diff --git a/_images/gradient-descent_19_1.png b/_images/gradient-descent_19_1.png
index f5d75774d..36cd0b2ba 100644
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diff --git a/_images/kernel-method_32_0.png b/_images/kernel-method_32_0.png
index bb25c20e7..9620e2f56 100644
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diff --git a/_images/kernel-method_39_1.png b/_images/kernel-method_39_1.png
index f3404a97f..52ec2b26d 100644
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diff --git a/_images/tools-of-the-trade_29_0.png b/_images/tools-of-the-trade_29_0.png
index ce0236f7e..07bada16d 100644
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diff --git a/_images/visualization-relationships_12_0.png b/_images/visualization-relationships_12_0.png
index 814c2b362..7a6cde5af 100644
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diff --git a/_images/visualization-relationships_16_0.png b/_images/visualization-relationships_16_0.png
index 778d0ded5..95c0e7590 100644
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diff --git a/_images/visualization-relationships_18_1.png b/_images/visualization-relationships_18_1.png
index da526cb9a..b56a1cc81 100644
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diff --git a/_images/visualization-relationships_20_0.png b/_images/visualization-relationships_20_0.png
index 60df73848..b2c3086f6 100644
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diff --git a/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb b/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb
index d49d239e4..4356f6dc7 100644
--- a/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb
+++ b/_sources/assignments/ml-fundamentals/create-a-regression-model.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "e20d2edc",
+   "id": "7f8c5d9c",
    "metadata": {},
    "source": [
     "# Create a regression model\n",
diff --git a/_sources/assignments/ml-fundamentals/exploring-visualizations.ipynb b/_sources/assignments/ml-fundamentals/exploring-visualizations.ipynb
index b8893c0c2..28b6e7d61 100644
--- a/_sources/assignments/ml-fundamentals/exploring-visualizations.ipynb
+++ b/_sources/assignments/ml-fundamentals/exploring-visualizations.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c6ca74f8",
+   "id": "54030063",
    "metadata": {},
    "source": [
     "# Exploring visualizations\n",
diff --git a/_sources/assignments/ml-fundamentals/parameter-play.ipynb b/_sources/assignments/ml-fundamentals/parameter-play.ipynb
index 452254a70..df47a8e2f 100644
--- a/_sources/assignments/ml-fundamentals/parameter-play.ipynb
+++ b/_sources/assignments/ml-fundamentals/parameter-play.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "bab39da2",
+   "id": "1407fd89",
    "metadata": {},
    "source": [
     "# Parameter play\n",
diff --git a/_sources/assignments/ml-fundamentals/regression-with-scikit-learn.ipynb b/_sources/assignments/ml-fundamentals/regression-with-scikit-learn.ipynb
index 029508848..9ea709ddd 100644
--- a/_sources/assignments/ml-fundamentals/regression-with-scikit-learn.ipynb
+++ b/_sources/assignments/ml-fundamentals/regression-with-scikit-learn.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "f7adfd63",
+   "id": "6f86010f",
    "metadata": {},
    "source": [
     "# Regression with Scikit-learn\n",
diff --git a/_sources/assignments/ml-fundamentals/retrying-some-regression.ipynb b/_sources/assignments/ml-fundamentals/retrying-some-regression.ipynb
index e26202a53..a8a8404cd 100644
--- a/_sources/assignments/ml-fundamentals/retrying-some-regression.ipynb
+++ b/_sources/assignments/ml-fundamentals/retrying-some-regression.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "5746f76c",
+   "id": "63285526",
    "metadata": {},
    "source": [
     "# Retrying some regression\n",
diff --git a/_sources/data-science/working-with-data/numpy.ipynb b/_sources/data-science/working-with-data/numpy.ipynb
index c53a46ca1..3b38e3b08 100644
--- a/_sources/data-science/working-with-data/numpy.ipynb
+++ b/_sources/data-science/working-with-data/numpy.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "d42f9283",
+   "id": "b5888482",
    "metadata": {},
    "source": [
     "# NumPy\n",
@@ -19,7 +19,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "fc137a63",
+   "id": "785cda3a",
    "metadata": {},
    "outputs": [
     {
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "24ccb95d",
+   "id": "4497576e",
    "metadata": {},
    "source": [
     "### Create a basic array\n",
@@ -53,7 +53,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "60efb52b",
+   "id": "a102ea7f",
    "metadata": {},
    "outputs": [
     {
@@ -75,7 +75,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "79fb360b",
+   "id": "45e91322",
    "metadata": {},
    "source": [
     "Besides creating an array from a sequence of elements, you can easily create an array filled with `0`’s:"
@@ -84,7 +84,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "87056673",
+   "id": "dfe4c7a0",
    "metadata": {},
    "outputs": [
     {
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed96c79b",
+   "id": "03a6c41b",
    "metadata": {},
    "source": [
     "Or an array filled with 1’s:"
@@ -113,7 +113,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "00a180bf",
+   "id": "30524d08",
    "metadata": {},
    "outputs": [
     {
@@ -133,7 +133,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ae3d7d40",
+   "id": "ebf9858c",
    "metadata": {},
    "source": [
     "Or even an empty array! The function `empty` creates an array whose initial content is random and depends on the state of the memory. The reason to use `empty` over `zeros` (or something similar) is speed - just make sure to fill every element afterwards!"
@@ -142,7 +142,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "2a4d1f70",
+   "id": "4e02ccfd",
    "metadata": {},
    "outputs": [
     {
@@ -162,7 +162,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "17cd44f8",
+   "id": "3dbd97c7",
    "metadata": {},
    "source": [
     "You can create an array with a range of elements:"
@@ -171,7 +171,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "a6c9eb69",
+   "id": "a0448381",
    "metadata": {},
    "outputs": [
     {
@@ -191,7 +191,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "97fe4079",
+   "id": "47cada4e",
    "metadata": {},
    "source": [
     "And even an array that contains a range of evenly spaced intervals. To do this, you will specify the **first number**, **last number**, and the **step size**."
@@ -200,7 +200,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "3a9ee670",
+   "id": "05de6608",
    "metadata": {},
    "outputs": [
     {
@@ -220,7 +220,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "43e227b2",
+   "id": "69708ef5",
    "metadata": {},
    "source": [
     "You can also use `np.linspace()` to create an array with values that are spaced linearly in a specified interval:"
@@ -229,7 +229,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "973239b9",
+   "id": "73c75e6d",
    "metadata": {},
    "outputs": [
     {
@@ -249,7 +249,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "20e1dd2c",
+   "id": "d4ed5418",
    "metadata": {},
    "source": [
     "While the default data type is floating point (`np.float64`), you can explicitly specify which data type you want using the `dtype` keyword."
@@ -258,7 +258,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "0fb481ef",
+   "id": "03b5f43a",
    "metadata": {},
    "outputs": [
     {
@@ -278,7 +278,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "041ef39d",
+   "id": "e5709a38",
    "metadata": {},
    "source": [
     "### Adding, removing, and sorting elements\n",
@@ -291,7 +291,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "aed933a7",
+   "id": "049e020a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -300,7 +300,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "37d480ec",
+   "id": "be71fbe1",
    "metadata": {},
    "source": [
     "You can quickly sort the numbers in ascending order with:"
@@ -309,7 +309,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "93ec8ca4",
+   "id": "de2aa8cf",
    "metadata": {},
    "outputs": [
     {
@@ -329,7 +329,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b5a0feff",
+   "id": "d6849175",
    "metadata": {},
    "source": [
     "In addition to sort, which returns a sorted copy of an array, you can use:\n",
@@ -345,7 +345,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "34866348",
+   "id": "76837ee3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -355,7 +355,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4c1f9394",
+   "id": "28bb0702",
    "metadata": {},
    "source": [
     "You can concatenate them with `np.concatenate()`."
@@ -364,7 +364,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "a8e8a5e9",
+   "id": "0f172ac3",
    "metadata": {},
    "outputs": [
     {
@@ -384,7 +384,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5055c367",
+   "id": "ad8a9627",
    "metadata": {},
    "source": [
     "Or, if you start with these arrays:"
@@ -393,7 +393,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "23100739",
+   "id": "b3d5e201",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -403,7 +403,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5350869b",
+   "id": "38230d50",
    "metadata": {},
    "source": [
     "You can concatenate them with:"
@@ -412,7 +412,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "d4ed74b9",
+   "id": "dcc748c4",
    "metadata": {},
    "outputs": [
     {
@@ -434,7 +434,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "30cd8b26",
+   "id": "b9aed6b6",
    "metadata": {},
    "source": [
     "In order to remove elements from an array, it’s simple to use indexing to select the elements that you want to keep.\n",
@@ -450,7 +450,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "f8986fa7",
+   "id": "930d5c5a",
    "metadata": {},
    "outputs": [
     {
@@ -475,7 +475,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "967a1f1f",
+   "id": "6de83562",
    "metadata": {},
    "outputs": [
     {
@@ -495,7 +495,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "44c736bf",
+   "id": "910fa2fa",
    "metadata": {},
    "source": [
     "- ndarray.shape\n",
@@ -505,7 +505,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "a9ccebf2",
+   "id": "4df0897a",
    "metadata": {},
    "outputs": [
     {
@@ -525,7 +525,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a3993a9b",
+   "id": "a8b7089e",
    "metadata": {},
    "source": [
     "- ndarray.size\n",
@@ -535,7 +535,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "593dd768",
+   "id": "4b82bdeb",
    "metadata": {},
    "outputs": [
     {
@@ -555,7 +555,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "75dea4f9",
+   "id": "c46827ca",
    "metadata": {},
    "source": [
     "- ndarray.dtype\n",
@@ -565,7 +565,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "2db2c6d8",
+   "id": "1a4fdec5",
    "metadata": {},
    "outputs": [
     {
@@ -586,7 +586,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "4fee3bd3",
+   "id": "46cb5a92",
    "metadata": {},
    "outputs": [
     {
@@ -606,7 +606,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a669a2a9",
+   "id": "63c6eebf",
    "metadata": {},
    "source": [
     "- ndarray.itemsize\n",
@@ -616,7 +616,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "c09e93a8",
+   "id": "2d8c0e24",
    "metadata": {},
    "outputs": [
     {
@@ -636,7 +636,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f744a7d",
+   "id": "6560f96f",
    "metadata": {},
    "source": [
     "- ndarray.data\n",
@@ -646,13 +646,13 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "39e7a739",
+   "id": "88e890a3",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<memory at 0x7f39491c1d40>"
+       "<memory at 0x7f806c85fd40>"
       ]
      },
      "execution_count": 23,
@@ -666,7 +666,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9a208b9b",
+   "id": "c3608ecd",
    "metadata": {},
    "source": [
     "### Reshape an array\n",
@@ -679,7 +679,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "06c93a54",
+   "id": "283a3027",
    "metadata": {},
    "outputs": [
     {
@@ -700,7 +700,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1441f194",
+   "id": "f77a9c2e",
    "metadata": {},
    "source": [
     "You can use `reshape()` to reshape your array. For example, you can reshape this array to an array with three rows and two columns:"
@@ -709,7 +709,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "d2d989d5",
+   "id": "27b6a6f4",
    "metadata": {},
    "outputs": [
     {
@@ -732,7 +732,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "599a27ca",
+   "id": "4f9818e1",
    "metadata": {},
    "source": [
     "With `np.reshape`, you can specify a few optional parameters:"
@@ -741,7 +741,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "68783c20",
+   "id": "2b0ab3e9",
    "metadata": {},
    "outputs": [
     {
@@ -761,7 +761,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4de39a60",
+   "id": "ec4b3d99",
    "metadata": {},
    "source": [
     "`a` is the array to be reshaped.\n",
@@ -782,7 +782,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "cb11349a",
+   "id": "c83a127f",
    "metadata": {},
    "outputs": [
     {
@@ -803,7 +803,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "03264962",
+   "id": "fb4550e1",
    "metadata": {},
    "source": [
     "You can use `np.newaxis` to add a new axis:"
@@ -812,7 +812,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "35ce5839",
+   "id": "343b677a",
    "metadata": {},
    "outputs": [
     {
@@ -833,7 +833,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0fbbe17f",
+   "id": "0b886e81",
    "metadata": {},
    "source": [
     "You can explicitly convert a 1D array with either a row vector or a column vector using `np.newaxis`. For example, you can convert a 1D array to a row vector by inserting an axis along the first dimension:"
@@ -842,7 +842,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "5edcdaaa",
+   "id": "2396425f",
    "metadata": {},
    "outputs": [
     {
@@ -863,7 +863,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c2c89a10",
+   "id": "e88e5084",
    "metadata": {},
    "source": [
     "Or, for a column vector, you can insert an axis along the second dimension:"
@@ -872,7 +872,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "e9262cf1",
+   "id": "f86eaec4",
    "metadata": {},
    "outputs": [
     {
@@ -893,7 +893,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6177dabb",
+   "id": "c2ceeaa3",
    "metadata": {},
    "source": [
     "You can also expand an array by inserting a new axis at a specified position with `np.expand_dims`.\n",
@@ -904,7 +904,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "65479436",
+   "id": "feaaf5f5",
    "metadata": {},
    "outputs": [
     {
@@ -925,7 +925,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "62baae17",
+   "id": "8179a047",
    "metadata": {},
    "source": [
     "You can use np.expand_dims to add an axis at index position 1 with:"
@@ -934,7 +934,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "0858aa93",
+   "id": "f2395964",
    "metadata": {},
    "outputs": [
     {
@@ -955,7 +955,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f4cdaeb",
+   "id": "e731906e",
    "metadata": {},
    "source": [
     "You can add an axis at index position 0 with:"
@@ -964,7 +964,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "f635fed2",
+   "id": "1e831b21",
    "metadata": {},
    "outputs": [
     {
@@ -985,7 +985,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9a52b417",
+   "id": "32c57fe6",
    "metadata": {},
    "source": [
     "### Indexing and slicing\n",
@@ -996,7 +996,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "4478a9aa",
+   "id": "e6828924",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1006,7 +1006,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "12aca91c",
+   "id": "fcf6182e",
    "metadata": {},
    "outputs": [
     {
@@ -1027,7 +1027,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "ec7c1d6e",
+   "id": "db06cad3",
    "metadata": {},
    "outputs": [
     {
@@ -1048,7 +1048,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "b47ac74f",
+   "id": "77460fae",
    "metadata": {},
    "outputs": [
     {
@@ -1069,7 +1069,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "b65e8f39",
+   "id": "50d3d7d5",
    "metadata": {},
    "outputs": [
     {
@@ -1089,7 +1089,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2062f21e",
+   "id": "390f7286",
    "metadata": {},
    "source": [
     "You may want to take a section of your array or specific array elements to use in further analysis or additional operations. To do that, you’ll need to subset, slice, and/or index your arrays.\n",
@@ -1102,7 +1102,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "ce39df14",
+   "id": "bfabe582",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1111,7 +1111,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4cf627fa",
+   "id": "98d7b447",
    "metadata": {},
    "source": [
     "You can easily print all of the values in the array that are less than 5."
@@ -1120,7 +1120,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "7a917f2c",
+   "id": "7b481bbb",
    "metadata": {},
    "outputs": [
     {
@@ -1140,7 +1140,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c4a80f02",
+   "id": "a1f20041",
    "metadata": {},
    "source": [
     "You can also select, for example, numbers that are equal to or greater than 5, and use that condition to index an array."
@@ -1149,7 +1149,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "8be48517",
+   "id": "abbbef5c",
    "metadata": {},
    "outputs": [
     {
@@ -1170,7 +1170,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5fa1b730",
+   "id": "5f9efe6e",
    "metadata": {},
    "source": [
     "You can select elements that are divisible by 2:"
@@ -1179,7 +1179,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "8b3171b7",
+   "id": "6fcf8c82",
    "metadata": {},
    "outputs": [
     {
@@ -1200,7 +1200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ef7461fb",
+   "id": "ac07b8bf",
    "metadata": {},
    "source": [
     "Or you can select elements that satisfy two conditions using the `&` and `|` operators:"
@@ -1209,7 +1209,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "9eed50a5",
+   "id": "a6c02448",
    "metadata": {},
    "outputs": [
     {
@@ -1230,7 +1230,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29f86723",
+   "id": "9aea32e3",
    "metadata": {},
    "source": [
     "You can also make use of the logical operators `&` and `|` in order to return boolean values that specify whether or not the values in an array fulfill a certain condition. This can be useful with arrays that contain names or other categorical values."
@@ -1239,7 +1239,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "6003d0d8",
+   "id": "52e1a2a9",
    "metadata": {},
    "outputs": [
     {
@@ -1262,7 +1262,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2bb6f36d",
+   "id": "6196ee04",
    "metadata": {},
    "source": [
     "You can also use `np.nonzero()` to select elements or indices from an array.\n",
@@ -1273,7 +1273,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "8b800a53",
+   "id": "84de3fc8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1282,7 +1282,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5a961d69",
+   "id": "62b27c24",
    "metadata": {},
    "source": [
     "You can use `np.nonzero()` to print the indices of elements that are, for example, less than 5:"
@@ -1291,7 +1291,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "3d62685b",
+   "id": "bf9b79b9",
    "metadata": {},
    "outputs": [
     {
@@ -1312,7 +1312,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35ca8cba",
+   "id": "be13baec",
    "metadata": {},
    "source": [
     "In this example, a tuple of arrays was returned: one for each dimension. The first array represents the row indices where these values are found, and the second array represents the column indices where the values are found.\n",
@@ -1323,7 +1323,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "8f21e63f",
+   "id": "6e50ff11",
    "metadata": {},
    "outputs": [
     {
@@ -1345,7 +1345,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a799850f",
+   "id": "2eecc043",
    "metadata": {},
    "source": [
     "You can also use `np.nonzero()` to print the elements in an array that are less than 5 with:"
@@ -1354,7 +1354,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "49469b0c",
+   "id": "e6439bbf",
    "metadata": {},
    "outputs": [
     {
@@ -1374,7 +1374,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b3380d45",
+   "id": "85a29814",
    "metadata": {},
    "source": [
     "If the element you’re looking for doesn’t exist in the array, then the returned array of indices will be empty. For example:"
@@ -1383,7 +1383,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "b4285507",
+   "id": "454088ea",
    "metadata": {},
    "outputs": [
     {
@@ -1404,7 +1404,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d53b5fbc",
+   "id": "8be2d548",
    "metadata": {},
    "source": [
     "### Create an array from existing data\n",
@@ -1417,7 +1417,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "54706621",
+   "id": "62890d3d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1426,7 +1426,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c766ba58",
+   "id": "e7bdf8cb",
    "metadata": {},
    "source": [
     "You can create a new array from a section of your array any time by specifying where you want to slice your array."
@@ -1435,7 +1435,7 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "02e1325d",
+   "id": "cf1bb50c",
    "metadata": {},
    "outputs": [
     {
@@ -1456,7 +1456,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "049a3c10",
+   "id": "b4a69e71",
    "metadata": {},
    "source": [
     "Here, you grabbed a section of your array from index position 3 through index position 8.\n",
@@ -1467,7 +1467,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "id": "799f7457",
+   "id": "e8d3491c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1479,7 +1479,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9bfa917c",
+   "id": "a1fd82bb",
    "metadata": {},
    "source": [
     "You can stack them vertically with `vstack`:"
@@ -1488,7 +1488,7 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "id": "4a0130d4",
+   "id": "2b133e0b",
    "metadata": {},
    "outputs": [
     {
@@ -1511,7 +1511,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e52a789",
+   "id": "cb12b0c2",
    "metadata": {},
    "source": [
     "Or stack them horizontally with hstack:"
@@ -1520,7 +1520,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "id": "1e4fedae",
+   "id": "bce35ecd",
    "metadata": {},
    "outputs": [
     {
@@ -1541,7 +1541,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9a5d31fc",
+   "id": "e28a6f04",
    "metadata": {},
    "source": [
     "You can split an array into several smaller arrays using `hsplit`. You can specify either the number of equally shaped arrays to return or the columns after which the division should occur.\n",
@@ -1552,7 +1552,7 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "id": "c5799472",
+   "id": "f99bd667",
    "metadata": {},
    "outputs": [
     {
@@ -1574,7 +1574,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9b1bdf6f",
+   "id": "cbc08091",
    "metadata": {},
    "source": [
     "If you wanted to split this array into three equally shaped arrays, you would run:"
@@ -1583,7 +1583,7 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "id": "35d2411e",
+   "id": "14c2c436",
    "metadata": {},
    "outputs": [
     {
@@ -1608,7 +1608,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7d09ed68",
+   "id": "35b81ae5",
    "metadata": {},
    "source": [
     "If you wanted to split your array after the third and fourth column, you’d run:"
@@ -1617,7 +1617,7 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "id": "d1d99f79",
+   "id": "dcaf2edb",
    "metadata": {},
    "outputs": [
     {
@@ -1642,7 +1642,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "54bd653b",
+   "id": "c81861d9",
    "metadata": {},
    "source": [
     "You can use the `view` method to create a new array object that looks at the same data as the original array (a shallow copy).\n",
@@ -1655,7 +1655,7 @@
   {
    "cell_type": "code",
    "execution_count": 58,
-   "id": "13933ea2",
+   "id": "234f0412",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1664,7 +1664,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8e3c6c29",
+   "id": "af503dcd",
    "metadata": {},
    "source": [
     "Now we create an array `b1` by slicing `a` and modify the first element of `b1`. This will modify the corresponding element in `a` as well!"
@@ -1673,7 +1673,7 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "id": "e4932af5",
+   "id": "845c5d61",
    "metadata": {},
    "outputs": [
     {
@@ -1695,7 +1695,7 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "id": "6449ba38",
+   "id": "106bbd4b",
    "metadata": {},
    "outputs": [
     {
@@ -1717,7 +1717,7 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "id": "02c6daa1",
+   "id": "a3cea45b",
    "metadata": {},
    "outputs": [
     {
@@ -1739,7 +1739,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "915560de",
+   "id": "09b0efa2",
    "metadata": {},
    "source": [
     "Using the `copy` method will make a complete copy of the array and its data (a deep copy). To use this on your array, you could run:"
@@ -1748,7 +1748,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "id": "ee8caaec",
+   "id": "e97bbc37",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1757,7 +1757,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c8bed026",
+   "id": "840ff748",
    "metadata": {},
    "source": [
     "## Array operations\n",
@@ -1770,7 +1770,7 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "id": "4a349415",
+   "id": "d34c3785",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1780,7 +1780,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7fb082fa",
+   "id": "354b812c",
    "metadata": {},
    "source": [
     "You can add the arrays together with the plus sign."
@@ -1789,7 +1789,7 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "id": "0d0708be",
+   "id": "044a9d38",
    "metadata": {},
    "outputs": [
     {
@@ -1809,7 +1809,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8bcd971c",
+   "id": "ca6b8eee",
    "metadata": {},
    "source": [
     "You can, of course, do more than just addition!"
@@ -1818,7 +1818,7 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "id": "10d49931",
+   "id": "02080dd2",
    "metadata": {},
    "outputs": [
     {
@@ -1839,7 +1839,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f9924a5c",
+   "id": "06df329f",
    "metadata": {},
    "source": [
     "Basic operations are simple with NumPy. If you want to find the sum of the elements in an array, you’d use `sum()`. This works for 1D arrays, 2D arrays, and arrays in higher dimensions."
@@ -1848,7 +1848,7 @@
   {
    "cell_type": "code",
    "execution_count": 66,
-   "id": "bb0fcf79",
+   "id": "d5942646",
    "metadata": {},
    "outputs": [
     {
@@ -1869,7 +1869,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "202fc319",
+   "id": "de4ae5f1",
    "metadata": {},
    "source": [
     "To add the rows or the columns in a 2D array, you would specify the axis.\n",
@@ -1880,7 +1880,7 @@
   {
    "cell_type": "code",
    "execution_count": 67,
-   "id": "7c587b0e",
+   "id": "797ef075",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1889,7 +1889,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0db5b5e7",
+   "id": "d6326b81",
    "metadata": {},
    "source": [
     "You can sum over the axis of rows with:"
@@ -1898,7 +1898,7 @@
   {
    "cell_type": "code",
    "execution_count": 68,
-   "id": "182a80b1",
+   "id": "879ed4b6",
    "metadata": {},
    "outputs": [
     {
@@ -1918,7 +1918,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "758c0601",
+   "id": "1d1d6798",
    "metadata": {},
    "source": [
     "You can sum over the axis of columns with:"
@@ -1927,7 +1927,7 @@
   {
    "cell_type": "code",
    "execution_count": 69,
-   "id": "93a8295a",
+   "id": "89742bc3",
    "metadata": {},
    "outputs": [
     {
@@ -1947,7 +1947,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "043250a0",
+   "id": "e05aea65",
    "metadata": {},
    "source": [
     "### Universal functions(ufunc)\n",
@@ -2038,7 +2038,7 @@
   {
    "cell_type": "code",
    "execution_count": 70,
-   "id": "9a215dfc",
+   "id": "2e7a2827",
    "metadata": {},
    "outputs": [
     {
@@ -2060,7 +2060,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d64eff6c",
+   "id": "e14d53f4",
    "metadata": {},
    "source": [
     "NumPy’s broadcasting rule relaxes this constraint when the arrays’ shapes meet certain constraints. The simplest broadcasting example occurs when an array and a scalar value are combined in an operation:"
@@ -2069,7 +2069,7 @@
   {
    "cell_type": "code",
    "execution_count": 71,
-   "id": "8e2e9d52",
+   "id": "3034ee65",
    "metadata": {},
    "outputs": [
     {
@@ -2091,7 +2091,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d705672b",
+   "id": "b32494fc",
    "metadata": {},
    "source": [
     "The result is equivalent to the previous example where `b` was an array.  NumPy is smart enough to use the original scalar value without actually making copies so that broadcasting operations are as memory and computationally efficient as possible.\n",
@@ -2133,7 +2133,7 @@
   {
    "cell_type": "code",
    "execution_count": 72,
-   "id": "46c0d2bf",
+   "id": "dfec7dfa",
    "metadata": {},
    "outputs": [
     {
@@ -2154,7 +2154,7 @@
   {
    "cell_type": "code",
    "execution_count": 73,
-   "id": "e709baf5",
+   "id": "b109968d",
    "metadata": {},
    "outputs": [
     {
@@ -2175,7 +2175,7 @@
   {
    "cell_type": "code",
    "execution_count": 74,
-   "id": "3c00aa67",
+   "id": "14573901",
    "metadata": {},
    "outputs": [
     {
@@ -2195,7 +2195,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b44670c3",
+   "id": "827494ec",
    "metadata": {},
    "source": [
     "Let’s start with this array, called “a”."
@@ -2204,7 +2204,7 @@
   {
    "cell_type": "code",
    "execution_count": 75,
-   "id": "4449fcff",
+   "id": "8fbcc6fd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2215,7 +2215,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce3fcc42",
+   "id": "9bb857c3",
    "metadata": {},
    "source": [
     "It’s very common to want to aggregate along a row or column. By default, every NumPy aggregation function will return the aggregate of the entire array. To find the sum or the minimum of the elements in your array, run:"
@@ -2224,7 +2224,7 @@
   {
    "cell_type": "code",
    "execution_count": 76,
-   "id": "45350736",
+   "id": "1365f7c7",
    "metadata": {},
    "outputs": [
     {
@@ -2244,7 +2244,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "46c11980",
+   "id": "7d979114",
    "metadata": {},
    "source": [
     "Or:"
@@ -2253,7 +2253,7 @@
   {
    "cell_type": "code",
    "execution_count": 77,
-   "id": "ea5ab3e7",
+   "id": "d01f3f34",
    "metadata": {},
    "outputs": [
     {
@@ -2273,7 +2273,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "15bfcfe8",
+   "id": "6b75981d",
    "metadata": {},
    "source": [
     "You can specify on which axis you want the aggregation function to be computed. For example, you can find the minimum value within each column by specifying `axis=0`."
@@ -2282,7 +2282,7 @@
   {
    "cell_type": "code",
    "execution_count": 78,
-   "id": "8ce375bd",
+   "id": "b877a6d2",
    "metadata": {},
    "outputs": [
     {
@@ -2302,7 +2302,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1762c1e5",
+   "id": "0f014088",
    "metadata": {},
    "source": [
     "The four values listed above correspond to the number of columns in your array. With a four-column array, you will get four values as your result.\n",
@@ -2325,7 +2325,7 @@
   {
    "cell_type": "code",
    "execution_count": 79,
-   "id": "58e8f5c8",
+   "id": "11a0d9b8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2335,7 +2335,7 @@
   {
    "cell_type": "code",
    "execution_count": 80,
-   "id": "f57ea41e",
+   "id": "e5904a16",
    "metadata": {},
    "outputs": [
     {
@@ -2356,7 +2356,7 @@
   {
    "cell_type": "code",
    "execution_count": 81,
-   "id": "f6618b66",
+   "id": "b1f20faa",
    "metadata": {},
    "outputs": [
     {
@@ -2376,7 +2376,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d9e8337f",
+   "id": "78347e54",
    "metadata": {},
    "source": [
     "It is not necessary to separate each dimension’s index into its own set of square brackets."
@@ -2385,7 +2385,7 @@
   {
    "cell_type": "code",
    "execution_count": 82,
-   "id": "8a2af3b4",
+   "id": "38396974",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2395,7 +2395,7 @@
   {
    "cell_type": "code",
    "execution_count": 83,
-   "id": "bbcc35b0",
+   "id": "14246c83",
    "metadata": {},
    "outputs": [
     {
@@ -2416,7 +2416,7 @@
   {
    "cell_type": "code",
    "execution_count": 84,
-   "id": "4cefdbb1",
+   "id": "d964c396",
    "metadata": {},
    "outputs": [
     {
@@ -2436,7 +2436,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d4116765",
+   "id": "f4db7183",
    "metadata": {},
    "source": [
     "Note that If one indexes a multidimensional array with fewer indices than dimensions, one gets a subdimensional array. For example:"
@@ -2445,7 +2445,7 @@
   {
    "cell_type": "code",
    "execution_count": 85,
-   "id": "e98b454b",
+   "id": "b3063d7f",
    "metadata": {},
    "outputs": [
     {
@@ -2465,7 +2465,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5a304a07",
+   "id": "302f6ed0",
    "metadata": {},
    "source": [
     "That is, each index specified selects the array corresponding to the rest of the dimensions selected. In the above example, choosing 0 means that the remaining dimension of length 5 is being left unspecified, and that what is returned is an array of that dimensionality and size. It must be noted that the returned array is a view, i.e., it is not a copy of the original, but points to the same values in memory as does the original array. In this case, the 1-D array at the first position (0) is returned. So using a single index on the returned array, results in a single element being returned. That is:"
@@ -2474,7 +2474,7 @@
   {
    "cell_type": "code",
    "execution_count": 86,
-   "id": "87c536b5",
+   "id": "b61a9fd7",
    "metadata": {},
    "outputs": [
     {
@@ -2494,7 +2494,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "761ba225",
+   "id": "35a67fd4",
    "metadata": {},
    "source": [
     "So note that `x[0, 2] == x[0][2]` though the second case is more inefficient as a new temporary array is created after the first index that is subsequently indexed by 2.\n",
@@ -2521,7 +2521,7 @@
   {
    "cell_type": "code",
    "execution_count": 87,
-   "id": "9d22d64a",
+   "id": "5d86e072",
    "metadata": {},
    "outputs": [
     {
@@ -2542,7 +2542,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8142b7d4",
+   "id": "c642e2bd",
    "metadata": {},
    "source": [
     "- Negative *i* and *j* are interpreted as *n + i* and *n + j* where *n* is the number of elements in the corresponding dimension. Negative *k* makes stepping go towards smaller indices. From the above example:"
@@ -2551,7 +2551,7 @@
   {
    "cell_type": "code",
    "execution_count": 88,
-   "id": "a6e27651",
+   "id": "c98b0c5a",
    "metadata": {},
    "outputs": [
     {
@@ -2572,7 +2572,7 @@
   {
    "cell_type": "code",
    "execution_count": 89,
-   "id": "d13f6700",
+   "id": "3a57ee72",
    "metadata": {},
    "outputs": [
     {
@@ -2592,7 +2592,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "095b0390",
+   "id": "fbf154df",
    "metadata": {},
    "source": [
     "- Assume *n* is the number of elements in the dimension being sliced. Then, if *i* is not given it defaults to 0 for *k > 0* and *n - 1* for *k < 0*. If *j* is not given it defaults to *n* for *k > 0* and *-n-1* for *k < 0*. If *k* is not given it defaults to 1. Note that `::` is the same as : and means select all indices along this axis. From the above example:"
@@ -2601,7 +2601,7 @@
   {
    "cell_type": "code",
    "execution_count": 90,
-   "id": "2cea56f9",
+   "id": "54fa2284",
    "metadata": {},
    "outputs": [
     {
@@ -2621,7 +2621,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3b827af9",
+   "id": "8cacdc50",
    "metadata": {},
    "source": [
     "- If the number of objects in the selection tuple is less than N, then `:` is assumed for any subsequent dimensions. For example:"
@@ -2630,7 +2630,7 @@
   {
    "cell_type": "code",
    "execution_count": 91,
-   "id": "20bb9974",
+   "id": "a6a38346",
    "metadata": {},
    "outputs": [
     {
@@ -2652,7 +2652,7 @@
   {
    "cell_type": "code",
    "execution_count": 92,
-   "id": "ffe7e2b1",
+   "id": "45fd34c7",
    "metadata": {},
    "outputs": [
     {
@@ -2674,7 +2674,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9ef2c35f",
+   "id": "bd80540c",
    "metadata": {},
    "source": [
     "- An integer, *i*, returns the same values as `i:i+1` **except** the dimensionality of the returned object is reduced by 1. In particular, a selection tuple with the *p*-th element an integer (and all other entries *:*) returns the corresponding sub-array with dimension *N - 1*. If *N = 1* then the returned object is an array scalar.\n",
@@ -2699,7 +2699,7 @@
   {
    "cell_type": "code",
    "execution_count": 93,
-   "id": "ceec2fb4",
+   "id": "cf487e2a",
    "metadata": {},
    "outputs": [
     {
@@ -2720,7 +2720,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1aff7de2",
+   "id": "cea60560",
    "metadata": {},
    "source": [
     "This is equivalent to:"
@@ -2729,7 +2729,7 @@
   {
    "cell_type": "code",
    "execution_count": 94,
-   "id": "7b49a3cd",
+   "id": "256b0b53",
    "metadata": {},
    "outputs": [
     {
@@ -2750,7 +2750,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7ded2831",
+   "id": "f0852ec8",
    "metadata": {},
    "source": [
     "Each `newaxis` object in the selection tuple serves to expand the dimensions of the resulting selection by one unit-length dimension. The added dimension is the position of the `newaxis` object in the selection tuple. `newaxis` is an alias for `None`, and `None` can be used in place of this with the same result. From the above example:"
@@ -2759,7 +2759,7 @@
   {
    "cell_type": "code",
    "execution_count": 95,
-   "id": "74bbbb53",
+   "id": "f3949020",
    "metadata": {},
    "outputs": [
     {
@@ -2780,7 +2780,7 @@
   {
    "cell_type": "code",
    "execution_count": 96,
-   "id": "7b394ddd",
+   "id": "9c51768b",
    "metadata": {},
    "outputs": [
     {
@@ -2800,7 +2800,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "778232cd",
+   "id": "d342e127",
    "metadata": {},
    "source": [
     "This can be handy to combine two arrays in a way that otherwise would require explicit reshaping operations. For example:"
@@ -2809,7 +2809,7 @@
   {
    "cell_type": "code",
    "execution_count": 97,
-   "id": "de655a5c",
+   "id": "adbe9075",
    "metadata": {},
    "outputs": [
     {
@@ -2834,7 +2834,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e4704ed9",
+   "id": "b17566e0",
    "metadata": {},
    "source": [
     "### Advanced indexing\n",
@@ -2857,7 +2857,7 @@
   {
    "cell_type": "code",
    "execution_count": 98,
-   "id": "caeb904c",
+   "id": "02932a10",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2867,7 +2867,7 @@
   {
    "cell_type": "code",
    "execution_count": 99,
-   "id": "a6266860",
+   "id": "7f8fdde1",
    "metadata": {},
    "outputs": [
     {
@@ -2888,7 +2888,7 @@
   {
    "cell_type": "code",
    "execution_count": 100,
-   "id": "b1b58910",
+   "id": "a8a7ea68",
    "metadata": {},
    "outputs": [
     {
@@ -2909,7 +2909,7 @@
   {
    "cell_type": "code",
    "execution_count": 101,
-   "id": "a34dda1e",
+   "id": "5ccb1438",
    "metadata": {},
    "outputs": [
     {
@@ -2929,7 +2929,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "80cf5e75",
+   "id": "96d8e32b",
    "metadata": {},
    "source": [
     "If the index values are out of bounds then an `IndexError` is thrown:"
@@ -2938,7 +2938,7 @@
   {
    "cell_type": "code",
    "execution_count": 102,
-   "id": "cd710942",
+   "id": "43a4fb4a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2948,7 +2948,7 @@
   {
    "cell_type": "code",
    "execution_count": 103,
-   "id": "d05ea078",
+   "id": "f065ee6a",
    "metadata": {},
    "outputs": [
     {
@@ -2969,7 +2969,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "42f0d065",
+   "id": "e7c13101",
    "metadata": {},
    "source": [
     "```py\n",
@@ -2999,7 +2999,7 @@
   {
    "cell_type": "code",
    "execution_count": 104,
-   "id": "5150ac27",
+   "id": "063101bc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3009,7 +3009,7 @@
   {
    "cell_type": "code",
    "execution_count": 105,
-   "id": "769e4f9b",
+   "id": "e4a0954b",
    "metadata": {},
    "outputs": [
     {
@@ -3034,7 +3034,7 @@
   {
    "cell_type": "code",
    "execution_count": 106,
-   "id": "346ea0c7",
+   "id": "f5399375",
    "metadata": {},
    "outputs": [
     {
@@ -3054,7 +3054,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce83ecd8",
+   "id": "54a9e869",
    "metadata": {},
    "source": [
     "In this case, if the index arrays have a matching shape, and there is an index array for each dimension of the array being indexed, the resultant array has the same shape as the index arrays, and the values correspond to the index set for each position in the index arrays. In this example, the first index value is 0 for both index arrays, and thus the first value of the resultant array is `y[0, 0]`. The next value is `y[2, 1]`, and the last is `y[4, 2]`.\n",
@@ -3077,7 +3077,7 @@
   {
    "cell_type": "code",
    "execution_count": 107,
-   "id": "dec489d5",
+   "id": "b5f16305",
    "metadata": {},
    "outputs": [
     {
@@ -3097,7 +3097,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16fce1af",
+   "id": "31c71ff4",
    "metadata": {},
    "source": [
     "Jumping to the next level of complexity, it is possible to only partially index an array with index arrays. It takes a bit of thought to understand what happens in such cases. For example if we just use one index array with y:"
@@ -3106,7 +3106,7 @@
   {
    "cell_type": "code",
    "execution_count": 108,
-   "id": "e5b6d2ba",
+   "id": "c844ded4",
    "metadata": {},
    "outputs": [
     {
@@ -3128,7 +3128,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fdf6289c",
+   "id": "ac086385",
    "metadata": {},
    "source": [
     "It results in the construction of a new array where each value of the index array selects one row from the array being indexed and the resultant array has the resulting shape (number of index elements, size of row).\n",
@@ -3143,7 +3143,7 @@
   {
    "cell_type": "code",
    "execution_count": 109,
-   "id": "a1dc3d6d",
+   "id": "0570bd1c",
    "metadata": {},
    "outputs": [
     {
@@ -3164,7 +3164,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c20e5a41",
+   "id": "a3dcf6fe",
    "metadata": {},
    "source": [
     "To achieve a behaviour similar to the basic slicing above, broadcasting can be used. The function `ix_` can help with this broadcasting. This is best understood with an example.\n",
@@ -3177,7 +3177,7 @@
   {
    "cell_type": "code",
    "execution_count": 110,
-   "id": "268d1657",
+   "id": "9bafc89a",
    "metadata": {},
    "outputs": [
     {
@@ -3206,7 +3206,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67e00413",
+   "id": "e2c55b65",
    "metadata": {},
    "source": [
     "However, since the indexing arrays above just repeat themselves, broadcasting can be used (compare operations such as `rows[:, np.newaxis] + columns`) to simplify this:"
@@ -3215,7 +3215,7 @@
   {
    "cell_type": "code",
    "execution_count": 111,
-   "id": "a6fe9f30",
+   "id": "7c316750",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3226,7 +3226,7 @@
   {
    "cell_type": "code",
    "execution_count": 112,
-   "id": "ef347053",
+   "id": "15afb73a",
    "metadata": {},
    "outputs": [
     {
@@ -3248,7 +3248,7 @@
   {
    "cell_type": "code",
    "execution_count": 113,
-   "id": "28dbb17f",
+   "id": "30c1524d",
    "metadata": {},
    "outputs": [
     {
@@ -3269,7 +3269,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "79640426",
+   "id": "90a1e37e",
    "metadata": {},
    "source": [
     "This broadcasting can also be achieved using the function `ix_`:"
@@ -3278,7 +3278,7 @@
   {
    "cell_type": "code",
    "execution_count": 114,
-   "id": "f209910e",
+   "id": "c1cc2872",
    "metadata": {},
    "outputs": [
     {
@@ -3299,7 +3299,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "78141d63",
+   "id": "4e044e8a",
    "metadata": {},
    "source": [
     "Note that without the `np.ix_` call, only the diagonal elements would be selected:"
@@ -3308,7 +3308,7 @@
   {
    "cell_type": "code",
    "execution_count": 115,
-   "id": "160f5d4f",
+   "id": "f13887ba",
    "metadata": {},
    "outputs": [
     {
@@ -3328,7 +3328,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "caed449a",
+   "id": "cb1e8cf2",
    "metadata": {},
    "source": [
     "This difference is the most important thing to remember about indexing with multiple advanced indices.\n",
@@ -3349,7 +3349,7 @@
   {
    "cell_type": "code",
    "execution_count": 116,
-   "id": "def11a0f",
+   "id": "05271311",
    "metadata": {},
    "outputs": [
     {
@@ -3370,7 +3370,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4c24f5b3",
+   "id": "ec5b9a35",
    "metadata": {},
    "source": [
     "Or wish to add a constant to all negative elements:"
@@ -3379,7 +3379,7 @@
   {
    "cell_type": "code",
    "execution_count": 117,
-   "id": "f85d658f",
+   "id": "291fff1d",
    "metadata": {},
    "outputs": [
     {
@@ -3401,7 +3401,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7b33c4f0",
+   "id": "034c1975",
    "metadata": {},
    "source": [
     "In general if an index includes a Boolean array, the result will be identical to inserting `obj.nonzero()` into the same position and using the integer array indexing mechanism described above. `x[ind_1, boolean_array, ind_2]` is equivalent to `x[(ind_1,) + boolean_array.nonzero() + (ind_2,)]`.\n",
@@ -3414,7 +3414,7 @@
   {
    "cell_type": "code",
    "execution_count": 118,
-   "id": "53eade9b",
+   "id": "c58aa8b3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3425,7 +3425,7 @@
   {
    "cell_type": "code",
    "execution_count": 119,
-   "id": "ece1b5d1",
+   "id": "21849360",
    "metadata": {},
    "outputs": [
     {
@@ -3446,7 +3446,7 @@
   {
    "cell_type": "code",
    "execution_count": 120,
-   "id": "c52c46bb",
+   "id": "e6924d97",
    "metadata": {},
    "outputs": [
     {
@@ -3467,7 +3467,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a3118f6f",
+   "id": "2cbb48f6",
    "metadata": {},
    "source": [
     "Here the 4th and 5th rows are selected from the indexed array and combined to make a 2-D array.\n",
@@ -3480,7 +3480,7 @@
   {
    "cell_type": "code",
    "execution_count": 121,
-   "id": "7708360b",
+   "id": "612560aa",
    "metadata": {},
    "outputs": [
     {
@@ -3503,7 +3503,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7087ef20",
+   "id": "4c8ed0c7",
    "metadata": {},
    "source": [
     "Combining multiple Boolean indexing arrays or a Boolean with an integer indexing array can best be understood with the `obj.nonzero()` analogy. The function `ix_` also supports boolean arrays and will work without any surprises.\n",
@@ -3516,7 +3516,7 @@
   {
    "cell_type": "code",
    "execution_count": 122,
-   "id": "41965626",
+   "id": "c8309bd2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3530,7 +3530,7 @@
   {
    "cell_type": "code",
    "execution_count": 123,
-   "id": "baf4625d",
+   "id": "6f96cb8a",
    "metadata": {},
    "outputs": [
     {
@@ -3551,7 +3551,7 @@
   {
    "cell_type": "code",
    "execution_count": 124,
-   "id": "43d558dc",
+   "id": "11741b5c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3561,7 +3561,7 @@
   {
    "cell_type": "code",
    "execution_count": 125,
-   "id": "9d2a6bab",
+   "id": "fd39a729",
    "metadata": {},
    "outputs": [
     {
@@ -3582,7 +3582,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "86f9ff85",
+   "id": "7ddc6b28",
    "metadata": {},
    "source": [
     "Without the n`p.ix_` call, only the diagonal elements would be selected.\n",
@@ -3593,7 +3593,7 @@
   {
    "cell_type": "code",
    "execution_count": 126,
-   "id": "5fd35947",
+   "id": "7ccf9464",
    "metadata": {},
    "outputs": [
     {
@@ -3615,7 +3615,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "99e52279",
+   "id": "564a7af0",
    "metadata": {},
    "source": [
     "##### Example 3\n",
@@ -3626,7 +3626,7 @@
   {
    "cell_type": "code",
    "execution_count": 127,
-   "id": "7f7a1f6b",
+   "id": "dce8c929",
    "metadata": {},
    "outputs": [
     {
@@ -3654,7 +3654,7 @@
   {
    "cell_type": "code",
    "execution_count": 128,
-   "id": "9cd72543",
+   "id": "bcd7a12b",
    "metadata": {},
    "outputs": [
     {
@@ -3678,7 +3678,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ad082716",
+   "id": "a7da394e",
    "metadata": {},
    "source": [
     "#### Combining advanced and basic indexing\n",
@@ -3691,7 +3691,7 @@
   {
    "cell_type": "code",
    "execution_count": 129,
-   "id": "721423b9",
+   "id": "dc2df71d",
    "metadata": {},
    "outputs": [
     {
@@ -3714,7 +3714,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7df99050",
+   "id": "28f3afe5",
    "metadata": {},
    "source": [
     "In effect, the slice and index array operation are independent. The slice operation extracts columns with index 1 and 2, (i.e. the 2nd and 3rd columns), followed by the index array operation which extracts rows with index 0, 2 and 4 (i.e the first, third and fifth rows). This is equivalent to:"
@@ -3723,7 +3723,7 @@
   {
    "cell_type": "code",
    "execution_count": 130,
-   "id": "f678ffe3",
+   "id": "d61f25a8",
    "metadata": {},
    "outputs": [
     {
@@ -3745,7 +3745,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7aad416f",
+   "id": "907b0b9d",
    "metadata": {},
    "source": [
     "A single advanced index can, for example, replace a slice and the result array will be the same. However, it is a copy and may have a different memory layout. A slice is preferable when it is possible. For example:"
@@ -3754,7 +3754,7 @@
   {
    "cell_type": "code",
    "execution_count": 131,
-   "id": "d25f4b37",
+   "id": "9a400503",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3767,7 +3767,7 @@
   {
    "cell_type": "code",
    "execution_count": 132,
-   "id": "3bffaf98",
+   "id": "95b3f42f",
    "metadata": {},
    "outputs": [
     {
@@ -3788,7 +3788,7 @@
   {
    "cell_type": "code",
    "execution_count": 133,
-   "id": "c719ffa9",
+   "id": "0dbd6221",
    "metadata": {},
    "outputs": [
     {
@@ -3808,7 +3808,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "23a538a8",
+   "id": "718eb852",
    "metadata": {},
    "source": [
     "The easiest way to understand a combination of multiple advanced indices may be to think in terms of the resulting shape. There are two parts to the indexing operation, the subspace defined by the basic indexing (excluding integers) and the subspace from the advanced indexing part. Two cases of index combination need to be distinguished:\n",
@@ -3834,7 +3834,7 @@
   {
    "cell_type": "code",
    "execution_count": 134,
-   "id": "d653d61c",
+   "id": "b840bdb4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3845,7 +3845,7 @@
   {
    "cell_type": "code",
    "execution_count": 135,
-   "id": "8428a5bc",
+   "id": "4031f15a",
    "metadata": {},
    "outputs": [
     {
@@ -3870,7 +3870,7 @@
   {
    "cell_type": "code",
    "execution_count": 136,
-   "id": "0556f002",
+   "id": "e16ef04d",
    "metadata": {},
    "outputs": [
     {
@@ -3891,7 +3891,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6a819f14",
+   "id": "022fb178",
    "metadata": {},
    "source": [
     "### Field access\n",
@@ -3908,7 +3908,7 @@
   {
    "cell_type": "code",
    "execution_count": 137,
-   "id": "8afb1ed2",
+   "id": "dc4dfd32",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3918,7 +3918,7 @@
   {
    "cell_type": "code",
    "execution_count": 138,
-   "id": "c9bc1cda",
+   "id": "437605af",
    "metadata": {},
    "outputs": [
     {
@@ -3939,7 +3939,7 @@
   {
    "cell_type": "code",
    "execution_count": 139,
-   "id": "af4ddce9",
+   "id": "1e9f160b",
    "metadata": {},
    "outputs": [
     {
@@ -3960,7 +3960,7 @@
   {
    "cell_type": "code",
    "execution_count": 140,
-   "id": "0bd0e82c",
+   "id": "e209d12a",
    "metadata": {},
    "outputs": [
     {
@@ -3981,7 +3981,7 @@
   {
    "cell_type": "code",
    "execution_count": 141,
-   "id": "5b1b68e8",
+   "id": "8f574141",
    "metadata": {},
    "outputs": [
     {
@@ -4001,7 +4001,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c501b6b5",
+   "id": "c1eed605",
    "metadata": {},
    "source": [
     "### Flat Iterator indexing\n",
@@ -4016,7 +4016,7 @@
   {
    "cell_type": "code",
    "execution_count": 142,
-   "id": "0a9c0a1d",
+   "id": "5ac958b9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4026,7 +4026,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a820c27b",
+   "id": "e5fa14f1",
    "metadata": {},
    "source": [
     "Or an array of the right size:"
@@ -4035,7 +4035,7 @@
   {
    "cell_type": "code",
    "execution_count": 143,
-   "id": "67e67af8",
+   "id": "03e4fa34",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4044,7 +4044,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a834aaaf",
+   "id": "48b8957e",
    "metadata": {},
    "source": [
     "Note that assignments may result in changes if assigning higher types to lower types (like floats to ints) or even exceptions (assigning complex to floats or ints):"
@@ -4053,7 +4053,7 @@
   {
    "cell_type": "code",
    "execution_count": 144,
-   "id": "90ccb7f8",
+   "id": "78392572",
    "metadata": {},
    "outputs": [
     {
@@ -4074,7 +4074,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "069aa7ba",
+   "id": "2d6aec14",
    "metadata": {},
    "source": [
     "```py\n",
@@ -4093,7 +4093,7 @@
   {
    "cell_type": "code",
    "execution_count": 145,
-   "id": "2429eb5c",
+   "id": "817f4e56",
    "metadata": {},
    "outputs": [
     {
@@ -4115,7 +4115,7 @@
   {
    "cell_type": "code",
    "execution_count": 146,
-   "id": "a6b44846",
+   "id": "8d24f864",
    "metadata": {},
    "outputs": [
     {
@@ -4136,7 +4136,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "95d8ed89",
+   "id": "fc9b6b02",
    "metadata": {},
    "source": [
     "Where people expect that the 1st location will be incremented by 3. In fact, it will only be incremented by 1. The reason is that a new array is extracted from the original (as a temporary) containing the values at 1, 1, 3, 1, then the value 1 is added to the temporary, and then the temporary is assigned back to the original array. Thus the value of the array at `x[1] + 1` is assigned to `x[1]` three times, rather than being incremented 3 times.\n",
@@ -4149,7 +4149,7 @@
   {
    "cell_type": "code",
    "execution_count": 147,
-   "id": "cd246fe5",
+   "id": "fcf9c20a",
    "metadata": {},
    "outputs": [
     {
@@ -4171,7 +4171,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c552db9",
+   "id": "86f6e843",
    "metadata": {},
    "source": [
     "So one can use code to construct tuples of any number of indices and then use these within an index.\n",
@@ -4182,7 +4182,7 @@
   {
    "cell_type": "code",
    "execution_count": 148,
-   "id": "57f76597",
+   "id": "13b4d1e0",
    "metadata": {},
    "outputs": [
     {
@@ -4203,7 +4203,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8f04c7f3",
+   "id": "159319b6",
    "metadata": {},
    "source": [
     "Likewise, ellipsis can be specified by code by using the Ellipsis object:"
@@ -4212,7 +4212,7 @@
   {
    "cell_type": "code",
    "execution_count": 149,
-   "id": "fae6ef2a",
+   "id": "9ca410fa",
    "metadata": {},
    "outputs": [
     {
@@ -4235,7 +4235,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "311bc41b",
+   "id": "767a18dd",
    "metadata": {},
    "source": [
     "For this reason, it is possible to use the output from the `np.nonzero()` function directly as an index since it always returns a tuple of index arrays.\n",
@@ -4246,7 +4246,7 @@
   {
    "cell_type": "code",
    "execution_count": 150,
-   "id": "25b456d8",
+   "id": "2580ea87",
    "metadata": {},
    "outputs": [
     {
@@ -4316,7 +4316,7 @@
   {
    "cell_type": "code",
    "execution_count": 151,
-   "id": "0581674b",
+   "id": "a9876d42",
    "metadata": {},
    "outputs": [
     {
@@ -4336,7 +4336,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "facacecd",
+   "id": "e878b8e8",
    "metadata": {},
    "source": [
     "## Structured arrays\n",
@@ -4349,7 +4349,7 @@
   {
    "cell_type": "code",
    "execution_count": 152,
-   "id": "c82e36f6",
+   "id": "4c4c0611",
    "metadata": {},
    "outputs": [
     {
@@ -4372,7 +4372,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5ed96385",
+   "id": "6df07549",
    "metadata": {},
    "source": [
     "Here `x` is a one-dimensional array of length two whose datatype is a structure with three fields: 1. A string of length 10 or less named `'name'`, 2. a 32-bit integer named `'age'`, and 3. a 32-bit float named `'weight'`.\n",
@@ -4383,7 +4383,7 @@
   {
    "cell_type": "code",
    "execution_count": 153,
-   "id": "4ccad27f",
+   "id": "842831f0",
    "metadata": {},
    "outputs": [
     {
@@ -4403,7 +4403,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4aefa26a",
+   "id": "72ddac9c",
    "metadata": {},
    "source": [
     "You can access and modify individual fields of a structured array by indexing with the field name:"
@@ -4412,7 +4412,7 @@
   {
    "cell_type": "code",
    "execution_count": 154,
-   "id": "5d173699",
+   "id": "e34d9aa7",
    "metadata": {},
    "outputs": [
     {
@@ -4433,7 +4433,7 @@
   {
    "cell_type": "code",
    "execution_count": 155,
-   "id": "6b6a4200",
+   "id": "126b416f",
    "metadata": {},
    "outputs": [
     {
@@ -4454,7 +4454,7 @@
   {
    "cell_type": "code",
    "execution_count": 156,
-   "id": "da8b24f8",
+   "id": "912cad13",
    "metadata": {},
    "outputs": [
     {
@@ -4475,7 +4475,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5ab9fdd5",
+   "id": "ca9d77da",
    "metadata": {},
    "source": [
     "Structured datatypes are designed to be able to mimic 'structs' in the C language, and share a similar memory layout. They are meant for interfacing with C code and for low-level manipulation of structured buffers, for example for interpreting binary blobs. For these purposes they support specialized features such as subarrays, nested datatypes, and unions, and allow control over the memory layout of the structure.\n",
@@ -4498,7 +4498,7 @@
   {
    "cell_type": "code",
    "execution_count": 157,
-   "id": "c2e8cc01",
+   "id": "a651d2ac",
    "metadata": {},
    "outputs": [
     {
@@ -4518,7 +4518,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "632de1e0",
+   "id": "85665077",
    "metadata": {},
    "source": [
     "If `fieldname` is the empty string `''`, the field will be given a default name of the form `f#`, where `#` is the integer index of the field, counting from 0 from the left:"
@@ -4527,7 +4527,7 @@
   {
    "cell_type": "code",
    "execution_count": 158,
-   "id": "b7600f3c",
+   "id": "5bfb5622",
    "metadata": {},
    "outputs": [
     {
@@ -4547,7 +4547,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ebeaece6",
+   "id": "7d7bda10",
    "metadata": {},
    "source": [
     "The byte offsets of the fields within the structure and the total structure itemsize are determined automatically.\n",
@@ -4560,7 +4560,7 @@
   {
    "cell_type": "code",
    "execution_count": 159,
-   "id": "b6d2bc6e",
+   "id": "dbfc5653",
    "metadata": {},
    "outputs": [
     {
@@ -4581,7 +4581,7 @@
   {
    "cell_type": "code",
    "execution_count": 160,
-   "id": "f7c6fefc",
+   "id": "9215972d",
    "metadata": {},
    "outputs": [
     {
@@ -4601,7 +4601,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8c10e670",
+   "id": "f22664b6",
    "metadata": {},
    "source": [
     "- A dictionary of field parameter arrays\n",
@@ -4614,7 +4614,7 @@
   {
    "cell_type": "code",
    "execution_count": 161,
-   "id": "c2d32750",
+   "id": "584c9e57",
    "metadata": {},
    "outputs": [
     {
@@ -4635,7 +4635,7 @@
   {
    "cell_type": "code",
    "execution_count": 162,
-   "id": "d1cbfcb0",
+   "id": "96debf43",
    "metadata": {},
    "outputs": [
     {
@@ -4658,7 +4658,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f7e5b2a3",
+   "id": "87a73b3e",
    "metadata": {},
    "source": [
     "Offsets may be chosen such that the fields overlap, though this will mean that assigning to one field may clobber any overlapping field’s data. As an exception, fields of `numpy.object_` type cannot overlap with other fields, because of the risk of clobbering the internal object pointer and then dereferencing it.\n",
@@ -4673,7 +4673,7 @@
   {
    "cell_type": "code",
    "execution_count": 163,
-   "id": "43546b17",
+   "id": "bb4ad3d2",
    "metadata": {},
    "outputs": [
     {
@@ -4693,7 +4693,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6e46def",
+   "id": "394a0bce",
    "metadata": {},
    "source": [
     "#### Manipulating and Displaying Structured Datatypes\n",
@@ -4704,7 +4704,7 @@
   {
    "cell_type": "code",
    "execution_count": 164,
-   "id": "550f4247",
+   "id": "8da55622",
    "metadata": {},
    "outputs": [
     {
@@ -4725,7 +4725,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "31887a1c",
+   "id": "ef61254c",
    "metadata": {},
    "source": [
     "The field names may be modified by assigning to the `names` attribute using a sequence of strings of the same length.\n",
@@ -4736,7 +4736,7 @@
   {
    "cell_type": "code",
    "execution_count": 165,
-   "id": "76f47ecb",
+   "id": "35e677f7",
    "metadata": {},
    "outputs": [
     {
@@ -4756,7 +4756,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e465f96",
+   "id": "5b0965e6",
    "metadata": {},
    "source": [
     "Both the `names` and `fields` attributes will equal `None` for unstructured arrays. The recommended way to test if a dtype is structured is with `if dt.names is not None` rather than `if dt.names`, to account for dtypes with 0 fields.\n",
@@ -4773,7 +4773,7 @@
   {
    "cell_type": "code",
    "execution_count": 166,
-   "id": "23295543",
+   "id": "808b8e57",
    "metadata": {},
    "outputs": [
     {
@@ -4793,7 +4793,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe463767",
+   "id": "102ab5a9",
    "metadata": {},
    "source": [
     "When using the first form of dictionary-based specification, the titles may be supplied as an extra `'titles'` key as described above. When using the second (discouraged) dictionary-based specification, the title can be supplied by providing a 3-element tuple `(datatype, offset, title)` instead of the usual 2-element tuple:"
@@ -4802,7 +4802,7 @@
   {
    "cell_type": "code",
    "execution_count": 167,
-   "id": "8e23d763",
+   "id": "5e2bdf5e",
    "metadata": {},
    "outputs": [
     {
@@ -4822,7 +4822,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "01baea80",
+   "id": "5dc79104",
    "metadata": {},
    "source": [
     "The `dtype.fields` dictionary will contain titles as keys, if any titles are used. This means effectively that a field with a title will be represented twice in the fields dictionary. The tuple values for these fields will also have a third element, the field title. Because of this, and because the `names` attribute preserves the field order while the `fields` attribute may not, it is recommended to iterate through the fields of a dtype using the `names` attribute of the dtype, which will not list titles, as in:"
@@ -4831,7 +4831,7 @@
   {
    "cell_type": "code",
    "execution_count": 168,
-   "id": "627ad17a",
+   "id": "8a797059",
    "metadata": {},
    "outputs": [
     {
@@ -4850,7 +4850,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2d5fed8f",
+   "id": "16de7203",
    "metadata": {},
    "source": [
     "### Indexing and Assignment to Structured arrays\n",
@@ -4867,7 +4867,7 @@
   {
    "cell_type": "code",
    "execution_count": 169,
-   "id": "75b4faf5",
+   "id": "b3c0653e",
    "metadata": {},
    "outputs": [
     {
@@ -4890,7 +4890,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bde6cbab",
+   "id": "e3161555",
    "metadata": {},
    "source": [
     "##### Assignment from Scalars\n",
@@ -4901,7 +4901,7 @@
   {
    "cell_type": "code",
    "execution_count": 170,
-   "id": "cbcd09b8",
+   "id": "3a8d4e40",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4911,7 +4911,7 @@
   {
    "cell_type": "code",
    "execution_count": 171,
-   "id": "e53e877a",
+   "id": "48873091",
    "metadata": {},
    "outputs": [
     {
@@ -4934,7 +4934,7 @@
   {
    "cell_type": "code",
    "execution_count": 172,
-   "id": "ee132ce3",
+   "id": "0f6129f7",
    "metadata": {},
    "outputs": [
     {
@@ -4956,7 +4956,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3bc68cc5",
+   "id": "2e114197",
    "metadata": {},
    "source": [
     "Structured arrays can also be assigned to unstructured arrays, but only if the structured datatype has just a single field:"
@@ -4965,7 +4965,7 @@
   {
    "cell_type": "code",
    "execution_count": 173,
-   "id": "6b80b416",
+   "id": "1e817a2d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4976,7 +4976,7 @@
   {
    "cell_type": "code",
    "execution_count": 174,
-   "id": "eb7d3420",
+   "id": "b276510e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4985,7 +4985,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4554b2a4",
+   "id": "d6cb1be7",
    "metadata": {},
    "source": [
     "```py\n",
@@ -5006,7 +5006,7 @@
   {
    "cell_type": "code",
    "execution_count": 175,
-   "id": "ec0922f5",
+   "id": "86c62c21",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5017,7 +5017,7 @@
   {
    "cell_type": "code",
    "execution_count": 176,
-   "id": "29f4eea2",
+   "id": "c00582b0",
    "metadata": {},
    "outputs": [
     {
@@ -5039,7 +5039,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "26eb2c63",
+   "id": "66dbb2d7",
    "metadata": {},
    "source": [
     "##### Assignment involving subarrays\n",
@@ -5056,7 +5056,7 @@
   {
    "cell_type": "code",
    "execution_count": 177,
-   "id": "cd694ccf",
+   "id": "7ab77ab8",
    "metadata": {},
    "outputs": [
     {
@@ -5078,7 +5078,7 @@
   {
    "cell_type": "code",
    "execution_count": 178,
-   "id": "b7c8796b",
+   "id": "1512b7c1",
    "metadata": {},
    "outputs": [
     {
@@ -5099,7 +5099,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "85a10b43",
+   "id": "1f9b14bd",
    "metadata": {},
    "source": [
     "The resulting array is a view into the original array. It shares the same memory locations and writing to the view will modify the original array."
@@ -5108,7 +5108,7 @@
   {
    "cell_type": "code",
    "execution_count": 179,
-   "id": "acfd1a61",
+   "id": "7408fddd",
    "metadata": {},
    "outputs": [
     {
@@ -5130,7 +5130,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f963c0a3",
+   "id": "5857f0c1",
    "metadata": {},
    "source": [
     "This view has the same dtype and itemsize as the indexed field, so it is typically a non-structured array, except in the case of nested structures."
@@ -5139,7 +5139,7 @@
   {
    "cell_type": "code",
    "execution_count": 180,
-   "id": "72bfa1e0",
+   "id": "5aa16f19",
    "metadata": {},
    "outputs": [
     {
@@ -5159,7 +5159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "10b66686",
+   "id": "3e10749c",
    "metadata": {},
    "source": [
     "If the accessed field is a subarray, the dimensions of the subarray are appended to the shape of the result:"
@@ -5168,7 +5168,7 @@
   {
    "cell_type": "code",
    "execution_count": 181,
-   "id": "d9fbe3ed",
+   "id": "62e622e5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5178,7 +5178,7 @@
   {
    "cell_type": "code",
    "execution_count": 182,
-   "id": "e125e942",
+   "id": "7a7ff9ff",
    "metadata": {},
    "outputs": [
     {
@@ -5199,7 +5199,7 @@
   {
    "cell_type": "code",
    "execution_count": 183,
-   "id": "110452d7",
+   "id": "c81fc737",
    "metadata": {},
    "outputs": [
     {
@@ -5219,7 +5219,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "067eca08",
+   "id": "0c7fbe27",
    "metadata": {},
    "source": [
     "##### Accessing multiple fields\n",
@@ -5232,7 +5232,7 @@
   {
    "cell_type": "code",
    "execution_count": 184,
-   "id": "f45d5149",
+   "id": "ae1f0721",
    "metadata": {},
    "outputs": [
     {
@@ -5254,7 +5254,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c5ad8dd0",
+   "id": "dc1fdde2",
    "metadata": {},
    "source": [
     "Assignment to the view modifies the original array. The view’s fields will be in the order they were indexed. Note that unlike for single-field indexing, the dtype of the view has the same itemsize as the original array, and has fields at the same offsets as in the original array, and unindexed fields are merely missing.\n",
@@ -5265,7 +5265,7 @@
   {
    "cell_type": "code",
    "execution_count": 185,
-   "id": "6a8b3d59",
+   "id": "85fe6386",
    "metadata": {},
    "outputs": [
     {
@@ -5287,7 +5287,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1993de1f",
+   "id": "83513e05",
    "metadata": {},
    "source": [
     "This obeys the structured array assignment rules described above. For example, this means that one can swap the values of two fields using appropriate multi-field indexes:"
@@ -5296,7 +5296,7 @@
   {
    "cell_type": "code",
    "execution_count": 186,
-   "id": "6ba06dd0",
+   "id": "d6698eb0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5305,7 +5305,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "36f03b8a",
+   "id": "cd8e9380",
    "metadata": {},
    "source": [
     "##### Indexing with an integer to get a structured scalar\n",
@@ -5316,7 +5316,7 @@
   {
    "cell_type": "code",
    "execution_count": 187,
-   "id": "aa4d413d",
+   "id": "dd10ae37",
    "metadata": {},
    "outputs": [
     {
@@ -5339,7 +5339,7 @@
   {
    "cell_type": "code",
    "execution_count": 188,
-   "id": "65efac41",
+   "id": "2dd9edfd",
    "metadata": {},
    "outputs": [
     {
@@ -5359,7 +5359,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "15a6d29e",
+   "id": "7021926f",
    "metadata": {},
    "source": [
     "Unlike other numpy scalars, structured scalars are mutable and act like views into the original array, such that modifying the scalar will modify the original array. Structured scalars also support access and assignment by field name:"
@@ -5368,7 +5368,7 @@
   {
    "cell_type": "code",
    "execution_count": 189,
-   "id": "e5433407",
+   "id": "38025152",
    "metadata": {},
    "outputs": [
     {
@@ -5391,7 +5391,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "791b2a6e",
+   "id": "267858ca",
    "metadata": {},
    "source": [
     "Similarly to tuples, structured scalars can also be indexed with an integer:"
@@ -5400,7 +5400,7 @@
   {
    "cell_type": "code",
    "execution_count": 190,
-   "id": "3593e923",
+   "id": "df9a2bb5",
    "metadata": {},
    "outputs": [
     {
@@ -5422,7 +5422,7 @@
   {
    "cell_type": "code",
    "execution_count": 191,
-   "id": "797c52c8",
+   "id": "a4d18340",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5431,7 +5431,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "febc77a1",
+   "id": "c04f3aa0",
    "metadata": {},
    "source": [
     "Thus, tuples might be thought of as the native Python equivalent to numpy’s structured types, much like native python integers are the equivalent to numpy’s integer types. Structured scalars may be converted to a tuple by calling `numpy.ndarray.item`:"
@@ -5440,7 +5440,7 @@
   {
    "cell_type": "code",
    "execution_count": 192,
-   "id": "0fd86175",
+   "id": "44d4f5b2",
    "metadata": {},
    "outputs": [
     {
@@ -5460,7 +5460,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32b69b12",
+   "id": "f30dc4b7",
    "metadata": {},
    "source": [
     "#### Viewing structured arrays containing objects\n",
@@ -5475,7 +5475,7 @@
   {
    "cell_type": "code",
    "execution_count": 193,
-   "id": "ecd3e689",
+   "id": "4fd31f79",
    "metadata": {},
    "outputs": [
     {
@@ -5497,7 +5497,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af2cb3cd",
+   "id": "424d1ba7",
    "metadata": {},
    "source": [
     "NumPy will promote individual field datatypes to perform the comparison. So the following is also valid (note the `'f4'` dtype for the `'a'` field):"
@@ -5506,7 +5506,7 @@
   {
    "cell_type": "code",
    "execution_count": 194,
-   "id": "672c39a2",
+   "id": "baaa67b7",
    "metadata": {},
    "outputs": [
     {
@@ -5527,7 +5527,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1bab8dc6",
+   "id": "80b3e916",
    "metadata": {},
    "source": [
     "To compare two structured arrays, it must be possible to promote them to a common dtype as returned by `numpy.result_type` and `np.promote_types`. This enforces that the number of fields, the field names, and the field titles must match precisely. When promotion is not possible, for example due to mismatching field names, NumPy will raise an error. Promotion between two structured dtypes results in a canonical dtype that ensures native byte-order for all fields:"
@@ -5536,7 +5536,7 @@
   {
    "cell_type": "code",
    "execution_count": 195,
-   "id": "5a895b7b",
+   "id": "84e4108a",
    "metadata": {},
    "outputs": [
     {
@@ -5557,7 +5557,7 @@
   {
    "cell_type": "code",
    "execution_count": 196,
-   "id": "a9b85563",
+   "id": "c3f17d88",
    "metadata": {},
    "outputs": [
     {
@@ -5577,7 +5577,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "559e4ec0",
+   "id": "cb87b09c",
    "metadata": {},
    "source": [
     "The resulting dtype from promotion is also guaranteed to be packed, meaning that all fields are ordered contiguously and any unnecessary padding is removed:"
@@ -5586,7 +5586,7 @@
   {
    "cell_type": "code",
    "execution_count": 197,
-   "id": "90507c63",
+   "id": "20a61efb",
    "metadata": {},
    "outputs": [
     {
@@ -5608,7 +5608,7 @@
   {
    "cell_type": "code",
    "execution_count": 198,
-   "id": "560bb176",
+   "id": "cf3b4857",
    "metadata": {},
    "outputs": [
     {
@@ -5628,7 +5628,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "087c3eaf",
+   "id": "9f4e3590",
    "metadata": {},
    "source": [
     "Note that the result prints without `offsets` or `itemsize` indicating no additional padding. If a structured dtype is created with `align=True` ensuring that `dtype.isalignedstruct` is true, this property is preserved:"
@@ -5637,7 +5637,7 @@
   {
    "cell_type": "code",
    "execution_count": 199,
-   "id": "cdab5905",
+   "id": "6363970b",
    "metadata": {},
    "outputs": [
     {
@@ -5659,7 +5659,7 @@
   {
    "cell_type": "code",
    "execution_count": 200,
-   "id": "6b9498d3",
+   "id": "e000dc13",
    "metadata": {},
    "outputs": [
     {
@@ -5680,7 +5680,7 @@
   {
    "cell_type": "code",
    "execution_count": 201,
-   "id": "a51a7440",
+   "id": "b47c2d91",
    "metadata": {},
    "outputs": [
     {
@@ -5700,7 +5700,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ebdd7ede",
+   "id": "fad22d96",
    "metadata": {},
    "source": [
     "When promoting multiple dtypes, the result is aligned if any of the inputs is:"
@@ -5709,7 +5709,7 @@
   {
    "cell_type": "code",
    "execution_count": 202,
-   "id": "a9cf2e32",
+   "id": "12393ade",
    "metadata": {},
    "outputs": [
     {
@@ -5729,7 +5729,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7dcadc00",
+   "id": "4a7da9ec",
    "metadata": {},
    "source": [
     "The `<` and `>` operators always return `False` when comparing void structured arrays, and arithmetic and bitwise operations are not supported.\n",
diff --git a/_sources/prerequisites/python-programming-basics.ipynb b/_sources/prerequisites/python-programming-basics.ipynb
index d277e404e..26e6b9080 100644
--- a/_sources/prerequisites/python-programming-basics.ipynb
+++ b/_sources/prerequisites/python-programming-basics.ipynb
@@ -4,14 +4,18 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "16159125",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
    "outputs": [],
    "source": [
     "# Install the necessary dependencies\n",
     "\n",
     "import sys\n",
     "import os\n",
-    "!{sys.executable} -m pip install --quiet jupyterlab_myst ipython"
+    "!{sys.executable} -m pip install --quiet jupyterlab_myst ipython pytest"
    ]
   },
   {
@@ -68,8 +72,14 @@
     "OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n",
     "SOFTWARE.\n",
     "\n",
-    "</details>\n",
-    "\n",
+    "</details>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bda5c0cb",
+   "metadata": {},
+   "source": [
     "# Python programming basics\n",
     "\n",
     "## Python syntax\n",
diff --git a/_sources/prerequisites/python-programming-introduction.ipynb b/_sources/prerequisites/python-programming-introduction.ipynb
index 4f8bf002a..a142cebcf 100644
--- a/_sources/prerequisites/python-programming-introduction.ipynb
+++ b/_sources/prerequisites/python-programming-introduction.ipynb
@@ -4,7 +4,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "55db1b3f",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
    "outputs": [],
    "source": [
     "# Install the necessary dependencies\n",
@@ -67,23 +71,7 @@
     "OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n",
     "SOFTWARE.\n",
     "\n",
-    "</details>\n",
-    "\n",
-    "# Python programming basics\n",
-    "\n",
-    "## Python syntax\n",
-    "\n",
-    "**Python syntax compared to other programming languages**\n",
-    "\n",
-    "- Python was designed for readability and has some similarities to the English language with influence from mathematics.\n",
-    "- Python uses new lines to complete a command, as opposed to other programming languages which often use semicolons or parentheses.\n",
-    "- Python relies on indentation, using whitespace, to define scope; such as the scope of loops, functions and classes. Other programming languages often use curly brackets for this purpose.\n",
-    "\n",
-    "### Python indentations\n",
-    "\n",
-    "While in other programming languages the indentation in code is for readability only, in Python the indentation is very important.\n",
-    "\n",
-    "Python uses indentation to indicate a block of code."
+    "</details>"
    ]
   },
   {
diff --git a/assignments/README.html b/assignments/README.html
index bdf4c818b..f0a8ef7a2 100644
--- a/assignments/README.html
+++ b/assignments/README.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>41. Self-paced assignments &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40. Self-paced assignments &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="41.3. First assignment" href="set-up-env/first-assignment.html" />
-    <link rel="prev" title="40. Model deployment" href="../machine-learning-productionization/model-deployment.html" />
+    <link rel="next" title="40.3. First assignment" href="set-up-env/first-assignment.html" />
+    <link rel="prev" title="39. Model deployment" href="../machine-learning-productionization/model-deployment.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -159,17 +159,17 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-   1. Python programming basics
+   1. Python programming introduction
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-   3. Python programming basics
+   2. Python programming basics
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-   4. Python programming advanced
+   3. Python programming advanced
   </a>
  </li>
 </ul>
@@ -181,7 +181,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../data-science/introduction/introduction.html">
-   5. Introduction
+   4. Introduction
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/>
   <label for="toctree-checkbox-1">
@@ -191,29 +191,29 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/introduction/defining-data-science.html">
-     5.1. Defining data science
+     4.1. Defining data science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/introduction/data-science-ethics.html">
-     5.2. Data Science ethics
+     4.2. Data Science ethics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/introduction/defining-data.html">
-     5.3. Defining data
+     4.3. Defining data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/introduction/introduction-to-statistics-and-probability.html">
-     5.4. Introduction to statistics and probability
+     4.4. Introduction to statistics and probability
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../data-science/working-with-data/working-with-data.html">
-   6. Working with data
+   5. Working with data
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/>
   <label for="toctree-checkbox-2">
@@ -223,22 +223,22 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/working-with-data/relational-databases.html">
-     6.1. Relational databases
+     5.1. Relational databases
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/working-with-data/non-relational-data.html">
-     6.2. Non-relational data
+     5.2. Non-relational data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/working-with-data/numpy.html">
-     6.3. NumPy
+     5.3. NumPy
     </a>
    </li>
    <li class="toctree-l2 has-children">
     <a class="reference internal" href="../data-science/working-with-data/pandas/pandas.html">
-     6.4. Pandas
+     5.4. Pandas
     </a>
     <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
     <label for="toctree-checkbox-3">
@@ -248,31 +248,31 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
     <ul>
      <li class="toctree-l3">
       <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
-       6.4.1. Introduction and Data Structures
+       5.4.1. Introduction and Data Structures
       </a>
      </li>
      <li class="toctree-l3">
       <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
-       6.4.2. Data Selection
+       5.4.2. Data Selection
       </a>
      </li>
      <li class="toctree-l3">
       <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
-       6.4.3. Advanced Pandas Techniques
+       5.4.3. Advanced Pandas Techniques
       </a>
      </li>
     </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/working-with-data/data-preparation.html">
-     6.5. Data preparation
+     5.5. Data preparation
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../data-science/data-visualization/data-visualization.html">
-   7. Data visualization
+   6. Data visualization
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
   <label for="toctree-checkbox-4">
@@ -282,29 +282,29 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-visualization/visualization-distributions.html">
-     7.1. Visualizing distributions
+     6.1. Visualizing distributions
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-visualization/visualization-proportions.html">
-     7.2. Visualizing proportions
+     6.2. Visualizing proportions
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-visualization/visualization-relationships.html">
-     7.3. Visualizing relationships: all about honey 🍯
+     6.3. Visualizing relationships: all about honey 🍯
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-visualization/meaningful-visualizations.html">
-     7.4. Making meaningful visualizations
+     6.4. Making meaningful visualizations
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../data-science/data-science-lifecycle/data-science-lifecycle.html">
-   8. Data Science lifecycle
+   7. Data Science lifecycle
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
   <label for="toctree-checkbox-5">
@@ -314,24 +314,24 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-lifecycle/introduction.html">
-     8.1. Introduction to the Data Science lifecycle
+     7.1. Introduction to the Data Science lifecycle
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-lifecycle/analyzing.html">
-     8.2. Analyzing
+     7.2. Analyzing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-lifecycle/communication.html">
-     8.3. Communication
+     7.3. Communication
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
-   9. Data Science in the cloud
+   8. Data Science in the cloud
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
   <label for="toctree-checkbox-6">
@@ -341,24 +341,24 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-in-the-cloud/introduction.html">
-     9.1. Introduction
+     8.1. Introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-in-the-cloud/the-low-code-no-code-way.html">
-     9.2. The “low code/no code” way
+     8.2. The “low code/no code” way
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-in-the-cloud/the-azure-ml-sdk-way.html">
-     9.3. Data Science in the cloud: The “Azure ML SDK” way
+     8.3. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../data-science/data-science-in-the-wild.html">
-   10. Data Science in the real world
+   9. Data Science in the real world
   </a>
  </li>
 </ul>
@@ -370,17 +370,17 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-fundamentals/ml-overview.html">
-   11. Machine Learning overview
+   10. Machine Learning overview
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-fundamentals/ml-summary.html">
-   12. Summary of machine learning fundamentals
+   11. Summary of machine learning fundamentals
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   13. Regression models for Machine Learning
+   12. Regression models for Machine Learning
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
   <label for="toctree-checkbox-7">
@@ -390,34 +390,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/tools-of-the-trade.html">
-     13.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/managing-data.html">
-     13.2. Managing data
+     12.2. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     13.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/regression/logistic-regression.html">
-     13.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   14. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../ml-fundamentals/classification/getting-started-with-classification.html">
-   15. Getting started with classification
+   14. Getting started with classification
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/>
   <label for="toctree-checkbox-8">
@@ -427,29 +427,29 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/introduction-to-classification.html">
-     15.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-fundamentals/classification/more-classifiers.html">
-     15.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
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+     40.3. First assignment
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+     40.4. Second assignment
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+     40.5. Project Plan​ Template
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+     40.6. Python programming introduction
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+     40.11. Write a data ethics case study
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
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+     40.36. Explore a planetary computer dataset
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+     40.39. Low code/no code Data Science project on Azure ML
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-     41.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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-     41.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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+     40.43. Machine Learning overview - assignment 2
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-     41.44. Regression with Scikit-learn
+     40.44. Regression with Scikit-learn
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-     41.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
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-     41.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
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+     40.47. Loss Function
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+     40.48. Gradient descent
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-     41.49. Linear Regression Implementation from Scratch
+     40.49. Linear Regression Implementation from Scratch
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-     41.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
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-     41.51. ML logistic regression - assignment 2
+     40.51. ML logistic regression - assignment 2
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+     40.52. ML neural network - Assignment 1
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+     40.53. Regression tools
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+     40.57. Create a regression model
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-     41.58. Linear and polynomial regression
+     40.58. Linear and polynomial regression
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+     40.59. Retrying some regression
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-     41.60. Pumpkin varieties and color
+     40.60. Pumpkin varieties and color
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-     41.61. Delicious asian and indian cuisines
+     40.61. Delicious asian and indian cuisines
     </a>
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-     41.62. Explore classification methods
+     40.62. Explore classification methods
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-     41.63. Kernel method assignment 1
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-     41.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
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     <a class="reference internal" href="ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     41.65. Support Vector Machines (SVM) - Classification
+     40.65. Support Vector Machines (SVM) - Classification
     </a>
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-     41.66. Decision Trees - Intro and Regression
+     40.66. Decision Trees - Intro and Regression
     </a>
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+     40.67. Decision Trees - Classification
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-     41.68. Model selection assignment 1
+     40.68. Model selection assignment 1
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-     41.69. Learning Curve To Identify Overfit &amp; Underfit
+     40.69. Learning Curve To Identify Overfit &amp; Underfit
     </a>
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-     41.70. Dropout and Batch Normalization
+     40.70. Dropout and Batch Normalization
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+     40.71. Lasso and Ridge Regression
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-     41.72. Regularized Linear Models
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+     40.73. Random forests intro and regression
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+     40.74. Random forests for classification
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-     41.75. Beyond random forests: more ensemble models
+     40.75. Beyond random forests: more ensemble models
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+     40.77. Hyperparameter tuning gradient boosting
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+     40.80. Random Forest Classifier with Feature Importance
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-     41.101. Intro to Autoencoders
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-     41.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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     <a class="reference internal" href="deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     41.103. Fun with Variational Autoencoders
+     40.103. Fun with Variational Autoencoders
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-     41.106. Neural Networks for Classification with TensorFlow
+     40.106. Neural Networks for Classification with TensorFlow
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-     41.112. DQN On Foreign Exchange Market
+     40.112. DQN On Foreign Exchange Market
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-     41.113. Art by gan
+     40.113. Art by gan
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-     41.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
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     <a class="reference internal" href="deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     41.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
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-     42.1. Python programming introduction
+     41.1. Python programming introduction
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-     42.2. Python programming basics
+     41.2. Python programming basics
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-     42.3. Python programming advanced
+     41.3. Python programming advanced
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     <a class="reference internal" href="../slides/data-science/data-science-introduction.html">
-     42.4. Data Science introduction
+     41.4. Data Science introduction
     </a>
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     <a class="reference internal" href="../slides/data-science/relational-vs-non-relational-database.html">
-     42.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
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-     42.6. NumPy and Pandas
+     41.6. NumPy and Pandas
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-     42.7. Data visualization
+     41.7. Data visualization
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-     42.8. Data Science lifecycle
+     41.8. Data Science lifecycle
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-     42.9. Data Science in the cloud
+     41.9. Data Science in the cloud
     </a>
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-     42.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
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    <li class="toctree-l2">
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-     42.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
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-     42.12. Linear Regression
+     41.12. Linear Regression
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-     42.13. Logistic Regression
+     41.13. Logistic Regression
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-     42.14. Logistic Regression
+     41.14. Logistic Regression
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-     42.15. Neural Network
+     41.15. Neural Network
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-     42.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
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-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
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-     42.18. Kernel method
+     41.18. Kernel method
     </a>
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-     42.19. Model Selection
+     41.19. Model Selection
     </a>
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-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
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     <a class="reference internal" href="../slides/deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
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@@ -1510,39 +1510,39 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#try-it-out">
-   41.1. Try it out
+   40.1. Try it out
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#binder">
-     41.1.1. Binder
+     40.1.1. Binder
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     <a class="reference internal nav-link" href="#optional-local">
-     41.1.2. [Optional]Local
+     40.1.2. [Optional]Local
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#github">
-     41.1.3. GitHub
+     40.1.3. GitHub
     </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#how-does-this-work">
-     41.1.4. How does this work?
+     40.1.4. How does this work?
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#development">
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+   40.2. Development
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   <ul class="nav section-nav flex-column">
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     <a class="reference internal nav-link" href="#environment">
-     41.2.1. Environment
+     40.2.1. Environment
     </a>
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   </ul>
@@ -1568,39 +1568,39 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#try-it-out">
-   41.1. Try it out
+   40.1. Try it out
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-     41.1.1. Binder
+     40.1.1. Binder
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#optional-local">
-     41.1.2. [Optional]Local
+     40.1.2. [Optional]Local
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#github">
-     41.1.3. GitHub
+     40.1.3. GitHub
     </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#how-does-this-work">
-     41.1.4. How does this work?
+     40.1.4. How does this work?
     </a>
    </li>
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+   40.2. Development
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+     40.2.1. Environment
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@@ -1616,18 +1616,18 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="self-paced-assignments">
-<h1><span class="section-number">41. </span>Self-paced assignments<a class="headerlink" href="#self-paced-assignments" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40. </span>Self-paced assignments<a class="headerlink" href="#self-paced-assignments" title="Permalink to this headline">#</a></h1>
 <p><a class="reference external" href="https://mybinder.org/v2/gh/ocademy-ai/assignments-binder-requirements/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252Focademy-ai%252Fmachine-learning%26urlpath%3Dlab%252Ftree%252Fmachine-learning%252F%26branch%3Dmain"><img alt="Binder" src="http://mybinder.org/badge_logo.svg" /></a></p>
 <p>Ocademy sets up the assignment as <a class="reference external" href="https://en.wikipedia.org/wiki/Test-driven_development">Test-driven Development</a> style. The questions are <a class="reference external" href="https://en.wikipedia.org/wiki/Unit_testing">unit testing</a> alike, implemented as a bunch of Jupyter Notebooks, and visualized by <a class="reference external" href="https://github.com/jupyterlab-contrib/jupyterlab-pytutor">jupyterlab-pytutor</a>. A real-world code quality assurance environment is simulated. You will learn how to handle software testing while working on the assignments.</p>
 <section id="try-it-out">
-<h2><span class="section-number">41.1. </span>Try it out<a class="headerlink" href="#try-it-out" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.1. </span>Try it out<a class="headerlink" href="#try-it-out" title="Permalink to this headline">#</a></h2>
 <p>There are multiple ways to do exercise with the assignment in self-paced mode. You MUST use Jupyter Lab as the runtime environment.</p>
 <section id="binder">
-<h3><span class="section-number">41.1.1. </span>Binder<a class="headerlink" href="#binder" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.1.1. </span>Binder<a class="headerlink" href="#binder" title="Permalink to this headline">#</a></h3>
 <p>Simply click on this <a class="reference external" href="https://mybinder.org/v2/gh/ocademy-ai/assignments-binder-requirements/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252Focademy-ai%252Fmachine-learning%26urlpath%3Dlab%252Ftree%252Fmachine-learning%252F%26branch%3Dmain"><img alt="Binder" src="http://mybinder.org/badge_logo.svg" /></a>.</p>
 </section>
 <section id="optional-local">
-<h3><span class="section-number">41.1.2. </span>[Optional]Local<a class="headerlink" href="#optional-local" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.1.2. </span>[Optional]Local<a class="headerlink" href="#optional-local" title="Permalink to this headline">#</a></h3>
 <p>All the assignment notebooks can be executed independently without any prerequisite, and in any Jupyter Lab environment. Below steps will guide you to create a work environment if you don’t have yet, which consists of some necessary dependencies and helpful Jupyter Lab extensions.</p>
 <p>1. Install Python &amp; Conda by following <a class="reference external" href="https://github.com/ocademy-ai/machine-learning/blob/main/CONTRIBUTING.md#install-python--conda">this</a>.</p>
 <p>2. Install Jupyter Lab by following <a class="reference external" href="https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html">this</a>.</p>
@@ -1649,7 +1649,7 @@ <h3><span class="section-number">41.1.2. </span>[Optional]Local<a class="headerl
 <p><img alt="Screenshot 2023-04-23 at 4 54 20 PM" src="https://user-images.githubusercontent.com/5424267/233873630-de9c1b75-c635-45bd-b4d0-889f0c917301.png" /></p>
 </section>
 <section id="github">
-<h3><span class="section-number">41.1.3. </span>GitHub<a class="headerlink" href="#github" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.1.3. </span>GitHub<a class="headerlink" href="#github" title="Permalink to this headline">#</a></h3>
 <p>To start working on the assignments, you could follow the  steps below:</p>
 <ol>
 <li><p>[First time only] Fork the <a class="reference external" href="https://github.com/ocademy-ai/machine-learning">repo</a> under your own GitHub account.
@@ -1699,7 +1699,7 @@ <h3><span class="section-number">41.1.3. </span>GitHub<a class="headerlink" href
 </div>
 </section>
 <section id="how-does-this-work">
-<h3><span class="section-number">41.1.4. </span>How does this work?<a class="headerlink" href="#how-does-this-work" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.1.4. </span>How does this work?<a class="headerlink" href="#how-does-this-work" title="Permalink to this headline">#</a></h3>
 <p>The assignment is fully automated by leveraging GitHub Action. The action is defined by <a class="reference external" href="https://github.com/ocademy-ai/machine-learning/blob/main/.github/workflows/assignment.yml">assignment.yml</a>, which handles the continuous integration workflow post a code push. It listens to the code change under <code class="docutils literal notranslate"><span class="pre">open-machine-learning-jupyter-book/assignments</span></code> path, gets the changed files, and runs unit testing against them.</p>
 <p><code class="docutils literal notranslate"><span class="pre">pytest</span></code> is the testing framework used for the assignments. To support unit testing for the Jupyter Notebook, <code class="docutils literal notranslate"><span class="pre">nbmake</span></code> is used as a plugin together with <code class="docutils literal notranslate"><span class="pre">pytest</span></code>.</p>
 <div class="admonition seealso">
@@ -1712,9 +1712,9 @@ <h3><span class="section-number">41.1.4. </span>How does this work?<a class="hea
 </section>
 </section>
 <section id="development">
-<h2><span class="section-number">41.2. </span>Development<a class="headerlink" href="#development" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.2. </span>Development<a class="headerlink" href="#development" title="Permalink to this headline">#</a></h2>
 <section id="environment">
-<h3><span class="section-number">41.2.1. </span>Environment<a class="headerlink" href="#environment" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.2.1. </span>Environment<a class="headerlink" href="#environment" title="Permalink to this headline">#</a></h3>
 <p>The environment is managed at <a class="reference external" href="https://github.com/ocademy-ai/assignments-binder-requirements">assignments-binder-requirements</a> by following <a class="reference external" href="https://discourse.jupyter.org/t/tip-speed-up-binder-launches-by-pulling-github-content-in-a-binder-link-with-nbgitpuller/922">this</a>.</p>
 <p>To update the environment,</p>
 <ol class="simple">
@@ -1764,13 +1764,13 @@ <h3><span class="section-number">41.2.1. </span>Environment<a class="headerlink"
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">40. </span>Model deployment</p>
+            <p class="prev-next-title"><span class="section-number">39. </span>Model deployment</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="set-up-env/first-assignment.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">41.3. </span>First assignment</p>
+        <p class="prev-next-title"><span class="section-number">40.3. </span>First assignment</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/data-science/analyzing-COVID-19-papers.html b/assignments/data-science/analyzing-COVID-19-papers.html
index a569dfca5..eb891ff99 100644
--- a/assignments/data-science/analyzing-COVID-19-papers.html
+++ b/assignments/data-science/analyzing-COVID-19-papers.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>41.22. Analyzing COVID-19 papers &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.22. Analyzing COVID-19 papers &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="41.23. Estimation of COVID-19 pandemic" href="estimation-of-COVID-19-pandemic.html" />
-    <link rel="prev" title="41.21. Soda profits" href="soda-profits.html" />
+    <link rel="next" title="40.23. Estimation of COVID-19 pandemic" href="estimation-of-COVID-19-pandemic.html" />
+    <link rel="prev" title="40.21. Soda profits" href="soda-profits.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -159,17 +159,17 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
-   1. Python programming basics
+   1. Python programming introduction
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../prerequisites/python-programming-basics.html">
-   3. Python programming basics
+   2. Python programming basics
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../prerequisites/python-programming-advanced.html">
-   4. Python programming advanced
+   3. Python programming advanced
   </a>
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 </ul>
@@ -181,7 +181,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../data-science/introduction/introduction.html">
-   5. Introduction
+   4. Introduction
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/introduction/defining-data-science.html">
-     5.1. Defining data science
+     4.1. Defining data science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/introduction/data-science-ethics.html">
-     5.2. Data Science ethics
+     4.2. Data Science ethics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/introduction/defining-data.html">
-     5.3. Defining data
+     4.3. Defining data
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/introduction/introduction-to-statistics-and-probability.html">
-     5.4. Introduction to statistics and probability
+     4.4. Introduction to statistics and probability
     </a>
    </li>
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  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../data-science/working-with-data/working-with-data.html">
-   6. Working with data
+   5. Working with data
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/relational-databases.html">
-     6.1. Relational databases
+     5.1. Relational databases
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/non-relational-data.html">
-     6.2. Non-relational data
+     5.2. Non-relational data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/numpy.html">
-     6.3. NumPy
+     5.3. NumPy
     </a>
    </li>
    <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas/pandas.html">
-     6.4. Pandas
+     5.4. Pandas
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     <ul>
      <li class="toctree-l3">
       <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
-       6.4.1. Introduction and Data Structures
+       5.4.1. Introduction and Data Structures
       </a>
      </li>
      <li class="toctree-l3">
       <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
-       6.4.2. Data Selection
+       5.4.2. Data Selection
       </a>
      </li>
      <li class="toctree-l3">
       <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
-       6.4.3. Advanced Pandas Techniques
+       5.4.3. Advanced Pandas Techniques
       </a>
      </li>
     </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
-     6.5. Data preparation
+     5.5. Data preparation
     </a>
    </li>
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../data-science/data-visualization/data-visualization.html">
-   7. Data visualization
+   6. Data visualization
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-visualization/visualization-distributions.html">
-     7.1. Visualizing distributions
+     6.1. Visualizing distributions
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-visualization/visualization-proportions.html">
-     7.2. Visualizing proportions
+     6.2. Visualizing proportions
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-visualization/visualization-relationships.html">
-     7.3. Visualizing relationships: all about honey 🍯
+     6.3. Visualizing relationships: all about honey 🍯
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-visualization/meaningful-visualizations.html">
-     7.4. Making meaningful visualizations
+     6.4. Making meaningful visualizations
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../data-science/data-science-lifecycle/data-science-lifecycle.html">
-   8. Data Science lifecycle
+   7. Data Science lifecycle
   </a>
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-science-lifecycle/introduction.html">
-     8.1. Introduction to the Data Science lifecycle
+     7.1. Introduction to the Data Science lifecycle
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-science-lifecycle/analyzing.html">
-     8.2. Analyzing
+     7.2. Analyzing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-science-lifecycle/communication.html">
-     8.3. Communication
+     7.3. Communication
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../data-science/data-science-in-the-cloud/data-science-in-the-cloud.html">
-   9. Data Science in the cloud
+   8. Data Science in the cloud
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-science-in-the-cloud/introduction.html">
-     9.1. Introduction
+     8.1. Introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-science-in-the-cloud/the-low-code-no-code-way.html">
-     9.2. The “low code/no code” way
+     8.2. The “low code/no code” way
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-science-in-the-cloud/the-azure-ml-sdk-way.html">
-     9.3. Data Science in the cloud: The “Azure ML SDK” way
+     8.3. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../data-science/data-science-in-the-wild.html">
-   10. Data Science in the real world
+   9. Data Science in the real world
   </a>
  </li>
 </ul>
@@ -370,17 +370,17 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/ml-overview.html">
-   11. Machine Learning overview
+   10. Machine Learning overview
   </a>
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  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
-   12. Summary of machine learning fundamentals
+   11. Summary of machine learning fundamentals
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   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   13. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     13.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     13.2. Managing data
+     12.2. Managing data
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     13.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     13.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
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  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   14. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   15. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     15.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     15.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     15.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     15.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
     </a>
    </li>
   </ul>
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  <li class="toctree-l1 has-children">
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-   16. parameter-optimization
+   15. parameter-optimization
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
-     16.1. Loss function
+     15.1. Loss function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
-     16.2. Gradient descent
+     15.2. Gradient descent
     </a>
    </li>
   </ul>
@@ -478,7 +478,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   17. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     17.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     17.2. K-Means clustering
+     16.2. K-Means clustering
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   <a class="reference internal" href="../../ml-advanced/ensemble-learning/getting-started-with-ensemble-learning.html">
-   18. Getting started with ensemble learning
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/bagging.html">
-     18.1. Bagging
+     17.1. Bagging
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/random-forest.html">
-     18.2. Random forest
+     17.2. Random forest
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/ensemble-learning/feature-importance.html">
-     18.3. Feature importance
+     17.3. Feature importance
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   <a class="reference internal" href="../../ml-advanced/gradient-boosting/introduction-to-gradient-boosting.html">
-   19. Introduction to Gradient Boosting
+   18. Introduction to Gradient Boosting
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting.html">
-     19.1. Gradient Boosting
+     18.1. Gradient Boosting
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/gradient-boosting-example.html">
-     19.2. Gradient boosting example
+     18.2. Gradient boosting example
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost.html">
-     19.3. XGBoost
+     18.3. XGBoost
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/gradient-boosting/xgboost-k-fold-cv-feature-importance.html">
-     19.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-advanced/unsupervised-learning.html">
-   20. Unsupervised learning
+   19. Unsupervised learning
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@@ -1673,7 +1673,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="analyzing-covid-19-papers">
-<h1><span class="section-number">41.22. </span>Analyzing COVID-19 papers<a class="headerlink" href="#analyzing-covid-19-papers" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.22. </span>Analyzing COVID-19 papers<a class="headerlink" href="#analyzing-covid-19-papers" title="Permalink to this headline">#</a></h1>
 <p>In this challenge, we will continue with the topic of COVID pandemic, and focus on processing scientific papers on the subject. There is <a class="reference external" href="https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge">CORD-19 Dataset</a> with more than 7000 (at the time of writing) papers on COVID, available with metadata and abstracts (and for about half of them there is also full text provided).</p>
 <p>A full example of analyzing this dataset using <a class="reference external" href="https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health/?WT.mc_id=academic-77958-bethanycheum">Text Analytics for Health</a> cognitive service is described <a class="reference external" href="https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/">in this blog post</a>. We will discuss simplified version of this analysis.</p>
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@@ -1686,7 +1686,7 @@ <h1><span class="section-number">41.22. </span>Analyzing COVID-19 papers<a class
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 <section id="getting-the-data">
-<h2><span class="section-number">41.22.1. </span>Getting the data<a class="headerlink" href="#getting-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.22.1. </span>Getting the data<a class="headerlink" href="#getting-the-data" title="Permalink to this headline">#</a></h2>
 <p>First, we need get the metadata for CORD papers that we will be working with.</p>
 <blockquote>
 <div><p><strong>NOTE</strong>: We do not provide a copy of the dataset as part of this repository. You may first need to download the <a class="reference external" href="https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge?select=metadata.csv"><code class="docutils literal notranslate"><span class="pre">metadata.csv</span></code></a> file from <a class="reference external" href="https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge">this dataset on Kaggle</a>. Registration with Kaggle may be required. You may also download the dataset without registration <a class="reference external" href="https://ai2-semanticscholar-cord-19.s3-us-west-2.amazonaws.com/historical_releases.html">from here</a>, but it will include all full texts in addition to metadata file.</p>
@@ -1716,7 +1716,7 @@ <h2><span class="section-number">41.22.1. </span>Getting the data<a class="heade
 <p>Interestingly, there are coronavirus-related papers that date back to 1880!</p>
 </section>
 <section id="structured-data-extraction">
-<h2><span class="section-number">41.22.2. </span>Structured data extraction<a class="headerlink" href="#structured-data-extraction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.22.2. </span>Structured data extraction<a class="headerlink" href="#structured-data-extraction" title="Permalink to this headline">#</a></h2>
 <p>Let’s see what kind of information we can easily extract from abstracts. One thing we might be interested in is to see which treatment strategies exist, and how they evolved over time. To begin with, we can manually compile the list of possible medications used to treat COVID, and also the list of diagnoses. We then go over them and search corresponding terms in the abstracts of papers.</p>
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 <section id="looking-for-trends-in-treatment-strategy">
-<h2><span class="section-number">41.22.3. </span>Looking for trends in treatment strategy<a class="headerlink" href="#looking-for-trends-in-treatment-strategy" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.22.3. </span>Looking for trends in treatment strategy<a class="headerlink" href="#looking-for-trends-in-treatment-strategy" title="Permalink to this headline">#</a></h2>
 <p>In the example above we have <code class="docutils literal notranslate"><span class="pre">sum</span></code>ed all values, but we can also do the same on a monthly basis:</p>
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@@ -1821,7 +1821,7 @@ <h2><span class="section-number">41.22.3. </span>Looking for trends in treatment
 </div>
 </section>
 <section id="computing-medicine-diagnosis-correspondence">
-<h2><span class="section-number">41.22.4. </span>Computing medicine-diagnosis correspondence<a class="headerlink" href="#computing-medicine-diagnosis-correspondence" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.22.4. </span>Computing medicine-diagnosis correspondence<a class="headerlink" href="#computing-medicine-diagnosis-correspondence" title="Permalink to this headline">#</a></h2>
 <p>One of the most interesting relationships we can look for is how different diagnoses are treated with different medicines. In order to visualize it, we need to compute <strong>co-occurence frequency map</strong>, which would show how many times two terms are mentioned in the same paper.</p>
 <p>Such a map is essentially a 2D matrix, which is best represented by <strong>numpy array</strong>. We will compute this map by walking through all abstracts, and marking entities that occur there:</p>
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@@ -1916,11 +1916,11 @@ <h2><span class="section-number">41.22.4. </span>Computing medicine-diagnosis co
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-<h2><span class="section-number">41.22.5. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.22.5. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>You have seen that we can use quite simple methods to extract information from non-structured data sources, such as text. In this example, we have taken the existing list of medications, but it would be much more powerful to use natural language processing (NLP) techniques to perform entity extraction from text. In <a class="reference external" href="https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/">this blog post</a> we describe how to use cloud services for entity extraction. Another option would be using Python NLP libraries such as <a class="reference external" href="https://www.nltk.org/">NLTK</a> - an approach for extracting information from text using NLTK is described <a class="reference external" href="https://www.nltk.org/book/ch07.html">here</a>.</p>
 </section>
 <section id="challenge">
-<h2><span class="section-number">41.22.6. </span>Challenge<a class="headerlink" href="#challenge" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.22.6. </span>Challenge<a class="headerlink" href="#challenge" title="Permalink to this headline">#</a></h2>
 <p>Continue to research the COVID paper data along the following lines:</p>
 <ol class="simple">
 <li><p>Build co-occurrence matrix of different medications, and see which medications often occur together (i.e. mentioned in one abstract). You can modify the code for building co-occurrence matrix for medications and diagnoses.</p></li>
@@ -1930,7 +1930,7 @@ <h2><span class="section-number">41.22.6. </span>Challenge<a class="headerlink"
 </ol>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.22.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.22.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1972,13 +1972,13 @@ <h2><span class="section-number">41.22.7. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.21. </span>Soda profits</p>
+            <p class="prev-next-title"><span class="section-number">40.21. </span>Soda profits</p>
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-        <p class="prev-next-title"><span class="section-number">41.23. </span>Estimation of COVID-19 pandemic</p>
+        <p class="prev-next-title"><span class="section-number">40.23. </span>Estimation of COVID-19 pandemic</p>
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     <link rel="index" title="Index" href="../../genindex.html" />
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+<h2><span class="section-number">40.27.1. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
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-     41.13. Apply your skills
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-     41.14. Try it in Excel
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-     41.16. Build your own custom vis
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-     41.17. Classifying datasets
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-     41.24. Data processing in Python
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-     41.27. Analyzing data
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-     41.35. Tell a story
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-     41.36. Explore a planetary computer dataset
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-     41.38. Market research
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-     41.39. Low code/no code Data Science project on Azure ML
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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-     41.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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-     41.45. Linear regression - California Housing
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-     41.46. Linear Regression Metrics
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-     41.47. Loss Function
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-     41.49. Linear Regression Implementation from Scratch
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-     41.51. ML logistic regression - assignment 2
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-     42.4. Data Science introduction
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-   41.9.1. Goal
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 </ul>
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                             <ul class="visible nav section-nav flex-column">
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 </ul>
@@ -1663,10 +1663,10 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="analyzing-text-about-data-science">
-<h1><span class="section-number">41.9. </span>Analyzing text about Data Science<a class="headerlink" href="#analyzing-text-about-data-science" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.9. </span>Analyzing text about Data Science<a class="headerlink" href="#analyzing-text-about-data-science" title="Permalink to this headline">#</a></h1>
 <p>In this example, let’s do a simple exercise that covers all steps of a traditional data science process. You do not have to write any code, you can just click on the cells below to execute them and observe the result. As a challenge, you are encouraged to try this code out with different data.</p>
 <section id="goal">
-<h2><span class="section-number">41.9.1. </span>Goal<a class="headerlink" href="#goal" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.9.1. </span>Goal<a class="headerlink" href="#goal" title="Permalink to this headline">#</a></h2>
 <p>In this section, we have been discussing different concepts related to Data Science. Let’s try to discover more related concepts by doing some <strong>text mining</strong>. We will start with a text about Data Science, extract keywords from it, and then try to visualize the result.</p>
 <p>As a text, I will use the page on Data Science from Wikipedia:</p>
 <div class="cell docutils container">
@@ -1684,7 +1684,7 @@ <h2><span class="section-number">41.9.1. </span>Goal<a class="headerlink" href="
 </div>
 </section>
 <section id="step-1-getting-the-data">
-<h2><span class="section-number">41.9.2. </span>Step 1: Getting the data<a class="headerlink" href="#step-1-getting-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.9.2. </span>Step 1: Getting the data<a class="headerlink" href="#step-1-getting-the-data" title="Permalink to this headline">#</a></h2>
 <p>First step in every data science process is getting the data. We will use <code class="docutils literal notranslate"><span class="pre">requests</span></code> library to do that as below.</p>
 <div class="cell docutils container">
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@@ -1732,7 +1732,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="step-2-transforming-the-data">
-<h2><span class="section-number">41.9.3. </span>Step 2: Transforming the data<a class="headerlink" href="#step-2-transforming-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.9.3. </span>Step 2: Transforming the data<a class="headerlink" href="#step-2-transforming-the-data" title="Permalink to this headline">#</a></h2>
 <p>The next step is to convert the data into the form suitable for processing. In our case, we have downloaded HTML source code from the page, and we need to convert it into plain text.</p>
 <p>There are many ways this can be done. We will use the simplest built-in <a class="reference external" href="https://docs.python.org/3/library/html.parser.html">HTMLParser</a> object from Python. We need to subclass the <code class="docutils literal notranslate"><span class="pre">HTMLParser</span></code> class and define the code that will collect all text inside HTML tags, except <code class="docutils literal notranslate"><span class="pre">&lt;script&gt;</span></code> and <code class="docutils literal notranslate"><span class="pre">&lt;style&gt;</span></code> tags.</p>
 <div class="cell docutils container">
@@ -1871,7 +1871,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="step-3-getting-insights">
-<h2><span class="section-number">41.9.4. </span>Step 3: Getting insights<a class="headerlink" href="#step-3-getting-insights" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.9.4. </span>Step 3: Getting insights<a class="headerlink" href="#step-3-getting-insights" title="Permalink to this headline">#</a></h2>
 <p>The most important step is to turn our data into some form from which we can draw insights. In our case, we want to extract keywords from the text, and see which keywords are more meaningful.</p>
 <p>We will use Python library called <a class="reference external" href="https://github.com/aneesha/RAKE">RAKE</a> for keyword extraction. First, let’s install this library in case it is not present:</p>
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@@ -1903,7 +1903,7 @@ <h2><span class="section-number">41.9.4. </span>Step 3: Getting insights<a class
 </div><p>We obtained a list terms together with associated degree of importance. As you can see, the most relevant disciplines, such as machine learning and big data, are present in the list at top positions.</p>
 </section>
 <section id="step-4-visualizing-the-result">
-<h2><span class="section-number">41.9.5. </span>Step 4: Visualizing the result<a class="headerlink" href="#step-4-visualizing-the-result" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.9.5. </span>Step 4: Visualizing the result<a class="headerlink" href="#step-4-visualizing-the-result" title="Permalink to this headline">#</a></h2>
 <p>People can interpret the data best in the visual form. Thus it often makes sense to visualize the data in order to draw some insights. We can use <code class="docutils literal notranslate"><span class="pre">matplotlib</span></code> library in Python to plot simple distribution of the keywords with their relevance:</p>
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 <p>In this exercise we have gone through a simple process of extracting some meaning from Wikipedia text, in the form of keywords and word cloud. This example is quite simple, but it demonstrates well all typical steps a data scientist will take when working with data, starting from data acquisition, up to visualization.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.9.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.9.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
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 <p>So far, you have worked with the Minnesota birds dataset to discover information about bird quantities and population density. Practice your application of these techniques by trying a different dataset, perhaps sourced from <a class="reference external" href="https://www.kaggle.com/">Kaggle</a>. Build a notebook to tell a story about this dataset, and make sure to use histograms when discussing it.</p>
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+<h2><span class="section-number">40.13.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
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-     41.10. Data Science scenarios
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-     41.11. Write a data ethics case study
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-     41.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.15. Dive into the beehive
+     40.15. Dive into the beehive
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
+     40.17. Classifying datasets
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-     41.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
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-     41.21. Soda profits
+     40.21. Soda profits
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-     41.22. Analyzing COVID-19 papers
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-     41.23. Estimation of COVID-19 pandemic
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-     41.24. Data processing in Python
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-     41.25. Evaluating data from a form
+     40.25. Evaluating data from a form
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-     41.26. Data preparation
+     40.26. Data preparation
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-     41.27. Analyzing data
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-     41.28. NYC taxi data in winter and summer
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-     41.29. Matplotlib applied
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-     41.35. Tell a story
+     40.35. Tell a story
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-     41.36. Explore a planetary computer dataset
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-     41.37. Exploring for answers
+     40.37. Exploring for answers
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-     41.38. Market research
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-     41.39. Low code/no code Data Science project on Azure ML
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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-     41.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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-     41.43. Machine Learning overview - assignment 2
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-     41.44. Regression with Scikit-learn
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-     41.45. Linear regression - California Housing
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-     41.46. Linear Regression Metrics
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-     41.47. Loss Function
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-     41.48. Gradient descent
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-     41.49. Linear Regression Implementation from Scratch
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               <div>
                 
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+<h1><span class="section-number">40.16. </span>Build your own custom vis<a class="headerlink" href="#build-your-own-custom-vis" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
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+<h2><span class="section-number">40.16.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>Using the code sample in this project to create a social network, mock up data of your own social interactions. You could map your usage of social media or make a diagram of your family members. Create an interesting web app that shows a unique visualization of a social network.</p>
 </section>
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 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
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 <p><strong>Structure Types</strong>: Structured, Semi-Structured, or Unstructured</p>
 <p><strong>Value Types</strong>: Qualitative or Quantitative</p>
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+     18.4. XGBoost + k-fold CV + Feature Importance
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-     41.5. Project Plan​ Template
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-     41.6. Python programming introduction
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-     41.8. Python programming advanced
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-     41.10. Data Science scenarios
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-     41.11. Write a data ethics case study
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-     41.12. Lines, scatters and bars
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-     41.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.15. Dive into the beehive
+     40.15. Dive into the beehive
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
+     40.17. Classifying datasets
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-     41.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
+     40.20. Displaying airport data
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-     41.21. Soda profits
+     40.21. Soda profits
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-     41.22. Analyzing COVID-19 papers
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-     41.23. Estimation of COVID-19 pandemic
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-     41.24. Data processing in Python
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-     41.25. Evaluating data from a form
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-     41.26. Data preparation
+     40.26. Data preparation
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-     41.27. Analyzing data
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-     41.28. NYC taxi data in winter and summer
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-     41.29. Matplotlib applied
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-     41.35. Tell a story
+     40.35. Tell a story
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-     41.36. Explore a planetary computer dataset
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-     41.37. Exploring for answers
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-     41.38. Market research
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-     41.39. Low code/no code Data Science project on Azure ML
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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-     41.42. Machine Learning overview - assignment 1
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-     41.43. Machine Learning overview - assignment 2
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-     41.44. Regression with Scikit-learn
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-     41.45. Linear regression - California Housing
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-     41.46. Linear Regression Metrics
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-     41.47. Loss Function
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-     41.48. Gradient descent
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-     41.49. Linear Regression Implementation from Scratch
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-     41.50. ML logistic regression - assignment 1
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-     41.51. ML logistic regression - assignment 2
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-     41.57. Create a regression model
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-     41.58. Linear and polynomial regression
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-     41.59. Retrying some regression
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     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id6">
-     41.26.1.23. Exercise:
+     40.26.1.23. Exercise:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#encoding-categorical-data">
-     41.26.1.24. Encoding categorical data
+     40.26.1.24. Encoding categorical data
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#removing-duplicate-data">
-   41.26.2. Removing duplicate data
+   40.26.2. Removing duplicate data
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#identifying-duplicates-duplicated">
-     41.26.2.1. Identifying duplicates:
+     40.26.2.1. Identifying duplicates:
      <code class="docutils literal notranslate">
       <span class="pre">
        duplicated
@@ -1772,7 +1772,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dropping-duplicates-drop-duplicates">
-     41.26.2.2. Dropping duplicates:
+     40.26.2.2. Dropping duplicates:
      <code class="docutils literal notranslate">
       <span class="pre">
        drop_duplicates
@@ -1784,7 +1784,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.26.3. Acknowledgments
+   40.26.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1808,7 +1808,7 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploring-dataframe-information">
-   41.26.1. Exploring
+   40.26.1. Exploring
    <code class="docutils literal notranslate">
     <span class="pre">
      DataFrame
@@ -1819,7 +1819,7 @@ <h2> Contents </h2>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dataframe-shape">
-     41.26.1.1.
+     40.26.1.1.
      <code class="docutils literal notranslate">
       <span class="pre">
        DataFrame.shape
@@ -1829,7 +1829,7 @@ <h2> Contents </h2>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dataframe-columns">
-     41.26.1.2.
+     40.26.1.2.
      <code class="docutils literal notranslate">
       <span class="pre">
        DataFrame.columns
@@ -1839,7 +1839,7 @@ <h2> Contents </h2>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dataframe-info">
-     41.26.1.3.
+     40.26.1.3.
      <code class="docutils literal notranslate">
       <span class="pre">
        DataFrame.info
@@ -1849,12 +1849,12 @@ <h2> Contents </h2>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dataframe-describe">
-     41.26.1.4. DataFrame.describe()
+     40.26.1.4. DataFrame.describe()
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dataframe-head">
-     41.26.1.5.
+     40.26.1.5.
      <code class="docutils literal notranslate">
       <span class="pre">
        DataFrame.head
@@ -1864,12 +1864,12 @@ <h2> Contents </h2>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exercise">
-     41.26.1.6. Exercise:
+     40.26.1.6. Exercise:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dataframe-tail">
-     41.26.1.7.
+     40.26.1.7.
      <code class="docutils literal notranslate">
       <span class="pre">
        DataFrame.tail
@@ -1879,12 +1879,12 @@ <h2> Contents </h2>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#missing-data">
-     41.26.1.8. Missing Data
+     40.26.1.8. Missing Data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#none-non-float-missing-data">
-     41.26.1.9.
+     40.26.1.9.
      <code class="docutils literal notranslate">
       <span class="pre">
        None
@@ -1895,7 +1895,7 @@ <h2> Contents </h2>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#nan-missing-float-values">
-     41.26.1.10.
+     40.26.1.10.
      <code class="docutils literal notranslate">
       <span class="pre">
        NaN
@@ -1906,12 +1906,12 @@ <h2> Contents </h2>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     41.26.1.11. Exercise:
+     40.26.1.11. Exercise:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#nan-and-none-null-values-in-pandas">
-     41.26.1.12.
+     40.26.1.12.
      <code class="docutils literal notranslate">
       <span class="pre">
        NaN
@@ -1928,74 +1928,74 @@ <h2> Contents </h2>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id2">
-     41.26.1.13. Exercise:
+     40.26.1.13. Exercise:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#detecting-null-values">
-     41.26.1.14. Detecting null values
+     40.26.1.14. Detecting null values
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id3">
-     41.26.1.15. Exercise:
+     40.26.1.15. Exercise:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dealing-with-missing-data">
-     41.26.1.16. Dealing with missing data
+     40.26.1.16. Dealing with missing data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dropping-null-values">
-     41.26.1.17. Dropping null values
+     40.26.1.17. Dropping null values
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id4">
-     41.26.1.18. Exercise:
+     40.26.1.18. Exercise:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#filling-null-values">
-     41.26.1.19. Filling null values
+     40.26.1.19. Filling null values
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#categorical-data-non-numeric">
-     41.26.1.20. Categorical Data(Non-numeric)
+     40.26.1.20. Categorical Data(Non-numeric)
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#numeric-data">
-     41.26.1.21. Numeric data
+     40.26.1.21. Numeric data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id5">
-     41.26.1.22. Exercise:
+     40.26.1.22. Exercise:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id6">
-     41.26.1.23. Exercise:
+     40.26.1.23. Exercise:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#encoding-categorical-data">
-     41.26.1.24. Encoding categorical data
+     40.26.1.24. Encoding categorical data
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#removing-duplicate-data">
-   41.26.2. Removing duplicate data
+   40.26.2. Removing duplicate data
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#identifying-duplicates-duplicated">
-     41.26.2.1. Identifying duplicates:
+     40.26.2.1. Identifying duplicates:
      <code class="docutils literal notranslate">
       <span class="pre">
        duplicated
@@ -2005,7 +2005,7 @@ <h2> Contents </h2>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dropping-duplicates-drop-duplicates">
-     41.26.2.2. Dropping duplicates:
+     40.26.2.2. Dropping duplicates:
      <code class="docutils literal notranslate">
       <span class="pre">
        drop_duplicates
@@ -2017,7 +2017,7 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.26.3. Acknowledgments
+   40.26.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -2031,9 +2031,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="data-preparation">
-<h1><span class="section-number">41.26. </span>Data preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.26. </span>Data preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">#</a></h1>
 <section id="exploring-dataframe-information">
-<h2><span class="section-number">41.26.1. </span>Exploring <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> information<a class="headerlink" href="#exploring-dataframe-information" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.26.1. </span>Exploring <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> information<a class="headerlink" href="#exploring-dataframe-information" title="Permalink to this headline">#</a></h2>
 <blockquote>
 <div><p><strong>Learning goal:</strong> By the end of this subsection, you should be comfortable finding general information about the data stored in pandas DataFrames.</p>
 </div></blockquote>
@@ -2051,7 +2051,7 @@ <h2><span class="section-number">41.26.1. </span>Exploring <code class="docutils
 </div>
 </div>
 <section id="dataframe-shape">
-<h3><span class="section-number">41.26.1.1. </span><code class="docutils literal notranslate"><span class="pre">DataFrame.shape</span></code><a class="headerlink" href="#dataframe-shape" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.1. </span><code class="docutils literal notranslate"><span class="pre">DataFrame.shape</span></code><a class="headerlink" href="#dataframe-shape" title="Permalink to this headline">#</a></h3>
 <p>We have loaded the Iris Dataset in the variable <code class="docutils literal notranslate"><span class="pre">iris_df</span></code>. Before diving into the data, it would be valuable to know the number of datapoints we have and the overall size of the dataset. It is useful to look at the volume of data we are dealing with.</p>
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@@ -2064,7 +2064,7 @@ <h3><span class="section-number">41.26.1.1. </span><code class="docutils literal
 <p><code class="docutils literal notranslate"><span class="pre">shape</span></code> here is an attribute of the dataframe and not a function, which is why it doesn’t end in a pair of parentheses.</p>
 </section>
 <section id="dataframe-columns">
-<h3><span class="section-number">41.26.1.2. </span><code class="docutils literal notranslate"><span class="pre">DataFrame.columns</span></code><a class="headerlink" href="#dataframe-columns" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.2. </span><code class="docutils literal notranslate"><span class="pre">DataFrame.columns</span></code><a class="headerlink" href="#dataframe-columns" title="Permalink to this headline">#</a></h3>
 <p>Let us now move into the 4 columns of data. What does each of them exactly represent? The <code class="docutils literal notranslate"><span class="pre">columns</span></code> attribute will give us the name of the columns in the dataframe.</p>
 <div class="cell docutils container">
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@@ -2076,7 +2076,7 @@ <h3><span class="section-number">41.26.1.2. </span><code class="docutils literal
 <p>As we can see, there are four(4) columns. The <code class="docutils literal notranslate"><span class="pre">columns</span></code> attribute tells us the name of the columns and basically nothing else. This attribute assumes importance when we want to identify the features a dataset contains.</p>
 </section>
 <section id="dataframe-info">
-<h3><span class="section-number">41.26.1.3. </span><code class="docutils literal notranslate"><span class="pre">DataFrame.info</span></code><a class="headerlink" href="#dataframe-info" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.3. </span><code class="docutils literal notranslate"><span class="pre">DataFrame.info</span></code><a class="headerlink" href="#dataframe-info" title="Permalink to this headline">#</a></h3>
 <p>The amount of data(given by the <code class="docutils literal notranslate"><span class="pre">shape</span></code> attribute) and the name of the features or columns(given by the <code class="docutils literal notranslate"><span class="pre">columns</span></code> attribute) tell us something about the dataset. Now, we would want to dive deeper into the dataset. The <code class="docutils literal notranslate"><span class="pre">DataFrame.info()</span></code> function is quite useful for this.</p>
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@@ -2092,7 +2092,7 @@ <h3><span class="section-number">41.26.1.3. </span><code class="docutils literal
 </ol>
 </section>
 <section id="dataframe-describe">
-<h3><span class="section-number">41.26.1.4. </span>DataFrame.describe()<a class="headerlink" href="#dataframe-describe" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.4. </span>DataFrame.describe()<a class="headerlink" href="#dataframe-describe" title="Permalink to this headline">#</a></h3>
 <p>Say we have a lot of numerical data in our dataset. Univariate statistical calculations such as the mean, median, quartiles etc. can be done on each of the columns individually. The <code class="docutils literal notranslate"><span class="pre">DataFrame.describe()</span></code> function provides us with a statistical summary of the numerical columns of a dataset.</p>
 <div class="cell docutils container">
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@@ -2104,7 +2104,7 @@ <h3><span class="section-number">41.26.1.4. </span>DataFrame.describe()<a class=
 <p>The output above shows the total number of data points, mean, standard deviation, minimum, lower quartile(25%), median(50%), upper quartile(75%) and the maximum value of each column.</p>
 </section>
 <section id="dataframe-head">
-<h3><span class="section-number">41.26.1.5. </span><code class="docutils literal notranslate"><span class="pre">DataFrame.head</span></code><a class="headerlink" href="#dataframe-head" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.5. </span><code class="docutils literal notranslate"><span class="pre">DataFrame.head</span></code><a class="headerlink" href="#dataframe-head" title="Permalink to this headline">#</a></h3>
 <p>With all the above functions and attributes, we have got a top level view of the dataset. We know how many data points are there, how many features are there, the data type of each feature and the number of non-null values for each feature.</p>
 <p>Now its time to look at the data itself. Let’s see what the first few rows(the first few datapoints) of our <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> look like:</p>
 <div class="cell docutils container">
@@ -2117,7 +2117,7 @@ <h3><span class="section-number">41.26.1.5. </span><code class="docutils literal
 <p>As the output here, we can see five(5) entries of the dataset. If we look at the index at the left, we find out that these are the first five rows.</p>
 </section>
 <section id="exercise">
-<h3><span class="section-number">41.26.1.6. </span>Exercise:<a class="headerlink" href="#exercise" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.6. </span>Exercise:<a class="headerlink" href="#exercise" title="Permalink to this headline">#</a></h3>
 <p>From the example given above, it is clear that, by default, <code class="docutils literal notranslate"><span class="pre">DataFrame.head</span></code> returns the first five rows of a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>. In the code cell below, can you figure out a way to display more than five rows?</p>
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@@ -2128,7 +2128,7 @@ <h3><span class="section-number">41.26.1.6. </span>Exercise:<a class="headerlink
 </div>
 </section>
 <section id="dataframe-tail">
-<h3><span class="section-number">41.26.1.7. </span><code class="docutils literal notranslate"><span class="pre">DataFrame.tail</span></code><a class="headerlink" href="#dataframe-tail" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.7. </span><code class="docutils literal notranslate"><span class="pre">DataFrame.tail</span></code><a class="headerlink" href="#dataframe-tail" title="Permalink to this headline">#</a></h3>
 <p>Another way of looking at the data can be from the end(instead of the beginning). The flipside of <code class="docutils literal notranslate"><span class="pre">DataFrame.head</span></code> is <code class="docutils literal notranslate"><span class="pre">DataFrame.tail</span></code>, which returns the last five rows of a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>:</p>
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@@ -2144,7 +2144,7 @@ <h3><span class="section-number">41.26.1.7. </span><code class="docutils literal
 </div></blockquote>
 </section>
 <section id="missing-data">
-<h3><span class="section-number">41.26.1.8. </span>Missing Data<a class="headerlink" href="#missing-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.8. </span>Missing Data<a class="headerlink" href="#missing-data" title="Permalink to this headline">#</a></h3>
 <p>Let us dive into missing data. Missing data occurs, when no value is stored in some of the columns.</p>
 <p>Let us take an example: say someone is conscious about his/her weight and doesn’t fill the weight field in a survey. Then, the weight value for that certain person will be missing.</p>
 <p>Most of the time, in real world datasets, missing values occur.</p>
@@ -2153,7 +2153,7 @@ <h3><span class="section-number">41.26.1.8. </span>Missing Data<a class="headerl
 <p>For missing values apart from floats, pandas uses the Python <code class="docutils literal notranslate"><span class="pre">None</span></code> object. While it might seem confusing that you will encounter two different kinds of values that say essentially the same thing, there are sound programmatic reasons for this design choice and, in practice, going this route enables pandas to deliver a good compromise for the vast majority of cases. Notwithstanding this, both <code class="docutils literal notranslate"><span class="pre">None</span></code> and <code class="docutils literal notranslate"><span class="pre">NaN</span></code> carry restrictions that you need to be mindful of with regards to how they can be used.</p>
 </section>
 <section id="none-non-float-missing-data">
-<h3><span class="section-number">41.26.1.9. </span><code class="docutils literal notranslate"><span class="pre">None</span></code>: non-float missing data<a class="headerlink" href="#none-non-float-missing-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.9. </span><code class="docutils literal notranslate"><span class="pre">None</span></code>: non-float missing data<a class="headerlink" href="#none-non-float-missing-data" title="Permalink to this headline">#</a></h3>
 <p>Because <code class="docutils literal notranslate"><span class="pre">None</span></code> comes from Python, it cannot be used in NumPy and pandas arrays that are not of data type <code class="docutils literal notranslate"><span class="pre">object</span></code>. Remember, NumPy arrays (and the data structures in pandas) can contain only one type of data. This is what gives them their tremendous power for large-scale data and computational work, but it also limits their flexibility. Such arrays have to upcast to the “lowest common denominator,” the data type that will encompass everything in the array. When <code class="docutils literal notranslate"><span class="pre">None</span></code> is in the array, it means you are working with Python objects.</p>
 <p>To see this in action, consider the following example array (note the <code class="docutils literal notranslate"><span class="pre">dtype</span></code> for it):</p>
 <div class="cell docutils container">
@@ -2178,7 +2178,7 @@ <h3><span class="section-number">41.26.1.9. </span><code class="docutils literal
 <p><strong>Key takeaway</strong>: Addition (and other operations) between integers and <code class="docutils literal notranslate"><span class="pre">None</span></code> values is undefined, which can limit what you can do with datasets that contain them.</p>
 </section>
 <section id="nan-missing-float-values">
-<h3><span class="section-number">41.26.1.10. </span><code class="docutils literal notranslate"><span class="pre">NaN</span></code>: missing float values<a class="headerlink" href="#nan-missing-float-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.10. </span><code class="docutils literal notranslate"><span class="pre">NaN</span></code>: missing float values<a class="headerlink" href="#nan-missing-float-values" title="Permalink to this headline">#</a></h3>
 <p>In contrast to <code class="docutils literal notranslate"><span class="pre">None</span></code>, NumPy (and therefore pandas) supports <code class="docutils literal notranslate"><span class="pre">NaN</span></code> for its fast, vectorized operations and ufuncs. The bad news is that any arithmetic performed on <code class="docutils literal notranslate"><span class="pre">NaN</span></code> always results in <code class="docutils literal notranslate"><span class="pre">NaN</span></code>. For example:</p>
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@@ -2205,7 +2205,7 @@ <h3><span class="section-number">41.26.1.10. </span><code class="docutils litera
 </div>
 </section>
 <section id="id1">
-<h3><span class="section-number">41.26.1.11. </span>Exercise:<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.11. </span>Exercise:<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># What happens if you add np.nan and None together?</span>
@@ -2216,7 +2216,7 @@ <h3><span class="section-number">41.26.1.11. </span>Exercise:<a class="headerlin
 <p>Remember: <code class="docutils literal notranslate"><span class="pre">NaN</span></code> is just for missing floating-point values; there is no <code class="docutils literal notranslate"><span class="pre">NaN</span></code> equivalent for integers, strings, or Booleans.</p>
 </section>
 <section id="nan-and-none-null-values-in-pandas">
-<h3><span class="section-number">41.26.1.12. </span><code class="docutils literal notranslate"><span class="pre">NaN</span></code> and <code class="docutils literal notranslate"><span class="pre">None</span></code>: null values in pandas<a class="headerlink" href="#nan-and-none-null-values-in-pandas" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.12. </span><code class="docutils literal notranslate"><span class="pre">NaN</span></code> and <code class="docutils literal notranslate"><span class="pre">None</span></code>: null values in pandas<a class="headerlink" href="#nan-and-none-null-values-in-pandas" title="Permalink to this headline">#</a></h3>
 <p>Even though <code class="docutils literal notranslate"><span class="pre">NaN</span></code> and <code class="docutils literal notranslate"><span class="pre">None</span></code> can behave somewhat differently, pandas is nevertheless built to handle them interchangeably. To see what we mean, consider a <code class="docutils literal notranslate"><span class="pre">Series</span></code> of integers:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2228,7 +2228,7 @@ <h3><span class="section-number">41.26.1.12. </span><code class="docutils litera
 </div>
 </section>
 <section id="id2">
-<h3><span class="section-number">41.26.1.13. </span>Exercise:<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.13. </span>Exercise:<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Now set an element of int_series equal to None.</span>
@@ -2248,7 +2248,7 @@ <h3><span class="section-number">41.26.1.13. </span>Exercise:<a class="headerlin
 <p>These are important methods to master and get comfortable with, so let’s go over them each in some depth.</p>
 </section>
 <section id="detecting-null-values">
-<h3><span class="section-number">41.26.1.14. </span>Detecting null values<a class="headerlink" href="#detecting-null-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.14. </span>Detecting null values<a class="headerlink" href="#detecting-null-values" title="Permalink to this headline">#</a></h3>
 <p>Now that we have understood the importance of missing values, we need to detect them in our dataset, before dealing with them.
 Both <code class="docutils literal notranslate"><span class="pre">isnull()</span></code> and <code class="docutils literal notranslate"><span class="pre">notnull()</span></code> are your primary methods for detecting null data. Both return Boolean masks over your data.</p>
 <div class="cell docutils container">
@@ -2277,7 +2277,7 @@ <h3><span class="section-number">41.26.1.14. </span>Detecting null values<a clas
 </div>
 </section>
 <section id="id3">
-<h3><span class="section-number">41.26.1.15. </span>Exercise:<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.15. </span>Exercise:<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Try running example3[example3.notnull()].</span>
@@ -2289,7 +2289,7 @@ <h3><span class="section-number">41.26.1.15. </span>Exercise:<a class="headerlin
 <p><strong>Key takeaway</strong>: Both the <code class="docutils literal notranslate"><span class="pre">isnull()</span></code> and <code class="docutils literal notranslate"><span class="pre">notnull()</span></code> methods produce similar results when you use them in DataFrames: they show the results and the index of those results, which will help you enormously as you wrestle with your data.</p>
 </section>
 <section id="dealing-with-missing-data">
-<h3><span class="section-number">41.26.1.16. </span>Dealing with missing data<a class="headerlink" href="#dealing-with-missing-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.16. </span>Dealing with missing data<a class="headerlink" href="#dealing-with-missing-data" title="Permalink to this headline">#</a></h3>
 <blockquote>
 <div><p><strong>Learning goal:</strong> By the end of this subsection, you should know how and when to replace or remove null values from DataFrames.</p>
 </div></blockquote>
@@ -2303,7 +2303,7 @@ <h3><span class="section-number">41.26.1.16. </span>Dealing with missing data<a
 <p>We will discuss both these methods and their pros and cons in details.</p>
 </section>
 <section id="dropping-null-values">
-<h3><span class="section-number">41.26.1.17. </span>Dropping null values<a class="headerlink" href="#dropping-null-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.17. </span>Dropping null values<a class="headerlink" href="#dropping-null-values" title="Permalink to this headline">#</a></h3>
 <p>The amount of data we pass on to our model has a direct effect on its performance. Dropping null values means that we are reducing the number of datapoints, and hence reducing the size of the dataset. So, it is advisable to drop rows with null values when the dataset is quite large.</p>
 <p>Another instance maybe that a certain row or column has a lot of missing values. Then, they maybe dropped because they wouldn’t add much value to our analysis as most of the data is missing for that row/column.</p>
 <p>Beyond identifying missing values, pandas provides a convenient means to remove null values from <code class="docutils literal notranslate"><span class="pre">Series</span></code> and <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>s. To see this in action, let’s return to <code class="docutils literal notranslate"><span class="pre">example3</span></code>. The <code class="docutils literal notranslate"><span class="pre">DataFrame.dropna()</span></code> function helps in dropping the rows with null values.</p>
@@ -2365,7 +2365,7 @@ <h3><span class="section-number">41.26.1.17. </span>Dropping null values<a class
 </ol>
 </section>
 <section id="id4">
-<h3><span class="section-number">41.26.1.18. </span>Exercise:<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.18. </span>Exercise:<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># How might you go about dropping just column 3?</span>
@@ -2385,12 +2385,12 @@ <h3><span class="section-number">41.26.1.18. </span>Exercise:<a class="headerlin
 <p>Here, the first and last row have been dropped, because they contain only two non-null values.</p>
 </section>
 <section id="filling-null-values">
-<h3><span class="section-number">41.26.1.19. </span>Filling null values<a class="headerlink" href="#filling-null-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.19. </span>Filling null values<a class="headerlink" href="#filling-null-values" title="Permalink to this headline">#</a></h3>
 <p>It sometimes makes sense to fill in missing values with ones which could be valid. There are a few techniques to fill null values. The first is using Domain Knowledge(knowledge of the subject on which the dataset is based) to somehow approximate the missing values.</p>
 <p>You could use <code class="docutils literal notranslate"><span class="pre">isnull</span></code> to do this in place, but that can be laborious, particularly if you have a lot of values to fill. Because this is such a common task in data science, pandas provides <code class="docutils literal notranslate"><span class="pre">fillna</span></code>, which returns a copy of the <code class="docutils literal notranslate"><span class="pre">Series</span></code> or <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> with the missing values replaced with one of your choosing. Let’s create another example <code class="docutils literal notranslate"><span class="pre">Series</span></code> to see how this works in practice.</p>
 </section>
 <section id="categorical-data-non-numeric">
-<h3><span class="section-number">41.26.1.20. </span>Categorical Data(Non-numeric)<a class="headerlink" href="#categorical-data-non-numeric" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.20. </span>Categorical Data(Non-numeric)<a class="headerlink" href="#categorical-data-non-numeric" title="Permalink to this headline">#</a></h3>
 <p>First let us consider non-numeric data. In datasets, we have columns with categorical data. Eg. Gender, True or False etc.</p>
 <p>In most of these cases, we replace missing values with the <code class="docutils literal notranslate"><span class="pre">mode</span></code> of the column. Say, we have 100 data points and 90 have said True, 8 have said False and 2 have not filled. Then, we can will the 2 with True, considering the full column.</p>
 <p>Again, here we can use domain knowledge here. Let us consider an example of filling with the mode.</p>
@@ -2433,7 +2433,7 @@ <h3><span class="section-number">41.26.1.20. </span>Categorical Data(Non-numeric
 <p>As we can see, the null value has been replaced. Needless to say, we could have written anything in place or <code class="docutils literal notranslate"><span class="pre">'True'</span></code> and it would have got substituted.</p>
 </section>
 <section id="numeric-data">
-<h3><span class="section-number">41.26.1.21. </span>Numeric data<a class="headerlink" href="#numeric-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.21. </span>Numeric data<a class="headerlink" href="#numeric-data" title="Permalink to this headline">#</a></h3>
 <p>Now, coming to numeric data. Here, we have a two common ways of replacing missing values:</p>
 <ol class="simple">
 <li><p>Replace with Median of the row</p></li>
@@ -2532,7 +2532,7 @@ <h3><span class="section-number">41.26.1.21. </span>Numeric data<a class="header
 </ol>
 </section>
 <section id="id5">
-<h3><span class="section-number">41.26.1.22. </span>Exercise:<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.22. </span>Exercise:<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># What happens if you try to fill null values with a string, like &#39;&#39;?</span>
@@ -2574,7 +2574,7 @@ <h3><span class="section-number">41.26.1.22. </span>Exercise:<a class="headerlin
 <p>Notice that when a previous value is not available for forward-filling, the null value remains.</p>
 </section>
 <section id="id6">
-<h3><span class="section-number">41.26.1.23. </span>Exercise:<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.23. </span>Exercise:<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># What output does example4.fillna(method=&#39;bfill&#39;, axis=1) produce?</span>
@@ -2598,7 +2598,7 @@ <h3><span class="section-number">41.26.1.23. </span>Exercise:<a class="headerlin
 </div></blockquote>
 </section>
 <section id="encoding-categorical-data">
-<h3><span class="section-number">41.26.1.24. </span>Encoding categorical data<a class="headerlink" href="#encoding-categorical-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.1.24. </span>Encoding categorical data<a class="headerlink" href="#encoding-categorical-data" title="Permalink to this headline">#</a></h3>
 <p>Machine learning models only deal with numbers and any form of numeric data. It won’t be able to tell the difference between a Yes and a No, but it would be able to distinguish between 0 and 1. So, after filling in the missing values, we need to do encode the categorical data to some numeric form for the model to understand.</p>
 <p>Encoding can be done in two ways. We will be discussing them next.</p>
 <p><strong>LABEL ENCODING</strong></p>
@@ -2682,13 +2682,13 @@ <h3><span class="section-number">41.26.1.24. </span>Encoding categorical data<a
 </section>
 </section>
 <section id="removing-duplicate-data">
-<h2><span class="section-number">41.26.2. </span>Removing duplicate data<a class="headerlink" href="#removing-duplicate-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.26.2. </span>Removing duplicate data<a class="headerlink" href="#removing-duplicate-data" title="Permalink to this headline">#</a></h2>
 <blockquote>
 <div><p><strong>Learning goal:</strong> By the end of this subsection, you should be comfortable identifying and removing duplicate values from DataFrames.</p>
 </div></blockquote>
 <p>In addition to missing data, you will often encounter duplicated data in real-world datasets. Fortunately, pandas provides an easy means of detecting and removing duplicate entries.</p>
 <section id="identifying-duplicates-duplicated">
-<h3><span class="section-number">41.26.2.1. </span>Identifying duplicates: <code class="docutils literal notranslate"><span class="pre">duplicated</span></code><a class="headerlink" href="#identifying-duplicates-duplicated" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.2.1. </span>Identifying duplicates: <code class="docutils literal notranslate"><span class="pre">duplicated</span></code><a class="headerlink" href="#identifying-duplicates-duplicated" title="Permalink to this headline">#</a></h3>
 <p>You can easily spot duplicate values using the <code class="docutils literal notranslate"><span class="pre">duplicated</span></code> method in pandas, which returns a Boolean mask indicating whether an entry in a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> is a duplicate of an earlier one. Let’s create another example <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> to see this in action.</p>
 <div class="cell docutils container">
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@@ -2708,7 +2708,7 @@ <h3><span class="section-number">41.26.2.1. </span>Identifying duplicates: <code
 </div>
 </section>
 <section id="dropping-duplicates-drop-duplicates">
-<h3><span class="section-number">41.26.2.2. </span>Dropping duplicates: <code class="docutils literal notranslate"><span class="pre">drop_duplicates</span></code><a class="headerlink" href="#dropping-duplicates-drop-duplicates" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.26.2.2. </span>Dropping duplicates: <code class="docutils literal notranslate"><span class="pre">drop_duplicates</span></code><a class="headerlink" href="#dropping-duplicates-drop-duplicates" title="Permalink to this headline">#</a></h3>
 <p><code class="docutils literal notranslate"><span class="pre">drop_duplicates</span></code> simply returns a copy of the data for which all of the <code class="docutils literal notranslate"><span class="pre">duplicated</span></code> values are <code class="docutils literal notranslate"><span class="pre">False</span></code>:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2731,7 +2731,7 @@ <h3><span class="section-number">41.26.2.2. </span>Dropping duplicates: <code cl
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.26.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.26.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter. Original Notebook source from <a class="reference external" href="https://github.com/leestott/intro-Datascience/blob/master/Course%20Materials/4-Cleaning_and_Manipulating-Reference.ipynb"><em>Data Science: Introduction to Machine Learning for Data Science Python and Machine Learning Studio</em></a> by Lee Stott</p>
 </section>
 </section>
@@ -2773,13 +2773,13 @@ <h2><span class="section-number">41.26.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.25. </span>Evaluating data from a form</p>
+            <p class="prev-next-title"><span class="section-number">40.25. </span>Evaluating data from a form</p>
         </div>
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-        <p class="prev-next-title"><span class="section-number">41.27. </span>Analyzing data</p>
+        <p class="prev-next-title"><span class="section-number">40.27. </span>Analyzing data</p>
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     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>41.24. Data processing in Python &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.24. Data processing in Python &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="41.25. Evaluating data from a form" href="evaluating-data-from-a-form.html" />
-    <link rel="prev" title="41.23. Estimation of COVID-19 pandemic" href="estimation-of-COVID-19-pandemic.html" />
+    <link rel="next" title="40.25. Evaluating data from a form" href="evaluating-data-from-a-form.html" />
+    <link rel="prev" title="40.23. Estimation of COVID-19 pandemic" href="estimation-of-COVID-19-pandemic.html" />
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-     5.2. Data Science ethics
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+     8.3. Data Science in the cloud: The “Azure ML SDK” way
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-     41.5. Project Plan​ Template
+     40.5. Project Plan​ Template
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-     41.6. Python programming introduction
+     40.6. Python programming introduction
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+     40.7. Python programming basics
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-     41.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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-     41.10. Data Science scenarios
+     40.10. Data Science scenarios
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-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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-     41.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
+     40.17. Classifying datasets
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     <a class="reference internal" href="small-diabetes-study.html">
-     41.18. Small diabetes study
+     40.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
+     40.20. Displaying airport data
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-     41.21. Soda profits
+     40.21. Soda profits
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-     41.22. Analyzing COVID-19 papers
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-     41.23. Estimation of COVID-19 pandemic
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-     41.24. Data processing in Python
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         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#covid-19-spread-modeling">
-   41.24.1. COVID-19 spread modeling
+   40.24.1. COVID-19 spread modeling
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#covid-19-papers-analysis">
-   41.24.2. COVID-19 papers analysis
+   40.24.2. COVID-19 papers analysis
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#rubric">
-   41.24.3. Rubric
+   40.24.3. Rubric
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.24.4. Acknowledgments
+   40.24.4. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1551,22 +1551,22 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#covid-19-spread-modeling">
-   41.24.1. COVID-19 spread modeling
+   40.24.1. COVID-19 spread modeling
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#covid-19-papers-analysis">
-   41.24.2. COVID-19 papers analysis
+   40.24.2. COVID-19 papers analysis
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#rubric">
-   41.24.3. Rubric
+   40.24.3. Rubric
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.24.4. Acknowledgments
+   40.24.4. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1580,10 +1580,10 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="data-processing-in-python">
-<h1><span class="section-number">41.24. </span>Data processing in Python<a class="headerlink" href="#data-processing-in-python" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.24. </span>Data processing in Python<a class="headerlink" href="#data-processing-in-python" title="Permalink to this headline">#</a></h1>
 <p>In this assignment, we will ask you to elaborate on the code we have started developing in these <a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/working-with-data/numpy.html#your-turn">challenges</a>. The assignment consists of two parts:</p>
 <section id="covid-19-spread-modeling">
-<h2><span class="section-number">41.24.1. </span>COVID-19 spread modeling<a class="headerlink" href="#covid-19-spread-modeling" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.24.1. </span>COVID-19 spread modeling<a class="headerlink" href="#covid-19-spread-modeling" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>[ ] Plot <span class="math notranslate nohighlight">\(R_t\)</span> graphs for 5-6 different countries on one plot for comparison, or using several plots side-by-side</p></li>
 <li><p>[ ] See how the number of deaths and recoveries correlate with a number of infected cases.</p></li>
@@ -1592,7 +1592,7 @@ <h2><span class="section-number">41.24.1. </span>COVID-19 spread modeling<a clas
 </ul>
 </section>
 <section id="covid-19-papers-analysis">
-<h2><span class="section-number">41.24.2. </span>COVID-19 papers analysis<a class="headerlink" href="#covid-19-papers-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.24.2. </span>COVID-19 papers analysis<a class="headerlink" href="#covid-19-papers-analysis" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>[ ] Build a co-occurrence matrix of different medications, and see which medications often occur together (i.e. mentioned in one abstract). You can modify the code for building a co-occurrence matrix for medications and diagnoses.</p></li>
 <li><p>[ ] Visualize this matrix using a heatmap.</p></li>
@@ -1601,7 +1601,7 @@ <h2><span class="section-number">41.24.2. </span>COVID-19 papers analysis<a clas
 </ul>
 </section>
 <section id="rubric">
-<h2><span class="section-number">41.24.3. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.24.3. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Exemplary</p></th>
@@ -1618,7 +1618,7 @@ <h2><span class="section-number">41.24.3. </span>Rubric<a class="headerlink" hre
 </table>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.24.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.24.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1660,13 +1660,13 @@ <h2><span class="section-number">41.24.4. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.23. </span>Estimation of COVID-19 pandemic</p>
+            <p class="prev-next-title"><span class="section-number">40.23. </span>Estimation of COVID-19 pandemic</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="evaluating-data-from-a-form.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">41.25. </span>Evaluating data from a form</p>
+        <p class="prev-next-title"><span class="section-number">40.25. </span>Evaluating data from a form</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html b/assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html
index ff3735e54..846ca1427 100644
--- a/assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html
+++ b/assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html
@@ -6,7 +6,7 @@
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>41.41. Data Science in the cloud: The “Azure ML SDK” way &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.41. Data Science in the cloud: The “Azure ML SDK” way &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="41.42. Machine Learning overview - assignment 1" href="../ml-fundamentals/ml-overview-iris.html" />
-    <link rel="prev" title="41.40. Data Science project using Azure ML SDK" href="data-science-project-using-azure-ml-sdk.html" />
+    <link rel="next" title="40.42. Machine Learning overview - assignment 1" href="../ml-fundamentals/ml-overview-iris.html" />
+    <link rel="prev" title="40.40. Data Science project using Azure ML SDK" href="data-science-project-using-azure-ml-sdk.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -159,17 +159,17 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
-   1. Python programming basics
+   1. Python programming introduction
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../prerequisites/python-programming-basics.html">
-   3. Python programming basics
+   2. Python programming basics
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../prerequisites/python-programming-advanced.html">
-   4. Python programming advanced
+   3. Python programming advanced
   </a>
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@@ -181,7 +181,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../data-science/introduction/introduction.html">
-   5. Introduction
+   4. Introduction
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@@ -191,29 +191,29 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/introduction/defining-data-science.html">
-     5.1. Defining data science
+     4.1. Defining data science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/introduction/data-science-ethics.html">
-     5.2. Data Science ethics
+     4.2. Data Science ethics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/introduction/defining-data.html">
-     5.3. Defining data
+     4.3. Defining data
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/introduction/introduction-to-statistics-and-probability.html">
-     5.4. Introduction to statistics and probability
+     4.4. Introduction to statistics and probability
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../data-science/working-with-data/working-with-data.html">
-   6. Working with data
+   5. Working with data
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/relational-databases.html">
-     6.1. Relational databases
+     5.1. Relational databases
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/non-relational-data.html">
-     6.2. Non-relational data
+     5.2. Non-relational data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/numpy.html">
-     6.3. NumPy
+     5.3. NumPy
     </a>
    </li>
    <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas/pandas.html">
-     6.4. Pandas
+     5.4. Pandas
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     <ul>
      <li class="toctree-l3">
       <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
-       6.4.1. Introduction and Data Structures
+       5.4.1. Introduction and Data Structures
       </a>
      </li>
      <li class="toctree-l3">
       <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
-       6.4.2. Data Selection
+       5.4.2. Data Selection
       </a>
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       <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
-       6.4.3. Advanced Pandas Techniques
+       5.4.3. Advanced Pandas Techniques
       </a>
      </li>
     </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
-     6.5. Data preparation
+     5.5. Data preparation
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+   6. Data visualization
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   <ul>
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     <a class="reference internal" href="../../data-science/data-visualization/visualization-distributions.html">
-     7.1. Visualizing distributions
+     6.1. Visualizing distributions
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-visualization/visualization-proportions.html">
-     7.2. Visualizing proportions
+     6.2. Visualizing proportions
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-visualization/visualization-relationships.html">
-     7.3. Visualizing relationships: all about honey 🍯
+     6.3. Visualizing relationships: all about honey 🍯
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-visualization/meaningful-visualizations.html">
-     7.4. Making meaningful visualizations
+     6.4. Making meaningful visualizations
     </a>
    </li>
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  <li class="toctree-l1 has-children">
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    <li class="toctree-l2">
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-     8.1. Introduction to the Data Science lifecycle
+     7.1. Introduction to the Data Science lifecycle
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-science-lifecycle/analyzing.html">
-     8.2. Analyzing
+     7.2. Analyzing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-science-lifecycle/communication.html">
-     8.3. Communication
+     7.3. Communication
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+   8. Data Science in the cloud
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     <a class="reference internal" href="../../data-science/data-science-in-the-cloud/introduction.html">
-     9.1. Introduction
+     8.1. Introduction
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     <a class="reference internal" href="../../data-science/data-science-in-the-cloud/the-low-code-no-code-way.html">
-     9.2. The “low code/no code” way
+     8.2. The “low code/no code” way
     </a>
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     <a class="reference internal" href="../../data-science/data-science-in-the-cloud/the-azure-ml-sdk-way.html">
-     9.3. Data Science in the cloud: The “Azure ML SDK” way
+     8.3. Data Science in the cloud: The “Azure ML SDK” way
     </a>
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  <li class="toctree-l1">
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-   10. Data Science in the real world
+   9. Data Science in the real world
   </a>
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@@ -370,17 +370,17 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   <a class="reference internal" href="../../ml-fundamentals/ml-overview.html">
-   11. Machine Learning overview
+   10. Machine Learning overview
   </a>
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  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
-   12. Summary of machine learning fundamentals
+   11. Summary of machine learning fundamentals
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   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
-   13. Regression models for Machine Learning
+   12. Regression models for Machine Learning
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     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     13.1. Tools of the trade
+     12.1. Tools of the trade
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
-     13.2. Managing data
+     12.2. Managing data
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
-     13.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     13.4. Logistic regression
+     12.4. Logistic regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   14. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
   </a>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
-   15. Getting started with classification
+   14. Getting started with classification
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/introduction-to-classification.html">
-     15.1. Introduction to classification
+     14.1. Introduction to classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/more-classifiers.html">
-     15.2. More classifiers
+     14.2. More classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/yet-other-classifiers.html">
-     15.3. Yet other classifiers
+     14.3. Yet other classifiers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/classification/applied-ml-build-a-web-app.html">
-     15.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/parameter-optimization.html">
-   16. parameter-optimization
+   15. parameter-optimization
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/loss-function.html">
-     16.1. Loss function
+     15.1. Loss function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html">
-     16.2. Gradient descent
+     15.2. Gradient descent
     </a>
    </li>
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@@ -478,7 +478,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   <a class="reference internal" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html">
-   17. Clustering models for Machine Learning
+   16. Clustering models for Machine Learning
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/introduction-to-clustering.html">
-     17.1. Introduction to clustering
+     16.1. Introduction to clustering
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-advanced/clustering/k-means-clustering.html">
-     17.2. K-Means clustering
+     16.2. K-Means clustering
     </a>
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-     41.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.16. Build your own custom vis
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-     41.17. Classifying datasets
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-     41.18. Small diabetes study
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-     41.35. Tell a story
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-     41.39. Low code/no code Data Science project on Azure ML
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
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-     41.45. Linear regression - California Housing
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-     41.95. Intro to TensorFlow for Deep Learning
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-     41.99. Google Stock Price Prediction RNN
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-     41.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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     </a>
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-     41.103. Fun with Variational Autoencoders
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-     41.106. Neural Networks for Classification with TensorFlow
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-     41.107. NN Classify 15 Fruits Assignment
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-     41.112. DQN On Foreign Exchange Market
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-     41.113. Art by gan
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-     41.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
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     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     41.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
    </li>
   </ul>
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   <ul>
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+     41.1. Python programming introduction
     </a>
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-     42.2. Python programming basics
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-     42.3. Python programming advanced
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-     42.4. Data Science introduction
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-     42.5. Relational vs. non-relational database
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-     42.6. NumPy and Pandas
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-     42.8. Data Science lifecycle
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-     42.9. Data Science in the cloud
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-     42.10. Data Science in real world
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-     42.11. Machine Learning overview
+     41.11. Machine Learning overview
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-     42.12. Linear Regression
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-     42.13. Logistic Regression
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-     42.14. Logistic Regression
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-     42.15. Neural Network
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-     42.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
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-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
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-     42.19. Model Selection
+     41.19. Model Selection
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-     42.20. Convolutional Neural Network
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+     41.21. Generative Adversarial Network
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         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   41.41.1. Introduction
+   40.41.1. Introduction
   </a>
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-   41.41.2. Azure Machine Learning SDK-specific imports
+   40.41.2. Azure Machine Learning SDK-specific imports
   </a>
  </li>
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-   41.41.3. Initialize Workspace
+   40.41.3. Initialize Workspace
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-   41.41.4. Create an Azure ML experiment
+   40.41.4. Create an Azure ML experiment
   </a>
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   <a class="reference internal nav-link" href="#create-a-compute-cluster">
-   41.41.5. Create a compute cluster
+   40.41.5. Create a compute cluster
   </a>
  </li>
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-   41.41.6. Data
+   40.41.6. Data
   </a>
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-   41.41.7. AutoML configuration
+   40.41.7. AutoML configuration
   </a>
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   <a class="reference internal nav-link" href="#automl-run">
-   41.41.8. AutoML run
+   40.41.8. AutoML run
   </a>
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   <a class="reference internal nav-link" href="#save-the-best-model">
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+   40.41.9. Save the best model
   </a>
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   <a class="reference internal nav-link" href="#deploy-the-best-model">
-   41.41.10. Deploy the best model
+   40.41.10. Deploy the best model
   </a>
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   <a class="reference internal nav-link" href="#consume-the-endpoint">
-   41.41.11. Consume the endpoint
+   40.41.11. Consume the endpoint
   </a>
  </li>
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+   40.41.12. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1654,62 +1654,62 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   41.41.1. Introduction
+   40.41.1. Introduction
   </a>
  </li>
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   <a class="reference internal nav-link" href="#azure-machine-learning-sdk-specific-imports">
-   41.41.2. Azure Machine Learning SDK-specific imports
+   40.41.2. Azure Machine Learning SDK-specific imports
   </a>
  </li>
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   <a class="reference internal nav-link" href="#initialize-workspace">
-   41.41.3. Initialize Workspace
+   40.41.3. Initialize Workspace
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   <a class="reference internal nav-link" href="#create-an-azure-ml-experiment">
-   41.41.4. Create an Azure ML experiment
+   40.41.4. Create an Azure ML experiment
   </a>
  </li>
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   <a class="reference internal nav-link" href="#create-a-compute-cluster">
-   41.41.5. Create a compute cluster
+   40.41.5. Create a compute cluster
   </a>
  </li>
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-   41.41.6. Data
+   40.41.6. Data
   </a>
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   <a class="reference internal nav-link" href="#automl-configuration">
-   41.41.7. AutoML configuration
+   40.41.7. AutoML configuration
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#automl-run">
-   41.41.8. AutoML run
+   40.41.8. AutoML run
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#save-the-best-model">
-   41.41.9. Save the best model
+   40.41.9. Save the best model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#deploy-the-best-model">
-   41.41.10. Deploy the best model
+   40.41.10. Deploy the best model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#consume-the-endpoint">
-   41.41.11. Consume the endpoint
+   40.41.11. Consume the endpoint
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.41.12. Acknowledgments
+   40.41.12. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1723,9 +1723,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="data-science-in-the-cloud-the-azure-ml-sdk-way">
-<h1><span class="section-number">41.41. </span>Data Science in the cloud: The “Azure ML SDK” way<a class="headerlink" href="#data-science-in-the-cloud-the-azure-ml-sdk-way" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.41. </span>Data Science in the cloud: The “Azure ML SDK” way<a class="headerlink" href="#data-science-in-the-cloud-the-azure-ml-sdk-way" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">41.41.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.41.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>In this notebook, we will learn how to use the Azure ML SDK to train, deploy and consume a model through Azure ML.</p>
 <p>Pre-requisites:</p>
 <ol class="simple">
@@ -1747,7 +1747,7 @@ <h2><span class="section-number">41.41.1. </span>Introduction<a class="headerlin
 </ol>
 </section>
 <section id="azure-machine-learning-sdk-specific-imports">
-<h2><span class="section-number">41.41.2. </span>Azure Machine Learning SDK-specific imports<a class="headerlink" href="#azure-machine-learning-sdk-specific-imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.41.2. </span>Azure Machine Learning SDK-specific imports<a class="headerlink" href="#azure-machine-learning-sdk-specific-imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">azureml.core</span> <span class="kn">import</span> <span class="n">Workspace</span><span class="p">,</span> <span class="n">Experiment</span>
@@ -1762,7 +1762,7 @@ <h2><span class="section-number">41.41.2. </span>Azure Machine Learning SDK-spec
 </div>
 </section>
 <section id="initialize-workspace">
-<h2><span class="section-number">41.41.3. </span>Initialize Workspace<a class="headerlink" href="#initialize-workspace" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.41.3. </span>Initialize Workspace<a class="headerlink" href="#initialize-workspace" title="Permalink to this headline">#</a></h2>
 <p>Initialize a workspace object from persisted configuration. Make sure the config file is present at .\config.json</p>
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 <section id="create-an-azure-ml-experiment">
-<h2><span class="section-number">41.41.4. </span>Create an Azure ML experiment<a class="headerlink" href="#create-an-azure-ml-experiment" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.41.4. </span>Create an Azure ML experiment<a class="headerlink" href="#create-an-azure-ml-experiment" title="Permalink to this headline">#</a></h2>
 <p>Let’s create an experiment named ‘aml-experiment’ in the workspace we just initialized.</p>
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-<h2><span class="section-number">41.41.5. </span>Create a compute cluster<a class="headerlink" href="#create-a-compute-cluster" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.41.5. </span>Create a compute cluster<a class="headerlink" href="#create-a-compute-cluster" title="Permalink to this headline">#</a></h2>
 <p>You will need to create a <a class="reference external" href="https://docs.microsoft.com/azure/machine-learning/concept-azure-machine-learning-architecture#compute-target">compute target</a> for your AutoML run.</p>
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+<h2><span class="section-number">40.41.12. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
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-            <p class="prev-next-title"><span class="section-number">41.40. </span>Data Science project using Azure ML SDK</p>
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-    <link rel="prev" title="41.39. Low code/no code Data Science project on Azure ML" href="low-code-no-code-data-science-project-on-azure-ml.html" />
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+     40.3. First assignment
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-     41.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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+     40.42. Machine Learning overview - assignment 1
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-<h1><span class="section-number">41.40. </span>Data Science project using Azure ML SDK<a class="headerlink" href="#data-science-project-using-azure-ml-sdk" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.40. </span>Data Science project using Azure ML SDK<a class="headerlink" href="#data-science-project-using-azure-ml-sdk" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
-<h2><span class="section-number">41.40.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.40.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>We saw how to use the Azure ML platform to train, deploy and consume a model with the Azure ML SDK. Now look around for some data that you could use to train another model, deploy it and consume it. You can look for datasets on <a class="reference external" href="https://kaggle.com">Kaggle</a> and <a class="reference external" href="https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-77958-bethanycheum&amp;ocid=AID3041109">Azure Open Datasets</a>.</p>
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+<h2><span class="section-number">40.40.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1633,13 +1633,13 @@ <h2><span class="section-number">41.40.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.39. </span>Low code/no code Data Science project on Azure ML</p>
+            <p class="prev-next-title"><span class="section-number">40.39. </span>Low code/no code Data Science project on Azure ML</p>
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-        <p class="prev-next-title"><span class="section-number">41.41. </span>Data Science in the cloud: The “Azure ML SDK” way</p>
+        <p class="prev-next-title"><span class="section-number">40.41. </span>Data Science in the cloud: The “Azure ML SDK” way</p>
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-    <link rel="prev" title="41.9. Analyzing text about Data Science" href="analyzing-text-about-data-science.html" />
+    <link rel="next" title="40.11. Write a data ethics case study" href="write-a-data-ethics-case-study.html" />
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-     41.5. Project Plan​ Template
+     40.5. Project Plan​ Template
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-     41.6. Python programming introduction
+     40.6. Python programming introduction
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-     41.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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-     41.10. Data Science scenarios
+     40.10. Data Science scenarios
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-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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-     41.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
+     40.17. Classifying datasets
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-     41.18. Small diabetes study
+     40.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
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-     41.21. Soda profits
+     40.21. Soda profits
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-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
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    <li class="toctree-l2">
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-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
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+   40.10.1. Instructions
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-   41.10.2. Rubric
+   40.10.2. Rubric
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   <a class="reference internal nav-link" href="#acknowledgments">
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 </ul>
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               <div>
                 
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+<h1><span class="section-number">40.10. </span>Data Science scenarios<a class="headerlink" href="#data-science-scenarios" title="Permalink to this headline">#</a></h1>
 <p>In this first assignment, we ask you to think about some real-life process or problem in different problem domains, and how you can improve it using the Data Science process. Think about the following:</p>
 <ol class="simple">
 <li><p>Which data can you collect?</p></li>
@@ -1584,7 +1584,7 @@ <h1><span class="section-number">41.10. </span>Data Science scenarios<a class="h
 <li><p>How can you use data to make sure you are being productive at work?</p></li>
 </ol>
 <section id="instructions">
-<h2><span class="section-number">41.10.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.10.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>Fill in the following table (substitute suggested problem domains for your own ones if needed):</p>
 <table class="colwidths-auto table">
 <thead>
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+<h2><span class="section-number">40.10.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Exemplary</p></th>
@@ -1635,7 +1635,7 @@ <h2><span class="section-number">41.10.2. </span>Rubric<a class="headerlink" hre
 </table>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.10.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.10.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1677,13 +1677,13 @@ <h2><span class="section-number">41.10.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.9. </span>Analyzing text about Data Science</p>
+            <p class="prev-next-title"><span class="section-number">40.9. </span>Analyzing text about Data Science</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="write-a-data-ethics-case-study.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">41.11. </span>Write a data ethics case study</p>
+        <p class="prev-next-title"><span class="section-number">40.11. </span>Write a data ethics case study</p>
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diff --git a/assignments/data-science/displaying-airport-data.html b/assignments/data-science/displaying-airport-data.html
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-    <title>41.20. Displaying airport data &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.20. Displaying airport data &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="41.21. Soda profits" href="soda-profits.html" />
-    <link rel="prev" title="41.19. Introduction to probability and statistics" href="introduction-to-statistics-and-probability.html" />
+    <link rel="next" title="40.21. Soda profits" href="soda-profits.html" />
+    <link rel="prev" title="40.19. Introduction to probability and statistics" href="introduction-to-statistics-and-probability.html" />
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 <ul class="nav bd-sidenav">
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   <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
-   1. Python programming basics
+   1. Python programming introduction
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-   4. Python programming advanced
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   <ul>
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-     5.2. Data Science ethics
+     4.2. Data Science ethics
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-     5.3. Defining data
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+     4.4. Introduction to statistics and probability
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-     6.2. Non-relational data
+     5.2. Non-relational data
     </a>
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     <a class="reference internal" href="../../data-science/working-with-data/numpy.html">
-     6.3. NumPy
+     5.3. NumPy
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     <a class="reference internal" href="../../data-science/working-with-data/pandas/pandas.html">
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       <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
-       6.4.2. Data Selection
+       5.4.2. Data Selection
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       <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
-       6.4.3. Advanced Pandas Techniques
+       5.4.3. Advanced Pandas Techniques
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     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
-     6.5. Data preparation
+     5.5. Data preparation
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-     7.1. Visualizing distributions
+     6.1. Visualizing distributions
     </a>
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     <a class="reference internal" href="../../data-science/data-visualization/visualization-proportions.html">
-     7.2. Visualizing proportions
+     6.2. Visualizing proportions
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     <a class="reference internal" href="../../data-science/data-visualization/visualization-relationships.html">
-     7.3. Visualizing relationships: all about honey 🍯
+     6.3. Visualizing relationships: all about honey 🍯
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-     7.4. Making meaningful visualizations
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-     15.2. More classifiers
+     14.2. More classifiers
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-     15.3. Yet other classifiers
+     14.3. Yet other classifiers
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+     14.4. Applied Machine Learning : build a web app
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
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-     41.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
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-     41.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
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+     41.11. Machine Learning overview
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+     41.16. Build an machine learning web application
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+     41.17. Unsupervised learning
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-     42.18. Kernel method
+     41.18. Kernel method
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-     42.19. Model Selection
+     41.19. Model Selection
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-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
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-     42.21. Generative Adversarial Network
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+   40.20.1. Database schema
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+   40.20.2. Loading data
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+   40.20.4. Acknowledgments
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   <a class="reference internal nav-link" href="#acknowledgments">
-   41.20.4. Acknowledgments
+   40.20.4. Acknowledgments
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  </li>
 </ul>
@@ -1643,10 +1643,10 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="displaying-airport-data">
-<h1><span class="section-number">41.20. </span>Displaying airport data<a class="headerlink" href="#displaying-airport-data" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.20. </span>Displaying airport data<a class="headerlink" href="#displaying-airport-data" title="Permalink to this headline">#</a></h1>
 <p>You have been provided a <a href="../../assets/data/airports.db">database</a> built on <a class="reference external" href="https://sqlite.org/index.html">SQLite</a> which contains information about airports.The schema is displayed below.</p>
 <section id="database-schema">
-<h2><span class="section-number">41.20.1. </span>Database schema<a class="headerlink" href="#database-schema" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.20.1. </span>Database schema<a class="headerlink" href="#database-schema" title="Permalink to this headline">#</a></h2>
 <p>A database’s schema is its table design and structure. The <strong>airports</strong> database has two tables, <code class="docutils literal notranslate"><span class="pre">cities</span></code>, which contain a list of cities in the United Kingdom and Ireland, and <code class="docutils literal notranslate"><span class="pre">airports</span></code>, which contains the list of all airports. Because some cities may have multiple airports, two tables were created to store the information. In this exercise, you will use joins to display information for different cities.</p>
 <table class="colwidths-auto table">
 <thead>
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+<h2><span class="section-number">40.20.2. </span>Loading data<a class="headerlink" href="#loading-data" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># install the necessary dependencies</span>
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 </section>
 <section id="assignment">
-<h2><span class="section-number">41.20.3. </span>Assignment<a class="headerlink" href="#assignment" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.20.3. </span>Assignment<a class="headerlink" href="#assignment" title="Permalink to this headline">#</a></h2>
 <p>Create queries to return the following information:</p>
 <ol class="simple">
 <li><p>all city names in the Cities table</p></li>
@@ -1766,7 +1766,7 @@ <h2><span class="section-number">41.20.3. </span>Assignment<a class="headerlink"
 </div>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.20.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.20.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a> and author <a class="reference external" href="https://github.com/BethanyJep">Pikachú</a>. They inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1808,13 +1808,13 @@ <h2><span class="section-number">41.20.4. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.19. </span>Introduction to probability and statistics</p>
+            <p class="prev-next-title"><span class="section-number">40.19. </span>Introduction to probability and statistics</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="soda-profits.html" title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">41.21. </span>Soda profits</p>
+        <p class="prev-next-title"><span class="section-number">40.21. </span>Soda profits</p>
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     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/data-science/dive-into-the-beehive.html b/assignments/data-science/dive-into-the-beehive.html
index 4958e606c..370b9515f 100644
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-    <title>41.15. Dive into the beehive &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.15. Dive into the beehive &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="41.16. Build your own custom vis" href="build-your-own-custom-vis.html" />
-    <link rel="prev" title="41.14. Try it in Excel" href="try-it-in-excel.html" />
+    <link rel="next" title="40.16. Build your own custom vis" href="build-your-own-custom-vis.html" />
+    <link rel="prev" title="40.14. Try it in Excel" href="try-it-in-excel.html" />
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+   1. Python programming introduction
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-     5.1. Defining data science
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     <a class="reference internal" href="../../data-science/introduction/data-science-ethics.html">
-     5.2. Data Science ethics
+     4.2. Data Science ethics
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-     41.5. Project Plan​ Template
+     40.5. Project Plan​ Template
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-     41.6. Python programming introduction
+     40.6. Python programming introduction
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+     40.7. Python programming basics
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+     40.9. Analyzing text about Data Science
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-     41.10. Data Science scenarios
+     40.10. Data Science scenarios
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-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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-     41.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.15. Dive into the beehive
+     40.15. Dive into the beehive
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
+     40.17. Classifying datasets
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-     41.18. Small diabetes study
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 <p>In this section you started looking at a dataset around bees and their honey production over a period of time that saw losses in the bee colony population overall. Dig deeper into this dataset and build a notebook that can tell the story of the health of the bee population, state by state and year by year. Do you discover anything interesting about this dataset?</p>
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+<h2><span class="section-number">40.15.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
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-    <link rel="prev" title="41.22. Analyzing COVID-19 papers" href="analyzing-COVID-19-papers.html" />
+    <link rel="next" title="40.24. Data processing in Python" href="data-processing-in-python.html" />
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-     41.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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+   40.23.5. Computing
    <span class="math notranslate nohighlight">
     \(R_t\)
    </span>
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  </li>
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   </a>
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 </ul>
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                             <ul class="visible nav section-nav flex-column">
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    <span class="math notranslate nohighlight">
     \(R_t\)
    </span>
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  </li>
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   </a>
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   </a>
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 </ul>
@@ -1691,9 +1691,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="estimation-of-covid-19-pandemic">
-<h1><span class="section-number">41.23. </span>Estimation of COVID-19 pandemic<a class="headerlink" href="#estimation-of-covid-19-pandemic" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.23. </span>Estimation of COVID-19 pandemic<a class="headerlink" href="#estimation-of-covid-19-pandemic" title="Permalink to this headline">#</a></h1>
 <section id="loading-data">
-<h2><span class="section-number">41.23.1. </span>Loading data<a class="headerlink" href="#loading-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.23.1. </span>Loading data<a class="headerlink" href="#loading-data" title="Permalink to this headline">#</a></h2>
 <p>We will use data on COVID-19 infected individuals, provided by the <a class="reference external" href="https://systems.jhu.edu/">Center for Systems Science and Engineering</a> (CSSE) at <a class="reference external" href="https://jhu.edu/">Johns Hopkins University</a>. Dataset is available in <a class="reference external" href="https://github.com/CSSEGISandData/COVID-19">this GitHub Repository</a>.</p>
 <div class="cell docutils container">
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@@ -1745,7 +1745,7 @@ <h2><span class="section-number">41.23.1. </span>Loading data<a class="headerlin
 </div>
 </section>
 <section id="making-sense-of-the-data">
-<h2><span class="section-number">41.23.2. </span>Making sense of the data<a class="headerlink" href="#making-sense-of-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.23.2. </span>Making sense of the data<a class="headerlink" href="#making-sense-of-the-data" title="Permalink to this headline">#</a></h2>
 <p>From the table above the role of province column is not clear. Let’s see the different values that are present in <code class="docutils literal notranslate"><span class="pre">Province/State</span></code> column:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1845,7 +1845,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="pre-processing-the-data">
-<h2><span class="section-number">41.23.3. </span>Pre-processing the data<a class="headerlink" href="#pre-processing-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.23.3. </span>Pre-processing the data<a class="headerlink" href="#pre-processing-the-data" title="Permalink to this headline">#</a></h2>
 <p>We are not interested in breaking countries down to further territories, thus we would first get rid of this breakdown and add information on all territories together, to get info for the whole country. This can be done using <code class="docutils literal notranslate"><span class="pre">groupby</span></code>:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2084,7 +2084,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="investigating-the-data">
-<h2><span class="section-number">41.23.4. </span>Investigating the data<a class="headerlink" href="#investigating-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.23.4. </span>Investigating the data<a class="headerlink" href="#investigating-the-data" title="Permalink to this headline">#</a></h2>
 <p>Let’s now switch to investigating a specific country. Let’s create a frame that contains the data on infections indexed by date:</p>
 <div class="cell docutils container">
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@@ -2942,7 +2942,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="computing-r-t">
-<h2><span class="section-number">41.23.5. </span>Computing <span class="math notranslate nohighlight">\(R_t\)</span><a class="headerlink" href="#computing-r-t" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.23.5. </span>Computing <span class="math notranslate nohighlight">\(R_t\)</span><a class="headerlink" href="#computing-r-t" title="Permalink to this headline">#</a></h2>
 <p>To see how infectious is the disease, we look at the <strong>basic reproduction number</strong> <span class="math notranslate nohighlight">\(R_0\)</span>, which indicated the number of people that an infected person would further infect. When <span class="math notranslate nohighlight">\(R_0\)</span> is more than 1, the epidemic is likely to spread.</p>
 <p><span class="math notranslate nohighlight">\(R_0\)</span> is a property of the disease itself, and does not take into account some protective measures that people may take to slow down the pandemic. During the pandemic progression, we can estimate the reproduction number <span class="math notranslate nohighlight">\(R_t\)</span> at any given time <span class="math notranslate nohighlight">\(t\)</span>. It has been shown that this number can be roughly estimated by taking a window of 8 days, and computing $<span class="math notranslate nohighlight">\(R_t=\frac{I_{t-7}+I_{t-6}+I_{t-5}+I_{t-4}}{I_{t-3}+I_{t-2}+I_{t-1}+I_t}\)</span><span class="math notranslate nohighlight">\(
 where \)</span>I_t<span class="math notranslate nohighlight">\( is the number of newly infected individuals on day \)</span>t$.</p>
@@ -3461,7 +3461,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="challenge">
-<h2><span class="section-number">41.23.6. </span>Challenge<a class="headerlink" href="#challenge" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.23.6. </span>Challenge<a class="headerlink" href="#challenge" title="Permalink to this headline">#</a></h2>
 <p>Now it is time for you to play more with the code and data! Here are a few suggestions you can experiment with:</p>
 <ul class="simple">
 <li><p>See the spread of the pandemic in different countries.</p></li>
@@ -3472,7 +3472,7 @@ <h2><span class="section-number">41.23.6. </span>Challenge<a class="headerlink"
 </ul>
 </section>
 <section id="references">
-<h2><span class="section-number">41.23.7. </span>References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.23.7. </span>References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
 <p>You may look at further studies of COVID epidemic spread in the following publications:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://soshnikov.com/science/sliding-sir-model-for-rt-estimation/">Sliding SIR Model for Rt Estimation during COVID Pandemic</a>, blog post by <a class="reference external" href="http://soshnikov.com">Dmitry Soshnikov</a></p></li>
@@ -3481,7 +3481,7 @@ <h2><span class="section-number">41.23.7. </span>References<a class="headerlink"
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.23.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.23.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
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-     41.5. Project Plan​ Template
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+<h2><span class="section-number">40.25.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
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+<h2><span class="section-number">40.25.2. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
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-            <p class="prev-next-title"><span class="section-number">41.24. </span>Data processing in Python</p>
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-    <link rel="prev" title="41.35. Tell a story" href="tell-a-story.html" />
+    <link rel="next" title="40.37. Exploring for answers" href="exploring-for-anwser.html" />
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-     41.5. Project Plan​ Template
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-     41.6. Python programming introduction
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-     41.8. Python programming advanced
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-     41.9. Analyzing text about Data Science
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-     41.10. Data Science scenarios
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-     41.11. Write a data ethics case study
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-     41.12. Lines, scatters and bars
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-     41.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.15. Dive into the beehive
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-     41.16. Build your own custom vis
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-     41.17. Classifying datasets
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-     41.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
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-     41.21. Soda profits
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-     41.22. Analyzing COVID-19 papers
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-     41.23. Estimation of COVID-19 pandemic
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-     41.24. Data processing in Python
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-     41.25. Evaluating data from a form
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-     41.26. Data preparation
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-     41.27. Analyzing data
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-     41.28. NYC taxi data in winter and summer
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-     41.29. Matplotlib applied
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-     41.35. Tell a story
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-     41.36. Explore a planetary computer dataset
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-     41.37. Exploring for answers
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-     41.38. Market research
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-     41.39. Low code/no code Data Science project on Azure ML
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+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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-     41.42. Machine Learning overview - assignment 1
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-     41.43. Machine Learning overview - assignment 2
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-     41.45. Linear regression - California Housing
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-     41.46. Linear Regression Metrics
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-     41.47. Loss Function
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-     41.48. Gradient descent
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-     41.49. Linear Regression Implementation from Scratch
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               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="explore-a-planetary-computer-dataset">
-<h1><span class="section-number">41.36. </span>Explore a planetary computer dataset<a class="headerlink" href="#explore-a-planetary-computer-dataset" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.36. </span>Explore a planetary computer dataset<a class="headerlink" href="#explore-a-planetary-computer-dataset" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
-<h2><span class="section-number">41.36.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.36.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>In this section, we talked about various data science application domains - with deep dives into examples related to research, sustainability, and digital humanities. In this assignment, you’ll explore one of these examples in more detail, and apply some of your learnings around data visualizations and analysis to derive insights about sustainability data.</p>
 <p>The <a class="reference external" href="https://planetarycomputer.microsoft.com/">Planetary Computer</a> project has datasets and APIs that can be accessed with an account - request one for access if you want to try the bonus step of the assignment. The site also provides an <a class="reference external" href="https://planetarycomputer.microsoft.com/explore">Explorer</a> feature that you can use without creating an account.</p>
 <p><code class="docutils literal notranslate"><span class="pre">Steps:</span></code>
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+<h2><span class="section-number">40.36.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
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-            <p class="prev-next-title"><span class="section-number">41.35. </span>Tell a story</p>
+            <p class="prev-next-title"><span class="section-number">40.35. </span>Tell a story</p>
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-        <p class="prev-next-title"><span class="section-number">41.37. </span>Exploring for answers</p>
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               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="exploring-for-answers">
-<h1><span class="section-number">41.37. </span>Exploring for answers<a class="headerlink" href="#exploring-for-answers" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.37. </span>Exploring for answers<a class="headerlink" href="#exploring-for-answers" title="Permalink to this headline">#</a></h1>
 <p>This is a continuation of the previous section’s <a class="reference internal" href="nyc-taxi-data-in-winter-and-summer.html"><span class="doc std std-doc">assignment</span></a>, where we briefly took a look at the data set. Now we will be taking a deeper look at the data.</p>
 <p>Again, the question the client wants to know: <strong>Do yellow taxi passengers in New York City tip drivers more in the winter or summer?</strong></p>
 <p>Your team is in the <a class="reference internal" href="../../data-science/data-science-lifecycle/analyzing.html"><span class="doc std std-doc">Analyzing</span></a> stage of the Data Science Lifecycle, where you are responsible for doing exploratory data analysis on the dataset. You have been provided a notebook and dataset that contains 200 taxi transactions from January and July 2019.</p>
 <section id="instructions">
-<h2><span class="section-number">41.37.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.37.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>Below data is from the <a class="reference external" href="https://docs.microsoft.com/en-us/azure/open-datasets/dataset-taxi-yellow?tabs=azureml-opendatasets">Taxi &amp; Limousine Commission</a>. Refer to the <a class="reference external" href="https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf">dataset’s dictionary</a> and <a class="reference external" href="https://www1.nyc.gov/assets/tlc/downloads/pdf/trip_record_user_guide.pdf">user guide</a> for more information about the data.</p>
 <p>Use some the techniques in this section to do your own EDA in the notebook (add cells if you’d like) and answer the following questions:</p>
 <ul class="simple">
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-<h2><span class="section-number">41.37.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.37.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
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@@ -1723,13 +1723,13 @@ <h2><span class="section-number">41.37.3. </span>Acknowledgments<a class="header
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-            <p class="prev-next-title"><span class="section-number">41.36. </span>Explore a planetary computer dataset</p>
+            <p class="prev-next-title"><span class="section-number">40.36. </span>Explore a planetary computer dataset</p>
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-        <p class="prev-next-title"><span class="section-number">41.38. </span>Market research</p>
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-    <link rel="prev" title="41.18. Small diabetes study" href="small-diabetes-study.html" />
+    <link rel="next" title="40.20. Displaying airport data" href="displaying-airport-data.html" />
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-     41.5. Project Plan​ Template
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-     41.6. Python programming introduction
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-     41.10. Data Science scenarios
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-     41.11. Write a data ethics case study
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-     41.12. Lines, scatters and bars
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-     41.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.15. Dive into the beehive
+     40.15. Dive into the beehive
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
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-     41.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
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-     41.21. Soda profits
+     40.21. Soda profits
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-     41.22. Analyzing COVID-19 papers
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-     41.23. Estimation of COVID-19 pandemic
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-     41.24. Data processing in Python
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-     41.25. Evaluating data from a form
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-     41.26. Data preparation
+     40.26. Data preparation
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-     41.27. Analyzing data
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-     41.28. NYC taxi data in winter and summer
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-     41.29. Matplotlib applied
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-     41.35. Tell a story
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-     41.36. Explore a planetary computer dataset
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-     41.37. Exploring for answers
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-     41.38. Market research
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-     41.39. Low code/no code Data Science project on Azure ML
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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-     41.42. Machine Learning overview - assignment 1
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-     41.43. Machine Learning overview - assignment 2
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-     41.44. Regression with Scikit-learn
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-     41.45. Linear regression - California Housing
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-     41.46. Linear Regression Metrics
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-     41.47. Loss Function
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-     41.48. Gradient descent
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-     41.49. Linear Regression Implementation from Scratch
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+   40.19.3. Normal distribution
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+   40.19.6. Simulating a normal distribution with the central limit theorem
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@@ -1695,7 +1695,7 @@ <h2> Contents </h2>
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   <section class="tex2jax_ignore mathjax_ignore" id="introduction-to-probability-and-statistics">
-<h1><span class="section-number">41.19. </span>Introduction to probability and statistics<a class="headerlink" href="#introduction-to-probability-and-statistics" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.19. </span>Introduction to probability and statistics<a class="headerlink" href="#introduction-to-probability-and-statistics" title="Permalink to this headline">#</a></h1>
 <p>In this notebook, we will play around with some of the concepts we have previously discussed. Many concepts from probability and statistics are well-represented in major libraries for data processing in Python, such as <code class="docutils literal notranslate"><span class="pre">numpy</span></code> and <code class="docutils literal notranslate"><span class="pre">pandas</span></code>.</p>
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-<h2><span class="section-number">41.19.1. </span>Random Variables and Distributions<a class="headerlink" href="#random-variables-and-distributions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.19.1. </span>Random Variables and Distributions<a class="headerlink" href="#random-variables-and-distributions" title="Permalink to this headline">#</a></h2>
 <p>Let’s start with drawing a sample of 30 values from a uniform distribution from <span class="math notranslate nohighlight">\(0\)</span> to <span class="math notranslate nohighlight">\(9\)</span>. We will also compute mean and variance.</p>
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-<h2><span class="section-number">41.19.2. </span>Analyzing Real Data<a class="headerlink" href="#analyzing-real-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.19.2. </span>Analyzing Real Data<a class="headerlink" href="#analyzing-real-data" title="Permalink to this headline">#</a></h2>
 <p>Mean and variance are very important when analyzing real-world data. Let’s load the data about baseball players from <a class="reference external" href="http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights">SOCR MLB Height/Weight Data</a></p>
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-<h2><span class="section-number">41.19.3. </span>Normal distribution<a class="headerlink" href="#normal-distribution" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.19.3. </span>Normal distribution<a class="headerlink" href="#normal-distribution" title="Permalink to this headline">#</a></h2>
 <p>Let’s create an artificial sample of weights that follows a normal distribution with the same mean and variance as our real data:</p>
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-<h2><span class="section-number">41.19.4. </span>Confidence intervals<a class="headerlink" href="#confidence-intervals" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.19.4. </span>Confidence intervals<a class="headerlink" href="#confidence-intervals" title="Permalink to this headline">#</a></h2>
 <p>Let’s now calculate confidence intervals for the weights and heights of baseball players. We will use the code <a class="reference external" href="https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data">from this stackoverflow discussion</a>:</p>
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-<h2><span class="section-number">41.19.5. </span>Hypothesis testing<a class="headerlink" href="#hypothesis-testing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.19.5. </span>Hypothesis testing<a class="headerlink" href="#hypothesis-testing" title="Permalink to this headline">#</a></h2>
 <p>Let’s explore different roles in our baseball players dataset:</p>
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-<h2><span class="section-number">41.19.6. </span>Simulating a normal distribution with the central limit theorem<a class="headerlink" href="#simulating-a-normal-distribution-with-the-central-limit-theorem" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.19.6. </span>Simulating a normal distribution with the central limit theorem<a class="headerlink" href="#simulating-a-normal-distribution-with-the-central-limit-theorem" title="Permalink to this headline">#</a></h2>
 <p>The pseudo-random generator in Python is designed to give us a uniform distribution. If we want to create a generator for normal distribution, we can use the central limit theorem. To get a normally distributed value we will just compute a mean of a uniform-generated sample.</p>
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-<h2><span class="section-number">41.19.7. </span>Correlation and evil baseball corp<a class="headerlink" href="#correlation-and-evil-baseball-corp" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.19.7. </span>Correlation and evil baseball corp<a class="headerlink" href="#correlation-and-evil-baseball-corp" title="Permalink to this headline">#</a></h2>
 <p>Correlation allows us to find relations between data sequences. In our toy example, let’s pretend there is an evil baseball corporation that pays its players according to their height - the taller the player is, the more money he/she gets. Suppose there is a base salary of <span class="math notranslate nohighlight">\(1000, and an additional bonus from \)</span>0 to $100, depending on height. We will take the real players from MLB, and compute their imaginary salaries:</p>
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@@ -2060,11 +2060,11 @@ <h2><span class="section-number">41.19.7. </span>Correlation and evil baseball c
 </div>
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 <section id="conclusion">
-<h2><span class="section-number">41.19.8. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.19.8. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>In this notebook we have learnt how to perform basic operations on data to compute statistical functions. We now know how to use a sound apparatus of math and statistics in order to prove some hypotheses, and how to compute confidence intervals for arbitrary variables given a data sample.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.19.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.19.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2106,13 +2106,13 @@ <h2><span class="section-number">41.19.9. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.18. </span>Small diabetes study</p>
+            <p class="prev-next-title"><span class="section-number">40.18. </span>Small diabetes study</p>
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-        <p class="prev-next-title"><span class="section-number">41.20. </span>Displaying airport data</p>
+        <p class="prev-next-title"><span class="section-number">40.20. </span>Displaying airport data</p>
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diff --git a/assignments/data-science/lines-scatters-and-bars.html b/assignments/data-science/lines-scatters-and-bars.html
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-    <title>41.12. Lines, scatters and bars &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.12. Lines, scatters and bars &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../genindex.html" />
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-    <link rel="next" title="41.13. Apply your skills" href="apply-your-skills.html" />
-    <link rel="prev" title="41.11. Write a data ethics case study" href="write-a-data-ethics-case-study.html" />
+    <link rel="next" title="40.13. Apply your skills" href="apply-your-skills.html" />
+    <link rel="prev" title="40.11. Write a data ethics case study" href="write-a-data-ethics-case-study.html" />
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-     41.35. Tell a story
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
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-     41.45. Linear regression - California Housing
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 <section id="instructions">
-<h2><span class="section-number">41.12.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.12.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>In this section, you worked with line charts, scatterplots, and bar charts to show interesting facts about this dataset. In this assignment, dig deeper into the dataset to discover a fact about a given type of bird. For example, create a notebook visualizing all the interesting data you can uncover about Snow Geese. Use the three plots mentioned above to tell a story in your notebook.</p>
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+<h2><span class="section-number">40.12.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.11. </span>Write a data ethics case study</p>
+            <p class="prev-next-title"><span class="section-number">40.11. </span>Write a data ethics case study</p>
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+    <link rel="next" title="40.40. Data Science project using Azure ML SDK" href="data-science-project-using-azure-ml-sdk.html" />
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-<h1><span class="section-number">41.39. </span>Low code/no code Data Science project on Azure ML<a class="headerlink" href="#low-code-no-code-data-science-project-on-azure-ml" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.39. </span>Low code/no code Data Science project on Azure ML<a class="headerlink" href="#low-code-no-code-data-science-project-on-azure-ml" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
-<h2><span class="section-number">41.39.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.39.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>We saw how to use the Azure ML platform to train, deploy and consume a model in a Low code/No code fashion. Now, look around for some data that you could use to train an other model, deploy it and consume it. You can look for datasets on <a class="reference external" href="https://kaggle.com">Kaggle</a> and <a class="reference external" href="https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-77958-bethanycheum&amp;ocid=AID3041109">Azure Open Datasets</a>.</p>
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+<h2><span class="section-number">40.39.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
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-    <link rel="prev" title="41.37. Exploring for answers" href="exploring-for-anwser.html" />
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-     41.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.23. Estimation of COVID-19 pandemic
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-     41.24. Data processing in Python
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-     41.35. Tell a story
+     40.35. Tell a story
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+     40.38. Market research
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-     41.39. Low code/no code Data Science project on Azure ML
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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-     41.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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+     40.43. Machine Learning overview - assignment 2
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-     41.45. Linear regression - California Housing
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-     41.46. Linear Regression Metrics
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-     41.47. Loss Function
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-     41.52. ML neural network - Assignment 1
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+<h2><span class="section-number">40.38.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>In the <a class="reference internal" href="../../data-science/data-science-in-the-cloud/introduction.html"><span class="doc std std-doc">Data science in the cloud</span></a> section, you learned that there are several important cloud providers. Do some market research to discover what each can offer to the Data Scientist. Are the offerings comparable? Write a paper to describe the offerings of three or more of these cloud providers.</p>
 </section>
 <section id="rubric">
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-            <p class="prev-next-title"><span class="section-number">41.37. </span>Exploring for answers</p>
+            <p class="prev-next-title"><span class="section-number">40.37. </span>Exploring for answers</p>
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-     41.5. Project Plan​ Template
+     40.5. Project Plan​ Template
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-     41.10. Data Science scenarios
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-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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-     41.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.15. Dive into the beehive
+     40.15. Dive into the beehive
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
+     40.17. Classifying datasets
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-     41.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
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+     40.34.1. Acknowledgments
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@@ -1702,7 +1702,7 @@ <h2> Contents </h2>
 See the License for the specific language governing permissions and
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-<h1><span class="section-number">41.29. </span>Matplotlib applied<a class="headerlink" href="#matplotlib-applied" title="Permalink to this headline">#</a></h1>
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+<h1><span class="section-number">40.31. </span>Bar Plots, Histograms, subplot2grid<a class="headerlink" href="#bar-plots-histograms-subplot2grid" title="Permalink to this headline">#</a></h1>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pclass_xt</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">crosstab</span><span class="p">(</span><span class="n">df_train</span><span class="p">[</span><span class="s2">&quot;Pclass&quot;</span><span class="p">],</span> <span class="n">df_train</span><span class="p">[</span><span class="s2">&quot;Survived&quot;</span><span class="p">])</span>
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 You can consider to use <code>numpy.sort</code> and <code>pandas.DataFrame.plot</code>.<br>
 </details>
 </div><section id="acknowledgments">
-<h2><span class="section-number">41.34.1. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.34.1. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Donne Martin for creating the open-source project <a href="https://github.com/donnemartin/data-science-ipython-notebooks">data-science-ipython-notebooks</a>, which inspires the majority of the content in this chapter.</p>
 </section>
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+            <p class="prev-next-title"><span class="section-number">40.28. </span>NYC taxi data in winter and summer</p>
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-     41.5. Project Plan​ Template
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-     41.6. Python programming introduction
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-     41.8. Python programming advanced
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-     41.9. Analyzing text about Data Science
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-     41.10. Data Science scenarios
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-     41.11. Write a data ethics case study
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-     41.12. Lines, scatters and bars
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-     41.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.15. Dive into the beehive
+     40.15. Dive into the beehive
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
+     40.17. Classifying datasets
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-     41.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
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-     41.21. Soda profits
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-     41.22. Analyzing COVID-19 papers
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-     41.23. Estimation of COVID-19 pandemic
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-     41.24. Data processing in Python
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-     41.25. Evaluating data from a form
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-     41.26. Data preparation
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-     41.27. Analyzing data
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-     41.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
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-     41.29. Matplotlib applied
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-     41.35. Tell a story
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-     41.36. Explore a planetary computer dataset
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-     41.37. Exploring for answers
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-     41.38. Market research
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-     41.39. Low code/no code Data Science project on Azure ML
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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-     41.42. Machine Learning overview - assignment 1
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-     41.43. Machine Learning overview - assignment 2
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-     41.44. Regression with Scikit-learn
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-     41.45. Linear regression - California Housing
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-     41.46. Linear Regression Metrics
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-     41.47. Loss Function
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-     41.48. Gradient descent
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-     41.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
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-     41.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
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               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="nyc-taxi-data-in-winter-and-summer">
-<h1><span class="section-number">41.28. </span>NYC taxi data in winter and summer<a class="headerlink" href="#nyc-taxi-data-in-winter-and-summer" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.28. </span>NYC taxi data in winter and summer<a class="headerlink" href="#nyc-taxi-data-in-winter-and-summer" title="Permalink to this headline">#</a></h1>
 <p>A client has approached your team for help in investigating a taxi customer’s seasonal spending habits in New York City. They want to know: <strong>Do yellow taxi passengers in New York City tip drivers more in the winter or summer?</strong></p>
 <p>Your team is in the <a class="reference external" href="../../data-science/data-science-lifecycle/introduction.md#Capturing">capturing</a> stage of the Data Science Lifecycle and you are in charge of handling the dataset. You have been provided a notebook and <a href="../../assets/data/taxi.csv">data</a> to explore.</p>
 <p>We will use Python to load yellow taxi trip data from the <a class="reference external" href="https://docs.microsoft.com/en-us/azure/open-datasets/dataset-taxi-yellow?tabs=azureml-opendatasets">NYC Taxi &amp; Limousine Commission</a>.
 You can also open the taxi data file in a text editor or spreadsheet software like Excel.</p>
 <section id="instructions">
-<h2><span class="section-number">41.28.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.28.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Assess whether or not the data in this dataset can help answer the question.</p></li>
 <li><p>Explore the <a class="reference external" href="https://data.cityofnewyork.us/browse?sortBy=most_accessed&amp;utf8=%E2%9C%93">NYC Open Data catalog</a>. Identify an additional dataset that could potentially be helpful in answering the client’s question.</p></li>
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 <thead>
 <tr class="row-odd"><th class="head"><p>Exemplary</p></th>
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 <section id="acknowledgments">
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+<h2><span class="section-number">40.28.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     41.103. Fun with Variational Autoencoders
+     40.103. Fun with Variational Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/time-series-forecasting-assignment.html">
-     41.104. Time Series Forecasting Assignment
+     40.104. Time Series Forecasting Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/nn-for-classification-assignment.html">
-     41.106. Neural Networks for Classification with TensorFlow
+     40.106. Neural Networks for Classification with TensorFlow
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
-     41.107. NN Classify 15 Fruits Assignment
+     40.107. NN Classify 15 Fruits Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     41.112. DQN On Foreign Exchange Market
+     40.112. DQN On Foreign Exchange Market
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan/art-by-gan.html">
-     41.113. Art by gan
+     40.113. Art by gan
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan/gan-introduction.html">
-     41.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     41.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../slides/introduction.html">
-   42. Slides
+   41. Slides
   </a>
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@@ -1209,107 +1209,107 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/python-programming/python-programming-introduction.html">
-     42.1. Python programming introduction
+     41.1. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/python-programming/python-programming-basics.html">
-     42.2. Python programming basics
+     41.2. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/python-programming/python-programming-advanced.html">
-     42.3. Python programming advanced
+     41.3. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-introduction.html">
-     42.4. Data Science introduction
+     41.4. Data Science introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/relational-vs-non-relational-database.html">
-     42.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/numpy-and-pandas.html">
-     42.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-visualization.html">
-     42.7. Data visualization
+     41.7. Data visualization
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-lifecycle.html">
-     42.8. Data Science lifecycle
+     41.8. Data Science lifecycle
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-in-the-cloud.html">
-     42.9. Data Science in the cloud
+     41.9. Data Science in the cloud
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-in-real-world.html">
-     42.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/ml-overview.html">
-     42.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/linear-regression.html">
-     42.12. Linear Regression
+     41.12. Linear Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression.html">
-     42.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression-condensed.html">
-     42.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/neural-network.html">
-     42.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/build-an-ml-web-app.html">
-     42.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/unsupervised-learning.html">
-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/kernel-method.html">
-     42.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/model-selection.html">
-     42.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1575,56 +1575,56 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction-to-probability-and-statistics">
-   41.18.1. Introduction to probability and statistics
+   40.18.1. Introduction to probability and statistics
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-compute-mean-values-and-variance-for-all-values">
-     41.18.1.1. Task 1: Compute mean values and variance for all values
+     40.18.1.1. Task 1: Compute mean values and variance for all values
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-plot-boxplots-for-bmi-bp-and-y-depending-on-gender">
-     41.18.1.2. Task 2: Plot boxplots for BMI, BP and Y depending on gender
+     40.18.1.2. Task 2: Plot boxplots for BMI, BP and Y depending on gender
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#part-1-get-the-data-ready">
-       41.18.1.2.1. Part 1: get the data ready
+       40.18.1.2.1. Part 1: get the data ready
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#part-2-plot-the-data">
-       41.18.1.2.2. Part 2: plot the data
+       40.18.1.2.2. Part 2: plot the data
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-what-is-the-distribution-of-age-sex-bmi-and-y-variables">
-     41.18.1.3. Task 3: What is the distribution of Age, Sex, BMI and Y variables?
+     40.18.1.3. Task 3: What is the distribution of Age, Sex, BMI and Y variables?
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-test-the-correlation-between-different-variables-and-disease-progression-y">
-     41.18.1.4. Task 4: Test the correlation between different variables and disease progression (Y)
+     40.18.1.4. Task 4: Test the correlation between different variables and disease progression (Y)
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-test-the-hypothesis-that-the-degree-of-diabetes-progression-is-different-between-men-and-women">
-     41.18.1.5. Task 5: Test the hypothesis that the degree of diabetes progression is different between men and women
+     40.18.1.5. Task 5: Test the hypothesis that the degree of diabetes progression is different between men and women
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#rubric">
-   41.18.2. Rubric
+   40.18.2. Rubric
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.18.3. Acknowledgments
+   40.18.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1648,56 +1648,56 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction-to-probability-and-statistics">
-   41.18.1. Introduction to probability and statistics
+   40.18.1. Introduction to probability and statistics
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-compute-mean-values-and-variance-for-all-values">
-     41.18.1.1. Task 1: Compute mean values and variance for all values
+     40.18.1.1. Task 1: Compute mean values and variance for all values
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-plot-boxplots-for-bmi-bp-and-y-depending-on-gender">
-     41.18.1.2. Task 2: Plot boxplots for BMI, BP and Y depending on gender
+     40.18.1.2. Task 2: Plot boxplots for BMI, BP and Y depending on gender
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#part-1-get-the-data-ready">
-       41.18.1.2.1. Part 1: get the data ready
+       40.18.1.2.1. Part 1: get the data ready
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#part-2-plot-the-data">
-       41.18.1.2.2. Part 2: plot the data
+       40.18.1.2.2. Part 2: plot the data
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-what-is-the-distribution-of-age-sex-bmi-and-y-variables">
-     41.18.1.3. Task 3: What is the distribution of Age, Sex, BMI and Y variables?
+     40.18.1.3. Task 3: What is the distribution of Age, Sex, BMI and Y variables?
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-test-the-correlation-between-different-variables-and-disease-progression-y">
-     41.18.1.4. Task 4: Test the correlation between different variables and disease progression (Y)
+     40.18.1.4. Task 4: Test the correlation between different variables and disease progression (Y)
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-test-the-hypothesis-that-the-degree-of-diabetes-progression-is-different-between-men-and-women">
-     41.18.1.5. Task 5: Test the hypothesis that the degree of diabetes progression is different between men and women
+     40.18.1.5. Task 5: Test the hypothesis that the degree of diabetes progression is different between men and women
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#rubric">
-   41.18.2. Rubric
+   40.18.2. Rubric
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.18.3. Acknowledgments
+   40.18.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1711,10 +1711,10 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="small-diabetes-study">
-<h1><span class="section-number">41.18. </span>Small diabetes study<a class="headerlink" href="#small-diabetes-study" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.18. </span>Small diabetes study<a class="headerlink" href="#small-diabetes-study" title="Permalink to this headline">#</a></h1>
 <p>In this assignment, we will work with a small dataset of diabetes patients taken from <a class="reference external" href="https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html">here</a>.</p>
 <section id="introduction-to-probability-and-statistics">
-<h2><span class="section-number">41.18.1. </span>Introduction to probability and statistics<a class="headerlink" href="#introduction-to-probability-and-statistics" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.18.1. </span>Introduction to probability and statistics<a class="headerlink" href="#introduction-to-probability-and-statistics" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
@@ -1742,7 +1742,7 @@ <h2><span class="section-number">41.18.1. </span>Introduction to probability and
 </ul>
 <p>Let’s study this dataset using methods of probability and statistics.</p>
 <section id="task-1-compute-mean-values-and-variance-for-all-values">
-<h3><span class="section-number">41.18.1.1. </span>Task 1: Compute mean values and variance for all values<a class="headerlink" href="#task-1-compute-mean-values-and-variance-for-all-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.18.1.1. </span>Task 1: Compute mean values and variance for all values<a class="headerlink" href="#task-1-compute-mean-values-and-variance-for-all-values" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">get_df_mean</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
@@ -1835,9 +1835,9 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="task-2-plot-boxplots-for-bmi-bp-and-y-depending-on-gender">
-<h3><span class="section-number">41.18.1.2. </span>Task 2: Plot boxplots for BMI, BP and Y depending on gender<a class="headerlink" href="#task-2-plot-boxplots-for-bmi-bp-and-y-depending-on-gender" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.18.1.2. </span>Task 2: Plot boxplots for BMI, BP and Y depending on gender<a class="headerlink" href="#task-2-plot-boxplots-for-bmi-bp-and-y-depending-on-gender" title="Permalink to this headline">#</a></h3>
 <section id="part-1-get-the-data-ready">
-<h4><span class="section-number">41.18.1.2.1. </span>Part 1: get the data ready<a class="headerlink" href="#part-1-get-the-data-ready" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.18.1.2.1. </span>Part 1: get the data ready<a class="headerlink" href="#part-1-get-the-data-ready" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">columns_to_plot</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;BMI&#39;</span><span class="p">,</span> <span class="s1">&#39;BP&#39;</span><span class="p">,</span> <span class="s1">&#39;Y&#39;</span><span class="p">]</span>
@@ -1925,7 +1925,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="part-2-plot-the-data">
-<h4><span class="section-number">41.18.1.2.2. </span>Part 2: plot the data<a class="headerlink" href="#part-2-plot-the-data" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.18.1.2.2. </span>Part 2: plot the data<a class="headerlink" href="#part-2-plot-the-data" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">df_boxplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">column</span><span class="p">):</span>
@@ -2005,7 +2005,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </div></section>
 </section>
 <section id="task-3-what-is-the-distribution-of-age-sex-bmi-and-y-variables">
-<h3><span class="section-number">41.18.1.3. </span>Task 3: What is the distribution of Age, Sex, BMI and Y variables?<a class="headerlink" href="#task-3-what-is-the-distribution-of-age-sex-bmi-and-y-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.18.1.3. </span>Task 3: What is the distribution of Age, Sex, BMI and Y variables?<a class="headerlink" href="#task-3-what-is-the-distribution-of-age-sex-bmi-and-y-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">df_plot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">column</span><span class="p">):</span>
@@ -2098,7 +2098,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="task-4-test-the-correlation-between-different-variables-and-disease-progression-y">
-<h3><span class="section-number">41.18.1.4. </span>Task 4: Test the correlation between different variables and disease progression (Y)<a class="headerlink" href="#task-4-test-the-correlation-between-different-variables-and-disease-progression-y" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.18.1.4. </span>Task 4: Test the correlation between different variables and disease progression (Y)<a class="headerlink" href="#task-4-test-the-correlation-between-different-variables-and-disease-progression-y" title="Permalink to this headline">#</a></h3>
 <blockquote>
 <div><p><strong>Hint</strong> Correlation matrix would give you the most useful information on which values are dependent.</p>
 </div></blockquote>
@@ -2168,7 +2168,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="task-5-test-the-hypothesis-that-the-degree-of-diabetes-progression-is-different-between-men-and-women">
-<h3><span class="section-number">41.18.1.5. </span>Task 5: Test the hypothesis that the degree of diabetes progression is different between men and women<a class="headerlink" href="#task-5-test-the-hypothesis-that-the-degree-of-diabetes-progression-is-different-between-men-and-women" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.18.1.5. </span>Task 5: Test the hypothesis that the degree of diabetes progression is different between men and women<a class="headerlink" href="#task-5-test-the-hypothesis-that-the-degree-of-diabetes-progression-is-different-between-men-and-women" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># get the correlation between &#39;SEX&#39; and &#39;Y&#39;</span>
@@ -2259,7 +2259,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </div></section>
 </section>
 <section id="rubric">
-<h2><span class="section-number">41.18.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.18.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Exemplary</p></th>
@@ -2276,7 +2276,7 @@ <h2><span class="section-number">41.18.2. </span>Rubric<a class="headerlink" hre
 </table>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.18.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.18.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2318,13 +2318,13 @@ <h2><span class="section-number">41.18.3. </span>Acknowledgments<a class="header
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 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a> and the data author <a class="reference external" href="https://github.com/paladique">Jasmine Greenaway</a>. They inspires the majority of the content in this chapter.</p>
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 <p>[1]. CocaCola excel. (n.d.). Retrieved 29 May 2023, from <a class="reference external" href="https://www.kaggle.com/datasets/yiyiwang0826/cocacola-excel">https://www.kaggle.com/datasets/yiyiwang0826/cocacola-excel</a></p>
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+     8.3. Data Science in the cloud: The “Azure ML SDK” way
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-     41.5. Project Plan​ Template
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-     41.6. Python programming introduction
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-     41.8. Python programming advanced
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-     41.9. Analyzing text about Data Science
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-     41.10. Data Science scenarios
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-     41.11. Write a data ethics case study
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-     41.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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     <a class="reference internal" href="try-it-in-excel.html">
-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.15. Dive into the beehive
+     40.15. Dive into the beehive
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     <a class="reference internal" href="build-your-own-custom-vis.html">
-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
+     40.17. Classifying datasets
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     <a class="reference internal" href="small-diabetes-study.html">
-     41.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
+     40.20. Displaying airport data
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-     41.21. Soda profits
+     40.21. Soda profits
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-     41.22. Analyzing COVID-19 papers
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     <a class="reference internal" href="estimation-of-COVID-19-pandemic.html">
-     41.23. Estimation of COVID-19 pandemic
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-     41.24. Data processing in Python
+     40.24. Data processing in Python
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-     41.25. Evaluating data from a form
+     40.25. Evaluating data from a form
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-     41.26. Data preparation
+     40.26. Data preparation
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-     41.27. Analyzing data
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-     41.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
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-     41.29. Matplotlib applied
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-     41.35. Tell a story
+     40.35. Tell a story
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-     41.36. Explore a planetary computer dataset
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-     41.37. Exploring for answers
+     40.37. Exploring for answers
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-     41.38. Market research
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-     41.39. Low code/no code Data Science project on Azure ML
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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    <li class="toctree-l2">
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-     41.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
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-     41.43. Machine Learning overview - assignment 2
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-     41.44. Regression with Scikit-learn
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    <li class="toctree-l2">
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-     41.45. Linear regression - California Housing
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    <li class="toctree-l2">
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-<h1><span class="section-number">41.35. </span>Tell a story<a class="headerlink" href="#tell-a-story" title="Permalink to this headline">#</a></h1>
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 <p>Data Science is all about storytelling. Pick any dataset and write a short paper about a story you could tell about it. What do you hope your dataset can reveal? What will you do if its revelations prove problematic? What if your data doesn’t easily unlock its secrets? Think of the scenarios your dataset might present and write them down.</p>
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 <p>A one-page essay is presented in .doc format with the dataset explained, documented, credited, and a coherent story is presented about it with detailed examples from the data. A shorter essay is presented in a less detailed format | The essay is found lacking in one of the above details.</p>
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 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
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+<h2><span class="section-number">40.14.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
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+    <link rel="next" title="40.12. Lines, scatters and bars" href="lines-scatters-and-bars.html" />
+    <link rel="prev" title="40.10. Data Science scenarios" href="data-science-scenarios.html" />
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-     41.5. Project Plan​ Template
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-     41.6. Python programming introduction
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-     41.7. Python programming basics
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-     41.8. Python programming advanced
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-     41.9. Analyzing text about Data Science
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-     41.10. Data Science scenarios
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-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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-     41.12. Lines, scatters and bars
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-     41.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.15. Dive into the beehive
+     40.15. Dive into the beehive
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
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-     41.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
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-     41.21. Soda profits
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-     41.22. Analyzing COVID-19 papers
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-     41.23. Estimation of COVID-19 pandemic
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-     41.24. Data processing in Python
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-     41.25. Evaluating data from a form
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-     41.26. Data preparation
+     40.26. Data preparation
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-     41.27. Analyzing data
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-     41.28. NYC taxi data in winter and summer
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-     41.29. Matplotlib applied
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-     41.35. Tell a story
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-     41.36. Explore a planetary computer dataset
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-     41.37. Exploring for answers
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-     41.38. Market research
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-     41.39. Low code/no code Data Science project on Azure ML
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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-     41.42. Machine Learning overview - assignment 1
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-     41.43. Machine Learning overview - assignment 2
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-     41.45. Linear regression - California Housing
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-     41.46. Linear Regression Metrics
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-     41.47. Loss Function
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-     41.48. Gradient descent
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-     41.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
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-     41.50. ML logistic regression - assignment 1
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-     41.51. ML logistic regression - assignment 2
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-     41.115. Generative Adversarial Networks (GANs)
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-     41.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
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-     42.3. Python programming advanced
+     41.3. Python programming advanced
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-     42.4. Data Science introduction
+     41.4. Data Science introduction
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-     42.5. Relational vs. non-relational database
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-     42.6. NumPy and Pandas
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-     42.7. Data visualization
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-     42.8. Data Science lifecycle
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-     42.9. Data Science in the cloud
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-     42.10. Data Science in real world
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-     42.11. Machine Learning overview
+     41.11. Machine Learning overview
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-     42.12. Linear Regression
+     41.12. Linear Regression
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-     42.13. Logistic Regression
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-     42.14. Logistic Regression
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-     42.16. Build an machine learning web application
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-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
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-     42.18. Kernel method
+     41.18. Kernel method
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-     42.19. Model Selection
+     41.19. Model Selection
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-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
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-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
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         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
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+   40.11.1. Instructions
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+   40.11.2. Rubric
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   <a class="reference internal nav-link" href="#acknowledgments">
-   41.11.3. Acknowledgments
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 </ul>
@@ -1544,17 +1544,17 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.11.3. Acknowledgments
+   40.11.3. Acknowledgments
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 </ul>
@@ -1568,9 +1568,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="write-a-data-ethics-case-study">
-<h1><span class="section-number">41.11. </span>Write a data ethics case study<a class="headerlink" href="#write-a-data-ethics-case-study" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.11. </span>Write a data ethics case study<a class="headerlink" href="#write-a-data-ethics-case-study" title="Permalink to this headline">#</a></h1>
 <section id="instructions">
-<h2><span class="section-number">41.11.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.11.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>You’ve learned about various <a class="reference external" href="../../data-science/introduction/data-science-ethics.md#ethics-challenges">data ethics challenges</a> and seen some examples of <a class="reference external" href="../../data-science/introduction/data-science-ethics.md#case-studies">case studies</a> reflecting data ethics challenges in real-world contexts.</p>
 <p>In this assignment, you’ll write your own case study reflecting a data ethics challenge from your own experience, or from a relevant real-world context you are familiar with. Just follow these steps:</p>
 <ol class="simple">
@@ -1580,7 +1580,7 @@ <h2><span class="section-number">41.11.1. </span>Instructions<a class="headerlin
 </ol>
 </section>
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-<h2><span class="section-number">41.11.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.11.2. </span>Rubric<a class="headerlink" href="#rubric" title="Permalink to this headline">#</a></h2>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Exemplary</p></th>
@@ -1597,7 +1597,7 @@ <h2><span class="section-number">41.11.2. </span>Rubric<a class="headerlink" hre
 </table>
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 <section id="acknowledgments">
-<h2><span class="section-number">41.11.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.11.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1639,13 +1639,13 @@ <h2><span class="section-number">41.11.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.10. </span>Data Science scenarios</p>
+            <p class="prev-next-title"><span class="section-number">40.10. </span>Data Science scenarios</p>
         </div>
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     <a class='right-next' id="next-link" href="lines-scatters-and-bars.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">41.12. </span>Lines, scatters and bars</p>
+        <p class="prev-next-title"><span class="section-number">40.12. </span>Lines, scatters and bars</p>
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-<h1><span class="section-number">41.101. </span>Intro to Autoencoders<a class="headerlink" href="#intro-to-autoencoders" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.101. </span>Intro to Autoencoders<a class="headerlink" href="#intro-to-autoencoders" title="Permalink to this headline">#</a></h1>
 <p>This tutorial introduces autoencoders with three examples: the basics, image denoising, and anomaly detection.</p>
 <p>An autoencoder is a special type of neural network that is trained to copy its input to its output. For example, given an image of a handwritten digit, an autoencoder first encodes the image into a lower dimensional latent representation, then decodes the latent representation back to an image. An autoencoder learns to compress the data while minimizing the reconstruction error.</p>
 <p>To learn more about autoencoders, please consider reading chapter 14 from <a class="reference external" href="https://www.deeplearningbook.org/">Deep Learning</a> by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.</p>
 <section id="import-tensorflow-and-other-libraries">
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+<h2><span class="section-number">40.101.1. </span>Import TensorFlow and other libraries<a class="headerlink" href="#import-tensorflow-and-other-libraries" title="Permalink to this headline">#</a></h2>
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+<h2><span class="section-number">40.101.3. </span>Load the dataset<a class="headerlink" href="#load-the-dataset" title="Permalink to this headline">#</a></h2>
 <p>To start, you will train the basic autoencoder using the Fashion MNIST dataset. Each image in this dataset is 28x28 pixels.</p>
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 <p><img alt="Basic autoencoder results" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/autoencoder/autoencoder/intro_autoencoder_result.png" /></p>
 <p>Define an autoencoder with two Dense layers: an <code class="docutils literal notranslate"><span class="pre">encoder</span></code>, which compresses the images into a 64 dimensional latent vector, and a <code class="docutils literal notranslate"><span class="pre">decoder</span></code>, that reconstructs the original image from the latent space.</p>
 <p>To define your model, use the <a class="reference external" href="https://www.tensorflow.org/guide/keras/custom_layers_and_models">Keras Model Subclassing API</a>.</p>
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 <p><img alt="Image denoising results" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/autoencoder/autoencoder/image_denoise_fmnist_results.png" /></p>
 <p>An autoencoder can also be trained to remove noise from images. In the following section, you will create a noisy version of the Fashion MNIST dataset by applying random noise to each image. You will then train an autoencoder using the noisy image as input, and the original image as the target.</p>
 <p>Let’s reimport the dataset to omit the modifications made earlier.</p>
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+<h3><span class="section-number">40.101.5.1. </span>Define a convolutional autoencoder<a class="headerlink" href="#define-a-convolutional-autoencoder" title="Permalink to this headline">#</a></h3>
 <p>In this example, you will train a convolutional autoencoder using  <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D">Conv2D</a> layers in the <code class="docutils literal notranslate"><span class="pre">encoder</span></code>, and <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2DTranspose">Conv2DTranspose</a> layers in the <code class="docutils literal notranslate"><span class="pre">decoder</span></code>.</p>
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 <p>In this example, you will train an autoencoder to detect anomalies on the <a class="reference external" href="http://www.timeseriesclassification.com/description.php?Dataset=ECG5000">ECG5000 dataset</a>. This dataset contains 5,000 <a class="reference external" href="https://en.wikipedia.org/wiki/Electrocardiography">Electrocardiograms</a>, each with 140 data points. You will use a simplified version of the dataset, where each example has been labeled either <code class="docutils literal notranslate"><span class="pre">0</span></code> (corresponding to an abnormal rhythm), or <code class="docutils literal notranslate"><span class="pre">1</span></code> (corresponding to a normal rhythm). You are interested in identifying the abnormal rhythms.</p>
 <p>Note: This is a labeled dataset, so you could phrase this as a supervised learning problem. The goal of this example is to illustrate anomaly detection concepts you can apply to larger datasets, where you do not have labels available (for example, if you had many thousands of normal rhythms, and only a small number of abnormal rhythms).</p>
 <p>How will you detect anomalies using an autoencoder? Recall that an autoencoder is trained to minimize reconstruction error. You will train an autoencoder on the normal rhythms only, then use it to reconstruct all the data. Our hypothesis is that the abnormal rhythms will have higher reconstruction error. You will then classify a rhythm as an anomaly if the reconstruction error surpasses a fixed threshold.</p>
 <section id="load-ecg-data">
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+<h3><span class="section-number">40.101.7.1. </span>Load ECG data<a class="headerlink" href="#load-ecg-data" title="Permalink to this headline">#</a></h3>
 <p>The dataset you will use is based on one from <a class="reference external" href="http://www.timeseriesclassification.com/description.php?Dataset=ECG5000">timeseriesclassification.com</a>.</p>
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+<h3><span class="section-number">40.101.7.3. </span>Detect anomalies<a class="headerlink" href="#detect-anomalies" title="Permalink to this headline">#</a></h3>
 <p>Detect anomalies by calculating whether the reconstruction loss is greater than a fixed threshold. In this tutorial, you will calculate the mean average error for normal examples from the training set, then classify future examples as anomalous if the reconstruction error is higher than one standard deviation from the training set.</p>
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 <p>To learn more about anomaly detection with autoencoders, check out this excellent <a class="reference external" href="https://anomagram.fastforwardlabs.com/#/">interactive example</a> built with TensorFlow.js by Victor Dibia. For a real-world use case, you can learn how <a class="reference external" href="https://blog.tensorflow.org/2020/04/how-airbus-detects-anomalies-iss-telemetry-data-tfx.html">Airbus Detects Anomalies in ISS Telemetry Data</a> using TensorFlow. To learn more about the basics, consider reading this <a class="reference external" href="https://blog.keras.io/building-autoencoders-in-keras.html">blog post</a> by François Chollet. For more details, check out chapter 14 from <a class="reference external" href="https://www.deeplearningbook.org/">Deep Learning</a> by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.</p>
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+<h2><span class="section-number">40.101.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to TensorFlow Core for creating the open-source course <a class="reference external" href="https://www.tensorflow.org/tutorials/generative/autoencoder">autoencoder</a>. It inspires the majority of the content in this chapter.</p>
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         <ul class="visible nav section-nav flex-column">
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+   40.102.1. Introduction
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+     40.102.3.1. Checking dataset by 2D plot
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    </li>
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     <a class="reference internal nav-link" href="#modeling">
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    </li>
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-     41.102.4.2. Training
+     40.102.4.2. Training
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-     41.102.4.3. Plotting the latent space after Dimension Reduction
+     40.102.4.3. Plotting the latent space after Dimension Reduction
     </a>
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     </a>
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   </a>
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-     41.102.5.1. Adding random noise to the image.
+     40.102.5.1. Adding random noise to the image.
     </a>
    </li>
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-     41.102.5.2. Plotting a noisy image.
+     40.102.5.2. Plotting a noisy image.
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    </li>
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+     40.102.5.3. Checking Noisy Dataset using Demension Reduction
     </a>
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+       40.102.5.3.1. 1) Noisy Dataset
       </a>
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       <a class="reference internal nav-link" href="#orignal-dataset">
-       41.102.5.3.2. 2) Orignal Dataset
+       40.102.5.3.2. 2) Orignal Dataset
       </a>
      </li>
     </ul>
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     </a>
    </li>
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     </a>
    </li>
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+     40.102.5.6. Checking results
     </a>
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  </li>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   41.102.6. Acknowledgments
+   40.102.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1704,110 +1704,110 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
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-   41.102.1. Introduction
+   40.102.1. Introduction
   </a>
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-   41.102.2. Loading and Scaling Datasets
+   40.102.2. Loading and Scaling Datasets
   </a>
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   <a class="reference internal nav-link" href="#load-model">
-   41.102.3. Load model
+   40.102.3. Load model
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-dataset-by-2d-plot">
-     41.102.3.1. Checking dataset by 2D plot
+     40.102.3.1. Checking dataset by 2D plot
     </a>
    </li>
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     <a class="reference internal nav-link" href="#checking-dataset-by-3d-plot">
-     41.102.3.2. Checking dataset by 3D plot
+     40.102.3.2. Checking dataset by 3D plot
     </a>
    </li>
   </ul>
  </li>
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   <a class="reference internal nav-link" href="#base-autoencoder">
-   41.102.4. Base Autoencoder
+   40.102.4. Base Autoencoder
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#modeling">
-     41.102.4.1. Modeling
+     40.102.4.1. Modeling
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#training">
-     41.102.4.2. Training
+     40.102.4.2. Training
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#plotting-the-latent-space-after-dimension-reduction">
-     41.102.4.3. Plotting the latent space after Dimension Reduction
+     40.102.4.3. Plotting the latent space after Dimension Reduction
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-results">
-     41.102.4.4. Checking results
+     40.102.4.4. Checking results
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#denoising-autoencoder">
-   41.102.5. Denoising Autoencoder
+   40.102.5. Denoising Autoencoder
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#adding-random-noise-to-the-image">
-     41.102.5.1. Adding random noise to the image.
+     40.102.5.1. Adding random noise to the image.
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#plotting-a-noisy-image">
-     41.102.5.2. Plotting a noisy image.
+     40.102.5.2. Plotting a noisy image.
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-noisy-dataset-using-demension-reduction">
-     41.102.5.3. Checking Noisy Dataset using Demension Reduction
+     40.102.5.3. Checking Noisy Dataset using Demension Reduction
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#noisy-dataset">
-       41.102.5.3.1. 1) Noisy Dataset
+       40.102.5.3.1. 1) Noisy Dataset
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#orignal-dataset">
-       41.102.5.3.2. 2) Orignal Dataset
+       40.102.5.3.2. 2) Orignal Dataset
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     41.102.5.4. Modeling
+     40.102.5.4. Modeling
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id2">
-     41.102.5.5. Training
+     40.102.5.5. Training
     </a>
    </li>
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     <a class="reference internal nav-link" href="#id3">
-     41.102.5.6. Checking results
+     40.102.5.6. Checking results
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.102.6. Acknowledgments
+   40.102.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1821,9 +1821,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="base-denoising-autoencoder-dimension-reduction">
-<h1><span class="section-number">41.102. </span>Base/Denoising Autoencoder &amp; Dimension Reduction<a class="headerlink" href="#base-denoising-autoencoder-dimension-reduction" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.102. </span>Base/Denoising Autoencoder &amp; Dimension Reduction<a class="headerlink" href="#base-denoising-autoencoder-dimension-reduction" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">41.102.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.102.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction/base_noise.gif" /></p>
 <p>Autoencoder is a neural network that simply copies input to output. In some ways, it looks like a simple neural network, but it makes a difficult neural network by constraining the network in various ways. For example, the number of neurons in the hidden layer is smaller than that of the input layer to compress the data (reduce the dimension), or add noise to the input data and then restore the original input. There are various autoencoders, such as learning These constraints prevent the autoencoder from simply copying the input directly to the output, and control it to learn how to represent the data efficiently.</p>
 <p>In this notebook, we will cover two autoencoders:</p>
@@ -1850,7 +1850,7 @@ <h2><span class="section-number">41.102.1. </span>Introduction<a class="headerli
 </div>
 </section>
 <section id="loading-and-scaling-datasets">
-<h2><span class="section-number">41.102.2. </span>Loading and Scaling Datasets<a class="headerlink" href="#loading-and-scaling-datasets" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.102.2. </span>Loading and Scaling Datasets<a class="headerlink" href="#loading-and-scaling-datasets" title="Permalink to this headline">#</a></h2>
 <p>Train a basic autoencoder using the Fashon MNIST dataset. Each image in this dataset is 28x28 pixels.
 Inputs are scaled for training.</p>
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-<h2><span class="section-number">41.102.3. </span>Load model<a class="headerlink" href="#load-model" title="Permalink to this headline">#</a></h2>
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 <div class="cell docutils container">
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 </div>
 </div>
 <section id="checking-dataset-by-2d-plot">
-<h3><span class="section-number">41.102.3.1. </span>Checking dataset by 2D plot<a class="headerlink" href="#checking-dataset-by-2d-plot" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.3.1. </span>Checking dataset by 2D plot<a class="headerlink" href="#checking-dataset-by-2d-plot" title="Permalink to this headline">#</a></h3>
 <p>Autoencoding can be thought of as a kind of dimensionality reduction process. Therefore, after compressing the fashion MNIST dataset through UMAP in two dimensions, let’s check how it is mapped for each label.</p>
 <div class="cell docutils container">
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@@ -1972,7 +1972,7 @@ <h3><span class="section-number">41.102.3.1. </span>Checking dataset by 2D plot<
 <p>Ref: <a class="reference external" href="https://umap-learn.readthedocs.io/en">https://umap-learn.readthedocs.io/en</a></p>
 </section>
 <section id="checking-dataset-by-3d-plot">
-<h3><span class="section-number">41.102.3.2. </span>Checking dataset by 3D plot<a class="headerlink" href="#checking-dataset-by-3d-plot" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.3.2. </span>Checking dataset by 3D plot<a class="headerlink" href="#checking-dataset-by-3d-plot" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">plotly</span>
@@ -1998,9 +1998,9 @@ <h3><span class="section-number">41.102.3.2. </span>Checking dataset by 3D plot<
 </section>
 </section>
 <section id="base-autoencoder">
-<h2><span class="section-number">41.102.4. </span>Base Autoencoder<a class="headerlink" href="#base-autoencoder" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.102.4. </span>Base Autoencoder<a class="headerlink" href="#base-autoencoder" title="Permalink to this headline">#</a></h2>
 <section id="modeling">
-<h3><span class="section-number">41.102.4.1. </span>Modeling<a class="headerlink" href="#modeling" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.4.1. </span>Modeling<a class="headerlink" href="#modeling" title="Permalink to this headline">#</a></h3>
 <p>An autoencoder always consists of two parts: an encoder and a decoder.</p>
 <ul class="simple">
 <li><p>Encoder (Recognition network): it transforms an input into an internal representation.</p></li>
@@ -2044,7 +2044,7 @@ <h3><span class="section-number">41.102.4.1. </span>Modeling<a class="headerlink
 </div>
 </section>
 <section id="training">
-<h3><span class="section-number">41.102.4.2. </span>Training<a class="headerlink" href="#training" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.4.2. </span>Training<a class="headerlink" href="#training" title="Permalink to this headline">#</a></h3>
 <p>Train the model using x_train as input and target. The encoder learns to compress the dataset into a latent space in 784 dimensions, and the decoder learns to reconstruct the original image.</p>
 <div class="cell docutils container">
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@@ -2075,7 +2075,7 @@ <h3><span class="section-number">41.102.4.2. </span>Training<a class="headerlink
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 </section>
 <section id="plotting-the-latent-space-after-dimension-reduction">
-<h3><span class="section-number">41.102.4.3. </span>Plotting the latent space after Dimension Reduction<a class="headerlink" href="#plotting-the-latent-space-after-dimension-reduction" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.4.3. </span>Plotting the latent space after Dimension Reduction<a class="headerlink" href="#plotting-the-latent-space-after-dimension-reduction" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_test</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;class&#39;</span><span class="p">])</span>
@@ -2095,7 +2095,7 @@ <h3><span class="section-number">41.102.4.3. </span>Plotting the latent space af
 <p>The <span class="math notranslate nohighlight">\(28*28\)</span> dimension input is compressed into the <span class="math notranslate nohighlight">\(7*7\)</span> latent space by the encoder. The latent space is compressed into 2D using Dimension Reduction. Although it is an approximate expression, it can be seen that each class is well clustered in the compressed latent space.</p>
 </section>
 <section id="checking-results">
-<h3><span class="section-number">41.102.4.4. </span>Checking results<a class="headerlink" href="#checking-results" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.4.4. </span>Checking results<a class="headerlink" href="#checking-results" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">n</span> <span class="o">=</span> <span class="mi">4</span>
@@ -2125,7 +2125,7 @@ <h3><span class="section-number">41.102.4.4. </span>Checking results<a class="he
 </section>
 </section>
 <section id="denoising-autoencoder">
-<h2><span class="section-number">41.102.5. </span>Denoising Autoencoder<a class="headerlink" href="#denoising-autoencoder" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.102.5. </span>Denoising Autoencoder<a class="headerlink" href="#denoising-autoencoder" title="Permalink to this headline">#</a></h2>
 <p>Another way to constrain the autoencoder to learn meaningful features is to add noise to the input and train it to reconstruct the original noise-free input. Noise can be generated by adding Gaussian noise to the input as shown in the figure below, or by randomly turning off the input unit (node) like a dropout.</p>
 <div class="cell docutils container">
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@@ -2148,7 +2148,7 @@ <h2><span class="section-number">41.102.5. </span>Denoising Autoencoder<a class=
 </div>
 </div>
 <section id="adding-random-noise-to-the-image">
-<h3><span class="section-number">41.102.5.1. </span>Adding random noise to the image.<a class="headerlink" href="#adding-random-noise-to-the-image" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.5.1. </span>Adding random noise to the image.<a class="headerlink" href="#adding-random-noise-to-the-image" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">noise_factor</span> <span class="o">=</span> <span class="mf">0.2</span>
@@ -2163,7 +2163,7 @@ <h3><span class="section-number">41.102.5.1. </span>Adding random noise to the i
 </div>
 </section>
 <section id="plotting-a-noisy-image">
-<h3><span class="section-number">41.102.5.2. </span>Plotting a noisy image.<a class="headerlink" href="#plotting-a-noisy-image" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.5.2. </span>Plotting a noisy image.<a class="headerlink" href="#plotting-a-noisy-image" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">n</span> <span class="o">=</span> <span class="mi">4</span>
@@ -2180,9 +2180,9 @@ <h3><span class="section-number">41.102.5.2. </span>Plotting a noisy image.<a cl
 </div>
 </section>
 <section id="checking-noisy-dataset-using-demension-reduction">
-<h3><span class="section-number">41.102.5.3. </span>Checking Noisy Dataset using Demension Reduction<a class="headerlink" href="#checking-noisy-dataset-using-demension-reduction" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.5.3. </span>Checking Noisy Dataset using Demension Reduction<a class="headerlink" href="#checking-noisy-dataset-using-demension-reduction" title="Permalink to this headline">#</a></h3>
 <section id="noisy-dataset">
-<h4><span class="section-number">41.102.5.3.1. </span>1) Noisy Dataset<a class="headerlink" href="#noisy-dataset" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.102.5.3.1. </span>1) Noisy Dataset<a class="headerlink" href="#noisy-dataset" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train_noisy_flat</span> <span class="o">=</span> <span class="n">x_train</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x_train_noisy</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
@@ -2208,7 +2208,7 @@ <h4><span class="section-number">41.102.5.3.1. </span>1) Noisy Dataset<a class="
 </div>
 </section>
 <section id="orignal-dataset">
-<h4><span class="section-number">41.102.5.3.2. </span>2) Orignal Dataset<a class="headerlink" href="#orignal-dataset" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.102.5.3.2. </span>2) Orignal Dataset<a class="headerlink" href="#orignal-dataset" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train_flat</span> <span class="o">=</span> <span class="n">x_train</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
@@ -2231,7 +2231,7 @@ <h4><span class="section-number">41.102.5.3.2. </span>2) Orignal Dataset<a class
 </section>
 </section>
 <section id="id1">
-<h3><span class="section-number">41.102.5.4. </span>Modeling<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.5.4. </span>Modeling<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Denoise</span><span class="p">(</span><span class="n">Model</span><span class="p">):</span>
@@ -2266,7 +2266,7 @@ <h3><span class="section-number">41.102.5.4. </span>Modeling<a class="headerlink
 </div>
 </section>
 <section id="id2">
-<h3><span class="section-number">41.102.5.5. </span>Training<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.5.5. </span>Training<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">autoencoder</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train_noisy</span><span class="p">,</span> <span class="n">x_train</span><span class="p">,</span>
@@ -2295,7 +2295,7 @@ <h3><span class="section-number">41.102.5.5. </span>Training<a class="headerlink
 </div>
 </section>
 <section id="id3">
-<h3><span class="section-number">41.102.5.6. </span>Checking results<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.102.5.6. </span>Checking results<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <p>Plots both the noisy and denoised images generated by the autoencoder.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2334,7 +2334,7 @@ <h3><span class="section-number">41.102.5.6. </span>Checking results<a class="he
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.102.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.102.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to TOH SEOK KIM for creating the Kaggle open-source project <a class="reference external" href="https://www.kaggle.com/code/ohseokkim/base-denoising-autoencoder-dimension-reduction">Base/Denoising Autoencoder + Dimension Reduction</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2376,13 +2376,13 @@ <h2><span class="section-number">41.102.6. </span>Acknowledgments<a class="heade
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.101. </span>Intro to Autoencoders</p>
+            <p class="prev-next-title"><span class="section-number">40.101. </span>Intro to Autoencoders</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="variational-autoencoder-and-faces-generation.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">41.103. </span>Fun with Variational Autoencoders</p>
+        <p class="prev-next-title"><span class="section-number">40.103. </span>Fun with Variational Autoencoders</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html b/assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html
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--- a/assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html
+++ b/assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>41.103. Fun with Variational Autoencoders &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.103. Fun with Variational Autoencoders &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="41.104. Time Series Forecasting Assignment" href="../time-series-forecasting-assignment.html" />
-    <link rel="prev" title="41.102. Base/Denoising Autoencoder &amp; Dimension Reduction" href="base-denoising-autoencoder-dimension-reduction.html" />
+    <link rel="next" title="40.104. Time Series Forecasting Assignment" href="../time-series-forecasting-assignment.html" />
+    <link rel="prev" title="40.102. Base/Denoising Autoencoder &amp; Dimension Reduction" href="base-denoising-autoencoder-dimension-reduction.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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   <a class="reference internal" href="../../../prerequisites/python-programming-introduction.html">
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+   1. Python programming introduction
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-     5.2. Data Science ethics
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   </a>
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   <a class="reference internal nav-link" href="#fork-it-and-give-it-an-upvote">
-   41.103.1. Fork it and give it an upvote.
+   40.103.1. Fork it and give it an upvote.
   </a>
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  <li class="toc-h2 nav-item toc-entry">
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-   41.103.2. A bit of theory
+   40.103.2. A bit of theory
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#load-datasets">
-   41.103.3. Load datasets
+   40.103.3. Load datasets
   </a>
  </li>
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   <a class="reference internal nav-link" href="#explore-the-data">
-   41.103.4. Explore the data
+   40.103.4. Explore the data
   </a>
  </li>
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-   41.103.5. Prepare the dataset
+   40.103.5. Prepare the dataset
   </a>
  </li>
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-   41.103.6. Building simple autoencoder
+   40.103.6. Building simple autoencoder
   </a>
  </li>
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-   41.103.7. Train autoencoder
+   40.103.7. Train autoencoder
   </a>
  </li>
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-   41.103.8. Sampling
+   40.103.8. Sampling
   </a>
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-   41.103.9. Adding smile and glasses
+   40.103.9. Adding smile and glasses
   </a>
  </li>
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-   41.103.10. Variational autoencoder
+   40.103.10. Variational autoencoder
   </a>
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+   40.103.11. Conclusion
   </a>
  </li>
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-   41.103.12. Acknowledgments
+   40.103.12. Acknowledgments
   </a>
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 </ul>
@@ -1723,10 +1723,10 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="fun-with-variational-autoencoders">
-<h1><span class="section-number">41.103. </span>Fun with Variational Autoencoders<a class="headerlink" href="#fun-with-variational-autoencoders" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.103. </span>Fun with Variational Autoencoders<a class="headerlink" href="#fun-with-variational-autoencoders" title="Permalink to this headline">#</a></h1>
 <p>This is a starter kernel to use <strong>Labelled Faces in the Wild (LFW) Dataset</strong> in order to maintain knowledge about main Autoencoder principles. PyTorch will be used for modelling.</p>
 <section id="fork-it-and-give-it-an-upvote">
-<h2><span class="section-number">41.103.1. </span>Fork it and give it an upvote.<a class="headerlink" href="#fork-it-and-give-it-an-upvote" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.1. </span>Fork it and give it an upvote.<a class="headerlink" href="#fork-it-and-give-it-an-upvote" title="Permalink to this headline">#</a></h2>
 <p><img alt="architecture" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/autoencoder/variational-autoencoder-and-faces-generation/architecture.png" /></p>
 <p>Useful links:</p>
 <ul class="simple">
@@ -1739,7 +1739,7 @@ <h2><span class="section-number">41.103.1. </span>Fork it and give it an upvote.
 </ul>
 </section>
 <section id="a-bit-of-theory">
-<h2><span class="section-number">41.103.2. </span>A bit of theory<a class="headerlink" href="#a-bit-of-theory" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.2. </span>A bit of theory<a class="headerlink" href="#a-bit-of-theory" title="Permalink to this headline">#</a></h2>
 <p>“Autoencoding” is a data compression algorithm where the compression and decompression functions are 1) data-specific, 2) lossy, and 3) learned automatically from examples rather than engineered by a human. Additionally, in almost all contexts where the term “autoencoder” is used, the compression and decompression functions are implemented with neural networks.</p>
 <ol class="simple">
 <li><p>Autoencoders are data-specific, which means that they will only be able to compress data similar to what they have been trained on. This is different from, say, the MPEG-2 Audio Layer III (MP3) compression algorithm, which only holds assumptions about “sound” in general, but not about specific types of sounds. An autoencoder trained on pictures of faces would do a rather poor job of compressing pictures of trees, because the features it would learn would be face-specific.</p></li>
@@ -1781,7 +1781,7 @@ <h2><span class="section-number">41.103.2. </span>A bit of theory<a class="heade
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 <section id="load-datasets">
-<h2><span class="section-number">41.103.3. </span>Load datasets<a class="headerlink" href="#load-datasets" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.3. </span>Load datasets<a class="headerlink" href="#load-datasets" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">datasets_url</span> <span class="o">=</span> <span class="s2">&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/autoencoder/variational-autoencoder-and-faces-generation/lfw-deepfunneled.zip&quot;</span>
@@ -1857,7 +1857,7 @@ <h2><span class="section-number">41.103.3. </span>Load datasets<a class="headerl
 </div>
 </section>
 <section id="explore-the-data">
-<h2><span class="section-number">41.103.4. </span>Explore the data<a class="headerlink" href="#explore-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.4. </span>Explore the data<a class="headerlink" href="#explore-the-data" title="Permalink to this headline">#</a></h2>
 <p>Image data is collected from DATASET_PATH and a dataset is created in which the person information for each image is extracted and used for subsequent data analysis or processing. Finally, the filter() function is used to limit the size of the dataset by retaining only information about people who appear less than 25 times.</p>
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 <section id="prepare-the-dataset">
-<h2><span class="section-number">41.103.5. </span>Prepare the dataset<a class="headerlink" href="#prepare-the-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.5. </span>Prepare the dataset<a class="headerlink" href="#prepare-the-dataset" title="Permalink to this headline">#</a></h2>
 <blockquote>
 <div><ul class="simple">
 <li><p>Reads the attribute data from the txt file at the specified path and stores it in a DataFrame object called df_attrs. The txt file is tab delimited, skipping the first line.</p></li>
@@ -1985,7 +1985,7 @@ <h2><span class="section-number">41.103.5. </span>Prepare the dataset<a class="h
 </div>
 </section>
 <section id="building-simple-autoencoder">
-<h2><span class="section-number">41.103.6. </span>Building simple autoencoder<a class="headerlink" href="#building-simple-autoencoder" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.6. </span>Building simple autoencoder<a class="headerlink" href="#building-simple-autoencoder" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dim_z</span><span class="o">=</span><span class="mi">100</span>
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-<h2><span class="section-number">41.103.7. </span>Train autoencoder<a class="headerlink" href="#train-autoencoder" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.7. </span>Train autoencoder<a class="headerlink" href="#train-autoencoder" title="Permalink to this headline">#</a></h2>
 <blockquote>
 <div><ul class="simple">
 <li><p>get_batch: It uses the Generator method to generate batches of a specified size by iterating over them. The amount of data generated is batch_size each time, until the entire data set is traversed.</p></li>
@@ -2257,7 +2257,7 @@ <h2><span class="section-number">41.103.7. </span>Train autoencoder<a class="hea
 </div>
 </section>
 <section id="sampling">
-<h2><span class="section-number">41.103.8. </span>Sampling<a class="headerlink" href="#sampling" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.8. </span>Sampling<a class="headerlink" href="#sampling" title="Permalink to this headline">#</a></h2>
 <p>Let’s generate some samples from random vectors</p>
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 </div>
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 <section id="adding-smile-and-glasses">
-<h2><span class="section-number">41.103.9. </span>Adding smile and glasses<a class="headerlink" href="#adding-smile-and-glasses" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.9. </span>Adding smile and glasses<a class="headerlink" href="#adding-smile-and-glasses" title="Permalink to this headline">#</a></h2>
 <p>Let’s find some attributes like smiles or glasses on the photo and try to add it to the photos which don’t have it. We will use the second dataset for it. It contains a bunch of such attributes.</p>
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@@ -2406,7 +2406,7 @@ <h2><span class="section-number">41.103.9. </span>Adding smile and glasses<a cla
 <p>While the concept is pretty straightforward the simple autoencoder have some disadvantages. Let’s explore them and try to do better.</p>
 </section>
 <section id="variational-autoencoder">
-<h2><span class="section-number">41.103.10. </span>Variational autoencoder<a class="headerlink" href="#variational-autoencoder" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.10. </span>Variational autoencoder<a class="headerlink" href="#variational-autoencoder" title="Permalink to this headline">#</a></h2>
 <p>So far we have trained our encoder to reconstruct the very same image that we’ve transfered to latent space. That means that when we’re trying to <strong>generate</strong> new image from the point decoder never met we’re getting <em>the best image it can produce</em>, but the quelity is not good enough.</p>
 <blockquote>
 <div><p><strong>In other words the encoded vectors may not be continuous in the latent space.</strong></p>
@@ -2582,12 +2582,12 @@ <h2><span class="section-number">41.103.10. </span>Variational autoencoder<a cla
 </div>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">41.103.11. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.11. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>Variational autoencoders are cool. Although models in this particular notebook are simple they let us design complex generative models of data, and fit them to large datasets. They can generate images of fictional celebrity faces and high-resolution digital artwork.
 These models also yield state-of-the-art machine learning results in image generation and reinforcement learning. Variational autoencoders (VAEs) were defined in 2013 by Kingma et al. and Rezende et al.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.103.12. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.103.12. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to SERGEI AVERKIEV for creating the Kaggle open-source project <a class="reference external" href="https://www.kaggle.com/code/averkij/variational-autoencoder-and-faces-generation">Variational Autoencoder and Faces Generation</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2629,13 +2629,13 @@ <h2><span class="section-number">41.103.12. </span>Acknowledgments<a class="head
         <i class="fas fa-angle-left"></i>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.102. </span>Base/Denoising Autoencoder &amp; Dimension Reduction</p>
+            <p class="prev-next-title"><span class="section-number">40.102. </span>Base/Denoising Autoencoder &amp; Dimension Reduction</p>
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-        <p class="prev-next-title"><span class="section-number">41.104. </span>Time Series Forecasting Assignment</p>
+        <p class="prev-next-title"><span class="section-number">40.104. </span>Time Series Forecasting Assignment</p>
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-    <title>41.90. How to choose cnn architecture mnist &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.90. How to choose cnn architecture mnist &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../../genindex.html" />
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-    <link rel="next" title="41.92. Sign Language Digits Classification with CNN" href="sign-language-digits-classification-with-cnn.html" />
-    <link rel="prev" title="41.89. Parameter play" href="../../ml-fundamentals/parameter-play.html" />
+    <link rel="next" title="40.92. Sign Language Digits Classification with CNN" href="sign-language-digits-classification-with-cnn.html" />
+    <link rel="prev" title="40.89. Parameter play" href="../../ml-fundamentals/parameter-play.html" />
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      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#build-convolutional-neural-networks">
-       41.90.8.1. Build convolutional neural networks
+       40.90.8.1. Build convolutional neural networks
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#create-validation-set-and-train-networks">
-       41.90.8.2. Create validation set and train networks
+       40.90.8.2. Create validation set and train networks
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#plot-accuracies">
-       41.90.8.3. Plot accuracies
+       40.90.8.3. Plot accuracies
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#summary">
-       41.90.8.4. Summary
+       40.90.8.4. Summary
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#how-many-feature-maps">
-     41.90.9. 2. How many feature maps?
+     40.90.9. 2. How many feature maps?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#experiment-2">
-     41.90.10. Experiment 2
+     40.90.10. Experiment 2
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       41.90.10.1. Build convolutional neural networks
+       40.90.10.1. Build convolutional neural networks
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id2">
-       41.90.10.2. Create validation set and train networks
+       40.90.10.2. Create validation set and train networks
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id3">
-       41.90.10.3. Plot accuracies
+       40.90.10.3. Plot accuracies
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id4">
-       41.90.10.4. Summary
+       40.90.10.4. Summary
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#how-large-a-dense-layer">
-     41.90.11. 3. How large a dense layer?
+     40.90.11. 3. How large a dense layer?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#experiment-3">
-     41.90.12. Experiment 3
+     40.90.12. Experiment 3
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id5">
-     41.90.13. Build convolutional neural networks
+     40.90.13. Build convolutional neural networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id6">
-     41.90.14. Create validation set and train networks
+     40.90.14. Create validation set and train networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id7">
-     41.90.15. Plot accuracies
+     40.90.15. Plot accuracies
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id8">
-       41.90.15.1. Summary
+       40.90.15.1. Summary
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#how-much-dropout">
-     41.90.16. 4. How much dropout?
+     40.90.16. 4. How much dropout?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#experiment-4">
-     41.90.17. Experiment 4
+     40.90.17. Experiment 4
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id9">
-     41.90.18. Build convolutional neural networks
+     40.90.18. Build convolutional neural networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id10">
-     41.90.19. Create validation set and train networks
+     40.90.19. Create validation set and train networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id11">
-     41.90.20. Plot accuracies
+     40.90.20. Plot accuracies
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id12">
-     41.90.21. Summary
+     40.90.21. Summary
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#advanced-features">
-     41.90.22. 5. Advanced features
+     40.90.22. 5. Advanced features
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id13">
-     41.90.23. Build convolutional neural networks
+     40.90.23. Build convolutional neural networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id14">
-     41.90.24. Create validation set and train networks
+     40.90.24. Create validation set and train networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#create-more-training-images-via-data-augmentation-and-train-network">
-     41.90.25. Create more training images via data augmentation and train network
+     40.90.25. Create more training images via data augmentation and train network
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id15">
-     41.90.26. Plot accuracies
+     40.90.26. Plot accuracies
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id16">
-       41.90.26.1. Summary
+       40.90.26.1. Summary
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#conclusion">
-     41.90.27. Conclusion
+     40.90.27. Conclusion
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.91. Acknowledgments
+   40.91. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1799,207 +1799,207 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#">
-   41.90. How to choose cnn architecture mnist
+   40.90. How to choose cnn architecture mnist
   </a>
   <ul class="visible nav section-nav flex-column">
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#what-is-the-best-cnn-architecture-for-mnist">
-     41.90.1. What is the best CNN architecture for MNIST?
+     40.90.1. What is the best CNN architecture for MNIST?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#basic-cnn-structure">
-     41.90.2. Basic CNN structure
+     40.90.2. Basic CNN structure
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#load-libraries">
-     41.90.3. Load libraries
+     40.90.3. Load libraries
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#load-the-data">
-     41.90.4. Load the data
+     40.90.4. Load the data
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#prepare-data-for-neural-network">
-     41.90.5. Prepare data for neural network
+     40.90.5. Prepare data for neural network
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#global-variables">
-     41.90.6. Global variables
+     40.90.6. Global variables
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#how-many-convolution-subsambling-pairs">
-     41.90.7. 1. How many convolution-subsambling pairs?
+     40.90.7. 1. How many convolution-subsambling pairs?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#experiment-1">
-     41.90.8. Experiment 1
+     40.90.8. Experiment 1
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#build-convolutional-neural-networks">
-       41.90.8.1. Build convolutional neural networks
+       40.90.8.1. Build convolutional neural networks
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#create-validation-set-and-train-networks">
-       41.90.8.2. Create validation set and train networks
+       40.90.8.2. Create validation set and train networks
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#plot-accuracies">
-       41.90.8.3. Plot accuracies
+       40.90.8.3. Plot accuracies
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#summary">
-       41.90.8.4. Summary
+       40.90.8.4. Summary
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#how-many-feature-maps">
-     41.90.9. 2. How many feature maps?
+     40.90.9. 2. How many feature maps?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#experiment-2">
-     41.90.10. Experiment 2
+     40.90.10. Experiment 2
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       41.90.10.1. Build convolutional neural networks
+       40.90.10.1. Build convolutional neural networks
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id2">
-       41.90.10.2. Create validation set and train networks
+       40.90.10.2. Create validation set and train networks
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id3">
-       41.90.10.3. Plot accuracies
+       40.90.10.3. Plot accuracies
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id4">
-       41.90.10.4. Summary
+       40.90.10.4. Summary
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#how-large-a-dense-layer">
-     41.90.11. 3. How large a dense layer?
+     40.90.11. 3. How large a dense layer?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#experiment-3">
-     41.90.12. Experiment 3
+     40.90.12. Experiment 3
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id5">
-     41.90.13. Build convolutional neural networks
+     40.90.13. Build convolutional neural networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id6">
-     41.90.14. Create validation set and train networks
+     40.90.14. Create validation set and train networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id7">
-     41.90.15. Plot accuracies
+     40.90.15. Plot accuracies
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id8">
-       41.90.15.1. Summary
+       40.90.15.1. Summary
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#how-much-dropout">
-     41.90.16. 4. How much dropout?
+     40.90.16. 4. How much dropout?
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#experiment-4">
-     41.90.17. Experiment 4
+     40.90.17. Experiment 4
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id9">
-     41.90.18. Build convolutional neural networks
+     40.90.18. Build convolutional neural networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id10">
-     41.90.19. Create validation set and train networks
+     40.90.19. Create validation set and train networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id11">
-     41.90.20. Plot accuracies
+     40.90.20. Plot accuracies
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id12">
-     41.90.21. Summary
+     40.90.21. Summary
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#advanced-features">
-     41.90.22. 5. Advanced features
+     40.90.22. 5. Advanced features
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id13">
-     41.90.23. Build convolutional neural networks
+     40.90.23. Build convolutional neural networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id14">
-     41.90.24. Create validation set and train networks
+     40.90.24. Create validation set and train networks
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#create-more-training-images-via-data-augmentation-and-train-network">
-     41.90.25. Create more training images via data augmentation and train network
+     40.90.25. Create more training images via data augmentation and train network
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id15">
-     41.90.26. Plot accuracies
+     40.90.26. Plot accuracies
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id16">
-       41.90.26.1. Summary
+       40.90.26.1. Summary
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#conclusion">
-     41.90.27. Conclusion
+     40.90.27. Conclusion
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.91. Acknowledgments
+   40.91. Acknowledgments
   </a>
  </li>
 </ul>
@@ -2013,19 +2013,19 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="how-to-choose-cnn-architecture-mnist">
-<h1><span class="section-number">41.90. </span>How to choose cnn architecture mnist<a class="headerlink" href="#how-to-choose-cnn-architecture-mnist" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.90. </span>How to choose cnn architecture mnist<a class="headerlink" href="#how-to-choose-cnn-architecture-mnist" title="Permalink to this headline">#</a></h1>
 <section id="what-is-the-best-cnn-architecture-for-mnist">
-<h2><span class="section-number">41.90.1. </span>What is the best CNN architecture for MNIST?<a class="headerlink" href="#what-is-the-best-cnn-architecture-for-mnist" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.1. </span>What is the best CNN architecture for MNIST?<a class="headerlink" href="#what-is-the-best-cnn-architecture-for-mnist" title="Permalink to this headline">#</a></h2>
 <p>There are so many choices for CNN architecture. How do we choose the best one? First we must define what <strong>best</strong> means. The best may be the simplest, or it may be the most efficient at producing accuracy while minimizing computational complexity. In this kernel, we will run experiments to find the most accurate and efficient CNN architecture for classifying MNIST handwritten digits.</p>
 <p>The best known MNIST classifier found on the internet achieves 99.8% accuracy!! That’s amazing. The best Kaggle kernel MNIST classifier achieves 99.75% [posted here][<a class="reference external" href="https://www.kaggle.com/cdeotte/25-million-images-0-99757-mnist">https://www.kaggle.com/cdeotte/25-million-images-0-99757-mnist</a>]. This kernel demostrates the experiments used to determine that kernel’s CNN architecture.</p>
 </section>
 <section id="basic-cnn-structure">
-<h2><span class="section-number">41.90.2. </span>Basic CNN structure<a class="headerlink" href="#basic-cnn-structure" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.2. </span>Basic CNN structure<a class="headerlink" href="#basic-cnn-structure" title="Permalink to this headline">#</a></h2>
 <p>A typical CNN design begins with feature extraction and finishes with classification. Feature extraction is performed by alternating convolution layers with subsambling layers. Classification is performed with dense layers followed by a final softmax layer. For image classification, this architecture performs better than an entirely fully connected feed forward neural network.
 <img alt="extract" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/cnn/how-to-choose-cnn-architecture-mnist/extract.png" /></p>
 </section>
 <section id="load-libraries">
-<h2><span class="section-number">41.90.3. </span>Load libraries<a class="headerlink" href="#load-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.3. </span>Load libraries<a class="headerlink" href="#load-libraries" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
@@ -2052,7 +2052,7 @@ <h2><span class="section-number">41.90.3. </span>Load libraries<a class="headerl
 </div>
 </section>
 <section id="load-the-data">
-<h2><span class="section-number">41.90.4. </span>Load the data<a class="headerlink" href="#load-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.4. </span>Load the data<a class="headerlink" href="#load-the-data" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/cnn/mnist_train.csv&quot;</span><span class="p">)</span>
@@ -2063,7 +2063,7 @@ <h2><span class="section-number">41.90.4. </span>Load the data<a class="headerli
 </div>
 </section>
 <section id="prepare-data-for-neural-network">
-<h2><span class="section-number">41.90.5. </span>Prepare data for neural network<a class="headerlink" href="#prepare-data-for-neural-network" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.5. </span>Prepare data for neural network<a class="headerlink" href="#prepare-data-for-neural-network" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Y_train</span> <span class="o">=</span> <span class="n">train</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span>
@@ -2084,7 +2084,7 @@ <h2><span class="section-number">41.90.5. </span>Prepare data for neural network
 </div>
 </section>
 <section id="global-variables">
-<h2><span class="section-number">41.90.6. </span>Global variables<a class="headerlink" href="#global-variables" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.6. </span>Global variables<a class="headerlink" href="#global-variables" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">annealer</span> <span class="o">=</span> <span class="n">LearningRateScheduler</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="mf">1e-3</span> <span class="o">*</span> <span class="mf">0.95</span><span class="o">**</span><span class="n">x</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
@@ -2095,7 +2095,7 @@ <h2><span class="section-number">41.90.6. </span>Global variables<a class="heade
 </div>
 </section>
 <section id="how-many-convolution-subsambling-pairs">
-<h2><span class="section-number">41.90.7. </span>1. How many convolution-subsambling pairs?<a class="headerlink" href="#how-many-convolution-subsambling-pairs" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.7. </span>1. How many convolution-subsambling pairs?<a class="headerlink" href="#how-many-convolution-subsambling-pairs" title="Permalink to this headline">#</a></h2>
 <p>First question, how many pairs of convolution-subsampling should we use? For example, our network could have 1, 2, or 3:</p>
 <ul class="simple">
 <li><p>784 - <strong>[24C5-P2]</strong> - 256 - 10</p></li>
@@ -2105,10 +2105,10 @@ <h2><span class="section-number">41.90.7. </span>1. How many convolution-subsamb
 <p>It’s typical to increase the number of feature maps for each subsequent pair as shown here.</p>
 </section>
 <section id="experiment-1">
-<h2><span class="section-number">41.90.8. </span>Experiment 1<a class="headerlink" href="#experiment-1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.8. </span>Experiment 1<a class="headerlink" href="#experiment-1" title="Permalink to this headline">#</a></h2>
 <p>Let’s see whether one, two, or three pairs is best. We are not doing four pairs since the image will be reduced too small before then. The input image is 28x28. After one pair, it’s 14x14. After two, it’s 7x7. After three it’s 4x4 (or 3x3 if we don’t use padding=‘same’). It doesn’t make sense to do a fourth convolution.</p>
 <section id="build-convolutional-neural-networks">
-<h3><span class="section-number">41.90.8.1. </span>Build convolutional neural networks<a class="headerlink" href="#build-convolutional-neural-networks" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.90.8.1. </span>Build convolutional neural networks<a class="headerlink" href="#build-convolutional-neural-networks" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">nets</span> <span class="o">=</span> <span class="mi">3</span>
@@ -2144,7 +2144,7 @@ <h3><span class="section-number">41.90.8.1. </span>Build convolutional neural ne
 </div>
 </section>
 <section id="create-validation-set-and-train-networks">
-<h3><span class="section-number">41.90.8.2. </span>Create validation set and train networks<a class="headerlink" href="#create-validation-set-and-train-networks" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.90.8.2. </span>Create validation set and train networks<a class="headerlink" href="#create-validation-set-and-train-networks" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train2</span><span class="p">,</span> <span class="n">X_val2</span><span class="p">,</span> <span class="n">Y_train2</span><span class="p">,</span> <span class="n">Y_val2</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.333</span><span class="p">)</span>
@@ -2175,7 +2175,7 @@ <h3><span class="section-number">41.90.8.2. </span>Create validation set and tra
 </div>
 </section>
 <section id="plot-accuracies">
-<h3><span class="section-number">41.90.8.3. </span>Plot accuracies<a class="headerlink" href="#plot-accuracies" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.90.8.3. </span>Plot accuracies<a class="headerlink" href="#plot-accuracies" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
@@ -2194,12 +2194,12 @@ <h3><span class="section-number">41.90.8.3. </span>Plot accuracies<a class="head
 </div>
 </section>
 <section id="summary">
-<h3><span class="section-number">41.90.8.4. </span>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.90.8.4. </span>Summary<a class="headerlink" href="#summary" title="Permalink to this headline">#</a></h3>
 <p>From the above experiment, it seems that 3 pairs of convolution-subsambling is slightly better than 2 pairs. However for efficiency, the improvement doesn’t warrant the additional computional cost, so let’s use 2.</p>
 </section>
 </section>
 <section id="how-many-feature-maps">
-<h2><span class="section-number">41.90.9. </span>2. How many feature maps?<a class="headerlink" href="#how-many-feature-maps" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.9. </span>2. How many feature maps?<a class="headerlink" href="#how-many-feature-maps" title="Permalink to this headline">#</a></h2>
 <p>In the previous experiement, we decided that two pairs is sufficient. How many feature maps should we include? For example, we could do</p>
 <ul class="simple">
 <li><p>784 - [<strong>8</strong>C5-P2] - [<strong>16</strong>C5-P2] - 256 - 10</p></li>
@@ -2211,9 +2211,9 @@ <h2><span class="section-number">41.90.9. </span>2. How many feature maps?<a cla
 </ul>
 </section>
 <section id="experiment-2">
-<h2><span class="section-number">41.90.10. </span>Experiment 2<a class="headerlink" href="#experiment-2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.10. </span>Experiment 2<a class="headerlink" href="#experiment-2" title="Permalink to this headline">#</a></h2>
 <section id="id1">
-<h3><span class="section-number">41.90.10.1. </span>Build convolutional neural networks<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.90.10.1. </span>Build convolutional neural networks<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">nets</span> <span class="o">=</span> <span class="mi">6</span>
@@ -2238,7 +2238,7 @@ <h3><span class="section-number">41.90.10.1. </span>Build convolutional neural n
 </div>
 </section>
 <section id="id2">
-<h3><span class="section-number">41.90.10.2. </span>Create validation set and train networks<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.90.10.2. </span>Create validation set and train networks<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train2</span><span class="p">,</span> <span class="n">X_val2</span><span class="p">,</span> <span class="n">Y_train2</span><span class="p">,</span> <span class="n">Y_val2</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.333</span><span class="p">)</span>
@@ -2269,7 +2269,7 @@ <h3><span class="section-number">41.90.10.2. </span>Create validation set and tr
 </div>
 </section>
 <section id="id3">
-<h3><span class="section-number">41.90.10.3. </span>Plot accuracies<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.90.10.3. </span>Plot accuracies<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
@@ -2288,12 +2288,12 @@ <h3><span class="section-number">41.90.10.3. </span>Plot accuracies<a class="hea
 </div>
 </section>
 <section id="id4">
-<h3><span class="section-number">41.90.10.4. </span>Summary<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.90.10.4. </span>Summary<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
 <p>From the above experiement, it appears that 32 maps in the first convolutional layer and 64 maps in the second convolutional layer is the best. Architectures with more maps only perform slightly better and are not worth the additonal computation cost.</p>
 </section>
 </section>
 <section id="how-large-a-dense-layer">
-<h2><span class="section-number">41.90.11. </span>3. How large a dense layer?<a class="headerlink" href="#how-large-a-dense-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.11. </span>3. How large a dense layer?<a class="headerlink" href="#how-large-a-dense-layer" title="Permalink to this headline">#</a></h2>
 <p>In our previous experiment, we decided on 32 and 64 maps in our convolutional layers. How many dense units should we use? For example we could use</p>
 <ul class="simple">
 <li><p>784 - [32C5-P2] - [64C5-P2] - <strong>0</strong> - 10</p></li>
@@ -2307,10 +2307,10 @@ <h2><span class="section-number">41.90.11. </span>3. How large a dense layer?<a
 </ul>
 </section>
 <section id="experiment-3">
-<h2><span class="section-number">41.90.12. </span>Experiment 3<a class="headerlink" href="#experiment-3" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.12. </span>Experiment 3<a class="headerlink" href="#experiment-3" title="Permalink to this headline">#</a></h2>
 </section>
 <section id="id5">
-<h2><span class="section-number">41.90.13. </span>Build convolutional neural networks<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.13. </span>Build convolutional neural networks<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">nets</span> <span class="o">=</span> <span class="mi">8</span>
@@ -2335,7 +2335,7 @@ <h2><span class="section-number">41.90.13. </span>Build convolutional neural net
 </div>
 </section>
 <section id="id6">
-<h2><span class="section-number">41.90.14. </span>Create validation set and train networks<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.14. </span>Create validation set and train networks<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train2</span><span class="p">,</span> <span class="n">X_val2</span><span class="p">,</span> <span class="n">Y_train2</span><span class="p">,</span> <span class="n">Y_val2</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.333</span><span class="p">)</span>
@@ -2367,7 +2367,7 @@ <h2><span class="section-number">41.90.14. </span>Create validation set and trai
 </div>
 </section>
 <section id="id7">
-<h2><span class="section-number">41.90.15. </span>Plot accuracies<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.15. </span>Plot accuracies<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
@@ -2385,22 +2385,22 @@ <h2><span class="section-number">41.90.15. </span>Plot accuracies<a class="heade
 </div>
 </div>
 <section id="id8">
-<h3><span class="section-number">41.90.15.1. </span>Summary<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.90.15.1. </span>Summary<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h3>
 <p>From this experiment, it appears that 128 units is the best. Dense layers with more units only perform slightly better and are not worth the additional computational cost. (We also tested using two consecutive dense layers instead of one, but that showed no benefit over a single dense layer.)</p>
 </section>
 </section>
 <section id="how-much-dropout">
-<h2><span class="section-number">41.90.16. </span>4. How much dropout?<a class="headerlink" href="#how-much-dropout" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.16. </span>4. How much dropout?<a class="headerlink" href="#how-much-dropout" title="Permalink to this headline">#</a></h2>
 <p>Dropout will prevent our network from overfitting thus helping our network generalize better. How much dropout should we add after each layer?</p>
 <ul class="simple">
 <li><p>0%, 10%, 20%, 30%, 40%, 50%, 60%, or 70%</p></li>
 </ul>
 </section>
 <section id="experiment-4">
-<h2><span class="section-number">41.90.17. </span>Experiment 4<a class="headerlink" href="#experiment-4" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.17. </span>Experiment 4<a class="headerlink" href="#experiment-4" title="Permalink to this headline">#</a></h2>
 </section>
 <section id="id9">
-<h2><span class="section-number">41.90.18. </span>Build convolutional neural networks<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.18. </span>Build convolutional neural networks<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">nets</span> <span class="o">=</span> <span class="mi">8</span>
@@ -2427,7 +2427,7 @@ <h2><span class="section-number">41.90.18. </span>Build convolutional neural net
 </div>
 </section>
 <section id="id10">
-<h2><span class="section-number">41.90.19. </span>Create validation set and train networks<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.19. </span>Create validation set and train networks<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train2</span><span class="p">,</span> <span class="n">X_val2</span><span class="p">,</span> <span class="n">Y_train2</span><span class="p">,</span> <span class="n">Y_val2</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.333</span><span class="p">)</span>
@@ -2459,7 +2459,7 @@ <h2><span class="section-number">41.90.19. </span>Create validation set and trai
 </div>
 </section>
 <section id="id11">
-<h2><span class="section-number">41.90.20. </span>Plot accuracies<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.20. </span>Plot accuracies<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
@@ -2478,11 +2478,11 @@ <h2><span class="section-number">41.90.20. </span>Plot accuracies<a class="heade
 </div>
 </section>
 <section id="id12">
-<h2><span class="section-number">41.90.21. </span>Summary<a class="headerlink" href="#id12" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.21. </span>Summary<a class="headerlink" href="#id12" title="Permalink to this headline">#</a></h2>
 <p>From this experiment, it appears that 40% dropout is the best.</p>
 </section>
 <section id="advanced-features">
-<h2><span class="section-number">41.90.22. </span>5. Advanced features<a class="headerlink" href="#advanced-features" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.22. </span>5. Advanced features<a class="headerlink" href="#advanced-features" title="Permalink to this headline">#</a></h2>
 <p>Instead of using one convolution layer of size 5x5, you can mimic 5x5 by using two consecutive 3x3 layers and it will be more nonlinear. Instead of using a max pooling layer, you can subsample by using a convolution layer with strides=2 and it will be learnable. Lastly, does batch normalization help? And does data augmentation help? Let’s test all four of these</p>
 <ul class="simple">
 <li><p>replace ‘32C5’ with ‘32C3-32C3’</p></li>
@@ -2492,7 +2492,7 @@ <h2><span class="section-number">41.90.22. </span>5. Advanced features<a class="
 </ul>
 </section>
 <section id="id13">
-<h2><span class="section-number">41.90.23. </span>Build convolutional neural networks<a class="headerlink" href="#id13" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.23. </span>Build convolutional neural networks<a class="headerlink" href="#id13" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">nets</span> <span class="o">=</span> <span class="mi">5</span>
@@ -2611,7 +2611,7 @@ <h2><span class="section-number">41.90.23. </span>Build convolutional neural net
 </div>
 </section>
 <section id="id14">
-<h2><span class="section-number">41.90.24. </span>Create validation set and train networks<a class="headerlink" href="#id14" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.24. </span>Create validation set and train networks<a class="headerlink" href="#id14" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train2</span><span class="p">,</span> <span class="n">X_val2</span><span class="p">,</span> <span class="n">Y_train2</span><span class="p">,</span> <span class="n">Y_val2</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
@@ -2643,7 +2643,7 @@ <h2><span class="section-number">41.90.24. </span>Create validation set and trai
 </div>
 </section>
 <section id="create-more-training-images-via-data-augmentation-and-train-network">
-<h2><span class="section-number">41.90.25. </span>Create more training images via data augmentation and train network<a class="headerlink" href="#create-more-training-images-via-data-augmentation-and-train-network" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.25. </span>Create more training images via data augmentation and train network<a class="headerlink" href="#create-more-training-images-via-data-augmentation-and-train-network" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">datagen</span> <span class="o">=</span> <span class="n">ImageDataGenerator</span><span class="p">(</span>
@@ -2673,7 +2673,7 @@ <h2><span class="section-number">41.90.25. </span>Create more training images vi
 </div>
 </section>
 <section id="id15">
-<h2><span class="section-number">41.90.26. </span>Plot accuracies<a class="headerlink" href="#id15" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.26. </span>Plot accuracies<a class="headerlink" href="#id15" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
@@ -2691,12 +2691,12 @@ <h2><span class="section-number">41.90.26. </span>Plot accuracies<a class="heade
 </div>
 </div>
 <section id="id16">
-<h3><span class="section-number">41.90.26.1. </span>Summary<a class="headerlink" href="#id16" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.90.26.1. </span>Summary<a class="headerlink" href="#id16" title="Permalink to this headline">#</a></h3>
 <p>From this experiment, we see that each of the four advanced features improve accuracy. The first model uses no advanced features. The second uses only the double convolution layer trick. The third uses only the learnable subsambling layer trick. The third model uses both of those techniques plus batch normalization. The last model employs all three of those techniques plus data augmentation and achieves the best accuracy of 99.5%! (Or more if we train longer.) (Experiments determing the best data augmentation hyper-parameters are posted at the end of the kernel <a class="reference external" href="https://www.kaggle.com/cdeotte/25-million-images-0-99757-mnist">here</a>.)</p>
 </section>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">41.90.27. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.90.27. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>Training convolutional neural networks is a random process. This makes experiments difficult because each time you run the same experiment, you get different results. Therefore, you must run your experiments dozens of times and take an average. This kernel was run dozens of times and it seems that the best CNN architecture for classifying MNIST handwritten digits is 784 - [32C5-P2] - [64C5-P2] - 128 - 10 with 40% dropout. Afterward, more experiments show that replacing ‘32C5’ with ‘32C3-32C3’ improves accuracy. And replacing ‘P2’ with ‘32C5S2’ improves accuracy. And adding batch normalizaiton and data augmentation improve the CNN. The best CNN found from the experiments here becomes</p>
 <ul class="simple">
 <li><p><strong>784 - [32C3-32C3-32C5S2] - [64C3-64C3-64C5S2] - 128 - 10</strong></p></li>
@@ -2705,7 +2705,7 @@ <h2><span class="section-number">41.90.27. </span>Conclusion<a class="headerlink
 </section>
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-<h1><span class="section-number">41.91. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.91. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/cdeotte">Chris Deotte</a> for creating <a class="reference external" href="https://www.kaggle.com/code/cdeotte/how-to-choose-cnn-architecture-mnist">how-to-choose-cnn-architecture-mnist</a>. It inspires the majority of the content in this chapter.</p>
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-     41.85. Case Study: Debugging in Regression
+     40.85. Case Study: Debugging in Regression
     </a>
    </li>
    <li class="toctree-l2">
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-     41.86. Study the solvers
+     40.86. Study the solvers
     </a>
    </li>
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-     41.87. Build classification models
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-     41.88. Build Classification Model
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-     41.89. Parameter play
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-     41.90. How to choose cnn architecture mnist
+     40.90. How to choose cnn architecture mnist
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="sign-language-digits-classification-with-cnn.html">
-     41.92. Sign Language Digits Classification with CNN
+     40.92. Sign Language Digits Classification with CNN
     </a>
    </li>
    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     41.94. Object Recognition in Images using CNN
+     40.94. Object Recognition in Images using CNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     41.95. Intro to TensorFlow for Deep Learning
+     40.95. Intro to TensorFlow for Deep Learning
     </a>
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     <a class="reference internal" href="../lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     41.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
     </a>
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-     41.99. Google Stock Price Prediction RNN
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     </a>
    </li>
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-     41.101. Intro to Autoencoders
+     40.101. Intro to Autoencoders
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-     41.102. Base/Denoising Autoencoder &amp; Dimension Reduction
+     40.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
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     <a class="reference internal" href="../autoencoder/variational-autoencoder-and-faces-generation.html">
-     41.103. Fun with Variational Autoencoders
+     40.103. Fun with Variational Autoencoders
     </a>
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-     41.104. Time Series Forecasting Assignment
+     40.104. Time Series Forecasting Assignment
     </a>
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     <a class="reference internal" href="../nn-for-classification-assignment.html">
-     41.106. Neural Networks for Classification with TensorFlow
+     40.106. Neural Networks for Classification with TensorFlow
     </a>
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     <a class="reference internal" href="../nn-classify-15-fruits-assignment.html">
-     41.107. NN Classify 15 Fruits Assignment
+     40.107. NN Classify 15 Fruits Assignment
     </a>
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-     41.112. DQN On Foreign Exchange Market
+     40.112. DQN On Foreign Exchange Market
     </a>
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-     41.113. Art by gan
+     40.113. Art by gan
     </a>
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     <a class="reference internal" href="../gan/gan-introduction.html">
-     41.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../overview/basic-classification-classify-images-of-clothing.html">
-     41.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
    </li>
   </ul>
  </li>
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@@ -1209,107 +1209,107 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/python-programming/python-programming-introduction.html">
-     42.1. Python programming introduction
+     41.1. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
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-     42.2. Python programming basics
+     41.2. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
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-     42.3. Python programming advanced
+     41.3. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/data-science-introduction.html">
-     42.4. Data Science introduction
+     41.4. Data Science introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/relational-vs-non-relational-database.html">
-     42.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
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-     42.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
    </li>
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-     42.7. Data visualization
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-     42.8. Data Science lifecycle
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-     42.9. Data Science in the cloud
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     </a>
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-     42.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/ml-overview.html">
-     42.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
    </li>
    <li class="toctree-l2">
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-     42.12. Linear Regression
+     41.12. Linear Regression
     </a>
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-     42.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
    </li>
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     <a class="reference internal" href="../../../slides/ml-fundamentals/logistic-regression-condensed.html">
-     42.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
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-     42.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/build-an-ml-web-app.html">
-     42.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/unsupervised-learning.html">
-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/kernel-method.html">
-     42.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/model-selection.html">
-     42.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1575,63 +1575,63 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#about-dataset">
-   41.94.1. About Dataset
+   40.94.1. About Dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#table-of-contents">
-   41.94.2. Table of Contents
+   40.94.2. Table of Contents
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#import-libaries">
-     41.94.2.1. Import Libaries
+     40.94.2.1. Import Libaries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploring-the-data">
-     41.94.2.2. Exploring the Data
+     40.94.2.2. Exploring the Data
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-and-validation-datasets">
-   41.94.3. Training and Validation Datasets
+   40.94.3. Training and Validation Datasets
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#training-data-single-batch-images">
-     41.94.3.1. training data single batch images
+     40.94.3.1. training data single batch images
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#convolutional-neural-network">
-   41.94.4. Convolutional Neural Network
+   40.94.4. Convolutional Neural Network
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#defining-the-model-convolutional-neural-network">
-     41.94.4.1. Defining the Model (Convolutional Neural Network)
+     40.94.4.1. Defining the Model (Convolutional Neural Network)
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-the-model">
-   41.94.5. Training the Model
+   40.94.5. Training the Model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#testing-with-individual-images">
-   41.94.6. Testing with individual images
+   40.94.6. Testing with individual images
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.94.7. Acknowledgments
+   40.94.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1655,63 +1655,63 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#about-dataset">
-   41.94.1. About Dataset
+   40.94.1. About Dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#table-of-contents">
-   41.94.2. Table of Contents
+   40.94.2. Table of Contents
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#import-libaries">
-     41.94.2.1. Import Libaries
+     40.94.2.1. Import Libaries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploring-the-data">
-     41.94.2.2. Exploring the Data
+     40.94.2.2. Exploring the Data
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-and-validation-datasets">
-   41.94.3. Training and Validation Datasets
+   40.94.3. Training and Validation Datasets
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#training-data-single-batch-images">
-     41.94.3.1. training data single batch images
+     40.94.3.1. training data single batch images
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#convolutional-neural-network">
-   41.94.4. Convolutional Neural Network
+   40.94.4. Convolutional Neural Network
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#defining-the-model-convolutional-neural-network">
-     41.94.4.1. Defining the Model (Convolutional Neural Network)
+     40.94.4.1. Defining the Model (Convolutional Neural Network)
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-the-model">
-   41.94.5. Training the Model
+   40.94.5. Training the Model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#testing-with-individual-images">
-   41.94.6. Testing with individual images
+   40.94.6. Testing with individual images
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.94.7. Acknowledgments
+   40.94.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1725,18 +1725,18 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="object-recognition-in-images-using-cnn">
-<h1><span class="section-number">41.94. </span>Object Recognition in Images using CNN<a class="headerlink" href="#object-recognition-in-images-using-cnn" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.94. </span>Object Recognition in Images using CNN<a class="headerlink" href="#object-recognition-in-images-using-cnn" title="Permalink to this headline">#</a></h1>
 <section id="about-dataset">
-<h2><span class="section-number">41.94.1. </span>About Dataset<a class="headerlink" href="#about-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.94.1. </span>About Dataset<a class="headerlink" href="#about-dataset" title="Permalink to this headline">#</a></h2>
 <p>CIFAR-10 is an established computer-vision dataset used for object recognition. It is a subset of the 80
 million tiny images dataset and consists of 60,000 32x32 color images containing one of 10 object classes,
 with 6000 images per class. It was collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.</p>
 <p><img alt="cifar10" src="https://miro.medium.com/max/709/1*LyV7_xga4jUHdx4_jHk1PQ.png" /></p>
 </section>
 <section id="table-of-contents">
-<h2><span class="section-number">41.94.2. </span>Table of Contents<a class="headerlink" href="#table-of-contents" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.94.2. </span>Table of Contents<a class="headerlink" href="#table-of-contents" title="Permalink to this headline">#</a></h2>
 <section id="import-libaries">
-<h3><span class="section-number">41.94.2.1. </span>Import Libaries<a class="headerlink" href="#import-libaries" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.94.2.1. </span>Import Libaries<a class="headerlink" href="#import-libaries" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
@@ -1759,7 +1759,7 @@ <h3><span class="section-number">41.94.2.1. </span>Import Libaries<a class="head
 </div>
 </section>
 <section id="exploring-the-data">
-<h3><span class="section-number">41.94.2.2. </span>Exploring the Data<a class="headerlink" href="#exploring-the-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.94.2.2. </span>Exploring the Data<a class="headerlink" href="#exploring-the-data" title="Permalink to this headline">#</a></h3>
 <p>Extract from tar archive file</p>
 <p>The dataset is extracted to the directory <code class="docutils literal notranslate"><span class="pre">tmp/object-recognition-in-images-using-cnn/cifar10</span></code>. It contains 2 folders <code class="docutils literal notranslate"><span class="pre">train</span></code> and <code class="docutils literal notranslate"><span class="pre">test</span></code>, containing the training set (50000 images) and test set (10000 images) respectively. Each of them contains 10 folders, one for each class of images. Let’s verify this using <code class="docutils literal notranslate"><span class="pre">os.listdir</span></code>.</p>
 <div class="cell docutils container">
@@ -1930,7 +1930,7 @@ <h3><span class="section-number">41.94.2.2. </span>Exploring the Data<a class="h
 </section>
 </section>
 <section id="training-and-validation-datasets">
-<h2><span class="section-number">41.94.3. </span>Training and Validation Datasets<a class="headerlink" href="#training-and-validation-datasets" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.94.3. </span>Training and Validation Datasets<a class="headerlink" href="#training-and-validation-datasets" title="Permalink to this headline">#</a></h2>
 <p>While building real world machine learning models, it is quite common to split the dataset into 3 parts:</p>
 <ol class="simple">
 <li><p><strong>Training set</strong> - used to train the model i.e. compute the loss and adjust the weights of the model using gradient descent.</p></li>
@@ -1990,7 +1990,7 @@ <h2><span class="section-number">41.94.3. </span>Training and Validation Dataset
 </div>
 </div>
 <section id="training-data-single-batch-images">
-<h3><span class="section-number">41.94.3.1. </span>training data single batch images<a class="headerlink" href="#training-data-single-batch-images" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.94.3.1. </span>training data single batch images<a class="headerlink" href="#training-data-single-batch-images" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">show_images_batch</span><span class="p">(</span><span class="n">train_dl</span><span class="p">)</span>
@@ -2008,9 +2008,9 @@ <h3><span class="section-number">41.94.3.1. </span>training data single batch im
 </section>
 </section>
 <section id="convolutional-neural-network">
-<h2><span class="section-number">41.94.4. </span>Convolutional Neural Network<a class="headerlink" href="#convolutional-neural-network" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.94.4. </span>Convolutional Neural Network<a class="headerlink" href="#convolutional-neural-network" title="Permalink to this headline">#</a></h2>
 <section id="defining-the-model-convolutional-neural-network">
-<h3><span class="section-number">41.94.4.1. </span>Defining the Model (Convolutional Neural Network)<a class="headerlink" href="#defining-the-model-convolutional-neural-network" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.94.4.1. </span>Defining the Model (Convolutional Neural Network)<a class="headerlink" href="#defining-the-model-convolutional-neural-network" title="Permalink to this headline">#</a></h3>
 <p>The 2D convolution is a fairly simple operation at heart: you start with a kernel, which is simply a small matrix of weights. This kernel “slides” over the 2D input data, performing an elementwise multiplication with the part of the input it is currently on, and then summing up the results into a single output pixel.</p>
 <img src="https://miro.medium.com/max/1070/1*Zx-ZMLKab7VOCQTxdZ1OAw.gif" style="max-width:400px;">
 <p>Let us implement a convolution operation on a 1 channel image with a 3x3 kernel.</p>
@@ -2219,7 +2219,7 @@ <h3><span class="section-number">41.94.4.1. </span>Defining the Model (Convoluti
 </section>
 </section>
 <section id="training-the-model">
-<h2><span class="section-number">41.94.5. </span>Training the Model<a class="headerlink" href="#training-the-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.94.5. </span>Training the Model<a class="headerlink" href="#training-the-model" title="Permalink to this headline">#</a></h2>
 <p>In this mode, the result of every computation will have requires_grad=False, even when the inputs have requires_grad=True.</p>
 <div class="cell docutils container">
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@@ -2335,7 +2335,7 @@ <h2><span class="section-number">41.94.5. </span>Training the Model<a class="hea
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 </section>
 <section id="testing-with-individual-images">
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+<h2><span class="section-number">40.94.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to datajameson for creating the Kaggle notebook <a class="reference external" href="https://www.kaggle.com/code/datajameson/cifar-10-object-recognition-cnn-explained">Cifar-10 Object Recognition(CNN) Explained</a>. It inspires the majority of the content in this chapter.</p>
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+   40.92. Sign Language Digits Classification with CNN
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+     40.92.2. Load data from numpy file
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+     40.92.5. Implementation of CNN
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+     40.92.7. Changin zoom level
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+     40.92.9. Changing rotaion, zoom
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                             <ul class="visible nav section-nav flex-column">
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+     40.92.4. Train Test Split
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+     40.92.5. Implementation of CNN
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-     41.92.6. Data Augmentation With Keras API
+     40.92.6. Data Augmentation With Keras API
     </a>
    </li>
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-     41.92.7. Changin zoom level
+     40.92.7. Changin zoom level
     </a>
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-     41.92.8. Changing rotaion
+     40.92.8. Changing rotaion
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-     41.92.9. Changing rotaion, zoom
+     40.92.9. Changing rotaion, zoom
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+     40.92.11. Conclusion
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@@ -1737,9 +1737,9 @@ <h2> Contents </h2>
               <div>
                 
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-<h1><span class="section-number">41.92. </span>Sign Language Digits Classification with CNN<a class="headerlink" href="#sign-language-digits-classification-with-cnn" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.92. </span>Sign Language Digits Classification with CNN<a class="headerlink" href="#sign-language-digits-classification-with-cnn" title="Permalink to this headline">#</a></h1>
 <section id="load-libraries">
-<h2><span class="section-number">41.92.1. </span>Load libraries<a class="headerlink" href="#load-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.92.1. </span>Load libraries<a class="headerlink" href="#load-libraries" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -1758,7 +1758,7 @@ <h2><span class="section-number">41.92.1. </span>Load libraries<a class="headerl
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-<h2><span class="section-number">41.92.2. </span>Load data from numpy file<a class="headerlink" href="#load-data-from-numpy-file" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.92.2. </span>Load data from numpy file<a class="headerlink" href="#load-data-from-numpy-file" title="Permalink to this headline">#</a></h2>
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@@ -1823,7 +1823,7 @@ <h2><span class="section-number">41.92.2. </span>Load data from numpy file<a cla
 </ul>
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-<h2><span class="section-number">41.92.3. </span>Preparing Data<a class="headerlink" href="#preparing-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.92.3. </span>Preparing Data<a class="headerlink" href="#preparing-data" title="Permalink to this headline">#</a></h2>
 <p>We will re-organize data to match labels and images correctly.</p>
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-<h2><span class="section-number">41.92.4. </span>Train Test Split<a class="headerlink" href="#train-test-split" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.92.4. </span>Train Test Split<a class="headerlink" href="#train-test-split" title="Permalink to this headline">#</a></h2>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
@@ -1896,10 +1896,10 @@ <h2><span class="section-number">41.92.4. </span>Train Test Split<a class="heade
 </ul>
 </section>
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-<h2><span class="section-number">41.92.5. </span>Implementation of CNN<a class="headerlink" href="#implementation-of-cnn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.92.5. </span>Implementation of CNN<a class="headerlink" href="#implementation-of-cnn" title="Permalink to this headline">#</a></h2>
 </section>
 <section id="data-augmentation-with-keras-api">
-<h2><span class="section-number">41.92.6. </span>Data Augmentation With Keras API<a class="headerlink" href="#data-augmentation-with-keras-api" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.92.6. </span>Data Augmentation With Keras API<a class="headerlink" href="#data-augmentation-with-keras-api" title="Permalink to this headline">#</a></h2>
 <p>Data augmentation is a technique which generates new training samples without changing labels of images. To generate new samples, some features of images are changed like brightness, rotation or zoom level. To apply it, ImageDataGenerator class is used in KERAS API. This class refers parameters and changes images. After complete the changing process, it returns new samples. This is important! ImageDataGenerator returns only new images. It means that out training dataset consists of different from original dataset. It provides more generalizaton for model anf of course it is desirable.</p>
 <p>So, in implementation of CNN part, we will use data augmentation and we will change rotation and zoom level of images. we chose these parameters with a simple logic. Think of test data that we might encounter in real life. we don’t always hold our hand at 90 degrees. So it is quite possible that we have a rotational change when using sign language. Likewise, the zoom level of the photo to be taken may also change. So we thought we could train my model better by creating a more general data set with these two parameters.  Let’s take a closer look at these parameters.</p>
 <ul class="simple">
@@ -1936,7 +1936,7 @@ <h2><span class="section-number">41.92.6. </span>Data Augmentation With Keras AP
 </div>
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-<h2><span class="section-number">41.92.7. </span>Changin zoom level<a class="headerlink" href="#changin-zoom-level" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.92.7. </span>Changin zoom level<a class="headerlink" href="#changin-zoom-level" title="Permalink to this headline">#</a></h2>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">datagen</span> <span class="o">=</span> <span class="n">ImageDataGenerator</span><span class="p">(</span><span class="n">zoom_range</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
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 </div>
 </section>
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-<h2><span class="section-number">41.92.8. </span>Changing rotaion<a class="headerlink" href="#changing-rotaion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.92.8. </span>Changing rotaion<a class="headerlink" href="#changing-rotaion" title="Permalink to this headline">#</a></h2>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">datagen</span> <span class="o">=</span> <span class="n">ImageDataGenerator</span><span class="p">(</span><span class="n">rotation_range</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
@@ -1960,7 +1960,7 @@ <h2><span class="section-number">41.92.8. </span>Changing rotaion<a class="heade
 </div>
 </section>
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-<h2><span class="section-number">41.92.9. </span>Changing rotaion, zoom<a class="headerlink" href="#changing-rotaion-zoom" title="Permalink to this headline">#</a></h2>
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+<h1><span class="section-number">40.93. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/gorkemgunay">Görkem Günay</a> for creating <a class="reference external" href="https://www.kaggle.com/code/gorkemgunay/sign-language-digits-classification-with-cnn">sign-language-digits-classification-with-cnn</a>. It inspires the majority of the content in this chapter.</p>
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 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/emrebulbul23">emrebulbul23</a> for creating <a class="reference external" href="https://www.kaggle.com/code/emrebulbul23/dqn-on-foreign-exchange-market">DQN on foreign exchange market</a>. It inspired the majority of the content in this article.</p>
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-<h1><span class="section-number">41.113. </span>Art by gan<a class="headerlink" href="#art-by-gan" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.113. </span>Art by gan<a class="headerlink" href="#art-by-gan" title="Permalink to this headline">#</a></h1>
 <p>In this Notebook, we will build a Generative Adversarial Network  (GAN) to illustrate the workings of a Generative Adversarial Network and to generate images. Generative modelling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data. As GANs work by identifying the patterns in the data, we will be using oil painted portraits. However, glancing over the dataset gives me an idea that it is going to be a long shot. The orientation and poses in the dataset vary vastly. Keeping that in mind we are still willing to give it a try. Only because portraits are our jam. We basically love oil painted portraits.</p>
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+<h2><span class="section-number">40.113.1. </span>Data loading &amp; Prepreprocessing<a class="headerlink" href="#data-loading-prepreprocessing" title="Permalink to this headline">#</a></h2>
 <p>For this project, We are using .jpg files of images of portraits. The dataset includes various artists. We are loading data as TensorFlow.Dataset, with a batch size of 64. We have reduced the image size to (64,64), presuming, it will be computationally less taxing on the GPU.</p>
 </section>
 <section id="loading-the-data">
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 <p>Most of the images are portraits. A portrait is a painting representation of a person, The face is predominantly depicted portraits along with expressions and postures. To represent the personality of the subject. Since our model is relative a smaller GAN we have reduced the size of the image.</p>
 </section>
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+<h2><span class="section-number">40.113.3. </span>Preprocessing the data<a class="headerlink" href="#preprocessing-the-data" title="Permalink to this headline">#</a></h2>
 <p><strong>Normalization:</strong> For the data normalization, we will convert the data in the range between 0 to 1. This helps in fast convergence and makes it easy for the computer to do calculations faster.
 Each of the three RGB channels in the image can take pixel values ranging from 0 to 256. Dividing it by 255 converts it to a range between 0 to 1.</p>
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 <p>Now the data is up as a tensorflow Dataset object and is Prepocessed. Next up is building the GAN.</p>
 </section>
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-<h2><span class="section-number">41.113.4. </span>Building GAN<a class="headerlink" href="#building-gan" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.113.4. </span>Building GAN<a class="headerlink" href="#building-gan" title="Permalink to this headline">#</a></h2>
 <p>GANs employs deep learning methods. It is a dexterous way of posing the problem as a supervised learning problem. It is composed of two models namely Generator and a Discriminator.</p>
 <p>Two models are trained simultaneously by an adversarial process. A generator (“the artist”) learns to create images that look like the dataset while a discriminator (“the art critic”) learns to tell real images apart from fakes.</p>
 <p>During training, the generator progressively becomes better at creating images that look real, while the discriminator becomes better at telling them apart. The process reaches equilibrium when the discriminator can no longer distinguish real images from fakes.</p>
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+<h2><span class="section-number">40.113.5. </span>The generator<a class="headerlink" href="#the-generator" title="Permalink to this headline">#</a></h2>
 <p>The Generator is a neural network that generates the images. It takes in a random noise as seed and outputs sample data. As the GAN’s training progresses the Generator output becomes more and more like the training set, as the Generator tries to improve the output so that the discrimination passes the output as a real image.</p>
 <p><strong>Following steps are involved in the models building</strong></p>
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 <p>Clearly, the output is a random seed containing noise as the Generator is not trained yet.</p>
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+<h2><span class="section-number">40.113.7. </span>The discriminator<a class="headerlink" href="#the-discriminator" title="Permalink to this headline">#</a></h2>
 <p>In GANs the Generator works along with the Discriminator.</p>
 <p>The Discriminator network decided whether the data is fake aka created by the Generator or real i.e. from the original input data. To do so it applies a binary classification method using a sigmoid function to get an output in the range of 0 to 1.</p>
 </section>
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 <p>Let us proceed and build the GAN architecture to train.</p>
 </section>
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+<h2><span class="section-number">40.113.9. </span>GAN compilation<a class="headerlink" href="#gan-compilation" title="Permalink to this headline">#</a></h2>
 <p>GAN training has two sections:</p>
 <p><strong>Section 1</strong>: The Discriminator is trained while the Generator is idle.
 The discriminator is trained real images and random noise (from an untrained generator). This trains it to tell between fake and real. This accommodates the discriminator to predict as fakes.</p>
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+<h2><span class="section-number">40.113.10. </span>Training the model<a class="headerlink" href="#training-the-model" title="Permalink to this headline">#</a></h2>
 <p>Calling the above created GAN function trains the generator and discriminator simultaneously.
 To implement the GAN we must define:</p>
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 </div>
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+<h2><span class="section-number">40.113.11. </span>Ploting the Learning Curves<a class="headerlink" href="#ploting-the-learning-curves" title="Permalink to this headline">#</a></h2>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
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 To get the art output we will create a function that saves the output portraits generated. We will be plotting the generated Portraits</p>
 </section>
 <section id="ai-makes-artwork">
-<h2><span class="section-number">41.113.12. </span>AI makes artwork<a class="headerlink" href="#ai-makes-artwork" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.113.12. </span>AI makes artwork<a class="headerlink" href="#ai-makes-artwork" title="Permalink to this headline">#</a></h2>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Number of images to be generate</span>
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 </div>
 </section>
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+<h2><span class="section-number">40.113.13. </span>Conculsion<a class="headerlink" href="#conculsion" title="Permalink to this headline">#</a></h2>
 <p>In the evaluation of the model: We can see that the GAN picked up the patterns in the portraits. It worked quite well. For further improvement,  as GANs are notorious for being data-hungry, I would consider increasing the dataset. There are many inconsistencies in the data which is rather complicated for the GAN to learn. Cleaning the data with some consistencies in the portrait styles would certainly help. Training it longer i.e. for more epochs would also help. Lastly, one can always strive to make a  more robust architecture for the Neural Networks.</p>
 </section>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="acknowledgments">
-<h1><span class="section-number">41.114. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.114. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/karnikakapoor">Karnika Kapoor</a> for creating <a class="reference external" href="https://www.kaggle.com/code/karnikakapoor/art-by-gan">art-by-gan</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
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-            <p class="prev-next-title"><span class="section-number">41.112. </span>DQN On Foreign Exchange Market</p>
+            <p class="prev-next-title"><span class="section-number">40.112. </span>DQN On Foreign Exchange Market</p>
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-     42.2. Python programming basics
+     41.2. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/python-programming/python-programming-advanced.html">
-     42.3. Python programming advanced
+     41.3. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/data-science-introduction.html">
-     42.4. Data Science introduction
+     41.4. Data Science introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/relational-vs-non-relational-database.html">
-     42.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/numpy-and-pandas.html">
-     42.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/data-visualization.html">
-     42.7. Data visualization
+     41.7. Data visualization
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/data-science-lifecycle.html">
-     42.8. Data Science lifecycle
+     41.8. Data Science lifecycle
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/data-science-in-the-cloud.html">
-     42.9. Data Science in the cloud
+     41.9. Data Science in the cloud
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/data-science-in-real-world.html">
-     42.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/ml-overview.html">
-     42.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/linear-regression.html">
-     42.12. Linear Regression
+     41.12. Linear Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/logistic-regression.html">
-     42.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/logistic-regression-condensed.html">
-     42.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/neural-network.html">
-     42.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/build-an-ml-web-app.html">
-     42.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/unsupervised-learning.html">
-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/kernel-method.html">
-     42.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/model-selection.html">
-     42.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1575,91 +1575,91 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-data">
-   41.115.1. Loading data
+   40.115.1. Loading data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-libraries">
-   41.115.2. Importing the libraries
+   40.115.2. Importing the libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#some-dogs">
-   41.115.3. Some dogs
+   40.115.3. Some dogs
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#image-preprocessing">
-   41.115.4. Image Preprocessing
+   40.115.4. Image Preprocessing
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#weights">
-   41.115.5. Weights
+   40.115.5. Weights
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#generator">
-   41.115.6. Generator
+   40.115.6. Generator
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#discriminator">
-   41.115.7. Discriminator
+   40.115.7. Discriminator
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#another-setup">
-   41.115.8. Another setup
+   40.115.8. Another setup
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training">
-   41.115.9. Training
+   40.115.9. Training
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#best-public-training">
-   41.115.10. Best public training
+   40.115.10. Best public training
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#parameters">
-     41.115.10.1. Parameters
+     40.115.10.1. Parameters
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#initialize-models-and-optimizers">
-     41.115.10.2. Initialize models and optimizers
+     40.115.10.2. Initialize models and optimizers
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#show-generated-images">
-   41.115.11. Show generated images
+   40.115.11. Show generated images
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-loop">
-   41.115.12. Training Loop
+   40.115.12. Training Loop
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#generation-example">
-   41.115.13. Generation example
+   40.115.13. Generation example
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#save-models">
-     41.115.13.1. Save models
+     40.115.13.1. Save models
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgement">
-   41.115.14. Acknowledgement
+   40.115.14. Acknowledgement
   </a>
  </li>
 </ul>
@@ -1683,91 +1683,91 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-data">
-   41.115.1. Loading data
+   40.115.1. Loading data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-libraries">
-   41.115.2. Importing the libraries
+   40.115.2. Importing the libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#some-dogs">
-   41.115.3. Some dogs
+   40.115.3. Some dogs
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#image-preprocessing">
-   41.115.4. Image Preprocessing
+   40.115.4. Image Preprocessing
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#weights">
-   41.115.5. Weights
+   40.115.5. Weights
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#generator">
-   41.115.6. Generator
+   40.115.6. Generator
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#discriminator">
-   41.115.7. Discriminator
+   40.115.7. Discriminator
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#another-setup">
-   41.115.8. Another setup
+   40.115.8. Another setup
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training">
-   41.115.9. Training
+   40.115.9. Training
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#best-public-training">
-   41.115.10. Best public training
+   40.115.10. Best public training
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#parameters">
-     41.115.10.1. Parameters
+     40.115.10.1. Parameters
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#initialize-models-and-optimizers">
-     41.115.10.2. Initialize models and optimizers
+     40.115.10.2. Initialize models and optimizers
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#show-generated-images">
-   41.115.11. Show generated images
+   40.115.11. Show generated images
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-loop">
-   41.115.12. Training Loop
+   40.115.12. Training Loop
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#generation-example">
-   41.115.13. Generation example
+   40.115.13. Generation example
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#save-models">
-     41.115.13.1. Save models
+     40.115.13.1. Save models
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgement">
-   41.115.14. Acknowledgement
+   40.115.14. Acknowledgement
   </a>
  </li>
 </ul>
@@ -1781,9 +1781,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="generative-adversarial-networks-gans">
-<h1><span class="section-number">41.115. </span>Generative Adversarial Networks (GANs)<a class="headerlink" href="#generative-adversarial-networks-gans" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.115. </span>Generative Adversarial Networks (GANs)<a class="headerlink" href="#generative-adversarial-networks-gans" title="Permalink to this headline">#</a></h1>
 <section id="loading-data">
-<h2><span class="section-number">41.115.1. </span>Loading data<a class="headerlink" href="#loading-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.1. </span>Loading data<a class="headerlink" href="#loading-data" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
@@ -1818,7 +1818,7 @@ <h2><span class="section-number">41.115.1. </span>Loading data<a class="headerli
 </div>
 </section>
 <section id="importing-the-libraries">
-<h2><span class="section-number">41.115.2. </span>Importing the libraries<a class="headerlink" href="#importing-the-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.2. </span>Importing the libraries<a class="headerlink" href="#importing-the-libraries" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
@@ -1847,7 +1847,7 @@ <h2><span class="section-number">41.115.2. </span>Importing the libraries<a clas
 </div>
 </section>
 <section id="some-dogs">
-<h2><span class="section-number">41.115.3. </span>Some dogs<a class="headerlink" href="#some-dogs" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.3. </span>Some dogs<a class="headerlink" href="#some-dogs" title="Permalink to this headline">#</a></h2>
 <p>The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world.</p>
 <div class="cell docutils container">
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@@ -1870,7 +1870,7 @@ <h2><span class="section-number">41.115.3. </span>Some dogs<a class="headerlink"
 </div>
 </section>
 <section id="image-preprocessing">
-<h2><span class="section-number">41.115.4. </span>Image Preprocessing<a class="headerlink" href="#image-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.4. </span>Image Preprocessing<a class="headerlink" href="#image-preprocessing" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">batch_size</span> <span class="o">=</span> <span class="mi">32</span>
@@ -1905,7 +1905,7 @@ <h2><span class="section-number">41.115.4. </span>Image Preprocessing<a class="h
 </div>
 </section>
 <section id="weights">
-<h2><span class="section-number">41.115.5. </span>Weights<a class="headerlink" href="#weights" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.5. </span>Weights<a class="headerlink" href="#weights" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">weights_init</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
@@ -1921,7 +1921,7 @@ <h2><span class="section-number">41.115.5. </span>Weights<a class="headerlink" h
 </div>
 </section>
 <section id="generator">
-<h2><span class="section-number">41.115.6. </span>Generator<a class="headerlink" href="#generator" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.6. </span>Generator<a class="headerlink" href="#generator" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">G</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
@@ -1959,7 +1959,7 @@ <h2><span class="section-number">41.115.6. </span>Generator<a class="headerlink"
 </div>
 </section>
 <section id="discriminator">
-<h2><span class="section-number">41.115.7. </span>Discriminator<a class="headerlink" href="#discriminator" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.7. </span>Discriminator<a class="headerlink" href="#discriminator" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">D</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
@@ -1992,7 +1992,7 @@ <h2><span class="section-number">41.115.7. </span>Discriminator<a class="headerl
 </div>
 </section>
 <section id="another-setup">
-<h2><span class="section-number">41.115.8. </span>Another setup<a class="headerlink" href="#another-setup" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.8. </span>Another setup<a class="headerlink" href="#another-setup" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Generator</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
@@ -2058,7 +2058,7 @@ <h2><span class="section-number">41.115.8. </span>Another setup<a class="headerl
 </div>
 </section>
 <section id="training">
-<h2><span class="section-number">41.115.9. </span>Training<a class="headerlink" href="#training" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.9. </span>Training<a class="headerlink" href="#training" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">EPOCH</span> <span class="o">=</span> <span class="mi">0</span>
@@ -2116,9 +2116,9 @@ <h2><span class="section-number">41.115.9. </span>Training<a class="headerlink"
 </div>
 </section>
 <section id="best-public-training">
-<h2><span class="section-number">41.115.10. </span>Best public training<a class="headerlink" href="#best-public-training" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.10. </span>Best public training<a class="headerlink" href="#best-public-training" title="Permalink to this headline">#</a></h2>
 <section id="parameters">
-<h3><span class="section-number">41.115.10.1. </span>Parameters<a class="headerlink" href="#parameters" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.115.10.1. </span>Parameters<a class="headerlink" href="#parameters" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">batch_size</span> <span class="o">=</span> <span class="mi">32</span>
@@ -2139,7 +2139,7 @@ <h3><span class="section-number">41.115.10.1. </span>Parameters<a class="headerl
 </div>
 </section>
 <section id="initialize-models-and-optimizers">
-<h3><span class="section-number">41.115.10.2. </span>Initialize models and optimizers<a class="headerlink" href="#initialize-models-and-optimizers" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.115.10.2. </span>Initialize models and optimizers<a class="headerlink" href="#initialize-models-and-optimizers" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">netG</span> <span class="o">=</span> <span class="n">Generator</span><span class="p">(</span><span class="n">nz</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
@@ -2177,7 +2177,7 @@ <h3><span class="section-number">41.115.10.2. </span>Initialize models and optim
 </section>
 </section>
 <section id="show-generated-images">
-<h2><span class="section-number">41.115.11. </span>Show generated images<a class="headerlink" href="#show-generated-images" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.11. </span>Show generated images<a class="headerlink" href="#show-generated-images" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">show_generated_img</span><span class="p">(</span><span class="n">n_images</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
@@ -2202,7 +2202,7 @@ <h2><span class="section-number">41.115.11. </span>Show generated images<a class
 </div>
 </section>
 <section id="training-loop">
-<h2><span class="section-number">41.115.12. </span>Training Loop<a class="headerlink" href="#training-loop" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.115.12. </span>Training Loop<a class="headerlink" href="#training-loop" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
@@ -2274,7 +2274,7 @@ <h2><span class="section-number">41.115.12. </span>Training Loop<a class="header
 </div>
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 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/jesucristo">jesucristo</a> for creating <a class="reference external" href="https://www.kaggle.com/code/jesucristo/gan-introduction/notebook">GAN Introduction</a>. It inspired the majority of the content in this article.</p>
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+<h1><span class="section-number">40.97. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment<a class="headerlink" href="#bitcoin-lstm-model-with-tweet-volume-and-sentiment" title="Permalink to this headline">#</a></h1>
 <section id="load-libraries">
-<h2><span class="section-number">41.97.1. </span>Load libraries<a class="headerlink" href="#load-libraries" title="Permalink to this headline">#</a></h2>
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+<h2><span class="section-number">40.97.3. </span>Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
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+<h2><span class="section-number">40.97.4. </span>LSTM Model<a class="headerlink" href="#lstm-model" title="Permalink to this headline">#</a></h2>
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-<h1><span class="section-number">41.98. </span>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.98. </span>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Permalink to this headline">#</a></h1>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/paul92s">Paul Simpson</a> for creating <a class="reference external" href="https://www.kaggle.com/code/paul92s/bitcoin-lstm-model-with-tweet-volume-and-sentiment">Bitcoin Lstm Model with Tweet Volume and Sentiment</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
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-    <link rel="prev" title="41.106. Neural Networks for Classification with TensorFlow" href="nn-for-classification-assignment.html" />
+    <link rel="next" title="40.112. DQN On Foreign Exchange Market" href="dqn/dqn-on-foreign-exchange-market.html" />
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+     40.11. Write a data ethics case study
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+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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   </a>
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+     40.110.1. Create and train the NN Model
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     </a>
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-   41.111. Acknowledgments
+   40.111. Acknowledgments
   </a>
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@@ -1691,11 +1691,11 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="nn-classify-15-fruits-assignment">
-<h1><span class="section-number">41.107. </span>NN Classify 15 Fruits Assignment<a class="headerlink" href="#nn-classify-15-fruits-assignment" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.107. </span>NN Classify 15 Fruits Assignment<a class="headerlink" href="#nn-classify-15-fruits-assignment" title="Permalink to this headline">#</a></h1>
 <p><img alt="Fruit Example" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/assignment/deep-learning/nn/examplefruit.png" /></p>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="data-collection">
-<h1><span class="section-number">41.108. </span>Data collection<a class="headerlink" href="#data-collection" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.108. </span>Data collection<a class="headerlink" href="#data-collection" title="Permalink to this headline">#</a></h1>
 <p>The database used in this study is comprising of 44406 fruit images, which we collected
 in a period of 6 months. The images where made with in our lab’s environment under different
 scenarios which we mention below. We captured all the images on a clear background with
@@ -1734,7 +1734,7 @@ <h1><span class="section-number">41.108. </span>Data collection<a class="headerl
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 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="load-and-visualize-the-dataset">
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+<h1><span class="section-number">40.109. </span>Load and visualize the dataset<a class="headerlink" href="#load-and-visualize-the-dataset" title="Permalink to this headline">#</a></h1>
 <p>The database used in this study is comprising of 70549 fruit images, which were collected in a period of 6 months. The images where made with in a lab’s environment under different scenarios which we mention below. All the images were captured on a clear background with resolution of 320×258 pixels.</p>
 <p>Type of fruits in the dataset:</p>
 <ul class="simple">
@@ -1910,7 +1910,7 @@ <h1><span class="section-number">41.109. </span>Load and visualize the dataset<a
 </div>
 </section>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># The pictures will be resized to have the same size for the neural network</span>
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 </div>
 </div>
 <section id="create-and-train-the-nn-model">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">cut_df</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">number_of_parts</span><span class="p">,</span> <span class="n">part</span><span class="p">):</span>
@@ -2106,7 +2106,7 @@ <h2><span class="section-number">41.110.1. </span>Create and train the NN Model<
 </div>
 </section>
 <section id="predictions-a-class-anchor-id-3-a">
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+<h2><span class="section-number">40.110.2. </span>Predictions<a class="anchor" id="3"></a><a class="headerlink" href="#predictions-a-class-anchor-id-3-a" title="Permalink to this headline">#</a></h2>
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 </div>
 </section>
 <section id="visualize-the-result-with-pictures-of-fruits">
-<h2><span class="section-number">41.110.3. </span>Visualize the result with pictures of fruits<a class="headerlink" href="#visualize-the-result-with-pictures-of-fruits" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.110.3. </span>Visualize the result with pictures of fruits<a class="headerlink" href="#visualize-the-result-with-pictures-of-fruits" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span>
@@ -2176,7 +2176,7 @@ <h2><span class="section-number">41.110.3. </span>Visualize the result with pict
 <!-- <strong>The predictions are very good with around 97% accuracy using only 1/5 of the dataset to train the model.</strong> --></section>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="acknowledgments">
-<h1><span class="section-number">41.111. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.111. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/databeru"> DATALIRA </a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/databeru/classify-15-fruits-with-tensorflow-acc-99-6"> Classify 15 Fruits with TensorFlow </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
@@ -2217,13 +2217,13 @@ <h1><span class="section-number">41.111. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.106. </span>Neural Networks for Classification with TensorFlow</p>
+            <p class="prev-next-title"><span class="section-number">40.106. </span>Neural Networks for Classification with TensorFlow</p>
         </div>
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-        <p class="prev-next-title"><span class="section-number">41.112. </span>DQN On Foreign Exchange Market</p>
+        <p class="prev-next-title"><span class="section-number">40.112. </span>DQN On Foreign Exchange Market</p>
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-    <link rel="prev" title="41.104. Time Series Forecasting Assignment" href="time-series-forecasting-assignment.html" />
+    <link rel="next" title="40.107. NN Classify 15 Fruits Assignment" href="nn-classify-15-fruits-assignment.html" />
+    <link rel="prev" title="40.104. Time Series Forecasting Assignment" href="time-series-forecasting-assignment.html" />
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+   1. Python programming introduction
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-     5.2. Data Science ethics
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+     40.5. Project Plan​ Template
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-     41.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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-     41.10. Data Science scenarios
+     40.10. Data Science scenarios
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-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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+     40.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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+     40.15. Dive into the beehive
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
+     40.17. Classifying datasets
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-     41.18. Small diabetes study
+     40.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
+     40.20. Displaying airport data
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-     41.21. Soda profits
+     40.21. Soda profits
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-     41.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
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-     41.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
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-     41.24. Data processing in Python
+     40.24. Data processing in Python
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+     40.26. Data preparation
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-     41.27. Analyzing data
+     40.27. Analyzing data
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-     41.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
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-     41.29. Matplotlib applied
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-     41.35. Tell a story
+     40.35. Tell a story
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-     41.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
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-     41.37. Exploring for answers
+     40.37. Exploring for answers
     </a>
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-     41.38. Market research
+     40.38. Market research
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#getting-the-fashion-data">
-   41.106.8. Getting the Fashion data
+   40.106.8. Getting the Fashion data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#looking-in-the-fashion-data">
-   41.106.9. Looking in the Fashion Data
+   40.106.9. Looking in the Fashion Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id1">
-   41.106.10. Preparing the Data
+   40.106.10. Preparing the Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id2">
-   41.106.11. Creating, Compiling, and Training a Model
+   40.106.11. Creating, Compiling, and Training a Model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualizing-the-model-results">
-   41.106.12. Visualizing the Model Results
+   40.106.12. Visualizing the Model Results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#model-evaluation">
-   41.106.13. Model Evaluation
+   40.106.13. Model Evaluation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#controlling-training-with-callbacks">
-   41.106.14. Controlling Training with Callbacks
+   40.106.14. Controlling Training with Callbacks
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#implementing-callbacks">
-     41.106.14.1. Implementing Callbacks
+     40.106.14.1. Implementing Callbacks
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#custom-callback">
-     41.106.14.2. Custom Callback
+     40.106.14.2. Custom Callback
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#using-tensorboard-for-model-visualization">
-   41.106.15. Using TensorBoard for Model Visualization
+   40.106.15. Using TensorBoard for Model Visualization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.106.16. Acknowledgments
+   40.106.16. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1693,101 +1693,101 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#getting-started-binary-classifier">
-   41.106.1. Getting Started: Binary Classifier
+   40.106.1. Getting Started: Binary Classifier
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#getting-the-data">
-     41.106.1.1. Getting the data
+     40.106.1.1. Getting the data
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#taking-a-look-in-the-data">
-   41.106.2. Taking a look in the data
+   40.106.2. Taking a look in the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#preparing-the-data">
-   41.106.3. Preparing the Data
+   40.106.3. Preparing the Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#creating-compiling-and-training-a-model">
-   41.106.4. Creating, Compiling and Training a Model
+   40.106.4. Creating, Compiling and Training a Model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualizing-the-results">
-   41.106.5. Visualizing the Results
+   40.106.5. Visualizing the Results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#evaluating-the-model">
-   41.106.6. Evaluating the Model
+   40.106.6. Evaluating the Model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#going-beyond-binary-classifier-to-multiclass-classifier-10-fashions-classifier">
-   41.106.7. Going Beyond Binary Classifier to Multiclass Classifier: 10 Fashions Classifier
+   40.106.7. Going Beyond Binary Classifier to Multiclass Classifier: 10 Fashions Classifier
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#getting-the-fashion-data">
-   41.106.8. Getting the Fashion data
+   40.106.8. Getting the Fashion data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#looking-in-the-fashion-data">
-   41.106.9. Looking in the Fashion Data
+   40.106.9. Looking in the Fashion Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id1">
-   41.106.10. Preparing the Data
+   40.106.10. Preparing the Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id2">
-   41.106.11. Creating, Compiling, and Training a Model
+   40.106.11. Creating, Compiling, and Training a Model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualizing-the-model-results">
-   41.106.12. Visualizing the Model Results
+   40.106.12. Visualizing the Model Results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#model-evaluation">
-   41.106.13. Model Evaluation
+   40.106.13. Model Evaluation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#controlling-training-with-callbacks">
-   41.106.14. Controlling Training with Callbacks
+   40.106.14. Controlling Training with Callbacks
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#implementing-callbacks">
-     41.106.14.1. Implementing Callbacks
+     40.106.14.1. Implementing Callbacks
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#custom-callback">
-     41.106.14.2. Custom Callback
+     40.106.14.2. Custom Callback
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#using-tensorboard-for-model-visualization">
-   41.106.15. Using TensorBoard for Model Visualization
+   40.106.15. Using TensorBoard for Model Visualization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.106.16. Acknowledgments
+   40.106.16. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1801,14 +1801,14 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="neural-networks-for-classification-with-tensorflow">
-<h1><span class="section-number">41.106. </span>Neural Networks for Classification with TensorFlow<a class="headerlink" href="#neural-networks-for-classification-with-tensorflow" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.106. </span>Neural Networks for Classification with TensorFlow<a class="headerlink" href="#neural-networks-for-classification-with-tensorflow" title="Permalink to this headline">#</a></h1>
 <section id="getting-started-binary-classifier">
-<h2><span class="section-number">41.106.1. </span>Getting Started: Binary Classifier<a class="headerlink" href="#getting-started-binary-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.1. </span>Getting Started: Binary Classifier<a class="headerlink" href="#getting-started-binary-classifier" title="Permalink to this headline">#</a></h2>
 <p>We will first practice building neural networks for binary classifier. In binary classification, we have two classes.</p>
 <p>We will use a classical cancer dataset to predict if a given patient has a malignant or benign based on their medical information. We will get it from sklearn datasets. You can read more about the dataset.</p>
 <p>The dataset contains two labels: malignant, benign.</p>
 <section id="getting-the-data">
-<h3><span class="section-number">41.106.1.1. </span>Getting the data<a class="headerlink" href="#getting-the-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.106.1.1. </span>Getting the data<a class="headerlink" href="#getting-the-data" title="Permalink to this headline">#</a></h3>
 <p>We will get the data from sklearn datasets.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1887,7 +1887,7 @@ <h3><span class="section-number">41.106.1.1. </span>Getting the data<a class="he
 </section>
 </section>
 <section id="taking-a-look-in-the-data">
-<h2><span class="section-number">41.106.2. </span>Taking a look in the data<a class="headerlink" href="#taking-a-look-in-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.2. </span>Taking a look in the data<a class="headerlink" href="#taking-a-look-in-the-data" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Looking from the head </span>
@@ -1917,7 +1917,7 @@ <h2><span class="section-number">41.106.2. </span>Taking a look in the data<a cl
 </div>
 </section>
 <section id="preparing-the-data">
-<h2><span class="section-number">41.106.3. </span>Preparing the Data<a class="headerlink" href="#preparing-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.3. </span>Preparing the Data<a class="headerlink" href="#preparing-the-data" title="Permalink to this headline">#</a></h2>
 <p>The data from sklearn is reasonably cleaned. Let’s split the data into train and test sets, and we will follow with scaling the values to be between 0 and 1.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1960,7 +1960,7 @@ <h2><span class="section-number">41.106.3. </span>Preparing the Data<a class="he
 <p>We are now ready to create, compile and train the model.</p>
 </section>
 <section id="creating-compiling-and-training-a-model">
-<h2><span class="section-number">41.106.4. </span>Creating, Compiling and Training a Model<a class="headerlink" href="#creating-compiling-and-training-a-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.4. </span>Creating, Compiling and Training a Model<a class="headerlink" href="#creating-compiling-and-training-a-model" title="Permalink to this headline">#</a></h2>
 <p>In TensorFlow, creating a model is only putting together an empty graphs. We are going to use Sequential API to stack the layers, from the input to output.</p>
 <p>In model compilaton, it’s where we specify the optimizer and loss function. Loss function is there for calculating the difference between the predictions and the actual output, and optimizer is there for reducing the loss.</p>
 <p>Also, if we are interested in tracking other metrics during training, we can specify them in <code class="docutils literal notranslate"><span class="pre">metric</span></code>.</p>
@@ -2013,7 +2013,7 @@ <h2><span class="section-number">41.106.4. </span>Creating, Compiling and Traini
 <p>‼️ If you retrain again, it will continue where it left. So, for example, if you train for 30 epochs, and you rerun the cell, it will train for same more epochs again.</p>
 </section>
 <section id="visualizing-the-results">
-<h2><span class="section-number">41.106.5. </span>Visualizing the Results<a class="headerlink" href="#visualizing-the-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.5. </span>Visualizing the Results<a class="headerlink" href="#visualizing-the-results" title="Permalink to this headline">#</a></h2>
 <p>Visualizing the model results after training is always a good way to learn what you can do to improve the performance.</p>
 <p>Let’s get a Pandas dataframe containing training loss and accuracy, and validation loss and accuracy.</p>
 <div class="cell docutils container">
@@ -2031,7 +2031,7 @@ <h2><span class="section-number">41.106.5. </span>Visualizing the Results<a clas
 <p>Let’s evaluate the model on the test set.</p>
 </section>
 <section id="evaluating-the-model">
-<h2><span class="section-number">41.106.6. </span>Evaluating the Model<a class="headerlink" href="#evaluating-the-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.6. </span>Evaluating the Model<a class="headerlink" href="#evaluating-the-model" title="Permalink to this headline">#</a></h2>
 <p>Quite often, you will want to test your model on the data that it never saw. This data is normally called <code class="docutils literal notranslate"><span class="pre">test</span> <span class="pre">set</span></code> and in more applied practice, you will only feed the test to the model after you have done your best to improve it.</p>
 <p>Let’s now evaluate the model on the test set. One thing to note here is that the test set must be preprocessed the same way we preprocessed the training set. The training set was rescaled and that was applied to the test set.</p>
 <p>If this is not obeyed, you would not know why you’re having poor results. Just look up on the next next cell how poor the accuracy will be if I evaluate the model on unscaled data when I trained it on scaled data.</p>
@@ -2149,13 +2149,13 @@ <h2><span class="section-number">41.106.6. </span>Evaluating the Model<a class="
 F1 score is the harmonic mean of precision and recall.</p>
 </section>
 <section id="going-beyond-binary-classifier-to-multiclass-classifier-10-fashions-classifier">
-<h2><span class="section-number">41.106.7. </span>Going Beyond Binary Classifier to Multiclass Classifier: 10 Fashions Classifier<a class="headerlink" href="#going-beyond-binary-classifier-to-multiclass-classifier-10-fashions-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.7. </span>Going Beyond Binary Classifier to Multiclass Classifier: 10 Fashions Classifier<a class="headerlink" href="#going-beyond-binary-classifier-to-multiclass-classifier-10-fashions-classifier" title="Permalink to this headline">#</a></h2>
 <p>So far, we have built a neural network for regression(in previous labs) and binary classification. And we have only been working with structured datasets(datasets in tabular format).</p>
 <p>Can the same neural networks we used be able to recognize images? In this next practice, we will turn the page to image classification. We will build a neural network for recognizing 10 different fashions and along the way, we will learn other things such as stopping the training upon a given condition is met, and using TensorBoard to visualize model.</p>
 <p>That is going to be cool! Let’s get started!</p>
 </section>
 <section id="getting-the-fashion-data">
-<h2><span class="section-number">41.106.8. </span>Getting the Fashion data<a class="headerlink" href="#getting-the-fashion-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.8. </span>Getting the Fashion data<a class="headerlink" href="#getting-the-fashion-data" title="Permalink to this headline">#</a></h2>
 <p>Let’s get the dataset from Keras.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2170,7 +2170,7 @@ <h2><span class="section-number">41.106.8. </span>Getting the Fashion data<a cla
 </div>
 </section>
 <section id="looking-in-the-fashion-data">
-<h2><span class="section-number">41.106.9. </span>Looking in the Fashion Data<a class="headerlink" href="#looking-in-the-fashion-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.9. </span>Looking in the Fashion Data<a class="headerlink" href="#looking-in-the-fashion-data" title="Permalink to this headline">#</a></h2>
 <p>As always, it is a best practice to peep into the images to see how they like.</p>
 <p>Let’s display the pixels values of a given image, image, and its corresponding label.</p>
 <div class="cell docutils container">
@@ -2241,7 +2241,7 @@ <h2><span class="section-number">41.106.9. </span>Looking in the Fashion Data<a
 <p>Now that we know the dataset that we are working with, let us do some few proprocessing before building a model.</p>
 </section>
 <section id="id1">
-<h2><span class="section-number">41.106.10. </span>Preparing the Data<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.10. </span>Preparing the Data<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
 <p>In many cases, real world images datasets are not that clean like fashion mnist.</p>
 <p>You may have to correct images that were incorrectly labeled, or you have labels in texts that need to be converted to numbers(most machine learning models accept numeric input), or scale the pixels values.</p>
 <p>The latter is what we are going to do. It is inarguable that scaling the images pixels to value between 0 and 1 increase the performance of the neural network, and hence the results. Let’s do it!!</p>
@@ -2260,7 +2260,7 @@ <h2><span class="section-number">41.106.10. </span>Preparing the Data<a class="h
 <p>We are now ready to build a neural network.</p>
 </section>
 <section id="id2">
-<h2><span class="section-number">41.106.11. </span>Creating, Compiling, and Training a Model<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.11. </span>Creating, Compiling, and Training a Model<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
 <p>There are few points to note before creating a model:</p>
 <ul class="simple">
 <li><p>When working with images, the shape of the input images has to be correctly provided. This is a common error done by many people, including me (before I learned it).</p></li>
@@ -2310,7 +2310,7 @@ <h2><span class="section-number">41.106.11. </span>Creating, Compiling, and Trai
 <p>But also, training mnist for 20 epochs is not slow that we would need to activate GPU. We will take an advantage of GPU in later labs.</p>
 </section>
 <section id="visualizing-the-model-results">
-<h2><span class="section-number">41.106.12. </span>Visualizing the Model Results<a class="headerlink" href="#visualizing-the-model-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.12. </span>Visualizing the Model Results<a class="headerlink" href="#visualizing-the-model-results" title="Permalink to this headline">#</a></h2>
 <p>Let’s visualize the model results to see how training went.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2329,7 +2329,7 @@ <h2><span class="section-number">41.106.12. </span>Visualizing the Model Results
 <p>Let’s see how the model performs on unseed data: test set.</p>
 </section>
 <section id="model-evaluation">
-<h2><span class="section-number">41.106.13. </span>Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.13. </span>Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># if you need a model trained, you can use this cell</span>
@@ -2360,11 +2360,11 @@ <h2><span class="section-number">41.106.13. </span>Model Evaluation<a class="hea
 <p>We could also find other Classification metrics based on True/False positives &amp; negatives such as precision, recall, but since we already saw how to compute them in the binary classifier, let’s see other interesting things: Controlling the training using callbacks and using TensorBoard.</p>
 </section>
 <section id="controlling-training-with-callbacks">
-<h2><span class="section-number">41.106.14. </span>Controlling Training with Callbacks<a class="headerlink" href="#controlling-training-with-callbacks" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.14. </span>Controlling Training with Callbacks<a class="headerlink" href="#controlling-training-with-callbacks" title="Permalink to this headline">#</a></h2>
 <p>We can use Callbacks functions to control the training.</p>
 <p>Take an example: we can stop training when the model is lo longer showing significant improvements on validation set. Or we can terminate training when a certain condition is met.</p>
 <section id="implementing-callbacks">
-<h3><span class="section-number">41.106.14.1. </span>Implementing Callbacks<a class="headerlink" href="#implementing-callbacks" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.106.14.1. </span>Implementing Callbacks<a class="headerlink" href="#implementing-callbacks" title="Permalink to this headline">#</a></h3>
 <p>There are various functionalities available in Keras Callbacks.</p>
 <p>Let’s start with how to use ModelCheckpoint to save the model when the performance on the validation set is best so far. By saving the best model on the validation set, we avoid things like overfitting which is a common issue in machine learning model training, neural network specifically. We also train for less time.</p>
 <p>I will rebuild a same model again.</p>
@@ -2540,7 +2540,7 @@ <h3><span class="section-number">41.106.14.1. </span>Implementing Callbacks<a cl
 </div>
 </section>
 <section id="custom-callback">
-<h3><span class="section-number">41.106.14.2. </span>Custom Callback<a class="headerlink" href="#custom-callback" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.106.14.2. </span>Custom Callback<a class="headerlink" href="#custom-callback" title="Permalink to this headline">#</a></h3>
 <p>Keras offers various functions for implementing custom callbacks that are very handy when you want to control the model training with a little bit of customization.</p>
 <p>You can do certain actions on almost every step of the training. Let’s stop the training when the accuracy is 95%.</p>
 <div class="cell docutils container">
@@ -2605,7 +2605,7 @@ <h3><span class="section-number">41.106.14.2. </span>Custom Callback<a class="he
 </section>
 </section>
 <section id="using-tensorboard-for-model-visualization">
-<h2><span class="section-number">41.106.15. </span>Using TensorBoard for Model Visualization<a class="headerlink" href="#using-tensorboard-for-model-visualization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.15. </span>Using TensorBoard for Model Visualization<a class="headerlink" href="#using-tensorboard-for-model-visualization" title="Permalink to this headline">#</a></h2>
 <p>Tensorboard is incredible tool used by many people (and not just only TensorFlow developers) to experiment with machine learning.</p>
 <p>With TensorBoard, you can:</p>
 <ul class="simple">
@@ -2696,7 +2696,7 @@ <h2><span class="section-number">41.106.15. </span>Using TensorBoard for Model V
 <p>As you can see, TensorBoard is very useful. The fact that you can use it to visualize the performance metrics, model graphs, and datasets as well.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.106.16. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.106.16. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Jean de Dieu Nyandwi for creating the open-source course <a class="reference external" href="https://nyandwi.com/machine_learning_complete/"> machine learning complete </a>. It inspires the majority of the content in this chapter.</p>
 </section>
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@@ -1575,86 +1575,86 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
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-   41.116.1. Load model
+   40.116.1. Load model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
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+   40.116.2. Overview: Deep learning, machine learning, and AI
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#overview-of-this-guide">
-   41.116.3. Overview of this guide
+   40.116.3. Overview of this guide
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#import-the-fashion-mnist-dataset">
-   41.116.4. Import the Fashion MNIST dataset
+   40.116.4. Import the Fashion MNIST dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#explore-the-data">
-   41.116.5. Explore the data
+   40.116.5. Explore the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#preprocess-the-data">
-   41.116.6. Preprocess the data
+   40.116.6. Preprocess the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#build-the-model">
-   41.116.7. Build the model
+   40.116.7. Build the model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#set-up-the-layers">
-     41.116.7.1. Set up the layers
+     40.116.7.1. Set up the layers
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#compile-the-model">
-     41.116.7.2. Compile the model
+     40.116.7.2. Compile the model
     </a>
    </li>
   </ul>
  </li>
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-   41.116.8. Train the model
+   40.116.8. Train the model
   </a>
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-     41.116.8.1. Feed the model
+     40.116.8.1. Feed the model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#evaluate-accuracy">
-     41.116.8.2. Evaluate accuracy
+     40.116.8.2. Evaluate accuracy
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
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-     41.116.8.3. Make predictions
+     40.116.8.3. Make predictions
     </a>
    </li>
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     <a class="reference internal nav-link" href="#verify-predictions">
-     41.116.8.4. Verify predictions
+     40.116.8.4. Verify predictions
     </a>
    </li>
   </ul>
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-   41.116.9. Use the trained model
+   40.116.9. Use the trained model
   </a>
  </li>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   41.116.10. Acknowledgments
+   40.116.10. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1678,86 +1678,86 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#load-model">
-   41.116.1. Load model
+   40.116.1. Load model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#overview-deep-learning-machine-learning-and-ai">
-   41.116.2. Overview: Deep learning, machine learning, and AI
+   40.116.2. Overview: Deep learning, machine learning, and AI
   </a>
  </li>
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   <a class="reference internal nav-link" href="#overview-of-this-guide">
-   41.116.3. Overview of this guide
+   40.116.3. Overview of this guide
   </a>
  </li>
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-   41.116.4. Import the Fashion MNIST dataset
+   40.116.4. Import the Fashion MNIST dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#explore-the-data">
-   41.116.5. Explore the data
+   40.116.5. Explore the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#preprocess-the-data">
-   41.116.6. Preprocess the data
+   40.116.6. Preprocess the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#build-the-model">
-   41.116.7. Build the model
+   40.116.7. Build the model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#set-up-the-layers">
-     41.116.7.1. Set up the layers
+     40.116.7.1. Set up the layers
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#compile-the-model">
-     41.116.7.2. Compile the model
+     40.116.7.2. Compile the model
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#train-the-model">
-   41.116.8. Train the model
+   40.116.8. Train the model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#feed-the-model">
-     41.116.8.1. Feed the model
+     40.116.8.1. Feed the model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#evaluate-accuracy">
-     41.116.8.2. Evaluate accuracy
+     40.116.8.2. Evaluate accuracy
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#make-predictions">
-     41.116.8.3. Make predictions
+     40.116.8.3. Make predictions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#verify-predictions">
-     41.116.8.4. Verify predictions
+     40.116.8.4. Verify predictions
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#use-the-trained-model">
-   41.116.9. Use the trained model
+   40.116.9. Use the trained model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.116.10. Acknowledgments
+   40.116.10. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1771,9 +1771,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="basic-classification-classify-images-of-clothing">
-<h1><span class="section-number">41.116. </span>Basic classification: Classify images of clothing<a class="headerlink" href="#basic-classification-classify-images-of-clothing" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.116. </span>Basic classification: Classify images of clothing<a class="headerlink" href="#basic-classification-classify-images-of-clothing" title="Permalink to this headline">#</a></h1>
 <section id="load-model">
-<h2><span class="section-number">41.116.1. </span>Load model<a class="headerlink" href="#load-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.116.1. </span>Load model<a class="headerlink" href="#load-model" title="Permalink to this headline">#</a></h2>
 <p>The following code has nothing to do with machine learning and can be skipped.</p>
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@@ -1818,7 +1818,7 @@ <h2><span class="section-number">41.116.1. </span>Load model<a class="headerlink
 </div>
 </section>
 <section id="overview-deep-learning-machine-learning-and-ai">
-<h2><span class="section-number">41.116.2. </span>Overview: Deep learning, machine learning, and AI<a class="headerlink" href="#overview-deep-learning-machine-learning-and-ai" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.116.2. </span>Overview: Deep learning, machine learning, and AI<a class="headerlink" href="#overview-deep-learning-machine-learning-and-ai" title="Permalink to this headline">#</a></h2>
 <p><img alt="ai-vs-machine-learning-vs-deep-learning" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/overview/ai-vs-machine-learning-vs-deep-learning.png" /></p>
 <p>Consider the following definitions to understand deep learning vs. machine learning vs. AI:</p>
 <ul class="simple">
@@ -1836,7 +1836,7 @@ <h2><span class="section-number">41.116.2. </span>Overview: Deep learning, machi
 <p>By using machine learning and deep learning techniques, you can build computer systems and applications that do tasks that are commonly associated with human intelligence. These tasks include image recognition, speech recognition, and language translation.</p>
 </section>
 <section id="overview-of-this-guide">
-<h2><span class="section-number">41.116.3. </span>Overview of this guide<a class="headerlink" href="#overview-of-this-guide" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.116.3. </span>Overview of this guide<a class="headerlink" href="#overview-of-this-guide" title="Permalink to this headline">#</a></h2>
 <p>This guide provide a  simple sample of deep learning. It will help you understand the model framework for deep learning.</p>
 <p>This guide trains a neural network model to classify images of clothing, like sneakers and shirts. It’s okay if you don’t understand all the details; this is a fast-paced overview of a complete TensorFlow program with the details explained as you go.</p>
 <p>This guide uses <a class="reference external" href="https://www.tensorflow.org/guide/keras">tf.keras</a>, a high-level API to build and train models in TensorFlow.</p>
@@ -1856,7 +1856,7 @@ <h2><span class="section-number">41.116.3. </span>Overview of this guide<a class
 </div>
 </section>
 <section id="import-the-fashion-mnist-dataset">
-<h2><span class="section-number">41.116.4. </span>Import the Fashion MNIST dataset<a class="headerlink" href="#import-the-fashion-mnist-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.116.4. </span>Import the Fashion MNIST dataset<a class="headerlink" href="#import-the-fashion-mnist-dataset" title="Permalink to this headline">#</a></h2>
 <p>This guide uses the <a class="reference external" href="https://github.com/zalandoresearch/fashion-mnist">Fashion MNIST</a> dataset which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels), as seen here:</p>
 <table>
   <tr><td>
@@ -1942,7 +1942,7 @@ <h2><span class="section-number">41.116.4. </span>Import the Fashion MNIST datas
 </div>
 </section>
 <section id="explore-the-data">
-<h2><span class="section-number">41.116.5. </span>Explore the data<a class="headerlink" href="#explore-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.116.5. </span>Explore the data<a class="headerlink" href="#explore-the-data" title="Permalink to this headline">#</a></h2>
 <p>Let’s explore the format of the dataset before training the model. The following shows there are 60,000 images in the training set, with each image represented as 28 x 28 pixels:</p>
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 </div>
 </section>
 <section id="preprocess-the-data">
-<h2><span class="section-number">41.116.6. </span>Preprocess the data<a class="headerlink" href="#preprocess-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.116.6. </span>Preprocess the data<a class="headerlink" href="#preprocess-the-data" title="Permalink to this headline">#</a></h2>
 <p>The data must be preprocessed before training the network. If you inspect the first image in the training set, you will see that the pixel values fall in the range of 0 to 255:</p>
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 </div>
 </section>
 <section id="build-the-model">
-<h2><span class="section-number">41.116.7. </span>Build the model<a class="headerlink" href="#build-the-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.116.7. </span>Build the model<a class="headerlink" href="#build-the-model" title="Permalink to this headline">#</a></h2>
 <p>Building the neural network requires configuring the layers of the model, then compiling the model.</p>
 <section id="set-up-the-layers">
-<h3><span class="section-number">41.116.7.1. </span>Set up the layers<a class="headerlink" href="#set-up-the-layers" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.116.7.1. </span>Set up the layers<a class="headerlink" href="#set-up-the-layers" title="Permalink to this headline">#</a></h3>
 <p>The basic building block of a neural network is the <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/layers"><em>layer</em></a>. Layers extract representations from the data fed into them. Hopefully, these representations are meaningful for the problem at hand.</p>
 <p>Most of deep learning consists of chaining together simple layers. Most layers, such as <code class="docutils literal notranslate"><span class="pre">tf.keras.layers.Dense</span></code>, have parameters that are learned during training.</p>
 <div class="cell docutils container">
@@ -2047,7 +2047,7 @@ <h3><span class="section-number">41.116.7.1. </span>Set up the layers<a class="h
 <p>After the pixels are flattened, the network consists of a sequence of two <code class="docutils literal notranslate"><span class="pre">tf.keras.layers.Dense</span></code> layers. These are densely connected, or fully connected, neural layers. The first <code class="docutils literal notranslate"><span class="pre">Dense</span></code> layer has 128 nodes (or neurons). The second (and last) layer returns a logits array with length of 10. Each node contains a score that indicates the current image belongs to one of the 10 classes.</p>
 </section>
 <section id="compile-the-model">
-<h3><span class="section-number">41.116.7.2. </span>Compile the model<a class="headerlink" href="#compile-the-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.116.7.2. </span>Compile the model<a class="headerlink" href="#compile-the-model" title="Permalink to this headline">#</a></h3>
 <p>Before the model is ready for training, it needs a few more settings. These are added during the model’s <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/Model#compile"><em>compile</em></a> step:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/losses"><em>Loss function</em></a> —This measures how accurate the model is during training. You want to minimize this function to “steer” the model in the right direction.</p></li>
@@ -2066,7 +2066,7 @@ <h3><span class="section-number">41.116.7.2. </span>Compile the model<a class="h
 </section>
 </section>
 <section id="train-the-model">
-<h2><span class="section-number">41.116.8. </span>Train the model<a class="headerlink" href="#train-the-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.116.8. </span>Train the model<a class="headerlink" href="#train-the-model" title="Permalink to this headline">#</a></h2>
 <p>Training the neural network model requires the following steps:</p>
 <ol class="simple">
 <li><p>Feed the training data to the model. In this example, the training data is in the <code class="docutils literal notranslate"><span class="pre">train_images</span></code> and <code class="docutils literal notranslate"><span class="pre">train_labels</span></code> arrays.</p></li>
@@ -2075,7 +2075,7 @@ <h2><span class="section-number">41.116.8. </span>Train the model<a class="heade
 <li><p>Verify that the predictions match the labels from the <code class="docutils literal notranslate"><span class="pre">test_labels</span></code> array.</p></li>
 </ol>
 <section id="feed-the-model">
-<h3><span class="section-number">41.116.8.1. </span>Feed the model<a class="headerlink" href="#feed-the-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.116.8.1. </span>Feed the model<a class="headerlink" href="#feed-the-model" title="Permalink to this headline">#</a></h3>
 <p>To start training,  call the <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit"><code class="docutils literal notranslate"><span class="pre">model.fit</span></code></a> method—so called because it “fits” the model to the training data:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2095,7 +2095,7 @@ <h3><span class="section-number">41.116.8.1. </span>Feed the model<a class="head
 <p>As the model trains, the loss and accuracy metrics are displayed. This model reaches an accuracy of about 0.91 (or 91%) on the training data.</p>
 </section>
 <section id="evaluate-accuracy">
-<h3><span class="section-number">41.116.8.2. </span>Evaluate accuracy<a class="headerlink" href="#evaluate-accuracy" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.116.8.2. </span>Evaluate accuracy<a class="headerlink" href="#evaluate-accuracy" title="Permalink to this headline">#</a></h3>
 <p>Next, compare how the model performs on the test dataset:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2113,7 +2113,7 @@ <h3><span class="section-number">41.116.8.2. </span>Evaluate accuracy<a class="h
 </ul>
 </section>
 <section id="make-predictions">
-<h3><span class="section-number">41.116.8.3. </span>Make predictions<a class="headerlink" href="#make-predictions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.116.8.3. </span>Make predictions<a class="headerlink" href="#make-predictions" title="Permalink to this headline">#</a></h3>
 <p>With the model trained, you can use it to make predictions about some images.
 Attach a softmax layer to convert the model’s linear outputs—<a class="reference external" href="https://developers.google.com/machine-learning/glossary#logits">logits</a>—to probabilities, which should be easier to interpret.</p>
 <div class="cell docutils container">
@@ -2194,7 +2194,7 @@ <h3><span class="section-number">41.116.8.3. </span>Make predictions<a class="he
 </div>
 </section>
 <section id="verify-predictions">
-<h3><span class="section-number">41.116.8.4. </span>Verify predictions<a class="headerlink" href="#verify-predictions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.116.8.4. </span>Verify predictions<a class="headerlink" href="#verify-predictions" title="Permalink to this headline">#</a></h3>
 <p>With the model trained, you can use it to make predictions about some images.</p>
 <p>Let’s look at the 0th image, predictions, and prediction array. Correct prediction labels are blue and incorrect prediction labels are red. The number gives the percentage (out of 100) for the predicted label.</p>
 <div class="cell docutils container">
@@ -2246,7 +2246,7 @@ <h3><span class="section-number">41.116.8.4. </span>Verify predictions<a class="
 </section>
 </section>
 <section id="use-the-trained-model">
-<h2><span class="section-number">41.116.9. </span>Use the trained model<a class="headerlink" href="#use-the-trained-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.116.9. </span>Use the trained model<a class="headerlink" href="#use-the-trained-model" title="Permalink to this headline">#</a></h2>
 <p>Finally, use the trained model to make a prediction about a single image.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2299,7 +2299,7 @@ <h2><span class="section-number">41.116.9. </span>Use the trained model<a class=
 <p>And the model predicts a label as expected.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.116.10. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.116.10. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Tensorflow docs for creating the Tensorflow notebook <a class="reference external" href="https://www.tensorflow.org/tutorials/keras/classification">Basic classification: Classify images of clothing</a>. It inspires the majority of the content in this chapter.</p>
 <p>Thanks to Microsoft for providing <a class="reference external" href="https://learn.microsoft.com/en-us/azure/machine-learning/concept-deep-learning-vs-machine-learning?view=azureml-api-2">overview of deep learning, machine learning, and AI</a>.</p>
 </section>
@@ -2342,13 +2342,13 @@ <h2><span class="section-number">41.116.10. </span>Acknowledgments<a class="head
         <i class="fas fa-angle-left"></i>
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-            <p class="prev-next-title"><span class="section-number">41.115. </span>Generative Adversarial Networks (GANs)</p>
+            <p class="prev-next-title"><span class="section-number">40.115. </span>Generative Adversarial Networks (GANs)</p>
         </div>
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-        <p class="prev-next-title"><span class="section-number">42. </span>Slides</p>
+        <p class="prev-next-title"><span class="section-number">41. </span>Slides</p>
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-    <link rel="next" title="41.101. Intro to Autoencoders" href="../autoencoder/autoencoder.html" />
-    <link rel="prev" title="41.97. Bitcoin LSTM Model with Tweet Volume and Sentiment" href="../lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html" />
+    <link rel="next" title="40.101. Intro to Autoencoders" href="../autoencoder/autoencoder.html" />
+    <link rel="prev" title="40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment" href="../lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html" />
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 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/ptheru">Priya</a> for creating <a class="reference external" href="https://www.kaggle.com/code/ptheru/google-stock-price-prediction-rnn">Google stock price prediction - RNN</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
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+     40.95.1. 1. What is TensorFlow
     </a>
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    <li class="toc-h2 nav-item toc-entry">
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-     41.95.2. 2. TensorFlow Model APIs
+     40.95.2. 2. TensorFlow Model APIs
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     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#sequential-api">
-       41.95.2.1. 2.1 Sequential API
+       40.95.2.1. 2.1 Sequential API
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       </a>
      </li>
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+     40.95.3. Model SubClassing
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
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-       41.95.4.1. 3.1 Intro to Tensors
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-       41.95.4.2. 3.2 Creating a Tensor with tf.constant()
+       40.95.4.2. 3.2 Creating a Tensor with tf.constant()
       </a>
      </li>
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-       41.95.4.3. 3.3 Creating a Tensor with tf.Variable()
+       40.95.4.3. 3.3 Creating a Tensor with tf.Variable()
       </a>
      </li>
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+       40.95.4.4. 3.4 Creating a Tensor from Existing Functions
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
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-       41.95.4.5. 3.5 Selecting Data in Tensor
+       40.95.4.5. 3.5 Selecting Data in Tensor
       </a>
      </li>
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-       41.95.4.6. 3.6 Performing Operations on Tensors
+       40.95.4.6. 3.6 Performing Operations on Tensors
       </a>
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-       41.95.4.7. 3.7 Manipulating the Shape of Tensor
+       40.95.4.7. 3.7 Manipulating the Shape of Tensor
       </a>
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  </li>
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-   41.96. Acknowledgments
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   </a>
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                             <ul class="visible nav section-nav flex-column">
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   </a>
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    <li class="toc-h2 nav-item toc-entry">
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-     41.95.1. 1. What is TensorFlow
+     40.95.1. 1. What is TensorFlow
     </a>
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     <a class="reference internal nav-link" href="#tensorflow-model-apis">
-     41.95.2. 2. TensorFlow Model APIs
+     40.95.2. 2. TensorFlow Model APIs
     </a>
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       <a class="reference internal nav-link" href="#sequential-api">
-       41.95.2.1. 2.1 Sequential API
+       40.95.2.1. 2.1 Sequential API
       </a>
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-       41.95.2.2. Functional API
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       </a>
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-     41.95.4. 3. The Basics of Tensors
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-       41.95.4.1. 3.1 Intro to Tensors
+       40.95.4.1. 3.1 Intro to Tensors
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      </li>
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-       41.95.4.2. 3.2 Creating a Tensor with tf.constant()
+       40.95.4.2. 3.2 Creating a Tensor with tf.constant()
       </a>
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       <a class="reference internal nav-link" href="#creating-a-tensor-with-tf-variable">
-       41.95.4.3. 3.3 Creating a Tensor with tf.Variable()
+       40.95.4.3. 3.3 Creating a Tensor with tf.Variable()
       </a>
      </li>
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       <a class="reference internal nav-link" href="#creating-a-tensor-from-existing-functions">
-       41.95.4.4. 3.4 Creating a Tensor from Existing Functions
+       40.95.4.4. 3.4 Creating a Tensor from Existing Functions
       </a>
      </li>
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       <a class="reference internal nav-link" href="#selecting-data-in-tensor">
-       41.95.4.5. 3.5 Selecting Data in Tensor
+       40.95.4.5. 3.5 Selecting Data in Tensor
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#performing-operations-on-tensors">
-       41.95.4.6. 3.6 Performing Operations on Tensors
+       40.95.4.6. 3.6 Performing Operations on Tensors
       </a>
      </li>
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+       40.95.4.7. 3.7 Manipulating the Shape of Tensor
       </a>
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  </li>
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+   40.96. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1766,18 +1766,18 @@ <h2> Contents </h2>
                 
   <p><a name='0'></a></p>
 <section class="tex2jax_ignore mathjax_ignore" id="intro-to-tensorflow-for-deep-learning">
-<h1><span class="section-number">41.95. </span>Intro to TensorFlow for Deep Learning<a class="headerlink" href="#intro-to-tensorflow-for-deep-learning" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.95. </span>Intro to TensorFlow for Deep Learning<a class="headerlink" href="#intro-to-tensorflow-for-deep-learning" title="Permalink to this headline">#</a></h1>
 <section id="what-is-tensorflow">
-<h2><span class="section-number">41.95.1. </span>1. What is TensorFlow<a class="headerlink" href="#what-is-tensorflow" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.95.1. </span>1. What is TensorFlow<a class="headerlink" href="#what-is-tensorflow" title="Permalink to this headline">#</a></h2>
 <p>TensorFlow is an open source and an end to end platform used for building machine learning models. Being end to end, you can prepare data, build models, diagnose, improve, and deploy them.</p>
 <p>TensorFlow uses <a class="reference external" href="https://keras.io">Keras</a> at its backend. Keras is a well beautifully designed API for building deep learning models in popular fields such as Computer Vision and Natural Language Processing.</p>
 <p>TensorFlow has got a strong community, from users, learning resources and whole range of technical supports. Not only it powers majority of Google apps such as YouTube, Maps and Google Photos, it is also widely used across startups and other big techs. If you would like to know who is using TensorFlow, <a class="reference external" href="https://www.tensorflow.org/about/case-studies">here you go!</a>.</p>
 </section>
 <section id="tensorflow-model-apis">
-<h2><span class="section-number">41.95.2. </span>2. TensorFlow Model APIs<a class="headerlink" href="#tensorflow-model-apis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.95.2. </span>2. TensorFlow Model APIs<a class="headerlink" href="#tensorflow-model-apis" title="Permalink to this headline">#</a></h2>
 <p>TensorFlow being suited for variety of tasks, there are 3 ways to build deep learning models.</p>
 <section id="sequential-api">
-<h3><span class="section-number">41.95.2.1. </span>2.1 Sequential API<a class="headerlink" href="#sequential-api" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.95.2.1. </span>2.1 Sequential API<a class="headerlink" href="#sequential-api" title="Permalink to this headline">#</a></h3>
 <p>This is the simplest model building option. When building a model, you start from the input to the output, no other way around. This API is suited for tasks that don’t require multiple inputs or outputs, or skip connections.</p>
 <p>Sequential API can be a good API to use for things like image classification or regression tasks. With things like object detection and segmentation, we need another API, which is Functional API.</p>
 <p>Below is how a sequential model looks like.</p>
@@ -1798,7 +1798,7 @@ <h3><span class="section-number">41.95.2.1. </span>2.1 Sequential API<a class="h
 </div>
 </section>
 <section id="functional-api">
-<h3><span class="section-number">41.95.2.2. </span>Functional API<a class="headerlink" href="#functional-api" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.95.2.2. </span>Functional API<a class="headerlink" href="#functional-api" title="Permalink to this headline">#</a></h3>
 <p>This type of API makes it easy to build models that can take multiple inputs/outputs, or skip connections.</p>
 <p>It is well suited in advanced things like object detection and segmentation. In object detection, there are two main involved things.</p>
 <p>One is recognizing the object(classification) and other is localizing the object(regression: predicting the bounding boxes coordinates).</p>
@@ -1820,7 +1820,7 @@ <h3><span class="section-number">41.95.2.2. </span>Functional API<a class="heade
 </section>
 </section>
 <section id="model-subclassing">
-<h2><span class="section-number">41.95.3. </span>Model SubClassing<a class="headerlink" href="#model-subclassing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.95.3. </span>Model SubClassing<a class="headerlink" href="#model-subclassing" title="Permalink to this headline">#</a></h2>
 <p>SubClassing API is for building custom models and having full control of every step in model building and training.</p>
 <p>In most cases, Sequential and Functional API will be all you need to build almost anything.</p>
 <p>Below is how a subclassing model looks like.</p>
@@ -1850,9 +1850,9 @@ <h2><span class="section-number">41.95.3. </span>Model SubClassing<a class="head
 </div>
 </section>
 <section id="the-basics-of-tensors">
-<h2><span class="section-number">41.95.4. </span>3. The Basics of Tensors<a class="headerlink" href="#the-basics-of-tensors" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.95.4. </span>3. The Basics of Tensors<a class="headerlink" href="#the-basics-of-tensors" title="Permalink to this headline">#</a></h2>
 <section id="intro-to-tensors">
-<h3><span class="section-number">41.95.4.1. </span>3.1 Intro to Tensors<a class="headerlink" href="#intro-to-tensors" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.95.4.1. </span>3.1 Intro to Tensors<a class="headerlink" href="#intro-to-tensors" title="Permalink to this headline">#</a></h3>
 <p>A tensor is a multidimensional array of the same data type. A tensor can be a scalar (single number), a vector, or a matrix.</p>
 <p>If you have used NumPy, tensors are like NumPy arrays, except that tensors have GPU(Graphical Processing Unit) support.</p>
 <p>A typical tensor has the following information:</p>
@@ -1882,7 +1882,7 @@ <h3><span class="section-number">41.95.4.1. </span>3.1 Intro to Tensors<a class=
 </ul>
 </section>
 <section id="creating-a-tensor-with-tf-constant">
-<h3><span class="section-number">41.95.4.2. </span>3.2 Creating a Tensor with tf.constant()<a class="headerlink" href="#creating-a-tensor-with-tf-constant" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.95.4.2. </span>3.2 Creating a Tensor with tf.constant()<a class="headerlink" href="#creating-a-tensor-with-tf-constant" title="Permalink to this headline">#</a></h3>
 <p>A Tensor can be a scalar, a vector or a matrix.
 Let’s use <code class="docutils literal notranslate"><span class="pre">tf.constant()</span></code> to create these tensor types.</p>
 <p>A tensor created with <code class="docutils literal notranslate"><span class="pre">tf.constant()</span></code> is immutable.</p>
@@ -2027,7 +2027,7 @@ <h3><span class="section-number">41.95.4.2. </span>3.2 Creating a Tensor with tf
 </div>
 </section>
 <section id="creating-a-tensor-with-tf-variable">
-<h3><span class="section-number">41.95.4.3. </span>3.3 Creating a Tensor with tf.Variable()<a class="headerlink" href="#creating-a-tensor-with-tf-variable" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.95.4.3. </span>3.3 Creating a Tensor with tf.Variable()<a class="headerlink" href="#creating-a-tensor-with-tf-variable" title="Permalink to this headline">#</a></h3>
 <p>A tensor created with <code class="docutils literal notranslate"><span class="pre">tf.constant()</span></code> is immutable, it can not be changed. Such kind of tensor can not be used as weights in neural networks because they need to be changed/updated in backpropogation for example.</p>
 <p>With <code class="docutils literal notranslate"><span class="pre">tf.Variable()</span></code>, we can create tensors that can be mutable and thus can be used in things like updating the weights of neural networks like said above.</p>
 <p>Creating variable tensor is as simple as the former.</p>
@@ -2058,7 +2058,7 @@ <h3><span class="section-number">41.95.4.3. </span>3.3 Creating a Tensor with tf
 </div>
 </section>
 <section id="creating-a-tensor-from-existing-functions">
-<h3><span class="section-number">41.95.4.4. </span>3.4 Creating a Tensor from Existing Functions<a class="headerlink" href="#creating-a-tensor-from-existing-functions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.95.4.4. </span>3.4 Creating a Tensor from Existing Functions<a class="headerlink" href="#creating-a-tensor-from-existing-functions" title="Permalink to this headline">#</a></h3>
 <p>There some types of uniform tensors that you would not want to create from scratch, when in fact, they are already built.</p>
 <p>Take an example of 1’s tensor, 0’s, and random tensors. Let’s create them.</p>
 <div class="cell docutils container">
@@ -2172,7 +2172,7 @@ <h3><span class="section-number">41.95.4.4. </span>3.4 Creating a Tensor from Ex
 <p>You can learn more about Random number generation at <a class="reference external" href="https://www.tensorflow.org/guide/random_numbers#the_tfrandomgenerator_class">TensorFlow docs</a>.</p>
 </section>
 <section id="selecting-data-in-tensor">
-<h3><span class="section-number">41.95.4.5. </span>3.5 Selecting Data in Tensor<a class="headerlink" href="#selecting-data-in-tensor" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.95.4.5. </span>3.5 Selecting Data in Tensor<a class="headerlink" href="#selecting-data-in-tensor" title="Permalink to this headline">#</a></h3>
 <p>We can also select values in any tensor, both single dimensional tensor and multi dimensional tensor.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2221,7 +2221,7 @@ <h3><span class="section-number">41.95.4.5. </span>3.5 Selecting Data in Tensor<
 </div>
 </section>
 <section id="performing-operations-on-tensors">
-<h3><span class="section-number">41.95.4.6. </span>3.6 Performing Operations on Tensors<a class="headerlink" href="#performing-operations-on-tensors" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.95.4.6. </span>3.6 Performing Operations on Tensors<a class="headerlink" href="#performing-operations-on-tensors" title="Permalink to this headline">#</a></h3>
 <p>All numeric operations can be performed on tensor. Let’s see few of them.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2272,7 +2272,7 @@ <h3><span class="section-number">41.95.4.6. </span>3.6 Performing Operations on
 <p>You can learn more at official docs, <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/math"><code class="docutils literal notranslate"><span class="pre">tf.math()</span></code></a> specifically. Almost all maths operations can be done on tensors.</p>
 </section>
 <section id="manipulating-the-shape-of-tensor">
-<h3><span class="section-number">41.95.4.7. </span>3.7 Manipulating the Shape of Tensor<a class="headerlink" href="#manipulating-the-shape-of-tensor" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.95.4.7. </span>3.7 Manipulating the Shape of Tensor<a class="headerlink" href="#manipulating-the-shape-of-tensor" title="Permalink to this headline">#</a></h3>
 <p>There are times you would want to reshape a tensor. Here is how to go about it.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2339,7 +2339,7 @@ <h3><span class="section-number">41.95.4.7. </span>3.7 Manipulating the Shape of
 </section>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="acknowledgments">
-<h1><span class="section-number">41.96. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.96. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
 <p>Thanks to <a class="reference external" href="https://github.com/Nyandwi">Jean de Dieu Nyandwi</a> for creating <a class="reference external" href="https://github.com/Nyandwi/machine_learning_complete/blob/main/7_intro_to_artificial%20_neural_networks_and_tensorflow/2_intro_to_tensorflow_for_deeplearning.ipynb">2_intro_to_tensorflow_for_deeplearning</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
@@ -2380,13 +2380,13 @@ <h1><span class="section-number">41.96. </span>Acknowledgments<a class="headerli
         <i class="fas fa-angle-left"></i>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.94. </span>Object Recognition in Images using CNN</p>
+            <p class="prev-next-title"><span class="section-number">40.94. </span>Object Recognition in Images using CNN</p>
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-        <p class="prev-next-title"><span class="section-number">41.97. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment</p>
+        <p class="prev-next-title"><span class="section-number">40.97. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment</p>
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-     41.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
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-     42.20. Convolutional Neural Network
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         <ul class="visible nav section-nav flex-column">
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#">
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+   40.104. Time Series Forecasting Assignment
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+     40.104.2. Imports
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    </li>
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+     40.104.3. Loading Data
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      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#csv-data-path">
-       41.104.3.1. CSV Data Path
+       40.104.3.1. CSV Data Path
       </a>
      </li>
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       <a class="reference internal nav-link" href="#reading-csv-file-with-pandas">
-       41.104.3.2. Reading CSV file with Pandas
+       40.104.3.2. Reading CSV file with Pandas
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
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+       40.104.3.3. Normalizing Data
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+       40.104.4.1. Data Preparation
       </a>
      </li>
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         </a>
        </li>
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+         40.104.4.2.2. CNN
         </a>
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-         41.104.4.2.3. Simple RNN
+         40.104.4.2.3. Simple RNN
         </a>
        </li>
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-         41.104.4.2.4. LSTM
+         40.104.4.2.4. LSTM
         </a>
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       </ul>
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    </li>
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-     41.104.5. Task 2
+     40.104.5. Task 2
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-       41.104.5.1. ANN
+       40.104.5.1. ANN
       </a>
      </li>
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       <a class="reference internal nav-link" href="#rnn">
-       41.104.5.2. RNN
+       40.104.5.2. RNN
       </a>
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-       41.104.5.3. CNN
+       40.104.5.3. CNN
       </a>
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       <a class="reference internal nav-link" href="#id4">
-       41.104.5.4. LSTM
+       40.104.5.4. LSTM
       </a>
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     </ul>
@@ -1692,7 +1692,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   41.105. Acknowledgments
+   40.105. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1716,86 +1716,86 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#">
-   41.104. Time Series Forecasting Assignment
+   40.104. Time Series Forecasting Assignment
   </a>
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    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#tasks">
-     41.104.1. Tasks
+     40.104.1. Tasks
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task1">
-       41.104.1.1. Task1
+       40.104.1.1. Task1
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task2">
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       </a>
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     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#imports">
-     41.104.2. Imports
+     40.104.2. Imports
     </a>
    </li>
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     <a class="reference internal nav-link" href="#loading-data">
-     41.104.3. Loading Data
+     40.104.3. Loading Data
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#csv-data-path">
-       41.104.3.1. CSV Data Path
+       40.104.3.1. CSV Data Path
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#reading-csv-file-with-pandas">
-       41.104.3.2. Reading CSV file with Pandas
+       40.104.3.2. Reading CSV file with Pandas
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#normalizing-data">
-       41.104.3.3. Normalizing Data
+       40.104.3.3. Normalizing Data
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     41.104.4. Task1
+     40.104.4. Task1
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#data-preparation">
-       41.104.4.1. Data Preparation
+       40.104.4.1. Data Preparation
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#modeling">
-       41.104.4.2. Modeling
+       40.104.4.2. Modeling
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h4 nav-item toc-entry">
         <a class="reference internal nav-link" href="#ann">
-         41.104.4.2.1. ANN
+         40.104.4.2.1. ANN
         </a>
        </li>
        <li class="toc-h4 nav-item toc-entry">
         <a class="reference internal nav-link" href="#cnn">
-         41.104.4.2.2. CNN
+         40.104.4.2.2. CNN
         </a>
        </li>
        <li class="toc-h4 nav-item toc-entry">
         <a class="reference internal nav-link" href="#simple-rnn">
-         41.104.4.2.3. Simple RNN
+         40.104.4.2.3. Simple RNN
         </a>
        </li>
        <li class="toc-h4 nav-item toc-entry">
         <a class="reference internal nav-link" href="#lstm">
-         41.104.4.2.4. LSTM
+         40.104.4.2.4. LSTM
         </a>
        </li>
       </ul>
@@ -1804,27 +1804,27 @@ <h2> Contents </h2>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2">
-     41.104.5. Task 2
+     40.104.5. Task 2
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      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id2">
-       41.104.5.1. ANN
+       40.104.5.1. ANN
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#rnn">
-       41.104.5.2. RNN
+       40.104.5.2. RNN
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id3">
-       41.104.5.3. CNN
+       40.104.5.3. CNN
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id4">
-       41.104.5.4. LSTM
+       40.104.5.4. LSTM
       </a>
      </li>
     </ul>
@@ -1833,7 +1833,7 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.105. Acknowledgments
+   40.105. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1847,21 +1847,21 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="time-series-forecasting-assignment">
-<h1><span class="section-number">41.104. </span>Time Series Forecasting Assignment<a class="headerlink" href="#time-series-forecasting-assignment" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.104. </span>Time Series Forecasting Assignment<a class="headerlink" href="#time-series-forecasting-assignment" title="Permalink to this headline">#</a></h1>
 <section id="tasks">
-<h2><span class="section-number">41.104.1. </span>Tasks<a class="headerlink" href="#tasks" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.104.1. </span>Tasks<a class="headerlink" href="#tasks" title="Permalink to this headline">#</a></h2>
 <p><img alt="types" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/assignment/deep-learning/time-series/time-series.jpg" /></p>
 <section id="task1">
-<h3><span class="section-number">41.104.1.1. </span>Task1<a class="headerlink" href="#task1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.104.1.1. </span>Task1<a class="headerlink" href="#task1" title="Permalink to this headline">#</a></h3>
 <p>Given 10 timesteps of features, predict the USDT of the next timestep (many to one)</p>
 </section>
 <section id="task2">
-<h3><span class="section-number">41.104.1.2. </span>Task2<a class="headerlink" href="#task2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.104.1.2. </span>Task2<a class="headerlink" href="#task2" title="Permalink to this headline">#</a></h3>
 <p>Given 10 timestaps of features, predict the USDT of those timesteps (many to many)</p>
 </section>
 </section>
 <section id="imports">
-<h2><span class="section-number">41.104.2. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.104.2. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -1887,9 +1887,9 @@ <h2><span class="section-number">41.104.2. </span>Imports<a class="headerlink" h
 </div>
 </section>
 <section id="loading-data">
-<h2><span class="section-number">41.104.3. </span>Loading Data<a class="headerlink" href="#loading-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.104.3. </span>Loading Data<a class="headerlink" href="#loading-data" title="Permalink to this headline">#</a></h2>
 <section id="csv-data-path">
-<h3><span class="section-number">41.104.3.1. </span>CSV Data Path<a class="headerlink" href="#csv-data-path" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.104.3.1. </span>CSV Data Path<a class="headerlink" href="#csv-data-path" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data_path</span> <span class="o">=</span> <span class="s1">&#39;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/time-series/timeseries.csv&#39;</span>
@@ -1899,7 +1899,7 @@ <h3><span class="section-number">41.104.3.1. </span>CSV Data Path<a class="heade
 </div>
 </section>
 <section id="reading-csv-file-with-pandas">
-<h3><span class="section-number">41.104.3.2. </span>Reading CSV file with Pandas<a class="headerlink" href="#reading-csv-file-with-pandas" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.104.3.2. </span>Reading CSV file with Pandas<a class="headerlink" href="#reading-csv-file-with-pandas" title="Permalink to this headline">#</a></h3>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">data_path</span><span class="p">)</span>
@@ -1916,7 +1916,7 @@ <h3><span class="section-number">41.104.3.2. </span>Reading CSV file with Pandas
 </div>
 </section>
 <section id="normalizing-data">
-<h3><span class="section-number">41.104.3.3. </span>Normalizing Data<a class="headerlink" href="#normalizing-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.104.3.3. </span>Normalizing Data<a class="headerlink" href="#normalizing-data" title="Permalink to this headline">#</a></h3>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">preprocessing</span>
@@ -1943,9 +1943,9 @@ <h3><span class="section-number">41.104.3.3. </span>Normalizing Data<a class="he
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 <section id="id1">
-<h2><span class="section-number">41.104.4. </span>Task1<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.104.4. </span>Task1<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
 <section id="data-preparation">
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 <div class="cell docutils container">
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@@ -2490,7 +2490,7 @@ <h3><span class="section-number">41.104.5.3. </span>CNN<a class="headerlink" hre
 </div>
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 <section id="id4">
-<h3><span class="section-number">41.104.5.4. </span>LSTM<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
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 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">lstm_builder</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">):</span>
@@ -2552,7 +2552,7 @@ <h3><span class="section-number">41.104.5.4. </span>LSTM<a class="headerlink" hr
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 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="acknowledgments">
-<h1><span class="section-number">41.105. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.105. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
 <p>Thanks to Taha Bouhsine for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/skywolfmo/wts2-time-series-forecasting-with-tf"> WTS2: Time Series Forecasting with TF </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
@@ -2593,13 +2593,13 @@ <h1><span class="section-number">41.105. </span>Acknowledgments<a class="headerl
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-            <p class="prev-next-title"><span class="section-number">41.103. </span>Fun with Variational Autoencoders</p>
+            <p class="prev-next-title"><span class="section-number">40.103. </span>Fun with Variational Autoencoders</p>
         </div>
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-        <p class="prev-next-title"><span class="section-number">41.106. </span>Neural Networks for Classification with TensorFlow</p>
+        <p class="prev-next-title"><span class="section-number">40.106. </span>Neural Networks for Classification with TensorFlow</p>
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-    <link rel="prev" title="41.82. Data engineering" href="data-engineering.html" />
+    <link rel="next" title="40.84. Case Study: Debugging in Classification" href="debugging-in-classification.html" />
+    <link rel="prev" title="40.82. Data engineering" href="data-engineering.html" />
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-     41.14. Try it in Excel
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-     41.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
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-     41.24. Data processing in Python
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-     41.37. Exploring for answers
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-     41.38. Market research
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-     41.40. Data Science project using Azure ML SDK
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
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-     41.42. Machine Learning overview - assignment 1
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-     41.45. Linear regression - California Housing
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-     41.46. Linear Regression Metrics
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-     41.47. Loss Function
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-     41.48. Gradient descent
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-     41.49. Linear Regression Implementation from Scratch
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+   40.83.1. Case Study: Debugging a Simple Model
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     <a class="reference internal nav-link" href="#create-the-dataset">
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+     40.83.1.1. Create the Dataset
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+     40.83.1.2. Fit Simple Data with Simple Model
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+     40.83.1.3. Solution: Getting Loss to Decrease
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-     41.83.1.4. Solution: Reaching Convergence
+     40.83.1.4. Solution: Reaching Convergence
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+   40.83.2. Exploding Gradients
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     <a class="reference internal nav-link" href="#cause-high-learning-rate">
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+     40.83.2.1. Cause: High Learning Rate
     </a>
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 </ul>
@@ -1712,7 +1712,7 @@ <h2> Contents </h2>
 See the License for the specific language governing permissions and
 limitations under the License.</p>
 </details><section class="tex2jax_ignore mathjax_ignore" id="counterintuitive-challenges-in-ml-debugging">
-<h1><span class="section-number">41.83. </span>Counterintuitive Challenges in ML Debugging<a class="headerlink" href="#counterintuitive-challenges-in-ml-debugging" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.83. </span>Counterintuitive Challenges in ML Debugging<a class="headerlink" href="#counterintuitive-challenges-in-ml-debugging" title="Permalink to this headline">#</a></h1>
 <p>In this Colab, you will explore why ML debugging is harder than traditional debugging by debugging a simple regression problem, with one feature and one label. You will:</p>
 <ul class="simple">
 <li><p>Create the dataset.</p></li>
@@ -1722,9 +1722,9 @@ <h1><span class="section-number">41.83. </span>Counterintuitive Challenges in ML
 </ul>
 <p>Please <strong>make a copy</strong> of this Colab before running it. Click on <em>File</em>, and then click on <em>Save a copy in Drive</em>.</p>
 <section id="case-study-debugging-a-simple-model">
-<h2><span class="section-number">41.83.1. </span>Case Study: Debugging a Simple Model<a class="headerlink" href="#case-study-debugging-a-simple-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.83.1. </span>Case Study: Debugging a Simple Model<a class="headerlink" href="#case-study-debugging-a-simple-model" title="Permalink to this headline">#</a></h2>
 <section id="create-the-dataset">
-<h3><span class="section-number">41.83.1.1. </span>Create the Dataset<a class="headerlink" href="#create-the-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.83.1.1. </span>Create the Dataset<a class="headerlink" href="#create-the-dataset" title="Permalink to this headline">#</a></h3>
 <p>Run the cells below to load libraries.</p>
 <div class="cell docutils container">
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@@ -1768,7 +1768,7 @@ <h3><span class="section-number">41.83.1.1. </span>Create the Dataset<a class="h
 </div>
 </section>
 <section id="fit-simple-data-with-simple-model">
-<h3><span class="section-number">41.83.1.2. </span>Fit Simple Data with Simple Model<a class="headerlink" href="#fit-simple-data-with-simple-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.83.1.2. </span>Fit Simple Data with Simple Model<a class="headerlink" href="#fit-simple-data-with-simple-model" title="Permalink to this headline">#</a></h3>
 <p>TensorFlow provides several different APIs. This Colab only demonstrates the Keras API since Keras lets you quickly train models in a few lines of code using high-level APIs. In Keras, the typical neural network is a <code class="docutils literal notranslate"><span class="pre">sequential</span></code> model with fully-connected, or <code class="docutils literal notranslate"><span class="pre">dense</span></code>, layers.</p>
 <p>Your dataset is simple. A neural network with just 1 neuron should learn your dataset. Define a neural network with 1 layer having 1 neuron using the model type <code class="docutils literal notranslate"><span class="pre">keras.Sequential</span></code> with the layer type <code class="docutils literal notranslate"><span class="pre">keras.layers.Dense</span></code>. To understand the Keras code, read the code comments. Then run the cell. The code prints the model summary to show a model with 1 layer and 2 parameters (weight and bias).</p>
 <div class="cell docutils container">
@@ -1819,7 +1819,7 @@ <h3><span class="section-number">41.83.1.2. </span>Fit Simple Data with Simple M
 <p>Consider these actions and experiment where necessary. Then read the following section for the solution.</p>
 </section>
 <section id="solution-getting-loss-to-decrease">
-<h3><span class="section-number">41.83.1.3. </span>Solution: Getting Loss to Decrease<a class="headerlink" href="#solution-getting-loss-to-decrease" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.83.1.3. </span>Solution: Getting Loss to Decrease<a class="headerlink" href="#solution-getting-loss-to-decrease" title="Permalink to this headline">#</a></h3>
 <p>Before trying to adjust specific model parameters, such as the hyperparameter values, you should first check for good development practices. Here, you should start with a linear model because of these two best practices:</p>
 <ul class="simple">
 <li><p>Regression: In a regression problem, the last layer must always be linear.</p></li>
@@ -1843,7 +1843,7 @@ <h3><span class="section-number">41.83.1.3. </span>Solution: Getting Loss to Dec
 <p>Your loss decreases, albeit slowly! You’re on the right track. How can you get your loss to converge? Experiment with the code above. For the solution, read the following section.</p>
 </section>
 <section id="solution-reaching-convergence">
-<h3><span class="section-number">41.83.1.4. </span>Solution: Reaching Convergence<a class="headerlink" href="#solution-reaching-convergence" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.83.1.4. </span>Solution: Reaching Convergence<a class="headerlink" href="#solution-reaching-convergence" title="Permalink to this headline">#</a></h3>
 <p>Your loss isn’t decreasing fast enough. From the guidance on <a class="reference external" href="https://developers.google.com/machine-learning/crash-course/reducing-loss/learning-rate">Learning Rate</a>, you know that you can increase the learning rate to train faster. Run the following code to increase the learning rate to 0.1. The the model reaches convergence quickly.</p>
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@@ -1873,7 +1873,7 @@ <h3><span class="section-number">41.83.1.4. </span>Solution: Reaching Convergenc
 <p>Yes, the predictions match the features very well.</p>
 </section>
 <section id="summary-of-case-study">
-<h3><span class="section-number">41.83.1.5. </span>Summary of Case Study<a class="headerlink" href="#summary-of-case-study" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.83.1.5. </span>Summary of Case Study<a class="headerlink" href="#summary-of-case-study" title="Permalink to this headline">#</a></h3>
 <p>When debugging ML models, you should first attempt to diagnose the problem and apply the appropriate fix. For example, if you had changed your optimizer using <code class="docutils literal notranslate"><span class="pre">optimizer='sgd'</span></code>, then your model also converges faster. However, the problem was not with the optimizer but with the learning rate. Changing the optimizer only helps because <code class="docutils literal notranslate"><span class="pre">optimizer='sgd'</span></code> has a higher default learning rate than <code class="docutils literal notranslate"><span class="pre">optimizer='adam'</span></code>.</p>
 <p>Alternatively, you could train the model for longer with the default learning rate. However, in real-world ML, models take long to train. You should keep your training cycles as short as possible. Therefore, increasing the learning rate is the correct fix.</p>
 <p>These options demonstrate how debugging in ML is n-dimensional, and therefore you must use your understanding of model mechanics to narrow down your options. Because running experiments in ML is time consuming, requires careful setup, and can be subject to reproducibility issues, it’s important to use your understanding of model mechanics to  narrow down options without having to experiment.</p>
@@ -1881,10 +1881,10 @@ <h3><span class="section-number">41.83.1.5. </span>Summary of Case Study<a class
 </section>
 </section>
 <section id="exploding-gradients">
-<h2><span class="section-number">41.83.2. </span>Exploding Gradients<a class="headerlink" href="#exploding-gradients" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.83.2. </span>Exploding Gradients<a class="headerlink" href="#exploding-gradients" title="Permalink to this headline">#</a></h2>
 <p>A common problem in model training is a loss that “explodes” or becomes <code class="docutils literal notranslate"><span class="pre">nan</span></code>. A common cause is anomalous feature data, such as outliers and <code class="docutils literal notranslate"><span class="pre">nan</span></code> values, or a high learning rate. The following sections demonstrate these causes.</p>
 <section id="cause-high-learning-rate">
-<h3><span class="section-number">41.83.2.1. </span>Cause: High Learning Rate<a class="headerlink" href="#cause-high-learning-rate" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.83.2.1. </span>Cause: High Learning Rate<a class="headerlink" href="#cause-high-learning-rate" title="Permalink to this headline">#</a></h3>
 <p>In this section, you will create data in the range [0,50] and show that the gradient explodes when you train the model using a learning rate of 0.01. Then you’ll reduce the learning rate to make the model converge.</p>
 <p>Create and visualize the data by running the following code.</p>
 <div class="cell docutils container">
@@ -1919,7 +1919,7 @@ <h3><span class="section-number">41.83.2.1. </span>Cause: High Learning Rate<a c
 </section>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">41.83.3. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.83.3. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>This Colab demonstrated the following principles.</p>
 <ul class="simple">
 <li><p>The n-dimensional nature of debugging in ML makes ML debugging hard.</p></li>
@@ -1967,13 +1967,13 @@ <h2><span class="section-number">41.83.3. </span>Conclusion<a class="headerlink"
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.82. </span>Data engineering</p>
+            <p class="prev-next-title"><span class="section-number">40.82. </span>Data engineering</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="debugging-in-classification.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">41.84. </span>Case Study: Debugging in Classification</p>
+        <p class="prev-next-title"><span class="section-number">40.84. </span>Case Study: Debugging in Classification</p>
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diff --git a/assignments/machine-learning-productionization/data-engineering.html b/assignments/machine-learning-productionization/data-engineering.html
index b1b1013d2..4b3822928 100644
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+    <title>40.82. Data engineering &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../genindex.html" />
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-    <link rel="next" title="41.83. Counterintuitive Challenges in ML Debugging" href="counterintuitive-challenges-in-ml-debugging.html" />
-    <link rel="prev" title="41.80. Random Forest Classifier with Feature Importance" href="../ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html" />
+    <link rel="next" title="40.83. Counterintuitive Challenges in ML Debugging" href="counterintuitive-challenges-in-ml-debugging.html" />
+    <link rel="prev" title="40.80. Random Forest Classifier with Feature Importance" href="../ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html" />
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
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-     41.45. Linear regression - California Housing
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-     41.46. Linear Regression Metrics
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-     42.4. Data Science introduction
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         <ul class="visible nav section-nav flex-column">
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+   40.82.1. Exploring dataset
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+   40.82.2. Dealing with missing data
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+   40.82.3. Removing duplicate data
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                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
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-   41.82.1. Exploring dataset
+   40.82.1. Exploring dataset
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+   40.82.2. Dealing with missing data
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+   40.82.3. Removing duplicate data
   </a>
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+   40.82.4. Handle inconsistent data
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 </ul>
@@ -1663,7 +1663,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="data-engineering">
-<h1><span class="section-number">41.82. </span>Data engineering<a class="headerlink" href="#data-engineering" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.82. </span>Data engineering<a class="headerlink" href="#data-engineering" title="Permalink to this headline">#</a></h1>
 <p>This assignment focuses on techniques for cleaning and transforming the data to handle challenges of missing, inaccurate, or incomplete data. Please refer to <a class="reference external" href="#data-engineering">Machine Learning productionization - Data engineering</a> to learn more.</p>
 <p>Fill <code class="docutils literal notranslate"><span class="pre">____</span></code> pieces of the below implementation in order to pass the assertions.</p>
 <!-- 
@@ -1675,7 +1675,7 @@ <h1><span class="section-number">41.82. </span>Data engineering<a class="headerl
 
 Identifying the group of attributes that uniquely identify a single record is very important and needs validation from a domain expert. Removing duplicates on this group leaves you with distinct records necessary for model training. This group acts as a key to performing multiple aggregate and transformations operations on the dataset like calculating rolling mean, backfilling null values, missing value imputation (details on this in next point), etc.
 True duplicates, i.e., instances of the same data point, are usually removed. In this way, the increase of the sample weight on these points is eliminated, and the risk of any artificial inflation in the performance metrics is reduced --><section id="exploring-dataset">
-<h2><span class="section-number">41.82.1. </span>Exploring dataset<a class="headerlink" href="#exploring-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.82.1. </span>Exploring dataset<a class="headerlink" href="#exploring-dataset" title="Permalink to this headline">#</a></h2>
 <blockquote>
 <div><p><strong>Learning goal</strong>: By the end of this subsection, you should be comfortable finding general information about the data stored in pandas DataFrames.</p>
 </div></blockquote>
@@ -1735,7 +1735,7 @@ <h2><span class="section-number">41.82.1. </span>Exploring dataset<a class="head
 <p><strong>Takeaway</strong>: Even just by looking at the metadata about the information in a DataFrame or the first and last few values in one, you can get an immediate idea about the size, shape, and content of the data you are dealing with.</p>
 </section>
 <section id="dealing-with-missing-data">
-<h2><span class="section-number">41.82.2. </span>Dealing with missing data<a class="headerlink" href="#dealing-with-missing-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.82.2. </span>Dealing with missing data<a class="headerlink" href="#dealing-with-missing-data" title="Permalink to this headline">#</a></h2>
 <p>Missing data can cause inaccuracies as well as weak or biased results. Sometimes these can be resolved by a “reload” of the data, filling in the missing values with computation and code like Python, or simply just removing the value and corresponding data. There are numerous reasons for why data may be missing and the actions that are taken to resolve these missing values can be dependent on how and why they went missing in the first place.</p>
 <blockquote>
 <div><p><strong>Learning goal</strong>: By the end of this subsection, you should know how to replace or remove null values from DataFrames.</p>
@@ -1845,7 +1845,7 @@ <h2><span class="section-number">41.82.2. </span>Dealing with missing data<a cla
 </div>
 </section>
 <section id="removing-duplicate-data">
-<h2><span class="section-number">41.82.3. </span>Removing duplicate data<a class="headerlink" href="#removing-duplicate-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.82.3. </span>Removing duplicate data<a class="headerlink" href="#removing-duplicate-data" title="Permalink to this headline">#</a></h2>
 <p>Data that has more than one occurrence can produce inaccurate results and usually should be removed. This can be a common occurrence when joining two or more datasets together. However, there are instances where duplication in joined datasets contain pieces that can provide additional information and may need to be preserved.</p>
 <blockquote>
 <div><p><strong>Learning goal</strong>: By the end of this subsection, you should be comfortable identifying and removing duplicate values from DataFrames.</p>
@@ -1888,7 +1888,7 @@ <h2><span class="section-number">41.82.3. </span>Removing duplicate data<a class
 </div>
 </section>
 <section id="handle-inconsistent-data">
-<h2><span class="section-number">41.82.4. </span>Handle inconsistent data<a class="headerlink" href="#handle-inconsistent-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.82.4. </span>Handle inconsistent data<a class="headerlink" href="#handle-inconsistent-data" title="Permalink to this headline">#</a></h2>
 <p>Depending on the source, data can have inconsistencies in how it’s presented. This can cause problems in searching for and representing the value, where it’s seen within the dataset but is not properly represented in visualizations or query results. Common formatting problems involve resolving whitespace, dates, and data types. Resolving formatting issues is typically up to the people who are using the data. For example, standards on how dates and numbers are presented can differ by country.</p>
 <blockquote>
 <div><p><strong>Learning goal</strong>: By the end of this subsection, you should know how to handle the inconsistent data format in the DataFrame.</p>
@@ -1936,7 +1936,7 @@ <h2><span class="section-number">41.82.4. </span>Handle inconsistent data<a clas
 </div>
 </section>
 <section id="at-last">
-<h2><span class="section-number">41.82.5. </span>At last<a class="headerlink" href="#at-last" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.82.5. </span>At last<a class="headerlink" href="#at-last" title="Permalink to this headline">#</a></h2>
 <p>Let’s apply all the methods above to make the data to be clean.</p>
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+<h2><span class="section-number">40.82.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It contributes some of the content in this chapter.</p>
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     <a class="reference internal" href="../deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
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+     40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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-     41.99. Google Stock Price Prediction RNN
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-     41.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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     <a class="reference internal" href="../deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     41.103. Fun with Variational Autoencoders
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-     41.106. Neural Networks for Classification with TensorFlow
+     40.106. Neural Networks for Classification with TensorFlow
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     <a class="reference internal" href="../deep-learning/nn-classify-15-fruits-assignment.html">
-     41.107. NN Classify 15 Fruits Assignment
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-     41.112. DQN On Foreign Exchange Market
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-     41.113. Art by gan
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-     41.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
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-     41.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
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-     42.1. Python programming introduction
+     41.1. Python programming introduction
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-     42.3. Python programming advanced
+     41.3. Python programming advanced
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-     42.4. Data Science introduction
+     41.4. Data Science introduction
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-     42.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
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-     42.6. NumPy and Pandas
+     41.6. NumPy and Pandas
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-     42.9. Data Science in the cloud
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-     42.10. Data Science in real world
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-     42.11. Machine Learning overview
+     41.11. Machine Learning overview
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-     42.12. Linear Regression
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-     42.13. Logistic Regression
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-     42.14. Logistic Regression
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-     42.16. Build an machine learning web application
+     41.16. Build an machine learning web application
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-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
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-     42.18. Kernel method
+     41.18. Kernel method
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-     42.19. Model Selection
+     41.19. Model Selection
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-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
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     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
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   </ul>
@@ -1575,101 +1575,101 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#load-mnist-data">
-   41.84.1. Load MNIST Data
+   40.84.1. Load MNIST Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#understanding-the-data-format">
-   41.84.2. Understanding the Data Format
+   40.84.2. Understanding the Data Format
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#do-you-have-imbalanced-classes">
-   41.84.3. Do you have Imbalanced Classes?
+   40.84.3. Do you have Imbalanced Classes?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#shuffle-and-split-dataset">
-   41.84.4. Shuffle and Split Dataset
+   40.84.4. Shuffle and Split Dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#process-data">
-   41.84.5. Process Data
+   40.84.5. Process Data
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#solution">
-     41.84.5.1. Solution
+     40.84.5.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#remove-all-zero-features">
-   41.84.6. Remove All-Zero Features?
+   40.84.6. Remove All-Zero Features?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#establish-baseline">
-   41.84.7. Establish Baseline
+   40.84.7. Establish Baseline
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#train-a-linear-model">
-   41.84.8. Train a Linear Model
+   40.84.8. Train a Linear Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     41.84.8.1. Solution
+     40.84.8.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#fixing-loss-calculation">
-   41.84.9. Fixing Loss Calculation
+   40.84.9. Fixing Loss Calculation
   </a>
  </li>
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   <a class="reference internal nav-link" href="#train-a-nonlinear-model">
-   41.84.10. Train a Nonlinear Model
+   40.84.10. Train a Nonlinear Model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#adding-a-second-layer">
-   41.84.11. Adding a Second Layer
+   40.84.11. Adding a Second Layer
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#check-for-training-validation-data-skew">
-   41.84.12. Check for Training/Validation Data Skew
+   40.84.12. Check for Training/Validation Data Skew
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#apply-dropout-regularization">
-   41.84.13. Apply Dropout Regularization
+   40.84.13. Apply Dropout Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#check-accuracy-for-data-slices">
-   41.84.14. Check Accuracy for Data Slices
+   40.84.14. Check Accuracy for Data Slices
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#testing-for-anomalous-values">
-   41.84.15. Testing for Anomalous Values
+   40.84.15. Testing for Anomalous Values
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#optional-pass-tests-by-fixing-loss-calculation">
-   41.84.16. Optional: Pass Tests by Fixing Loss Calculation
+   40.84.16. Optional: Pass Tests by Fixing Loss Calculation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   41.84.17. Conclusion
+   40.84.17. Conclusion
   </a>
  </li>
 </ul>
@@ -1693,101 +1693,101 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#load-mnist-data">
-   41.84.1. Load MNIST Data
+   40.84.1. Load MNIST Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#understanding-the-data-format">
-   41.84.2. Understanding the Data Format
+   40.84.2. Understanding the Data Format
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#do-you-have-imbalanced-classes">
-   41.84.3. Do you have Imbalanced Classes?
+   40.84.3. Do you have Imbalanced Classes?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#shuffle-and-split-dataset">
-   41.84.4. Shuffle and Split Dataset
+   40.84.4. Shuffle and Split Dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#process-data">
-   41.84.5. Process Data
+   40.84.5. Process Data
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#solution">
-     41.84.5.1. Solution
+     40.84.5.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#remove-all-zero-features">
-   41.84.6. Remove All-Zero Features?
+   40.84.6. Remove All-Zero Features?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#establish-baseline">
-   41.84.7. Establish Baseline
+   40.84.7. Establish Baseline
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#train-a-linear-model">
-   41.84.8. Train a Linear Model
+   40.84.8. Train a Linear Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     41.84.8.1. Solution
+     40.84.8.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#fixing-loss-calculation">
-   41.84.9. Fixing Loss Calculation
+   40.84.9. Fixing Loss Calculation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#train-a-nonlinear-model">
-   41.84.10. Train a Nonlinear Model
+   40.84.10. Train a Nonlinear Model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#adding-a-second-layer">
-   41.84.11. Adding a Second Layer
+   40.84.11. Adding a Second Layer
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#check-for-training-validation-data-skew">
-   41.84.12. Check for Training/Validation Data Skew
+   40.84.12. Check for Training/Validation Data Skew
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#apply-dropout-regularization">
-   41.84.13. Apply Dropout Regularization
+   40.84.13. Apply Dropout Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#check-accuracy-for-data-slices">
-   41.84.14. Check Accuracy for Data Slices
+   40.84.14. Check Accuracy for Data Slices
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#testing-for-anomalous-values">
-   41.84.15. Testing for Anomalous Values
+   40.84.15. Testing for Anomalous Values
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#optional-pass-tests-by-fixing-loss-calculation">
-   41.84.16. Optional: Pass Tests by Fixing Loss Calculation
+   40.84.16. Optional: Pass Tests by Fixing Loss Calculation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   41.84.17. Conclusion
+   40.84.17. Conclusion
   </a>
  </li>
 </ul>
@@ -1812,7 +1812,7 @@ <h2> Contents </h2>
 See the License for the specific language governing permissions and
 limitations under the License.</p>
 </details><section class="tex2jax_ignore mathjax_ignore" id="case-study-debugging-in-classification">
-<h1><span class="section-number">41.84. </span>Case Study: Debugging in Classification<a class="headerlink" href="#case-study-debugging-in-classification" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.84. </span>Case Study: Debugging in Classification<a class="headerlink" href="#case-study-debugging-in-classification" title="Permalink to this headline">#</a></h1>
 <p>This Colab quickly demonstrates a few concepts related to debugging classification models. You will explore potential problems in implementing these tasks:</p>
 <ul class="simple">
 <li><p>Calculating loss for classification problems.</p></li>
@@ -1822,7 +1822,7 @@ <h1><span class="section-number">41.84. </span>Case Study: Debugging in Classifi
 </ul>
 <p>Please <strong>make a copy</strong> of this Colab before running it. Click on <em>File</em>, and then click on <em>Save a copy in Drive</em>.</p>
 <section id="load-mnist-data">
-<h2><span class="section-number">41.84.1. </span>Load MNIST Data<a class="headerlink" href="#load-mnist-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.1. </span>Load MNIST Data<a class="headerlink" href="#load-mnist-data" title="Permalink to this headline">#</a></h2>
 <p>MNIST is a dataset of images of the numbers 0 to 9. The problem is to classify the images as numbers. Setup libraries and load the MNIST dataset. Display the first few rows to verify that the data loaded. You’ll explore the data format after the data loads.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1860,7 +1860,7 @@ <h2><span class="section-number">41.84.1. </span>Load MNIST Data<a class="header
 </div>
 </section>
 <section id="understanding-the-data-format">
-<h2><span class="section-number">41.84.2. </span>Understanding the Data Format<a class="headerlink" href="#understanding-the-data-format" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.2. </span>Understanding the Data Format<a class="headerlink" href="#understanding-the-data-format" title="Permalink to this headline">#</a></h2>
 <p>Each row represents one labeled example. Column 0 represents the label that a human rater has assigned for one handwritten digit. For example, if Column 0 contains ‘6’, then a human rater interpreted the handwritten character as the digit ‘6’.  The ten digits 0-9 are each represented, with a unique class label for each possible digit. Thus, this is a multi-class classification problem with 10 classes.</p>
 <p><img alt="img" src="https://www.tensorflow.org/versions/r0.11/images/MNIST-Matrix.png" /></p>
 <p>Columns 1 through 784 contain the feature values, one per pixel for the 28×28=784 pixel values. The pixel values are on a gray scale in which 0 represents white, 255 represents black, and values between 0 and 255 represent shades of gray. Most of the pixel values are 0; you may want to take a minute to confirm that they aren’t all 0.  Modify the form below and run the code to view data for a given example.</p>
@@ -1875,7 +1875,7 @@ <h2><span class="section-number">41.84.2. </span>Understanding the Data Format<a
 </div>
 </section>
 <section id="do-you-have-imbalanced-classes">
-<h2><span class="section-number">41.84.3. </span>Do you have Imbalanced Classes?<a class="headerlink" href="#do-you-have-imbalanced-classes" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.3. </span>Do you have Imbalanced Classes?<a class="headerlink" href="#do-you-have-imbalanced-classes" title="Permalink to this headline">#</a></h2>
 <p>As we read in the course, imbalanced classes make classification harder. Let’s look at the distribution of classes. Do you have imbalanced classes?</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1892,7 +1892,7 @@ <h2><span class="section-number">41.84.3. </span>Do you have Imbalanced Classes?
 <p>The preceding graph shows that the 10 classes are roughly equally represented.</p>
 </section>
 <section id="shuffle-and-split-dataset">
-<h2><span class="section-number">41.84.4. </span>Shuffle and Split Dataset<a class="headerlink" href="#shuffle-and-split-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.4. </span>Shuffle and Split Dataset<a class="headerlink" href="#shuffle-and-split-dataset" title="Permalink to this headline">#</a></h2>
 <p>As part of <a class="reference external" href="https://developers.google.com/machine-learning/testing-debugging/common/data-errors">Data Debugging</a> best practices, ensure your splits are statistically equivalent by shuffling your data to remove any pre-existing order.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1908,7 +1908,7 @@ <h2><span class="section-number">41.84.4. </span>Shuffle and Split Dataset<a cla
 </div>
 </section>
 <section id="process-data">
-<h2><span class="section-number">41.84.5. </span>Process Data<a class="headerlink" href="#process-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.5. </span>Process Data<a class="headerlink" href="#process-data" title="Permalink to this headline">#</a></h2>
 <p>Scale the data values to <code class="docutils literal notranslate"><span class="pre">[0,1]</span></code> since the values are bounded to <code class="docutils literal notranslate"><span class="pre">[0,255]</span></code> and do not contain outliers. Then check that the scaled data values are as expected by generating summary statistics using the <code class="docutils literal notranslate"><span class="pre">DataFrame.describe()</span></code> function.</p>
 <p>Run the following cell to scale data and generate statistics. This cell takes a few minutes to run.</p>
 <div class="cell docutils container">
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 </div>
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-<h3><span class="section-number">41.84.5.1. </span>Solution<a class="headerlink" href="#solution" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.84.5.1. </span>Solution<a class="headerlink" href="#solution" title="Permalink to this headline">#</a></h3>
 <p>Start exploring your data by generating a high-level summary using <code class="docutils literal notranslate"><span class="pre">Dataframe.describe()</span></code>.</p>
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@@ -1980,11 +1980,11 @@ <h3><span class="section-number">41.84.5.1. </span>Solution<a class="headerlink"
 </section>
 </section>
 <section id="remove-all-zero-features">
-<h2><span class="section-number">41.84.6. </span>Remove All-Zero Features?<a class="headerlink" href="#remove-all-zero-features" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.6. </span>Remove All-Zero Features?<a class="headerlink" href="#remove-all-zero-features" title="Permalink to this headline">#</a></h2>
 <p>You might think that getting NaNs and discovering that some features were all-zero is good luck because those features can be discarded. However, your training data and validation data might have different all-zero features. Since you should not use validation data to make modeling decisions, you cannot remove only those features that are all-zero in both. Furthermore, data in the future might have different characteristics. There are pros and cons in either case. This Colab keeps the features since reducing the feature set is not a concern.</p>
 </section>
 <section id="establish-baseline">
-<h2><span class="section-number">41.84.7. </span>Establish Baseline<a class="headerlink" href="#establish-baseline" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.7. </span>Establish Baseline<a class="headerlink" href="#establish-baseline" title="Permalink to this headline">#</a></h2>
 <p>Following development best practices, you should establish a baseline. The simplest baseline is predicting the most common class. You saw that the most common class is 1. Let’s check the accuracy when always predicting 1.</p>
 <div class="cell docutils container">
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@@ -1996,7 +1996,7 @@ <h2><span class="section-number">41.84.7. </span>Establish Baseline<a class="hea
 <p>Your baseline accuracy is about 11%. Should be easy to beat, right?</p>
 </section>
 <section id="train-a-linear-model">
-<h2><span class="section-number">41.84.8. </span>Train a Linear Model<a class="headerlink" href="#train-a-linear-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.8. </span>Train a Linear Model<a class="headerlink" href="#train-a-linear-model" title="Permalink to this headline">#</a></h2>
 <p>Let’s start nice and easy with a linear model. All we need is an accuracy &gt; 11%.</p>
 <p>First, let’s define a function to plot our loss and accuracy curves. The function will also print the final loss and accuracy. Instead of using <code class="docutils literal notranslate"><span class="pre">verbose=1</span></code>, you can call the function.</p>
 <div class="cell docutils container">
@@ -2058,12 +2058,12 @@ <h2><span class="section-number">41.84.8. </span>Train a Linear Model<a class="h
 <p>Wow, that accuracy is terrible! What could the cause be?</p>
 <p>Hint: You followed the same procedure as for the previous regression problem. Do you need an adaptation for a classification problem? Experiment with the code above or skip to the solution below.</p>
 <section id="id1">
-<h3><span class="section-number">41.84.8.1. </span>Solution<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.84.8.1. </span>Solution<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <p>In regression, the last layer uses a linear activation function. In classification, the last layer cannot use a linear transform. Instead, one option is a softmax transform. Furthermore, in regression, the loss is calculated using MSE while in classification, loss is calculated using crossentropy. Before running your model, if you wrote a test to validate the output values, your test would detect the anomalous output. You’ll look at such a test later. Move onto the next section to fix the loss calculation.</p>
 </section>
 </section>
 <section id="fixing-loss-calculation">
-<h2><span class="section-number">41.84.9. </span>Fixing Loss Calculation<a class="headerlink" href="#fixing-loss-calculation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.9. </span>Fixing Loss Calculation<a class="headerlink" href="#fixing-loss-calculation" title="Permalink to this headline">#</a></h2>
 <p>Since your labels are integers instead of one-hot encodings, use <code class="docutils literal notranslate"><span class="pre">sparse_categorical_crossentropy</span></code> instead of <code class="docutils literal notranslate"><span class="pre">categorical_crossentropy</span></code> so that you avoid converting the integers to one-hot encoding.</p>
 <p>Retrain the model with the new loss calculation by running the following cell. Look through the code to note the changes. What do you think of the result?</p>
 <div class="cell docutils container">
@@ -2090,7 +2090,7 @@ <h2><span class="section-number">41.84.9. </span>Fixing Loss Calculation<a class
 <p>Your loss curves are much better. Your accuracy has improved too. You’re on the right track.</p>
 </section>
 <section id="train-a-nonlinear-model">
-<h2><span class="section-number">41.84.10. </span>Train a Nonlinear Model<a class="headerlink" href="#train-a-nonlinear-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.10. </span>Train a Nonlinear Model<a class="headerlink" href="#train-a-nonlinear-model" title="Permalink to this headline">#</a></h2>
 <p>Switch to a nonlinear model by modifying the code below to use relu activation functions instead of linear activation functions. Run the code. What do you observe?</p>
 <div class="cell docutils container">
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@@ -2115,7 +2115,7 @@ <h2><span class="section-number">41.84.10. </span>Train a Nonlinear Model<a clas
 <p>The quality of the nonlinear model is significantly better than of the linear model. Progress! Move onto the next section.</p>
 </section>
 <section id="adding-a-second-layer">
-<h2><span class="section-number">41.84.11. </span>Adding a Second Layer<a class="headerlink" href="#adding-a-second-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.11. </span>Adding a Second Layer<a class="headerlink" href="#adding-a-second-layer" title="Permalink to this headline">#</a></h2>
 <p>Increasing the model’s capacity significantly improved your results. Perhaps you can continue this strategy by adding a second relu layer. Run the following code cell to train the model with another relu layer.</p>
 <div class="cell docutils container">
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@@ -2142,7 +2142,7 @@ <h2><span class="section-number">41.84.11. </span>Adding a Second Layer<a class=
 <p>Does your model begin to overfit the training data if you train for long enough? (Your model starts overfitting training data at the point when your validation loss starts increasing.)</p>
 </section>
 <section id="check-for-training-validation-data-skew">
-<h2><span class="section-number">41.84.12. </span>Check for Training/Validation Data Skew<a class="headerlink" href="#check-for-training-validation-data-skew" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.12. </span>Check for Training/Validation Data Skew<a class="headerlink" href="#check-for-training-validation-data-skew" title="Permalink to this headline">#</a></h2>
 <p>Our validation accuracy is a little worse than our training accuracy. While this result is always expected, you should check for typical errors. The commonest cause is having different distributions of data and labels in training and validation. Confirm that the distribution of classes in training and validation data is similar.</p>
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@@ -2161,7 +2161,7 @@ <h2><span class="section-number">41.84.12. </span>Check for Training/Validation
 </div>
 </section>
 <section id="apply-dropout-regularization">
-<h2><span class="section-number">41.84.13. </span>Apply Dropout Regularization<a class="headerlink" href="#apply-dropout-regularization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.13. </span>Apply Dropout Regularization<a class="headerlink" href="#apply-dropout-regularization" title="Permalink to this headline">#</a></h2>
 <p>Dropout regularization is a common regularization method that removes a random selection of a fixed number of units in a network layer for a single gradient step. Typically, dropout will improve generalization at a dropout rate of between 10% and 50% of neurons.</p>
 <p>Try to reduce the divergence between training and validation loss by using dropout regularization with values between 0.1 and 0.5. Dropout does not improve the results in this case. However, at a dropout of 0.5, the difference in loss decreases, though both training and validation loss decrease in absolute terms.</p>
 <div class="cell docutils container">
@@ -2204,7 +2204,7 @@ <h2><span class="section-number">41.84.13. </span>Apply Dropout Regularization<a
 0.5 | 0.97 | 0.94</p>
 </section>
 <section id="check-accuracy-for-data-slices">
-<h2><span class="section-number">41.84.14. </span>Check Accuracy for Data Slices<a class="headerlink" href="#check-accuracy-for-data-slices" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.14. </span>Check Accuracy for Data Slices<a class="headerlink" href="#check-accuracy-for-data-slices" title="Permalink to this headline">#</a></h2>
 <p>For classification problems, you should always check the metrics by class to ensure your model predicts well across all classes. Check accuracy on the 10 classes by running the next cell by using the function <code class="docutils literal notranslate"><span class="pre">sklearn.metrics.classification_report</span></code> from the scikit-learn library. In the output, the rows with indices 0 to 9 correspond to the classes for the labels 0 to 9. The columns “Precision”, “Recall”, and “<a class="reference external" href="https://en.wikipedia.org/wiki/F1_score">F1-Score</a>” correspond to the respective classification metrics for each class. “Support” is the number of examples for the class in question. For example, for the label “4”, when predicting on 464 examples labelled “4”, the model has a precision of 0.98, a recall of 0.97, and a F1 score of 0.98.</p>
 <p>The classification metrics are very uniform across all classes, which is perfect. In your classification problem, in case any metric is lower for a class, then you should investigate why the model has lower-quality predictions for that class.</p>
 <div class="cell docutils container">
@@ -2218,7 +2218,7 @@ <h2><span class="section-number">41.84.14. </span>Check Accuracy for Data Slices
 </div>
 </section>
 <section id="testing-for-anomalous-values">
-<h2><span class="section-number">41.84.15. </span>Testing for Anomalous Values<a class="headerlink" href="#testing-for-anomalous-values" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.15. </span>Testing for Anomalous Values<a class="headerlink" href="#testing-for-anomalous-values" title="Permalink to this headline">#</a></h2>
 <p>In the section <a class="reference external" href="https://colab.corp.google.com/google_src/cloud/karangill/mlcc/google3/engedu/ml/capitalg/colab/testing_debugging_classification.ipynb#scrollTo=B6AOgLcC5nwp">Train a Linear Model</a>, you debugged an incorrect calculation of loss. Before running your model, if you wrote a test to validate the output values, your test would detect the anomalous output. For example, you could test whether the distribution of predicted labels on the training dataset is similar to the actual distribution of training labels. A simple statistical implementation of this concept is to compare the standard deviation and mean of the predicted and actual labels.</p>
 <p>First, check the standard deviation and mean of the actual labels.</p>
 <div class="cell docutils container">
@@ -2297,7 +2297,7 @@ <h2><span class="section-number">41.84.15. </span>Testing for Anomalous Values<a
 <p>Given equally represented classes, the predicted labels are clearly skewed. From this plot, a possible explanation is that your loss calculation does not appear to be weighting all classes equally. This anomaly is a hint that your loss calculation is incorrect.</p>
 </section>
 <section id="optional-pass-tests-by-fixing-loss-calculation">
-<h2><span class="section-number">41.84.16. </span>Optional: Pass Tests by Fixing Loss Calculation<a class="headerlink" href="#optional-pass-tests-by-fixing-loss-calculation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.16. </span>Optional: Pass Tests by Fixing Loss Calculation<a class="headerlink" href="#optional-pass-tests-by-fixing-loss-calculation" title="Permalink to this headline">#</a></h2>
 <p>As an optional exercise, you can attempt to pass the tests by fixing the loss calculation. You will need to:</p>
 <ol class="simple">
 <li><p>Fix the loss calculation by setting the output layer’s activation to <code class="docutils literal notranslate"><span class="pre">softmax</span></code>.</p></li>
@@ -2307,7 +2307,7 @@ <h2><span class="section-number">41.84.16. </span>Optional: Pass Tests by Fixing
 </ol>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">41.84.17. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.84.17. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>The Colab demonstrated the following principles:</p>
 <ul class="simple">
 <li><p>Calculate loss correctly for your problem.</p></li>
@@ -2356,13 +2356,13 @@ <h2><span class="section-number">41.84.17. </span>Conclusion<a class="headerlink
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+            <p class="prev-next-title"><span class="section-number">40.83. </span>Counterintuitive Challenges in ML Debugging</p>
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+     40.3. First assignment
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+     40.5. Project Plan​ Template
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-     41.6. Python programming introduction
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-     41.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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-     41.10. Data Science scenarios
+     40.10. Data Science scenarios
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-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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-     41.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.15. Dive into the beehive
+     40.15. Dive into the beehive
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
+     40.17. Classifying datasets
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-     41.18. Small diabetes study
+     40.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
+     40.20. Displaying airport data
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-     41.21. Soda profits
+     40.21. Soda profits
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-     41.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
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-     41.23. Estimation of COVID-19 pandemic
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-     41.24. Data processing in Python
+     40.24. Data processing in Python
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+     40.26. Data preparation
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-     41.27. Analyzing data
+     40.27. Analyzing data
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-     41.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
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-     41.29. Matplotlib applied
+     40.29. Matplotlib applied
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-     41.35. Tell a story
+     40.35. Tell a story
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-     41.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
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-     41.37. Exploring for answers
+     40.37. Exploring for answers
     </a>
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-     41.38. Market research
+     40.38. Market research
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-     41.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
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-     41.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
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    <li class="toctree-l2">
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
    <li class="toctree-l2">
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-     41.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
     </a>
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     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#use-a-nonlinear-model">
-   41.85.10. Use a Nonlinear Model
+   40.85.10. Use a Nonlinear Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id3">
-     41.85.10.1. Solution
+     40.85.10.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#optimize-your-model">
-   41.85.11. Optimize Your Model
+   40.85.11. Optimize Your Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id4">
-     41.85.11.1. Hint
+     40.85.11.1. Hint
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id5">
-     41.85.11.2. Solution
+     40.85.11.2. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#check-for-implementation-bugs-using-reduced-dataset">
-   41.85.12. Check for Implementation Bugs using Reduced Dataset
+   40.85.12. Check for Implementation Bugs using Reduced Dataset
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id6">
-     41.85.12.1. Solution
+     40.85.12.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#trying-a-very-complex-model">
-   41.85.13. Trying a Very Complex Model
+   40.85.13. Trying a Very Complex Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#results">
-     41.85.13.1. Results
+     40.85.13.1. Results
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   41.85.14. Conclusion
+   40.85.14. Conclusion
   </a>
  </li>
 </ul>
@@ -1723,131 +1723,131 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#setup">
-   41.85.1. Setup
+   40.85.1. Setup
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#check-correlation-matrix">
-   41.85.2. Check Correlation Matrix
+   40.85.2. Check Correlation Matrix
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#validate-input-data-against-data-schema">
-   41.85.3. Validate Input Data against Data Schema
+   40.85.3. Validate Input Data against Data Schema
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#solution">
-     41.85.3.1. Solution
+     40.85.3.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#split-and-normalize-data">
-   41.85.4. Split and Normalize Data
+   40.85.4. Split and Normalize Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#test-engineered-data">
-   41.85.5. Test Engineered Data
+   40.85.5. Test Engineered Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#check-splits-for-statistical-equivalence">
-   41.85.6. Check Splits for Statistical Equivalence
+   40.85.6. Check Splits for Statistical Equivalence
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#establish-a-baseline">
-   41.85.7. Establish a Baseline
+   40.85.7. Establish a Baseline
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#linear-model">
-   41.85.8. Linear Model
+   40.85.8. Linear Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#hint">
-     41.85.8.1. Hint
+     40.85.8.1. Hint
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     41.85.8.2. Solution
+     40.85.8.2. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#add-feature-to-linear-model">
-   41.85.9. Add Feature to Linear Model
+   40.85.9. Add Feature to Linear Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id2">
-     41.85.9.1. Solution
+     40.85.9.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#use-a-nonlinear-model">
-   41.85.10. Use a Nonlinear Model
+   40.85.10. Use a Nonlinear Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id3">
-     41.85.10.1. Solution
+     40.85.10.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#optimize-your-model">
-   41.85.11. Optimize Your Model
+   40.85.11. Optimize Your Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id4">
-     41.85.11.1. Hint
+     40.85.11.1. Hint
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id5">
-     41.85.11.2. Solution
+     40.85.11.2. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#check-for-implementation-bugs-using-reduced-dataset">
-   41.85.12. Check for Implementation Bugs using Reduced Dataset
+   40.85.12. Check for Implementation Bugs using Reduced Dataset
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id6">
-     41.85.12.1. Solution
+     40.85.12.1. Solution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#trying-a-very-complex-model">
-   41.85.13. Trying a Very Complex Model
+   40.85.13. Trying a Very Complex Model
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#results">
-     41.85.13.1. Results
+     40.85.13.1. Results
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   41.85.14. Conclusion
+   40.85.14. Conclusion
   </a>
  </li>
 </ul>
@@ -1872,7 +1872,7 @@ <h2> Contents </h2>
 See the License for the specific language governing permissions and
 limitations under the License.</p>
 </details><section class="tex2jax_ignore mathjax_ignore" id="case-study-debugging-in-regression">
-<h1><span class="section-number">41.85. </span>Case Study: Debugging in Regression<a class="headerlink" href="#case-study-debugging-in-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.85. </span>Case Study: Debugging in Regression<a class="headerlink" href="#case-study-debugging-in-regression" title="Permalink to this headline">#</a></h1>
 <p>In this Colab, you will learn how to debug a regression problem through a case study. You will:</p>
 <ol class="simple">
 <li><p>Set up the problem.</p></li>
@@ -1883,7 +1883,7 @@ <h1><span class="section-number">41.85. </span>Case Study: Debugging in Regressi
 <li><p>Debug your chosen model.</p></li>
 </ol>
 <section id="setup">
-<h2><span class="section-number">41.85.1. </span>Setup<a class="headerlink" href="#setup" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.1. </span>Setup<a class="headerlink" href="#setup" title="Permalink to this headline">#</a></h2>
 <p>This Colab uses the wine quality dataset&lt;sup#[1]&lt;/sup#, which is hosted at <a class="reference external" href="https://archive.ics.uci.edu/ml/datasets/wine+quality">UCI</a>. This dataset contains data on the physicochemical properties of wine along with wine quality ratings. The problem is to predict wine quality (0-10) from physicochemical properties.</p>
 <p>Please <strong>make a copy</strong> of this Colab before running it. Click on <em>File</em>, and then click on <em>Save a copy in Drive</em>.</p>
 <p>&lt;small#[1] Modeling wine preferences by data mining from physicochemical properties. P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Decision Support Systems, Elsevier, 47(4):547-553, 2009.&lt;/small#</p>
@@ -1920,7 +1920,7 @@ <h2><span class="section-number">41.85.1. </span>Setup<a class="headerlink" href
 </div>
 </section>
 <section id="check-correlation-matrix">
-<h2><span class="section-number">41.85.2. </span>Check Correlation Matrix<a class="headerlink" href="#check-correlation-matrix" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.2. </span>Check Correlation Matrix<a class="headerlink" href="#check-correlation-matrix" title="Permalink to this headline">#</a></h2>
 <p>Before developing your ML model, you need to select features. To find informative features, check the correlation matrix by running the following cell. Which features are informative?</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1934,7 +1934,7 @@ <h2><span class="section-number">41.85.2. </span>Check Correlation Matrix<a clas
 <p><code class="docutils literal notranslate"><span class="pre">alcohol</span></code> is most highly correlated with <code class="docutils literal notranslate"><span class="pre">quality</span></code>. Looking for other informative features, notice that <code class="docutils literal notranslate"><span class="pre">volatile</span> <span class="pre">acidity</span></code> correlates with <code class="docutils literal notranslate"><span class="pre">quality</span></code> but not with <code class="docutils literal notranslate"><span class="pre">alcohol</span></code>, making it a good second feature. Remember that a correlation matrix is not helpful if predictive signals are encoded in combinations of features.</p>
 </section>
 <section id="validate-input-data-against-data-schema">
-<h2><span class="section-number">41.85.3. </span>Validate Input Data against Data Schema<a class="headerlink" href="#validate-input-data-against-data-schema" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.3. </span>Validate Input Data against Data Schema<a class="headerlink" href="#validate-input-data-against-data-schema" title="Permalink to this headline">#</a></h2>
 <p>Before processing your data, you should validate the data against a data schema as described in <a class="reference external" href="https://developers.google.com/machine-learning/testing-debugging/common/data-errors">Data and Feature Debugging</a>.</p>
 <p>First, define a function that validates data against a schema.</p>
 <div class="cell docutils container">
@@ -2025,7 +2025,7 @@ <h2><span class="section-number">41.85.3. </span>Validate Input Data against Dat
 </div>
 </div>
 <section id="solution">
-<h3><span class="section-number">41.85.3.1. </span>Solution<a class="headerlink" href="#solution" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.85.3.1. </span>Solution<a class="headerlink" href="#solution" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">wine_schema</span> <span class="o">=</span> <span class="p">{</span>
@@ -2061,7 +2061,7 @@ <h3><span class="section-number">41.85.3.1. </span>Solution<a class="headerlink"
 </section>
 </section>
 <section id="split-and-normalize-data">
-<h2><span class="section-number">41.85.4. </span>Split and Normalize Data<a class="headerlink" href="#split-and-normalize-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.4. </span>Split and Normalize Data<a class="headerlink" href="#split-and-normalize-data" title="Permalink to this headline">#</a></h2>
 <p>Split the dataset into data and labels.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2089,7 +2089,7 @@ <h2><span class="section-number">41.85.4. </span>Split and Normalize Data<a clas
 </div>
 </section>
 <section id="test-engineered-data">
-<h2><span class="section-number">41.85.5. </span>Test Engineered Data<a class="headerlink" href="#test-engineered-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.5. </span>Test Engineered Data<a class="headerlink" href="#test-engineered-data" title="Permalink to this headline">#</a></h2>
 <p>After normalizing your data, you should test your engineered data for errors as described in <a class="reference external" href="https://developers.google.com/machine-learning/testing-debugging/common/data-errors">Data and Feature Debugging</a>. In this section, you will test that engineering data:</p>
 <ul class="simple">
 <li><p>Has the expected number of rows and columns.</p></li>
@@ -2136,7 +2136,7 @@ <h2><span class="section-number">41.85.5. </span>Test Engineered Data<a class="h
 </div>
 </section>
 <section id="check-splits-for-statistical-equivalence">
-<h2><span class="section-number">41.85.6. </span>Check Splits for Statistical Equivalence<a class="headerlink" href="#check-splits-for-statistical-equivalence" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.6. </span>Check Splits for Statistical Equivalence<a class="headerlink" href="#check-splits-for-statistical-equivalence" title="Permalink to this headline">#</a></h2>
 <p>As described in the <a class="reference external" href="https://developers.google.com/machine-learning/testing-debugging/common/data-errors">Data Debugging</a> guidelines, before developing your model, you should check that your training and validation splits are equally representative. Assuming a training:validation split of 80:20, compare the mean and the standard deviation of the splits by running the next two code cells. Note that this comparison is not a rigorous test for statistical equivalence but simply a quick and dirty comparison of the splits.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2171,7 +2171,7 @@ <h2><span class="section-number">41.85.6. </span>Check Splits for Statistical Eq
 <p>Ensuring your splits are statistically equivalent is a good development practice. In general, following good development practices will simplify your model debugging. To learn about testing for statistical equivalence, see <a class="reference external" href="https://journals.sagepub.com/doi/10.1177/1948550617697177">Equivalence Tests Lakens, D.</a>.</p>
 </section>
 <section id="establish-a-baseline">
-<h2><span class="section-number">41.85.7. </span>Establish a Baseline<a class="headerlink" href="#establish-a-baseline" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.7. </span>Establish a Baseline<a class="headerlink" href="#establish-a-baseline" title="Permalink to this headline">#</a></h2>
 <p>For a regression problem, the simplest baseline to predict the average value. Run the following code to calculate the mean-squared error (MSE) loss on the training split using the average value as a baseline. Your loss is approximately 0.75. Any model should beat this loss to justify its use.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2184,7 +2184,7 @@ <h2><span class="section-number">41.85.7. </span>Establish a Baseline<a class="h
 </div>
 </section>
 <section id="linear-model">
-<h2><span class="section-number">41.85.8. </span>Linear Model<a class="headerlink" href="#linear-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.8. </span>Linear Model<a class="headerlink" href="#linear-model" title="Permalink to this headline">#</a></h2>
 <p>Following good ML dev practice, let’s start with a linear model that uses the most informative feature from the correlation matrix: <code class="docutils literal notranslate"><span class="pre">alcohol</span></code>. Even if this model performs badly, we can still use it as a baseline. This model should beat our previous baseline’s MSE of 0.75.</p>
 <p>First, let’s define a function to plot our loss and accuracy curves. The function will also print the final loss and accuracy. Instead of using <code class="docutils literal notranslate"><span class="pre">verbose=1</span></code>, you can call the function.</p>
 <div class="cell docutils container">
@@ -2238,7 +2238,7 @@ <h2><span class="section-number">41.85.8. </span>Linear Model<a class="headerlin
 </div>
 </div>
 <section id="hint">
-<h3><span class="section-number">41.85.8.1. </span>Hint<a class="headerlink" href="#hint" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.85.8.1. </span>Hint<a class="headerlink" href="#hint" title="Permalink to this headline">#</a></h3>
 <p>The loss decreases but very slowly. Possible fixes are:</p>
 <ul class="simple">
 <li><p>Increase number of epochs.</p></li>
@@ -2248,7 +2248,7 @@ <h3><span class="section-number">41.85.8.1. </span>Hint<a class="headerlink" hre
 <p>Play with these three parameters in the code above to decrease the loss.</p>
 </section>
 <section id="id1">
-<h3><span class="section-number">41.85.8.2. </span>Solution<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.85.8.2. </span>Solution<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <p>Run the following code cell to train the model using a reduced batch size of 100. Reducing the batch size leads to a greater decrease in loss per epoch. The minimum achievable loss is about 0.64. This is a significant increase over our baseline of 0.75.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2277,7 +2277,7 @@ <h3><span class="section-number">41.85.8.2. </span>Solution<a class="headerlink"
 </section>
 </section>
 <section id="add-feature-to-linear-model">
-<h2><span class="section-number">41.85.9. </span>Add Feature to Linear Model<a class="headerlink" href="#add-feature-to-linear-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.9. </span>Add Feature to Linear Model<a class="headerlink" href="#add-feature-to-linear-model" title="Permalink to this headline">#</a></h2>
 <p>Try adding a feature to the linear model. Since you need to combine the two features into one prediction for regression, you’ll also need to add a second layer. Modify the code below to implement the following changes:</p>
 <ol class="simple">
 <li><p>Add <code class="docutils literal notranslate"><span class="pre">'volatile</span> <span class="pre">acidity'</span></code> to the features in <code class="docutils literal notranslate"><span class="pre">wineFeaturesSimple</span></code>.</p></li>
@@ -2312,7 +2312,7 @@ <h2><span class="section-number">41.85.9. </span>Add Feature to Linear Model<a c
 </div>
 </div>
 <section id="id2">
-<h3><span class="section-number">41.85.9.1. </span>Solution<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.85.9.1. </span>Solution<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <p>Run the following code to add the second feature and the second layer. The training loss is about 0.59, a small decrease from the previous loss of 0.64.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2343,7 +2343,7 @@ <h3><span class="section-number">41.85.9.1. </span>Solution<a class="headerlink"
 </section>
 </section>
 <section id="use-a-nonlinear-model">
-<h2><span class="section-number">41.85.10. </span>Use a Nonlinear Model<a class="headerlink" href="#use-a-nonlinear-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.10. </span>Use a Nonlinear Model<a class="headerlink" href="#use-a-nonlinear-model" title="Permalink to this headline">#</a></h2>
 <p>Let’s try a nonlinear model. Modify the code below to make the following changes:</p>
 <ol class="simple">
 <li><p>Change the first layer to use <code class="docutils literal notranslate"><span class="pre">relu</span></code>. (Output layer stays linear since this is a regression problem.)</p></li>
@@ -2375,7 +2375,7 @@ <h2><span class="section-number">41.85.10. </span>Use a Nonlinear Model<a class=
 </div>
 </div>
 <section id="id3">
-<h3><span class="section-number">41.85.10.1. </span>Solution<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.85.10.1. </span>Solution<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <p>Run the following cell to use a <code class="docutils literal notranslate"><span class="pre">relu</span></code> activation in your first hidden layer. Your loss stays about the same, perhaps declining negligibly to 0.58.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2404,7 +2404,7 @@ <h3><span class="section-number">41.85.10.1. </span>Solution<a class="headerlink
 </section>
 </section>
 <section id="optimize-your-model">
-<h2><span class="section-number">41.85.11. </span>Optimize Your Model<a class="headerlink" href="#optimize-your-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.11. </span>Optimize Your Model<a class="headerlink" href="#optimize-your-model" title="Permalink to this headline">#</a></h2>
 <p>We have two features with one hidden layer but didn’t see an improvement. At this point, it’s tempting to use all your features with a high-capacity network. However, you must resist the temptation. Instead, follow the guidance in <a class="reference external" href="https://developers.google.com/machine-learning/testing-debugging/common/optimization">Model Optimization</a> to improve model performance. For a hint and for a discussion, see the following text sections.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2434,11 +2434,11 @@ <h2><span class="section-number">41.85.11. </span>Optimize Your Model<a class="h
 </div>
 </div>
 <section id="id4">
-<h3><span class="section-number">41.85.11.1. </span>Hint<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.85.11.1. </span>Hint<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
 <p>You can try to reduce loss by adding features, adding layers, or playing with the hyperparameters. Before adding more features, check the correlation matrix. Don’t expect your loss to decrease by much. Sadly, that is a common experience in machine learning!</p>
 </section>
 <section id="id5">
-<h3><span class="section-number">41.85.11.2. </span>Solution<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.85.11.2. </span>Solution<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
 <p>Run the following code to:</p>
 <ul class="simple">
 <li><p>Add the features chlorides and density.</p></li>
@@ -2476,7 +2476,7 @@ <h3><span class="section-number">41.85.11.2. </span>Solution<a class="headerlink
 </section>
 </section>
 <section id="check-for-implementation-bugs-using-reduced-dataset">
-<h2><span class="section-number">41.85.12. </span>Check for Implementation Bugs using Reduced Dataset<a class="headerlink" href="#check-for-implementation-bugs-using-reduced-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.12. </span>Check for Implementation Bugs using Reduced Dataset<a class="headerlink" href="#check-for-implementation-bugs-using-reduced-dataset" title="Permalink to this headline">#</a></h2>
 <p>Your loss isn’t decreasing by much. Perhaps your model has an implementation bug. From the <a class="reference external" href="https://developers.google.com/machine-learning/testing-debugging/common/model-errors">Model Debugging</a> guidelines, a quick test for implementation bugs is to obtain a low loss on a reduced dataset of, say, 10 examples. Remember, passing this test does not validate your modeling approach but only checks for basic implementation bugs. In your ML problem, if your model passes this test, then continue debugging your model to train on your full dataset.</p>
 <p>In the following code, experiment with the learning rate, batch size, and number of epochs. Can you reach a low loss? Choose hyperparameter values that let you iterate quickly.</p>
 <div class="cell docutils container">
@@ -2508,7 +2508,7 @@ <h2><span class="section-number">41.85.12. </span>Check for Implementation Bugs
 </div>
 </div>
 <section id="id6">
-<h3><span class="section-number">41.85.12.1. </span>Solution<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.85.12.1. </span>Solution<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h3>
 <p>Run the following code cell to train the model using these hyperparameter values:</p>
 <ul class="simple">
 <li><p>learning rate = 0.01</p></li>
@@ -2546,7 +2546,7 @@ <h3><span class="section-number">41.85.12.1. </span>Solution<a class="headerlink
 </section>
 </section>
 <section id="trying-a-very-complex-model">
-<h2><span class="section-number">41.85.13. </span>Trying a Very Complex Model<a class="headerlink" href="#trying-a-very-complex-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.13. </span>Trying a Very Complex Model<a class="headerlink" href="#trying-a-very-complex-model" title="Permalink to this headline">#</a></h2>
 <p>Let’s go all in and use a very complex model with all the features. For science! And to satisfy ourselves that a simple model is indeed better. Let’s use all 11 features with 3 fully-connected relu layers and a final linear layer. The next cell takes a while to run. Skip to the results in the cell after if you like.</p>
 <div class="cell docutils container">
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@@ -2571,14 +2571,14 @@ <h2><span class="section-number">41.85.13. </span>Trying a Very Complex Model<a
 </div>
 </div>
 <section id="results">
-<h3><span class="section-number">41.85.13.1. </span>Results<a class="headerlink" href="#results" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.85.13.1. </span>Results<a class="headerlink" href="#results" title="Permalink to this headline">#</a></h3>
 <p>If you train for long enough, the minimum achievable MSE is around 0.52, which is a decrease of 0.04 from the previous best loss of 0.56. This decrease probably isn’t worth the performance and complexity cost of using all your features and a deeper network. However, that decision depends on the objectives you’re optimizing for.</p>
 <p>If you train the model long enough, the validation loss actually starts increasing while training loss continues to decrease. This divergence in loss curves means your model is overfitting. The overfitting results from the closer fit that your very complex model can learn. Stick with the simpler model. You’ll be happier and live longer!</p>
 <p>If you do want to optimize your loss, then play with the model to find the minimum achievable training loss before overfitting sets in. Try playing with the network parameters in the code cell above to achieve a loss of 0.51. But be warned—optimizing your loss could take a lot of trial and error…</p>
 </section>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">41.85.14. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.85.14. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>This Colab demonstrated the following principles:</p>
 <ul class="simple">
 <li><p>The most important step in machine learning is understanding your data.</p></li>
@@ -2627,13 +2627,13 @@ <h2><span class="section-number">41.85.14. </span>Conclusion<a class="headerlink
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.84. </span>Case Study: Debugging in Classification</p>
+            <p class="prev-next-title"><span class="section-number">40.84. </span>Case Study: Debugging in Classification</p>
         </div>
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-        <p class="prev-next-title"><span class="section-number">41.86. </span>Study the solvers</p>
+        <p class="prev-next-title"><span class="section-number">40.86. </span>Study the solvers</p>
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+    <title>40.75. Beyond random forests: more ensemble models &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="next" title="41.76. Decision trees" href="decision-trees.html" />
-    <link rel="prev" title="41.74. Random forests for classification" href="random-forests-for-classification.html" />
+    <link rel="next" title="40.76. Decision trees" href="decision-trees.html" />
+    <link rel="prev" title="40.74. Random forests for classification" href="random-forests-for-classification.html" />
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-<h1><span class="section-number">41.75. </span>Beyond random forests: more ensemble models<a class="headerlink" href="#beyond-random-forests-more-ensemble-models" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.75. </span>Beyond random forests: more ensemble models<a class="headerlink" href="#beyond-random-forests-more-ensemble-models" title="Permalink to this headline">#</a></h1>
 <p>Ensembles are the combination of different machine learning models, where by averaging the results of all models trained together on the same dataset, the result can be away good than any individual model. These types of models can be used for all kinds of tasks, including classification, regression and detecting anomalies.</p>
 <p>In his book <em><a class="reference external" href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/">Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow</a></em>, <em>Aurelion Geron</em> gave a perfect analogy describing the ensemble learning: “Suppose you pose a complex question to thousands of random people, then aggregate their answers. In many cases you will find that this aggregated answer is better than an expert’s answer. This is called the <em>wisdom of the crowd</em>. Similarly, if you aggregate the predictions of a group of predictors (such as classifiers or regressors), you will often get better predictions than with the best individual predictor. A group of predictors is called an ensemble; thus, this technique is called Ensemble Learning, and an Ensemble Learning algorithm is called an Ensemble method.”</p>
 <p>Random Forests that was used in the previous assignment is an example of ensemble models. It combined different decision trees. Usually, these types of models are classified into three groups that will be covered in this assignment:</p>
@@ -1783,10 +1783,10 @@ <h1><span class="section-number">41.75. </span>Beyond random forests: more ensem
 <p>Most of the above algorithms are implemented in Sklearn, except XGBoost and Stacking which is implemented indirectly.</p>
 <p>Like said in the beginning, these types of models can be used for both regression and classification, but this assignment will consider classification domain and for giving focus on the ensemble models, we will use the same dataset we used previouly, in random forests.</p>
 <section id="ensemble-methods-for-classification">
-<h2><span class="section-number">41.75.1. </span>Ensemble methods for classification<a class="headerlink" href="#ensemble-methods-for-classification" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.75.1. </span>Ensemble methods for classification<a class="headerlink" href="#ensemble-methods-for-classification" title="Permalink to this headline">#</a></h2>
 </section>
 <section id="imports">
-<h2><span class="section-number">41.75.2. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.75.2. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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@@ -1801,7 +1801,7 @@ <h2><span class="section-number">41.75.2. </span>Imports<a class="headerlink" hr
 </div>
 </section>
 <section id="loading-the-data">
-<h2><span class="section-number">41.75.3. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.75.3. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
 <p>In this assignment, we will use Random forests to build a classifier that identify the increase or decrease of the electricity using “the data that was collected from the Australian New South Wales Electricity Market. In this market, prices are not fixed and are affected by demand and supply of the market. They are set every five minutes. Electricity transfers to/from the neighboring state of Victoria were done to alleviate fluctuations.”</p>
 <p>“The dataset contains 45,312 instances dated from 7 May 1996 to 5 December 1998. Each example of the dataset refers to a period of 30 minutes, i.e. there are 48 instances for each time period of one day. Each example on the dataset has 5 fields, the day of week, the time stamp, the New South Wales electricity demand, the Victoria electricity demand, the scheduled electricity transfer between states and the class label. The class label identifies the change of the price (UP or DOWN) in New South Wales relative to a moving average of the last 24 hours (and removes the impact of longer term price trends). Source: <a class="reference external" href="https://www.openml.org/d/151">Open ML electricity</a>.</p>
 <p>Here are the information about the features:</p>
@@ -1856,9 +1856,9 @@ <h2><span class="section-number">41.75.3. </span>Loading the data<a class="heade
 </div>
 </section>
 <section id="tasks-and-roles">
-<h2><span class="section-number">41.75.4. </span>Tasks and roles<a class="headerlink" href="#tasks-and-roles" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.75.4. </span>Tasks and roles<a class="headerlink" href="#tasks-and-roles" title="Permalink to this headline">#</a></h2>
 <section id="task-1-exploratory-data-analysis">
-<h3><span class="section-number">41.75.4.1. </span>Task 1: Exploratory data analysis<a class="headerlink" href="#task-1-exploratory-data-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.75.4.1. </span>Task 1: Exploratory data analysis<a class="headerlink" href="#task-1-exploratory-data-analysis" title="Permalink to this headline">#</a></h3>
 <p>Before doing exploratory analysis, as always, let’s split the data into training and test sets.</p>
 <div class="cell docutils container">
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@@ -1961,7 +1961,7 @@ <h3><span class="section-number">41.75.4.1. </span>Task 1: Exploratory data anal
 <p>In the above correlation matrix, you can see that class feature is not there and this is because it still has categorical values.</p>
 </section>
 <section id="task-2-more-data-exploration">
-<h3><span class="section-number">41.75.4.2. </span>Task 2: More data exploration<a class="headerlink" href="#task-2-more-data-exploration" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.75.4.2. </span>Task 2: More data exploration<a class="headerlink" href="#task-2-more-data-exploration" title="Permalink to this headline">#</a></h3>
 <p>Before preprocessing the data, let’s take a look into specific features.</p>
 <p>Let’s see how many Ups/Downs are in the class feature.</p>
 <div class="cell docutils container">
@@ -2047,7 +2047,7 @@ <h3><span class="section-number">41.75.4.2. </span>Task 2: More data exploration
 </div>
 </section>
 <section id="task-3-data-preprocessing">
-<h3><span class="section-number">41.75.4.3. </span>Task 3: Data preprocessing<a class="headerlink" href="#task-3-data-preprocessing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.75.4.3. </span>Task 3: Data preprocessing<a class="headerlink" href="#task-3-data-preprocessing" title="Permalink to this headline">#</a></h3>
 <p>It is here that we prepare the data to be in the proper format for the machine learning model.</p>
 <p>Let’s encode the categorical feature <code class="docutils literal notranslate"><span class="pre">class</span></code>. But before that, let’s take training input data and labels.</p>
 <div class="cell docutils container">
@@ -2101,9 +2101,9 @@ <h3><span class="section-number">41.75.4.3. </span>Task 3: Data preprocessing<a
 <p>But again if you look at the dat, the <code class="docutils literal notranslate"><span class="pre">day</span></code> feature is not normalized as other features. We can normalize it or leave it but for now let’s go ahead and train the random forests classifier.</p>
 </section>
 <section id="task-4-training-ensemble-classifiers">
-<h3><span class="section-number">41.75.4.4. </span>Task 4: Training ensemble classifiers<a class="headerlink" href="#task-4-training-ensemble-classifiers" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.75.4.4. </span>Task 4: Training ensemble classifiers<a class="headerlink" href="#task-4-training-ensemble-classifiers" title="Permalink to this headline">#</a></h3>
 <section id="voting-classifier">
-<h4><span class="section-number">41.75.4.4.1. </span>4.1: Voting classifier<a class="headerlink" href="#voting-classifier" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.75.4.4.1. </span>4.1: Voting classifier<a class="headerlink" href="#voting-classifier" title="Permalink to this headline">#</a></h4>
 <p>Let’s assume that you have trained 3 different classifiers on the training data but none of these classifiers had an oustanding results.</p>
 <p>The idea of Voting ensemble technique is fairly simple. We can aggregate the results of all those 3 classifiers and the result will be good than any single classifier.</p>
 <p>Let’s train 3 classifiers on the training data and then we will go ahead and aggregate their results.</p>
@@ -2204,7 +2204,7 @@ <h4><span class="section-number">41.75.4.4.1. </span>4.1: Voting classifier<a cl
 <p>As we can see, it slightly outperformed all individual classifiers. It’s small increments in the accuracy but most of the time, it will be always better than any individual classifier.</p>
 </section>
 <section id="bagging-classifier">
-<h4><span class="section-number">41.75.4.4.2. </span>4.2: Bagging classifier<a class="headerlink" href="#bagging-classifier" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.75.4.4.2. </span>4.2: Bagging classifier<a class="headerlink" href="#bagging-classifier" title="Permalink to this headline">#</a></h4>
 <p>Instead of training different algorithms on the same data and average their results as Voting does, Bagging ensemble method train a single or multiple classifier/regressors on different subsets of the training data and aggregate the results on all subsets.</p>
 <p>When this is used with complex models (that overfit data easily) like decision trees, the overfitting can be reduced.</p>
 <p>Let’s use bagging to train 500 decision trees on different subsets of data and then average the predictions on those subsets.</p>
@@ -2237,7 +2237,7 @@ <h4><span class="section-number">41.75.4.4.2. </span>4.2: Bagging classifier<a c
 <p>Wow, this is much better. Bagging ensembles work well and they outperform Voting ensembles a lot. Other remarkable thing about them is that they are able to overcome the overfitting especially when used with decision trees (decision trees tend to overfit easily). Let’s now see other type of ensemble models: Gradient Boosting Classifier.</p>
 </section>
 <section id="gradient-boosting-classifier">
-<h4><span class="section-number">41.75.4.4.3. </span>4.3: Gradient boosting classifier<a class="headerlink" href="#gradient-boosting-classifier" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.75.4.4.3. </span>4.3: Gradient boosting classifier<a class="headerlink" href="#gradient-boosting-classifier" title="Permalink to this headline">#</a></h4>
 <p>Gradient boosting works like bagging methods. The only difference is that instead of training models on subsets of training data, models(decision trees) are trained in a sequence where each tree model takes the error of the previous tree to correct it and the sequence goes on…</p>
 <p>Simply, the initial model is trained on the full data, but then the next models minimizes the previous errors.</p>
 <p>Just like other models, gradient boosting classifier has hyperparameters but the most important ones are number of estimators or trees (<code class="docutils literal notranslate"><span class="pre">n_estimators</span></code>) and learning rate (<code class="docutils literal notranslate"><span class="pre">learning_rate</span></code>).</p>
@@ -2268,7 +2268,7 @@ <h4><span class="section-number">41.75.4.4.3. </span>4.3: Gradient boosting clas
 <p>For classification problems beyond two classes, Scikit-Learn recommend using <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradientBoostingClassifier">Histogram Gradient Boosting Classifier</a> which is a faster version of the Gradient Boosting Classifier. It is inspired by <a class="reference external" href="https://github.com/Microsoft/LightGBM">Microsoft Light Gradient Boosting Machine (LightGBM)</a>. LightGBM is fater, low memory usage, better performance, support distributed training and GPU, and can handle large datasets.</p>
 </section>
 <section id="adaboost-classifier">
-<h4><span class="section-number">41.75.4.4.4. </span>4.4: AdaBoost classifier<a class="headerlink" href="#adaboost-classifier" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.75.4.4.4. </span>4.4: AdaBoost classifier<a class="headerlink" href="#adaboost-classifier" title="Permalink to this headline">#</a></h4>
 <p>AdaBoost is another ensemble model in class of boosting methods. It’s very much like gradient boost but instead of minimizing the error of the consecutive models, it updates the weights.</p>
 <p>So, the first model (decision tree) is trained on the full training data, the next model weights are updated based off the previous weights and so forth.</p>
 <p>The main parameters to tune to make AdaBoost work well are number of estimators and the maximum depth of each estimator.</p>
@@ -2300,7 +2300,7 @@ <h4><span class="section-number">41.75.4.4.4. </span>4.4: AdaBoost classifier<a
 <p>Again, you can tune the number of estimators and the depth of the base estimator. The base estimator is Decision Trees by default.</p>
 </section>
 <section id="stacking-classifier">
-<h4><span class="section-number">41.75.4.4.5. </span>4.5: Stacking classifier<a class="headerlink" href="#stacking-classifier" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.75.4.4.5. </span>4.5: Stacking classifier<a class="headerlink" href="#stacking-classifier" title="Permalink to this headline">#</a></h4>
 <p>Also referred to as Stacked Generalization, it is an ensemble method employed to reduce the biases and the errors rate made by multiple predictors(individual models/estimators).</p>
 <p>Instead of averaging the predictions made by individual models or predictors, in stacking, multiple models are trained on the full training data and then the final model that is trained on different subsets of the training data takes the predictions of the former models and find the final predictions.</p>
 <p>From its <a class="reference external" href="http://machine-learning.martinsewell.com/ensembles/stacking/Wolpert1992.pdf">original paper</a>, it is noted that it can be seen as the sophisticated version of the cross validation (per the reason why the final estimator is trained on the subsets or folds of the training data).</p>
@@ -2340,7 +2340,7 @@ <h4><span class="section-number">41.75.4.4.5. </span>4.5: Stacking classifier<a
 </section>
 </section>
 <section id="task-5-evaluating-the-ensemble-model-on-the-test-set">
-<h3><span class="section-number">41.75.4.5. </span>Task 5: Evaluating the ensemble model on the test set<a class="headerlink" href="#task-5-evaluating-the-ensemble-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.75.4.5. </span>Task 5: Evaluating the ensemble model on the test set<a class="headerlink" href="#task-5-evaluating-the-ensemble-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
 <p>Let’s say that all we have been doing was trying to find an ensemble model that can fit well the data and so we want to test it on the test set before shipping it into production.</p>
 <p>To narrow the choice down, we will use A Gradient Boosting method.</p>
 <p>As always, we will prepare the test set in the same way that we prepared the training set. Let’s go!</p>
@@ -2383,14 +2383,14 @@ <h3><span class="section-number">41.75.4.5. </span>Task 5: Evaluating the ensemb
 </section>
 </section>
 <section id="final-note">
-<h2><span class="section-number">41.75.5. </span>Final note<a class="headerlink" href="#final-note" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.75.5. </span>Final note<a class="headerlink" href="#final-note" title="Permalink to this headline">#</a></h2>
 <p>This notebook was all about ensemble learning methods. There is truly a <code class="docutils literal notranslate"><span class="pre">wisdom</span> <span class="pre">in</span> <span class="pre">the</span> <span class="pre">crowd</span></code>(CC: Aurelion Geron). By averaging the results of different models, we are able to improve the overall prediction.</p>
 <p>There is a notion that ensemble models are slow and expensive to run in production. It’s true, but with the advent of computation power and how incredible these types of ML models work well, that’s no longer issue for someone. They are complex algorithms but they can also reduce complexity. How?</p>
 <p>Think about it: Instead of building a single complex model (typically a neural network), you can build small models that will train and compute predictions faster and then average their results using a given ensemble method. By that way, you are leveraging ensemble methods to reduce complexity.</p>
 <p>To learn more about the ensemble models, you can refer to this chapter <code class="docutils literal notranslate"><span class="pre">Chapter</span> <span class="pre">7</span> <span class="pre">Ensemble</span> <span class="pre">Learning</span> <span class="pre">and</span> <span class="pre">Random</span> <span class="pre">Forests</span></code> of the book <a class="reference external" href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/">Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow</a>.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.75.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.75.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Nyandwi for creating the open-source course <a class="reference external" href="https://github.com/Nyandwi/machine_learning_complete">Machine Learning complete</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2432,13 +2432,13 @@ <h2><span class="section-number">41.75.6. </span>Acknowledgments<a class="header
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-            <p class="prev-next-title"><span class="section-number">41.74. </span>Random forests for classification</p>
+            <p class="prev-next-title"><span class="section-number">40.74. </span>Random forests for classification</p>
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-        <p class="prev-next-title"><span class="section-number">41.76. </span>Decision trees</p>
+        <p class="prev-next-title"><span class="section-number">40.76. </span>Decision trees</p>
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-    <link rel="next" title="41.77. Hyperparameter tuning gradient boosting" href="../gradient-boosting/hyperparameter-tuning-gradient-boosting.html" />
-    <link rel="prev" title="41.75. Beyond random forests: more ensemble models" href="beyond-random-forests-more-ensemble-models.html" />
+    <link rel="next" title="40.77. Hyperparameter tuning gradient boosting" href="../gradient-boosting/hyperparameter-tuning-gradient-boosting.html" />
+    <link rel="prev" title="40.75. Beyond random forests: more ensemble models" href="beyond-random-forests-more-ensemble-models.html" />
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+       40.76.2.1.1. Entropy
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      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#toy-example">
-       41.76.2.1.2. Toy Example
+       40.76.2.1.2. Toy Example
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-tree-building-algorithm">
-     41.76.2.2. Task 2: Tree-building algorithm
+     40.76.2.2. Task 2: Tree-building algorithm
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-other-quality-criteria-for-splits-in-classification-problems">
-     41.76.2.3. Task 3: Other quality criteria for splits in classification problems
+     40.76.2.3. Task 3: Other quality criteria for splits in classification problems
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       41.76.2.3.1. Example
+       40.76.2.3.1. Example
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#how-can-we-read-such-a-tree">
-       41.76.2.3.2. How can we “read” such a tree?
+       40.76.2.3.2. How can we “read” such a tree?
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-how-a-decision-tree-works-with-numerical-features">
-     41.76.2.4. Task 4: How a decision tree works with numerical features
+     40.76.2.4. Task 4: How a decision tree works with numerical features
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-crucial-tree-parameters">
-     41.76.2.5. Task 5: Crucial tree parameters
+     40.76.2.5. Task 5: Crucial tree parameters
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-6-class-decisiontreeclassifier-in-scikit-learn">
-     41.76.2.6. Task 6: Class DecisionTreeClassifier in Scikit-learn
+     40.76.2.6. Task 6: Class DecisionTreeClassifier in Scikit-learn
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-7-decision-tree-in-a-regression-problem">
-     41.76.2.7. Task 7: Decision tree in a regression problem
+     40.76.2.7. Task 7: Decision tree in a regression problem
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id2">
-       41.76.2.7.1. Example
+       40.76.2.7.1. Example
       </a>
      </li>
     </ul>
@@ -1662,39 +1662,39 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#choosing-model-parameters-and-cross-validation">
-   41.76.3. Choosing model parameters and cross-validation
+   40.76.3. Choosing model parameters and cross-validation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#application-examples-and-complex-cases">
-   41.76.4. Application examples and complex cases
+   40.76.4. Application examples and complex cases
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-decision-trees-in-a-customer-churn-prediction-task">
-     41.76.4.1. Task 1: Decision trees in a customer churn prediction task
+     40.76.4.1. Task 1: Decision trees in a customer churn prediction task
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-complex-case-for-decision-trees">
-     41.76.4.2. Task 2: Complex case for decision trees
+     40.76.4.2. Task 2: Complex case for decision trees
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-decision-trees-in-a-task-of-mnist-handwritten-digits-recognition">
-     41.76.4.3. Task 3: Decision trees in a task of MNIST handwritten digits recognition
+     40.76.4.3. Task 3: Decision trees in a task of MNIST handwritten digits recognition
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#pros-and-cons-of-decision-tree">
-   41.76.5. Pros and cons of decision tree
+   40.76.5. Pros and cons of decision tree
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.76.6. Acknowledgments
+   40.76.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1718,83 +1718,83 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   41.76.1. Introduction
+   40.76.1. Introduction
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    <li class="toc-h3 nav-item toc-entry">
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-     41.76.1.1. Example
+     40.76.1.1. Example
     </a>
    </li>
   </ul>
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   <a class="reference internal nav-link" href="#decision-tree">
-   41.76.2. Decision tree
+   40.76.2. Decision tree
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-how-to-build-a-decision-tree">
-     41.76.2.1. Task 1: How to build a decision tree
+     40.76.2.1. Task 1: How to build a decision tree
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#entropy">
-       41.76.2.1.1. Entropy
+       40.76.2.1.1. Entropy
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#toy-example">
-       41.76.2.1.2. Toy Example
+       40.76.2.1.2. Toy Example
       </a>
      </li>
     </ul>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-tree-building-algorithm">
-     41.76.2.2. Task 2: Tree-building algorithm
+     40.76.2.2. Task 2: Tree-building algorithm
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-other-quality-criteria-for-splits-in-classification-problems">
-     41.76.2.3. Task 3: Other quality criteria for splits in classification problems
+     40.76.2.3. Task 3: Other quality criteria for splits in classification problems
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       41.76.2.3.1. Example
+       40.76.2.3.1. Example
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#how-can-we-read-such-a-tree">
-       41.76.2.3.2. How can we “read” such a tree?
+       40.76.2.3.2. How can we “read” such a tree?
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-how-a-decision-tree-works-with-numerical-features">
-     41.76.2.4. Task 4: How a decision tree works with numerical features
+     40.76.2.4. Task 4: How a decision tree works with numerical features
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-crucial-tree-parameters">
-     41.76.2.5. Task 5: Crucial tree parameters
+     40.76.2.5. Task 5: Crucial tree parameters
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-6-class-decisiontreeclassifier-in-scikit-learn">
-     41.76.2.6. Task 6: Class DecisionTreeClassifier in Scikit-learn
+     40.76.2.6. Task 6: Class DecisionTreeClassifier in Scikit-learn
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-7-decision-tree-in-a-regression-problem">
-     41.76.2.7. Task 7: Decision tree in a regression problem
+     40.76.2.7. Task 7: Decision tree in a regression problem
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id2">
-       41.76.2.7.1. Example
+       40.76.2.7.1. Example
       </a>
      </li>
     </ul>
@@ -1803,39 +1803,39 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#choosing-model-parameters-and-cross-validation">
-   41.76.3. Choosing model parameters and cross-validation
+   40.76.3. Choosing model parameters and cross-validation
   </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#application-examples-and-complex-cases">
-   41.76.4. Application examples and complex cases
+   40.76.4. Application examples and complex cases
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-decision-trees-in-a-customer-churn-prediction-task">
-     41.76.4.1. Task 1: Decision trees in a customer churn prediction task
+     40.76.4.1. Task 1: Decision trees in a customer churn prediction task
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-complex-case-for-decision-trees">
-     41.76.4.2. Task 2: Complex case for decision trees
+     40.76.4.2. Task 2: Complex case for decision trees
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-decision-trees-in-a-task-of-mnist-handwritten-digits-recognition">
-     41.76.4.3. Task 3: Decision trees in a task of MNIST handwritten digits recognition
+     40.76.4.3. Task 3: Decision trees in a task of MNIST handwritten digits recognition
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#pros-and-cons-of-decision-tree">
-   41.76.5. Pros and cons of decision tree
+   40.76.5. Pros and cons of decision tree
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.76.6. Acknowledgments
+   40.76.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1849,9 +1849,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="decision-trees">
-<h1><span class="section-number">41.76. </span>Decision trees<a class="headerlink" href="#decision-trees" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.76. </span>Decision trees<a class="headerlink" href="#decision-trees" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">41.76.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.76.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>Before we dive into the material for this week’s article, let’s talk about the kind of problem that we are going to solve and its place in the exciting field of machine learning. T. Mitchell’s book “Machine Learning” (1997) gives a classic, general definition of machine learning as follows:</p>
 <blockquote>
 <div><p>A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.</p>
@@ -1867,14 +1867,14 @@ <h2><span class="section-number">41.76.1. </span>Introduction<a class="headerlin
 <p>A good overview is provided in the “Machine Learning basics” chapter of <a class="reference external" href="http://www.deeplearningbook.org">“Deep Learning”</a> (by Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016).</p>
 <p>Experience E  refers to data (we can’t go anywhere without it). Machine learning algorithms can be divided into those that are trained in supervised or unsupervised manner. In unsupervised learning tasks, one has a set consisting of instances described by a set of features. In supervised learning problems, there’s also a target variable, which is what we would like to be able to predict, known for each instance in a training set.</p>
 <section id="example">
-<h3><span class="section-number">41.76.1.1. </span>Example<a class="headerlink" href="#example" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.76.1.1. </span>Example<a class="headerlink" href="#example" title="Permalink to this headline">#</a></h3>
 <p>Classification and regression are supervised learning problems. For example, as a credit institution, we may want to predict loan defaults based on the data accumulated about our clients. Here, the experience E is the available training data: a set of instances (clients), a collection of features (such as age, salary, type of loan, past loan defaults, etc.) for each, and a target variable (whether they defaulted on the loan). This target variable is just a fact of loan default (1 or 0), so recall that this is a (binary) classification problem. If you were instead predicting by how much time the loan payment is overdue, this would become a regression problem.</p>
 <p>Finally, the third term used in the definition of machine learning is a metric of the algorithm’s performance evaluation P. Such metrics differ for various problems and algorithms, and we’ll discuss them as we study new algorithms. For now, we’ll refer to a simple metric for classification algorithms, the proportion of correct answers – accuracy – on the test set.</p>
 <p>Let’s take a look at two supervised learning problems: classification and regression.</p>
 </section>
 </section>
 <section id="decision-tree">
-<h2><span class="section-number">41.76.2. </span>Decision tree<a class="headerlink" href="#decision-tree" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.76.2. </span>Decision tree<a class="headerlink" href="#decision-tree" title="Permalink to this headline">#</a></h2>
 <p>We begin our overview of classification and regression methods with one of the most popular ones – a decision tree. Decision trees are used in everyday life decisions, not just in machine learning. Flow diagrams are actually visual representations of decision trees. For example, Higher School of Economics publishes information diagrams to make the lives of its employees easier. Here is a snippet of instructions for publishing a paper on the Institution portal.</p>
 <p><img align='center' src='https://habrastorage.org/webt/wh/ty/tf/whtytf2hxotbnhymqno6vmcivr4.png' width=60%><br></p>
 <p>In terms of machine learning, one can see it as a simple classifier that determines the appropriate form of publication (book, article, chapter of the book, preprint, publication in the “Higher School of Economics and the Media”) based on the content (book, pamphlet, paper), type of journal, original publication type (scientific journal, proceedings), etc.</p>
@@ -1884,18 +1884,18 @@ <h2><span class="section-number">41.76.2. </span>Decision tree<a class="headerli
 <p>The decision tree as a machine learning algorithm is essentially the same thing as the diagram shown above; we incorporate a stream of logical rules of the form “feature <span class="math notranslate nohighlight">\(a\)</span> value is less than <span class="math notranslate nohighlight">\(x\)</span> and feature <span class="math notranslate nohighlight">\(b\)</span> value is less than <span class="math notranslate nohighlight">\(y\)</span> … =&gt; Category 1” into a tree-like data structure. The advantage of this algorithm is that they are easily interpretable. For example, using the above scheme, the bank can explain to the client why they were denied for a loan: e.g the client does not own a house and her income is less than 5,000.</p>
 <p>As we’ll see later, many other models, although more accurate, do not have this property and can be regarded as more of a “black box” approach, where it is harder to interpret how the input data was transformed into the output. Due to this “understandability” and similarity to human decision-making (you can easily explain your model to your boss), decision trees have gained immense popularity. C4.5, a representative of this group of classification methods, is even the first in the list of 10 best data mining algorithms (“Top 10 Algorithms in Data Mining”, Knowledge and Information Systems, 2008. <a class="reference external" href="https://www.researchgate.net/publication/29467751_Top_10_algorithms_in_data_mining">ResearchGate</a>).</p>
 <section id="task-1-how-to-build-a-decision-tree">
-<h3><span class="section-number">41.76.2.1. </span>Task 1: How to build a decision tree<a class="headerlink" href="#task-1-how-to-build-a-decision-tree" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.76.2.1. </span>Task 1: How to build a decision tree<a class="headerlink" href="#task-1-how-to-build-a-decision-tree" title="Permalink to this headline">#</a></h3>
 <p>Earlier, we saw that the decision to grant a loan is made based on age, assets, income, and other variables. But what variable to look at first? Let’s discuss a simple example where all the variables are binary.</p>
 <p>Recall the game of “20 Questions”, which is often referenced when introducing decision trees. You’ve probably played this game – one person thinks of a celebrity while the other tries to guess by asking only “Yes” or “No” questions. What question will the guesser ask first? Of course, they will ask the one that narrows down the number of the remaining options the most. Asking “Is it Angelina Jolie?” would, in the case of a negative response, leave all but one celebrity in the realm of possibility. In contrast, asking “Is the celebrity a woman?” would reduce the possibilities to roughly half. That is to say, the “gender” feature separates the celebrity dataset much better than other features like “Angelina Jolie”, “Spanish”, or “loves football.” This reasoning corresponds to the concept of information gain based on entropy.</p>
 <section id="entropy">
-<h4><span class="section-number">41.76.2.1.1. </span>Entropy<a class="headerlink" href="#entropy" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.76.2.1.1. </span>Entropy<a class="headerlink" href="#entropy" title="Permalink to this headline">#</a></h4>
 <p>Shannon’s entropy is defined for a system with N possible states as follows:</p>
 <div class="math notranslate nohighlight">
 \[\Large S = -\sum_{i=1}^{N}p_i \log_2{p_i},\]</div>
 <p>where <span class="math notranslate nohighlight">\(p_i\)</span> is the probability of finding the system in the <span class="math notranslate nohighlight">\(i\)</span>-th state. This is a very important concept used in physics, information theory, and other areas. Entropy can be described as the degree of chaos in the system. The higher the entropy, the less ordered the system and vice versa. This will help us formalize “effective data splitting”, which we alluded to in the context of “20 Questions”.</p>
 </section>
 <section id="toy-example">
-<h4><span class="section-number">41.76.2.1.2. </span>Toy Example<a class="headerlink" href="#toy-example" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.76.2.1.2. </span>Toy Example<a class="headerlink" href="#toy-example" title="Permalink to this headline">#</a></h4>
 <p>To illustrate how entropy can help us identify good features for building a decision tree, let’s look at a toy example. We will predict the color of the ball based on its position.</p>
 <p><img align='center' src='https://habrastorage.org/webt/mu/vl/mt/muvlmtd2njeqf18trbldenpqvnm.png'><br></p>
 <p>There are 9 blue balls and 11 yellow balls. If we randomly pull out a ball, then it will be blue with probability <span class="math notranslate nohighlight">\(p_1=\frac{9}{20}\)</span> and yellow with probability <span class="math notranslate nohighlight">\(p_2=\frac{11}{20}\)</span>, which gives us an entropy <span class="math notranslate nohighlight">\(S_0 = -\frac{9}{20}\log_2{\frac{9}{20}}-\frac{11}{20}\log_2{\frac{11}{20}} \approx 1\)</span>. This value by itself may not tell us much, but let’s see how the value changes if we were to break the balls into two groups: with the position less than or equal to 12 and greater than 12.</p>
@@ -1913,7 +1913,7 @@ <h4><span class="section-number">41.76.2.1.2. </span>Toy Example<a class="header
 </section>
 </section>
 <section id="task-2-tree-building-algorithm">
-<h3><span class="section-number">41.76.2.2. </span>Task 2: Tree-building algorithm<a class="headerlink" href="#task-2-tree-building-algorithm" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.76.2.2. </span>Task 2: Tree-building algorithm<a class="headerlink" href="#task-2-tree-building-algorithm" title="Permalink to this headline">#</a></h3>
 <p>We can make sure that the tree built in the previous example is optimal: it took only 5 “questions” (conditioned on the variable <span class="math notranslate nohighlight">\(x\)</span>) to perfectly fit a decision tree to the training set. Under other split conditions, the resulting tree would be deeper, i.e. take more “questions” to reach an answer.</p>
 <p>At the heart of the popular algorithms for decision tree construction, such as ID3 or C4.5, lies the principle of greedy maximization of information gain: at each step, the algorithm chooses the variable that gives the greatest information gain upon splitting. Then the procedure is repeated recursively until the entropy is zero (or some small value to account for overfitting). Different algorithms use different heuristics for “early stopping” or “cut-off” to avoid constructing an overfitted tree.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">build</span><span class="p">(</span><span class="n">L</span><span class="p">):</span>
@@ -1929,7 +1929,7 @@ <h3><span class="section-number">41.76.2.2. </span>Task 2: Tree-building algorit
 </div>
 </section>
 <section id="task-3-other-quality-criteria-for-splits-in-classification-problems">
-<h3><span class="section-number">41.76.2.3. </span>Task 3: Other quality criteria for splits in classification problems<a class="headerlink" href="#task-3-other-quality-criteria-for-splits-in-classification-problems" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.76.2.3. </span>Task 3: Other quality criteria for splits in classification problems<a class="headerlink" href="#task-3-other-quality-criteria-for-splits-in-classification-problems" title="Permalink to this headline">#</a></h3>
 <p>We discussed how entropy allows us to formalize partitions in a tree. But this is only one heuristic; there exists others:</p>
 <ul class="simple">
 <li><p>Gini uncertainty (Gini impurity): <span class="math notranslate nohighlight">\(G = 1 - \sum\limits_k (p_k)^2\)</span>. Maximizing this criterion can be interpreted as the maximization of the number of pairs of objects of the same class that are in the same subtree (not to be confused with the Gini index).</p></li>
@@ -1981,7 +1981,7 @@ <h3><span class="section-number">41.76.2.3. </span>Task 3: Other quality criteri
 </div>
 </div>
 <section id="id1">
-<h4><span class="section-number">41.76.2.3.1. </span>Example<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.76.2.3.1. </span>Example<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
 <p>Let’s consider fitting a decision tree to some synthetic data. We will generate samples from two classes, both normal distributions but with different means.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2059,12 +2059,12 @@ <h4><span class="section-number">41.76.2.3.1. </span>Example<a class="headerlink
 <p>And how does the tree itself look? We see that the tree “cuts” the space into 8 rectangles, i.e. the tree has 8 leaves. Within each rectangle, the tree will make the prediction according to the majority label of the objects inside it.</p>
 <img src="https://habrastorage.org/webt/z0/yp/wm/z0ypwmvjko4r2mymcohy7ovevwe.png" /></section>
 <section id="how-can-we-read-such-a-tree">
-<h4><span class="section-number">41.76.2.3.2. </span>How can we “read” such a tree?<a class="headerlink" href="#how-can-we-read-such-a-tree" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.76.2.3.2. </span>How can we “read” such a tree?<a class="headerlink" href="#how-can-we-read-such-a-tree" title="Permalink to this headline">#</a></h4>
 <p>In the beginning, there were 200 samples (instances), 100 of each class. The entropy of the initial state was maximal, <span class="math notranslate nohighlight">\(S=1\)</span>. Then, the first partition of the samples into 2 groups was made by comparing the value of <span class="math notranslate nohighlight">\(x_2\)</span> with <span class="math notranslate nohighlight">\(1.211\)</span> (find this part of the border in the picture above). With that, the entropy of both left and right groups decreased. The process continues up to depth 3. In this visualization, the more samples of the first class, the darker the orange color of the vertex; the more samples of the second class, the darker the blue. At the beginning, the number of samples from two classes is equal, so the root node of the tree is white.</p>
 </section>
 </section>
 <section id="task-4-how-a-decision-tree-works-with-numerical-features">
-<h3><span class="section-number">41.76.2.4. </span>Task 4: How a decision tree works with numerical features<a class="headerlink" href="#task-4-how-a-decision-tree-works-with-numerical-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.76.2.4. </span>Task 4: How a decision tree works with numerical features<a class="headerlink" href="#task-4-how-a-decision-tree-works-with-numerical-features" title="Permalink to this headline">#</a></h3>
 <p>Suppose we have a numeric feature “Age” that has a lot of unique values. A decision tree will look for the best (according to some criterion of information gain) split by checking binary attributes such as “Age &lt;17”, “Age &lt; 22.87”, and so on. But what if the age range is large? Or what if another quantitative variable, “salary”, can also be “cut” in many ways? There will be too many binary attributes to select from at each step during tree construction. To resolve this problem, heuristics are usually used to limit the number of thresholds to which we compare the quantitative variable.</p>
 <p>Let’s consider an example. Suppose we have the following dataset:</p>
 <div class="cell docutils container">
@@ -2495,7 +2495,7 @@ <h3><span class="section-number">41.76.2.4. </span>Task 4: How a decision tree w
 <p>More examples of numeric feature discretization can be found in posts like <a class="reference external" href="http://kevinmeurer.com/a-simple-guide-to-entropy-based-discretization/">“A Simple Guide to Entropy-Based Discretization”</a> or <a class="reference external" href="http://clear-lines.com/blog/post/Discretizing-a-continuous-variable-using-Entropy.aspx">“Discretizing a continuous variable using Entropy”</a>. One of the most prominent scientific papers on this subject is “On the handling of continuous-valued attributes in decision tree generation” (UM Fayyad. KB Irani, “Machine Learning”, 1992).</p>
 </section>
 <section id="task-5-crucial-tree-parameters">
-<h3><span class="section-number">41.76.2.5. </span>Task 5: Crucial tree parameters<a class="headerlink" href="#task-5-crucial-tree-parameters" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.76.2.5. </span>Task 5: Crucial tree parameters<a class="headerlink" href="#task-5-crucial-tree-parameters" title="Permalink to this headline">#</a></h3>
 <p>Technically, you can build a decision tree until each leaf has exactly one instance, but this is not common in practice when building a single tree because it will be <em>overfitted</em>, or too tuned to the training set, and will not predict labels for new data well. At the bottom of the tree, at some great depth, there will be partitions on less important features (e.g. whether a client came from Leeds or New York). We can exaggerate this story further and find that all four clients who came to the bank for a loan in green trousers did not return the loan. Even if that were true in training, we do not want our classification model to generate such specific rules.</p>
 <p>There are two exceptions where the trees are built to the maximum depth:</p>
 <ul class="simple">
@@ -2511,7 +2511,7 @@ <h3><span class="section-number">41.76.2.5. </span>Task 5: Crucial tree paramete
 </ul>
 </section>
 <section id="task-6-class-decisiontreeclassifier-in-scikit-learn">
-<h3><span class="section-number">41.76.2.6. </span>Task 6: Class DecisionTreeClassifier in Scikit-learn<a class="headerlink" href="#task-6-class-decisiontreeclassifier-in-scikit-learn" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.76.2.6. </span>Task 6: Class DecisionTreeClassifier in Scikit-learn<a class="headerlink" href="#task-6-class-decisiontreeclassifier-in-scikit-learn" title="Permalink to this headline">#</a></h3>
 <p>The main parameters of the <a class="reference external" href="http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html"><code class="docutils literal notranslate"><span class="pre">sklearn.tree.DecisionTreeClassifier</span></code></a> class are:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">max_depth</span></code> – the maximum depth of the tree;</p></li>
@@ -2521,7 +2521,7 @@ <h3><span class="section-number">41.76.2.6. </span>Task 6: Class DecisionTreeCla
 <p>The parameters of the tree need to be set depending on input data, and it is usually done by means of <em>cross-validation</em>, more on this below.</p>
 </section>
 <section id="task-7-decision-tree-in-a-regression-problem">
-<h3><span class="section-number">41.76.2.7. </span>Task 7: Decision tree in a regression problem<a class="headerlink" href="#task-7-decision-tree-in-a-regression-problem" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.76.2.7. </span>Task 7: Decision tree in a regression problem<a class="headerlink" href="#task-7-decision-tree-in-a-regression-problem" title="Permalink to this headline">#</a></h3>
 <p>When predicting a numeric variable, the idea of a tree construction remains the same, but the quality criteria changes:</p>
 <ul class="simple">
 <li><p>Variance:</p></li>
@@ -2530,7 +2530,7 @@ <h3><span class="section-number">41.76.2.7. </span>Task 7: Decision tree in a re
 \[\Large D = \frac{1}{\ell} \sum\limits_{i =1}^{\ell} (y_i - \frac{1}{\ell} \sum\limits_{j=1}^{\ell} y_j)^2, \]</div>
 <p>where <span class="math notranslate nohighlight">\(\ell\)</span> is the number of samples in a leaf, <span class="math notranslate nohighlight">\(y_i\)</span> is the value of the target variable. Simply put, by minimizing the variance, we look for features that divide the training set in such a way that the values of the target feature in each leaf are roughly equal.</p>
 <section id="id2">
-<h4><span class="section-number">41.76.2.7.1. </span>Example<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.76.2.7.1. </span>Example<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
 <p>Let’s generate some data distributed by the function <span class="math notranslate nohighlight">\(f(x) = e^{-x ^ 2} + 1.5 * e^{-(x - 2) ^ 2}\)</span> with some noise. Then we will train a tree with this data and predictions that the tree makes.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2588,7 +2588,7 @@ <h4><span class="section-number">41.76.2.7.1. </span>Example<a class="headerlink
 </section>
 </section>
 <section id="choosing-model-parameters-and-cross-validation">
-<h2><span class="section-number">41.76.3. </span>Choosing model parameters and cross-validation<a class="headerlink" href="#choosing-model-parameters-and-cross-validation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.76.3. </span>Choosing model parameters and cross-validation<a class="headerlink" href="#choosing-model-parameters-and-cross-validation" title="Permalink to this headline">#</a></h2>
 <p>The main task of learning algorithms is to be able to <em>generalize</em> to unseen data. Since we cannot immediately check the model performance on new, incoming data (because we do not know the true values of the target variable yet), it is necessary to sacrifice a small portion of the data to check the quality of the model on it.</p>
 <p>This is often done in one of two ways:</p>
 <ul class="simple">
@@ -2601,9 +2601,9 @@ <h2><span class="section-number">41.76.3. </span>Choosing model parameters and c
 <p>Cross-validation is a very important technique in machine learning and can also be applied in statistics and econometrics. It helps with hyperparameter tuning, model comparison, feature evaluation, etc. More details can be found <a class="reference external" href="https://sebastianraschka.com/blog/2016/model-evaluation-selection-part1.html">here</a> (blog post by Sebastian Raschka) or in any classic textbook on machine (statistical) learning.</p>
 </section>
 <section id="application-examples-and-complex-cases">
-<h2><span class="section-number">41.76.4. </span>Application examples and complex cases<a class="headerlink" href="#application-examples-and-complex-cases" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.76.4. </span>Application examples and complex cases<a class="headerlink" href="#application-examples-and-complex-cases" title="Permalink to this headline">#</a></h2>
 <section id="task-1-decision-trees-in-a-customer-churn-prediction-task">
-<h3><span class="section-number">41.76.4.1. </span>Task 1: Decision trees in a customer churn prediction task<a class="headerlink" href="#task-1-decision-trees-in-a-customer-churn-prediction-task" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.76.4.1. </span>Task 1: Decision trees in a customer churn prediction task<a class="headerlink" href="#task-1-decision-trees-in-a-customer-churn-prediction-task" title="Permalink to this headline">#</a></h3>
 <p>Let’s read data into a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> and preprocess it. Store <em>State</em> in a separate <code class="docutils literal notranslate"><span class="pre">Series</span></code> object for now and remove it from the dataframe. We will
 train the first model without the <em>State</em> feature, and then we will see if it helps.</p>
 <div class="cell docutils container">
@@ -2928,7 +2928,7 @@ <h3><span class="section-number">41.76.4.1. </span>Task 1: Decision trees in a c
 </div>
 </section>
 <section id="task-2-complex-case-for-decision-trees">
-<h3><span class="section-number">41.76.4.2. </span>Task 2: Complex case for decision trees<a class="headerlink" href="#task-2-complex-case-for-decision-trees" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.76.4.2. </span>Task 2: Complex case for decision trees<a class="headerlink" href="#task-2-complex-case-for-decision-trees" title="Permalink to this headline">#</a></h3>
 <p>To continue the discussion of the pros and cons of the methods in question, let’s consider a simple classification task, where a tree would perform well but does it in an “overly complicated” manner. Let’s create a set of points on a plane (2 features), each point will be one of two classes (+1 for red, or -1 for yellow). If you look at it as a classification problem, it seems very simple: the classes are separated by a line.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2976,7 +2976,7 @@ <h3><span class="section-number">41.76.4.2. </span>Task 2: Complex case for deci
 <img src="https://habrastorage.org/webt/az/ld/yj/azldyj0ija_sxkk438m959qpf0w.png" /><p>Obviously, decision trees don’t work as well as linear classifiers in this case</p>
 </section>
 <section id="task-3-decision-trees-in-a-task-of-mnist-handwritten-digits-recognition">
-<h3><span class="section-number">41.76.4.3. </span>Task 3: Decision trees in a task of MNIST handwritten digits recognition<a class="headerlink" href="#task-3-decision-trees-in-a-task-of-mnist-handwritten-digits-recognition" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.76.4.3. </span>Task 3: Decision trees in a task of MNIST handwritten digits recognition<a class="headerlink" href="#task-3-decision-trees-in-a-task-of-mnist-handwritten-digits-recognition" title="Permalink to this headline">#</a></h3>
 <p>Now let’s have a look at how decision trees performs on a real-world task. We will use the <code class="docutils literal notranslate"><span class="pre">sklearn</span></code> built-in dataset on handwritten digits.</p>
 <p>Pictures here are 8x8 matrices (intensity of white color for each pixel). Then each such matrix is “unfolded” into a vector of length 64, and we obtain a feature description of an object.</p>
 <p>Let’s draw some handwritten digits. We see that they are distinguishable.</p>
@@ -3119,7 +3119,7 @@ <h3><span class="section-number">41.76.4.3. </span>Task 3: Decision trees in a t
 </section>
 </section>
 <section id="pros-and-cons-of-decision-tree">
-<h2><span class="section-number">41.76.5. </span>Pros and cons of decision tree<a class="headerlink" href="#pros-and-cons-of-decision-tree" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.76.5. </span>Pros and cons of decision tree<a class="headerlink" href="#pros-and-cons-of-decision-tree" title="Permalink to this headline">#</a></h2>
 <p>Pros:</p>
 <ul class="simple">
 <li><p>Generation of clear human-understandable classification rules, e.g. “if age &lt;25 and is interested in motorcycles, deny the loan”. This property is called interpretability of the model.</p></li>
@@ -3140,7 +3140,7 @@ <h2><span class="section-number">41.76.5. </span>Pros and cons of decision tree<
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.76.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.76.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/kashnitsky">Yury Kashnitsky</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/kashnitsky/topic-3-decision-trees-and-knn/notebook">Decision Trees and kNN</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -3182,13 +3182,13 @@ <h2><span class="section-number">41.76.6. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.75. </span>Beyond random forests: more ensemble models</p>
+            <p class="prev-next-title"><span class="section-number">40.75. </span>Beyond random forests: more ensemble models</p>
         </div>
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-        <p class="prev-next-title"><span class="section-number">41.77. </span>Hyperparameter tuning gradient boosting</p>
+        <p class="prev-next-title"><span class="section-number">40.77. </span>Hyperparameter tuning gradient boosting</p>
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diff --git a/assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html b/assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html
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--- a/assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html
+++ b/assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>41.80. Random Forest Classifier with Feature Importance &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.80. Random Forest Classifier with Feature Importance &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
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     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="41.82. Data engineering" href="../../machine-learning-productionization/data-engineering.html" />
-    <link rel="prev" title="41.79. Boosting with tuning" href="../gradient-boosting/boosting-with-tuning.html" />
+    <link rel="next" title="40.82. Data engineering" href="../../machine-learning-productionization/data-engineering.html" />
+    <link rel="prev" title="40.79. Boosting with tuning" href="../gradient-boosting/boosting-with-tuning.html" />
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      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#rename-column-names">
-       41.80.4.3. Rename column names
+       40.80.4.3. Rename column names
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#view-summary-of-dataset">
-       41.80.4.4. View summary of dataset
+       40.80.4.4. View summary of dataset
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#check-the-data-types-of-columns">
-       41.80.4.5. Check the data types of columns
+       40.80.4.5. Check the data types of columns
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#view-statistical-properties-of-dataset">
-       41.80.4.6. View statistical properties of dataset
+       40.80.4.6. View statistical properties of dataset
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#check-with-assert-statement">
-       41.80.4.7. Check with ASSERT statement
+       40.80.4.7. Check with ASSERT statement
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#functional-approach-to-eda">
-       41.80.4.8. Functional approach to EDA
+       40.80.4.8. Functional approach to EDA
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#explore-categorical-variables">
-     41.80.5. Explore Categorical Variables
+     40.80.5. Explore Categorical Variables
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#find-categorical-variables">
-       41.80.5.1. Find categorical variables
+       40.80.5.1. Find categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#preview-categorical-variables">
-       41.80.5.2. Preview categorical variables
+       40.80.5.2. Preview categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#summary-of-categorical-variables">
-       41.80.5.3. Summary of categorical variables
+       40.80.5.3. Summary of categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#frequency-distribution-of-categorical-variables">
-       41.80.5.4. Frequency distribution of categorical variables
+       40.80.5.4. Frequency distribution of categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#percentage-of-frequency-distribution-of-values">
-       41.80.5.5. Percentage of frequency distribution of values
+       40.80.5.5. Percentage of frequency distribution of values
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#explore-the-variables">
-       41.80.5.6. Explore the variables
+       40.80.5.6. Explore the variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#number-of-labels-cardinality">
-       41.80.5.7. Number of labels: Cardinality
+       40.80.5.7. Number of labels: Cardinality
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#declare-feature-vector-and-target-variable">
-     41.80.6. Declare feature vector and target variable
+     40.80.6. Declare feature vector and target variable
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#split-data-into-separate-training-and-test-set">
-     41.80.7. Split data into separate training and test set
+     40.80.7. Split data into separate training and test set
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#feature-engineering">
-     41.80.8. Feature Engineering
+     40.80.8. Feature Engineering
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#display-categorical-variables-in-training-set">
-       41.80.8.1. Display categorical variables in training set
+       40.80.8.1. Display categorical variables in training set
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#display-numerical-variables-in-training-set">
-       41.80.8.2. Display numerical variables in training set
+       40.80.8.2. Display numerical variables in training set
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#engineering-missing-values-in-categorical-variables">
-       41.80.8.3. Engineering missing values in categorical variables
+       40.80.8.3. Engineering missing values in categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#encode-categorical-variables">
-       41.80.8.4. Encode categorical variables
+       40.80.8.4. Encode categorical variables
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#feature-scaling">
-     41.80.9. Feature Scaling
+     40.80.9. Feature Scaling
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#random-forest-classifier-model-with-default-parameters">
-     41.80.10. Random Forest Classifier model with default parameters
+     40.80.10. Random Forest Classifier model with default parameters
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#random-forest-classifier-model-with-100-decision-trees">
-     41.80.11. Random Forest Classifier model with 100 Decision Trees
+     40.80.11. Random Forest Classifier model with 100 Decision Trees
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#build-the-random-forest-model-on-selected-features">
-     41.80.12. Build the Random Forest model on selected features
+     40.80.12. Build the Random Forest model on selected features
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#confusion-matrix">
-     41.80.13. Confusion matrix
+     40.80.13. Confusion matrix
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-report">
-     41.80.14. Classification Report
+     40.80.14. Classification Report
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.81. Acknowledgments
+   40.81. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1779,187 +1779,187 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#">
-   41.80. Random Forest Classifier with Feature Importance
+   40.80. Random Forest Classifier with Feature Importance
   </a>
   <ul class="visible nav section-nav flex-column">
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-problem-statement">
-     41.80.1. The problem statement
+     40.80.1. The problem statement
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#import-libraries">
-     41.80.2. Import libraries
+     40.80.2. Import libraries
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#import-dataset">
-     41.80.3. Import dataset
+     40.80.3. Import dataset
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploratory-data-analysis">
-     41.80.4. Exploratory data analysis
+     40.80.4. Exploratory data analysis
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#view-dimensions-of-dataset">
-       41.80.4.1. View dimensions of dataset
+       40.80.4.1. View dimensions of dataset
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#preview-the-dataset-a-class-anchor-id-4-2-a">
-       41.80.4.2. Preview the dataset
+       40.80.4.2. Preview the dataset
        <a class="anchor" id="4.2">
        </a>
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#rename-column-names">
-       41.80.4.3. Rename column names
+       40.80.4.3. Rename column names
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#view-summary-of-dataset">
-       41.80.4.4. View summary of dataset
+       40.80.4.4. View summary of dataset
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#check-the-data-types-of-columns">
-       41.80.4.5. Check the data types of columns
+       40.80.4.5. Check the data types of columns
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#view-statistical-properties-of-dataset">
-       41.80.4.6. View statistical properties of dataset
+       40.80.4.6. View statistical properties of dataset
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#check-with-assert-statement">
-       41.80.4.7. Check with ASSERT statement
+       40.80.4.7. Check with ASSERT statement
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#functional-approach-to-eda">
-       41.80.4.8. Functional approach to EDA
+       40.80.4.8. Functional approach to EDA
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#explore-categorical-variables">
-     41.80.5. Explore Categorical Variables
+     40.80.5. Explore Categorical Variables
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#find-categorical-variables">
-       41.80.5.1. Find categorical variables
+       40.80.5.1. Find categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#preview-categorical-variables">
-       41.80.5.2. Preview categorical variables
+       40.80.5.2. Preview categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#summary-of-categorical-variables">
-       41.80.5.3. Summary of categorical variables
+       40.80.5.3. Summary of categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#frequency-distribution-of-categorical-variables">
-       41.80.5.4. Frequency distribution of categorical variables
+       40.80.5.4. Frequency distribution of categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#percentage-of-frequency-distribution-of-values">
-       41.80.5.5. Percentage of frequency distribution of values
+       40.80.5.5. Percentage of frequency distribution of values
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#explore-the-variables">
-       41.80.5.6. Explore the variables
+       40.80.5.6. Explore the variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#number-of-labels-cardinality">
-       41.80.5.7. Number of labels: Cardinality
+       40.80.5.7. Number of labels: Cardinality
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#declare-feature-vector-and-target-variable">
-     41.80.6. Declare feature vector and target variable
+     40.80.6. Declare feature vector and target variable
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#split-data-into-separate-training-and-test-set">
-     41.80.7. Split data into separate training and test set
+     40.80.7. Split data into separate training and test set
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#feature-engineering">
-     41.80.8. Feature Engineering
+     40.80.8. Feature Engineering
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#display-categorical-variables-in-training-set">
-       41.80.8.1. Display categorical variables in training set
+       40.80.8.1. Display categorical variables in training set
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#display-numerical-variables-in-training-set">
-       41.80.8.2. Display numerical variables in training set
+       40.80.8.2. Display numerical variables in training set
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#engineering-missing-values-in-categorical-variables">
-       41.80.8.3. Engineering missing values in categorical variables
+       40.80.8.3. Engineering missing values in categorical variables
       </a>
      </li>
      <li class="toc-h3 nav-item toc-entry">
       <a class="reference internal nav-link" href="#encode-categorical-variables">
-       41.80.8.4. Encode categorical variables
+       40.80.8.4. Encode categorical variables
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#feature-scaling">
-     41.80.9. Feature Scaling
+     40.80.9. Feature Scaling
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#random-forest-classifier-model-with-default-parameters">
-     41.80.10. Random Forest Classifier model with default parameters
+     40.80.10. Random Forest Classifier model with default parameters
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#random-forest-classifier-model-with-100-decision-trees">
-     41.80.11. Random Forest Classifier model with 100 Decision Trees
+     40.80.11. Random Forest Classifier model with 100 Decision Trees
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#build-the-random-forest-model-on-selected-features">
-     41.80.12. Build the Random Forest model on selected features
+     40.80.12. Build the Random Forest model on selected features
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#confusion-matrix">
-     41.80.13. Confusion matrix
+     40.80.13. Confusion matrix
     </a>
    </li>
    <li class="toc-h2 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-report">
-     41.80.14. Classification Report
+     40.80.14. Classification Report
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h1 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.81. Acknowledgments
+   40.81. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1973,13 +1973,13 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="random-forest-classifier-with-feature-importance">
-<h1><span class="section-number">41.80. </span>Random Forest Classifier with Feature Importance<a class="headerlink" href="#random-forest-classifier-with-feature-importance" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.80. </span>Random Forest Classifier with Feature Importance<a class="headerlink" href="#random-forest-classifier-with-feature-importance" title="Permalink to this headline">#</a></h1>
 <section id="the-problem-statement">
-<h2><span class="section-number">41.80.1. </span>The problem statement<a class="headerlink" href="#the-problem-statement" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.1. </span>The problem statement<a class="headerlink" href="#the-problem-statement" title="Permalink to this headline">#</a></h2>
 <p>We try to make predictions where the prediction task is to determine whether a person makes over 50K a year. We implement Random Forest Classification with Python and Scikit-Learn. So, to answer the question, we build a Random Forest classifier to predict whether a person makes over 50K a year.</p>
 </section>
 <section id="import-libraries">
-<h2><span class="section-number">41.80.2. </span>Import libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.2. </span>Import libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># This Python 3 environment comes with many helpful analytics libraries installed</span>
@@ -2024,7 +2024,7 @@ <h2><span class="section-number">41.80.2. </span>Import libraries<a class="heade
 </div>
 </section>
 <section id="import-dataset">
-<h2><span class="section-number">41.80.3. </span>Import dataset<a class="headerlink" href="#import-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.3. </span>Import dataset<a class="headerlink" href="#import-dataset" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="s1">&#39;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/random-forest-income_evaluation.csv&#39;</span>
@@ -2036,9 +2036,9 @@ <h2><span class="section-number">41.80.3. </span>Import dataset<a class="headerl
 </div>
 </section>
 <section id="exploratory-data-analysis">
-<h2><span class="section-number">41.80.4. </span>Exploratory data analysis<a class="headerlink" href="#exploratory-data-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.4. </span>Exploratory data analysis<a class="headerlink" href="#exploratory-data-analysis" title="Permalink to this headline">#</a></h2>
 <section id="view-dimensions-of-dataset">
-<h3><span class="section-number">41.80.4.1. </span>View dimensions of dataset<a class="headerlink" href="#view-dimensions-of-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.4.1. </span>View dimensions of dataset<a class="headerlink" href="#view-dimensions-of-dataset" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># print the shape</span>
@@ -2050,7 +2050,7 @@ <h3><span class="section-number">41.80.4.1. </span>View dimensions of dataset<a
 <p>We can see that there are 32561 instances and 15 attributes in the data set.</p>
 </section>
 <section id="preview-the-dataset-a-class-anchor-id-4-2-a">
-<h3><span class="section-number">41.80.4.2. </span>Preview the dataset <a class="anchor" id="4.2"></a><a class="headerlink" href="#preview-the-dataset-a-class-anchor-id-4-2-a" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.4.2. </span>Preview the dataset <a class="anchor" id="4.2"></a><a class="headerlink" href="#preview-the-dataset-a-class-anchor-id-4-2-a" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
@@ -2060,7 +2060,7 @@ <h3><span class="section-number">41.80.4.2. </span>Preview the dataset <a class=
 </div>
 </section>
 <section id="rename-column-names">
-<h3><span class="section-number">41.80.4.3. </span>Rename column names<a class="headerlink" href="#rename-column-names" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.4.3. </span>Rename column names<a class="headerlink" href="#rename-column-names" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">col_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;age&#39;</span><span class="p">,</span> <span class="s1">&#39;workclass&#39;</span><span class="p">,</span> <span class="s1">&#39;fnlwgt&#39;</span><span class="p">,</span> <span class="s1">&#39;education&#39;</span><span class="p">,</span> <span class="s1">&#39;education_num&#39;</span><span class="p">,</span> <span class="s1">&#39;marital_status&#39;</span><span class="p">,</span> <span class="s1">&#39;occupation&#39;</span><span class="p">,</span> <span class="s1">&#39;relationship&#39;</span><span class="p">,</span>
@@ -2075,7 +2075,7 @@ <h3><span class="section-number">41.80.4.3. </span>Rename column names<a class="
 </div>
 </section>
 <section id="view-summary-of-dataset">
-<h3><span class="section-number">41.80.4.4. </span>View summary of dataset<a class="headerlink" href="#view-summary-of-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.4.4. </span>View summary of dataset<a class="headerlink" href="#view-summary-of-dataset" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
@@ -2085,7 +2085,7 @@ <h3><span class="section-number">41.80.4.4. </span>View summary of dataset<a cla
 </div>
 </section>
 <section id="check-the-data-types-of-columns">
-<h3><span class="section-number">41.80.4.5. </span>Check the data types of columns<a class="headerlink" href="#check-the-data-types-of-columns" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.4.5. </span>Check the data types of columns<a class="headerlink" href="#check-the-data-types-of-columns" title="Permalink to this headline">#</a></h3>
 <p>The above <a class="reference external" href="http://df.info">df.info</a>() command gives us the number of filled values along with the data types of columns.</p>
 <p>If we simply want to check the data type of a particular column, we can use the following command.</p>
 <div class="cell docutils container">
@@ -2097,7 +2097,7 @@ <h3><span class="section-number">41.80.4.5. </span>Check the data types of colum
 </div>
 </section>
 <section id="view-statistical-properties-of-dataset">
-<h3><span class="section-number">41.80.4.6. </span>View statistical properties of dataset<a class="headerlink" href="#view-statistical-properties-of-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.4.6. </span>View statistical properties of dataset<a class="headerlink" href="#view-statistical-properties-of-dataset" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
@@ -2131,7 +2131,7 @@ <h3><span class="section-number">41.80.4.6. </span>View statistical properties o
 </div>
 </section>
 <section id="check-with-assert-statement">
-<h3><span class="section-number">41.80.4.7. </span>Check with ASSERT statement<a class="headerlink" href="#check-with-assert-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.4.7. </span>Check with ASSERT statement<a class="headerlink" href="#check-with-assert-statement" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">assert</span> <span class="n">pd</span><span class="o">.</span><span class="n">notnull</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">()</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
@@ -2141,7 +2141,7 @@ <h3><span class="section-number">41.80.4.7. </span>Check with ASSERT statement<a
 </div>
 </section>
 <section id="functional-approach-to-eda">
-<h3><span class="section-number">41.80.4.8. </span>Functional approach to EDA<a class="headerlink" href="#functional-approach-to-eda" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.4.8. </span>Functional approach to EDA<a class="headerlink" href="#functional-approach-to-eda" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">initial_eda</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
@@ -2173,9 +2173,9 @@ <h3><span class="section-number">41.80.4.8. </span>Functional approach to EDA<a
 </section>
 </section>
 <section id="explore-categorical-variables">
-<h2><span class="section-number">41.80.5. </span>Explore Categorical Variables<a class="headerlink" href="#explore-categorical-variables" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.5. </span>Explore Categorical Variables<a class="headerlink" href="#explore-categorical-variables" title="Permalink to this headline">#</a></h2>
 <section id="find-categorical-variables">
-<h3><span class="section-number">41.80.5.1. </span>Find categorical variables<a class="headerlink" href="#find-categorical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.5.1. </span>Find categorical variables<a class="headerlink" href="#find-categorical-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">categorical</span> <span class="o">=</span> <span class="p">[</span><span class="n">var</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="n">df</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span><span class="o">==</span><span class="s1">&#39;O&#39;</span><span class="p">]</span>
@@ -2189,7 +2189,7 @@ <h3><span class="section-number">41.80.5.1. </span>Find categorical variables<a
 </div>
 </section>
 <section id="preview-categorical-variables">
-<h3><span class="section-number">41.80.5.2. </span>Preview categorical variables<a class="headerlink" href="#preview-categorical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.5.2. </span>Preview categorical variables<a class="headerlink" href="#preview-categorical-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="n">categorical</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
@@ -2199,10 +2199,10 @@ <h3><span class="section-number">41.80.5.2. </span>Preview categorical variables
 </div>
 </section>
 <section id="summary-of-categorical-variables">
-<h3><span class="section-number">41.80.5.3. </span>Summary of categorical variables<a class="headerlink" href="#summary-of-categorical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.5.3. </span>Summary of categorical variables<a class="headerlink" href="#summary-of-categorical-variables" title="Permalink to this headline">#</a></h3>
 </section>
 <section id="frequency-distribution-of-categorical-variables">
-<h3><span class="section-number">41.80.5.4. </span>Frequency distribution of categorical variables<a class="headerlink" href="#frequency-distribution-of-categorical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.5.4. </span>Frequency distribution of categorical variables<a class="headerlink" href="#frequency-distribution-of-categorical-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">categorical</span><span class="p">:</span> 
@@ -2214,7 +2214,7 @@ <h3><span class="section-number">41.80.5.4. </span>Frequency distribution of cat
 </div>
 </section>
 <section id="percentage-of-frequency-distribution-of-values">
-<h3><span class="section-number">41.80.5.5. </span>Percentage of frequency distribution of values<a class="headerlink" href="#percentage-of-frequency-distribution-of-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.5.5. </span>Percentage of frequency distribution of values<a class="headerlink" href="#percentage-of-frequency-distribution-of-values" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">categorical</span><span class="p">:</span>
@@ -2225,7 +2225,7 @@ <h3><span class="section-number">41.80.5.5. </span>Percentage of frequency distr
 </div>
 </section>
 <section id="explore-the-variables">
-<h3><span class="section-number">41.80.5.6. </span>Explore the variables<a class="headerlink" href="#explore-the-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.5.6. </span>Explore the variables<a class="headerlink" href="#explore-the-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># check for missing values</span>
@@ -2514,7 +2514,7 @@ <h3><span class="section-number">41.80.5.6. </span>Explore the variables<a class
 </div>
 </section>
 <section id="number-of-labels-cardinality">
-<h3><span class="section-number">41.80.5.7. </span>Number of labels: Cardinality<a class="headerlink" href="#number-of-labels-cardinality" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.5.7. </span>Number of labels: Cardinality<a class="headerlink" href="#number-of-labels-cardinality" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># check for cardinality in categorical variables</span>
@@ -2530,7 +2530,7 @@ <h3><span class="section-number">41.80.5.7. </span>Number of labels: Cardinality
 </section>
 </section>
 <section id="declare-feature-vector-and-target-variable">
-<h2><span class="section-number">41.80.6. </span>Declare feature vector and target variable<a class="headerlink" href="#declare-feature-vector-and-target-variable" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.6. </span>Declare feature vector and target variable<a class="headerlink" href="#declare-feature-vector-and-target-variable" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">&#39;income&#39;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
@@ -2542,7 +2542,7 @@ <h2><span class="section-number">41.80.6. </span>Declare feature vector and targ
 </div>
 </section>
 <section id="split-data-into-separate-training-and-test-set">
-<h2><span class="section-number">41.80.7. </span>Split data into separate training and test set<a class="headerlink" href="#split-data-into-separate-training-and-test-set" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.7. </span>Split data into separate training and test set<a class="headerlink" href="#split-data-into-separate-training-and-test-set" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
@@ -2563,9 +2563,9 @@ <h2><span class="section-number">41.80.7. </span>Split data into separate traini
 </div>
 </section>
 <section id="feature-engineering">
-<h2><span class="section-number">41.80.8. </span>Feature Engineering<a class="headerlink" href="#feature-engineering" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.8. </span>Feature Engineering<a class="headerlink" href="#feature-engineering" title="Permalink to this headline">#</a></h2>
 <section id="display-categorical-variables-in-training-set">
-<h3><span class="section-number">41.80.8.1. </span>Display categorical variables in training set<a class="headerlink" href="#display-categorical-variables-in-training-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.8.1. </span>Display categorical variables in training set<a class="headerlink" href="#display-categorical-variables-in-training-set" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">categorical</span> <span class="o">=</span> <span class="p">[</span><span class="n">col</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">X_train</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="n">X_train</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">dtypes</span> <span class="o">==</span> <span class="s1">&#39;O&#39;</span><span class="p">]</span>
@@ -2577,7 +2577,7 @@ <h3><span class="section-number">41.80.8.1. </span>Display categorical variables
 </div>
 </section>
 <section id="display-numerical-variables-in-training-set">
-<h3><span class="section-number">41.80.8.2. </span>Display numerical variables in training set<a class="headerlink" href="#display-numerical-variables-in-training-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.8.2. </span>Display numerical variables in training set<a class="headerlink" href="#display-numerical-variables-in-training-set" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">numerical</span> <span class="o">=</span> <span class="p">[</span><span class="n">col</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">X_train</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="n">X_train</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">dtypes</span> <span class="o">!=</span> <span class="s1">&#39;O&#39;</span><span class="p">]</span>
@@ -2589,7 +2589,7 @@ <h3><span class="section-number">41.80.8.2. </span>Display numerical variables i
 </div>
 </section>
 <section id="engineering-missing-values-in-categorical-variables">
-<h3><span class="section-number">41.80.8.3. </span>Engineering missing values in categorical variables<a class="headerlink" href="#engineering-missing-values-in-categorical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.8.3. </span>Engineering missing values in categorical variables<a class="headerlink" href="#engineering-missing-values-in-categorical-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># print percentage of missing values in the categorical variables in training set</span>
@@ -2662,7 +2662,7 @@ <h3><span class="section-number">41.80.8.3. </span>Engineering missing values in
 <p>We can see that there are no missing values in X_train and X_test.</p>
 </section>
 <section id="encode-categorical-variables">
-<h3><span class="section-number">41.80.8.4. </span>Encode categorical variables<a class="headerlink" href="#encode-categorical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.80.8.4. </span>Encode categorical variables<a class="headerlink" href="#encode-categorical-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># preview categorical variables in X_train</span>
@@ -2729,7 +2729,7 @@ <h3><span class="section-number">41.80.8.4. </span>Encode categorical variables<
 </section>
 </section>
 <section id="feature-scaling">
-<h2><span class="section-number">41.80.9. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.9. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cols</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">columns</span>
@@ -2767,7 +2767,7 @@ <h2><span class="section-number">41.80.9. </span>Feature Scaling<a class="header
 <p>We now have X_train dataset ready to be fed into the Random Forest classifier. We will do it as follows.</p>
 </section>
 <section id="random-forest-classifier-model-with-default-parameters">
-<h2><span class="section-number">41.80.10. </span>Random Forest Classifier model with default parameters<a class="headerlink" href="#random-forest-classifier-model-with-default-parameters" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.10. </span>Random Forest Classifier model with default parameters<a class="headerlink" href="#random-forest-classifier-model-with-default-parameters" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># import Random Forest classifier</span>
@@ -2805,7 +2805,7 @@ <h2><span class="section-number">41.80.10. </span>Random Forest Classifier model
 </div>
 </section>
 <section id="random-forest-classifier-model-with-100-decision-trees">
-<h2><span class="section-number">41.80.11. </span>Random Forest Classifier model with 100 Decision Trees<a class="headerlink" href="#random-forest-classifier-model-with-100-decision-trees" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.11. </span>Random Forest Classifier model with 100 Decision Trees<a class="headerlink" href="#random-forest-classifier-model-with-100-decision-trees" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate the classifier with n_estimators = 100</span>
@@ -2861,7 +2861,7 @@ <h2><span class="section-number">41.80.11. </span>Random Forest Classifier model
 </div>
 </section>
 <section id="build-the-random-forest-model-on-selected-features">
-<h2><span class="section-number">41.80.12. </span>Build the Random Forest model on selected features<a class="headerlink" href="#build-the-random-forest-model-on-selected-features" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.12. </span>Build the Random Forest model on selected features<a class="headerlink" href="#build-the-random-forest-model-on-selected-features" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># drop the least important feature from X_train and X_test</span>
@@ -2901,7 +2901,7 @@ <h2><span class="section-number">41.80.12. </span>Build the Random Forest model
 </div>
 </section>
 <section id="confusion-matrix">
-<h2><span class="section-number">41.80.13. </span>Confusion matrix<a class="headerlink" href="#confusion-matrix" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.13. </span>Confusion matrix<a class="headerlink" href="#confusion-matrix" title="Permalink to this headline">#</a></h2>
 <p>A confusion matrix is a tool for summarizing the performance of a classification algorithm. A confusion matrix will give us a clear picture of classification model performance and the types of errors produced by the model. It gives us a summary of correct and incorrect predictions broken down by each category. The summary is represented in a tabular form.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2930,7 +2930,7 @@ <h2><span class="section-number">41.80.13. </span>Confusion matrix<a class="head
 </div>
 </section>
 <section id="classification-report">
-<h2><span class="section-number">41.80.14. </span>Classification Report<a class="headerlink" href="#classification-report" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.80.14. </span>Classification Report<a class="headerlink" href="#classification-report" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">classification_report</span>
@@ -2943,7 +2943,7 @@ <h2><span class="section-number">41.80.14. </span>Classification Report<a class=
 </section>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="acknowledgments">
-<h1><span class="section-number">41.81. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.81. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h1>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/prashant111">prashant111</a> for creating <a class="reference external" href="https://www.kaggle.com/code/prashant111/random-forest-classifier-feature-importance">random-forest-classifier-feature-importance</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 
@@ -2984,13 +2984,13 @@ <h1><span class="section-number">41.81. </span>Acknowledgments<a class="headerli
         <i class="fas fa-angle-left"></i>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.79. </span>Boosting with tuning</p>
+            <p class="prev-next-title"><span class="section-number">40.79. </span>Boosting with tuning</p>
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+     40.74.2.4. Task 4: Training random forests classifier
     </a>
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-     41.74.2.5. Task5: Evaluating random forests classifier
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     </a>
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-     41.74.2.6. Task 6: Improving random forests
+     40.74.2.6. Task 6: Improving random forests
     </a>
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-     41.74.2.7. Task 7: Evaluating the model on the test set
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     </a>
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     </a>
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+     40.74.2.4. Task 4: Training random forests classifier
     </a>
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-     41.74.2.5. Task5: Evaluating random forests classifier
+     40.74.2.5. Task5: Evaluating random forests classifier
     </a>
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    <li class="toc-h3 nav-item toc-entry">
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-     41.74.2.6. Task 6: Improving random forests
+     40.74.2.6. Task 6: Improving random forests
     </a>
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-     41.74.2.7. Task 7: Evaluating the model on the test set
+     40.74.2.7. Task 7: Evaluating the model on the test set
     </a>
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-     41.74.2.8. Task 8: Feature importance
+     40.74.2.8. Task 8: Feature importance
     </a>
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-   41.74.3. Acknowledgments
+   40.74.3. Acknowledgments
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@@ -1717,12 +1717,12 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="random-forests-for-classification">
-<h1><span class="section-number">41.74. </span>Random forests for classification<a class="headerlink" href="#random-forests-for-classification" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.74. </span>Random forests for classification<a class="headerlink" href="#random-forests-for-classification" title="Permalink to this headline">#</a></h1>
 <p>Random Forests are powerful machine learning algorithms used for supervised classification and regression. Random forests works by averaging the predictions of the multiple and randomized decision trees. Decision trees tends to overfit and so by combining multiple decision trees, the effect of overfitting can be minimized.</p>
 <p>Random Forests are type of ensemble models. More about ensembles models in the next assignment.</p>
 <p>Different to other learning algorithms, random forests provide a way to find the importance of each feature and this is implemented in Sklearn.</p>
 <section id="imports">
-<h2><span class="section-number">41.74.1. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.74.1. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -1737,7 +1737,7 @@ <h2><span class="section-number">41.74.1. </span>Imports<a class="headerlink" hr
 </div>
 </section>
 <section id="loading-the-data">
-<h2><span class="section-number">41.74.2. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.74.2. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
 <p>In this assignment, we will use Random forests to build a classifier that identify the increase or decrease of the electricity using “the data that was collected from the Australian New South Wales Electricity Market. In this market, prices are not fixed and are affected by demand and supply of the market. They are set every five minutes. Electricity transfers to/from the neighboring state of Victoria were done to alleviate fluctuations.”</p>
 <p>“The dataset contains 45,312 instances dated from 7 May 1996 to 5 December 1998. Each example of the dataset refers to a period of 30 minutes, i.e. there are 48 instances for each time period of one day. Each example on the dataset has 5 fields, the day of week, the time stamp, the New South Wales electricity demand, the Victoria electricity demand, the scheduled electricity transfer between states and the class label. The class label identifies the change of the price (UP or DOWN) in New South Wales relative to a moving average of the last 24 hours (and removes the impact of longer term price trends). Source: <a class="reference external" href="https://www.openml.org/d/151">Open ML electricity</a>.</p>
 <p>Here are the information about the features:</p>
@@ -1791,7 +1791,7 @@ <h2><span class="section-number">41.74.2. </span>Loading the data<a class="heade
 </div>
 </div>
 <section id="task-1-exploratory-data-analysis">
-<h3><span class="section-number">41.74.2.1. </span>Task 1: Exploratory data analysis<a class="headerlink" href="#task-1-exploratory-data-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.74.2.1. </span>Task 1: Exploratory data analysis<a class="headerlink" href="#task-1-exploratory-data-analysis" title="Permalink to this headline">#</a></h3>
 <p>Before doing exploratory analysis, as always, let’s split the data into training and test sets.</p>
 <div class="cell docutils container">
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@@ -1887,7 +1887,7 @@ <h3><span class="section-number">41.74.2.1. </span>Task 1: Exploratory data anal
 <p>In the above correlation matrix, you can see that class feature is not there and this is because it still has categorical values.</p>
 </section>
 <section id="task-2-more-data-exploration">
-<h3><span class="section-number">41.74.2.2. </span>Task 2: More data exploration<a class="headerlink" href="#task-2-more-data-exploration" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.74.2.2. </span>Task 2: More data exploration<a class="headerlink" href="#task-2-more-data-exploration" title="Permalink to this headline">#</a></h3>
 <p>Before preprocessing the data, let’s take a look into specific features.</p>
 <p>Let’s see how many Ups/Downs are in the class feature.</p>
 <div class="cell docutils container">
@@ -1973,7 +1973,7 @@ <h3><span class="section-number">41.74.2.2. </span>Task 2: More data exploration
 </div>
 </section>
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-<h3><span class="section-number">41.74.2.3. </span>Task 3: Data preprocessing<a class="headerlink" href="#task-3-data-preprocessing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.74.2.3. </span>Task 3: Data preprocessing<a class="headerlink" href="#task-3-data-preprocessing" title="Permalink to this headline">#</a></h3>
 <p>It is here that we prepare the data to be in the proper format for the machine learning model.</p>
 <p>Let’s encode the categorical feature <code class="docutils literal notranslate"><span class="pre">class</span></code>. But before that, let’s take training input data and labels.</p>
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@@ -2012,7 +2012,7 @@ <h3><span class="section-number">41.74.2.3. </span>Task 3: Data preprocessing<a
 <p>But again if you look at the data, the <code class="docutils literal notranslate"><span class="pre">day</span></code> feature is not normalized as other features. We can normalize it or leave it but for now let’s go ahead and train the random forests classifier.</p>
 </section>
 <section id="task-4-training-random-forests-classifier">
-<h3><span class="section-number">41.74.2.4. </span>Task 4: Training random forests classifier<a class="headerlink" href="#task-4-training-random-forests-classifier" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.74.2.4. </span>Task 4: Training random forests classifier<a class="headerlink" href="#task-4-training-random-forests-classifier" title="Permalink to this headline">#</a></h3>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
@@ -2033,7 +2033,7 @@ <h3><span class="section-number">41.74.2.4. </span>Task 4: Training random fores
 </div>
 </section>
 <section id="task5-evaluating-random-forests-classifier">
-<h3><span class="section-number">41.74.2.5. </span>Task5: Evaluating random forests classifier<a class="headerlink" href="#task5-evaluating-random-forests-classifier" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.74.2.5. </span>Task5: Evaluating random forests classifier<a class="headerlink" href="#task5-evaluating-random-forests-classifier" title="Permalink to this headline">#</a></h3>
 <p>Let’s build 3 functions to display accuracy, confusion matrix, and classification report.</p>
 <ul class="simple">
 <li><p>Accuracy provide a percentage score of the model’s ability to make correct predictions.</p></li>
@@ -2124,7 +2124,7 @@ <h3><span class="section-number">41.74.2.5. </span>Task5: Evaluating random fore
 <p>The model clearly overfitted the data. Let’s see how we can regularize it.</p>
 </section>
 <section id="task-6-improving-random-forests">
-<h3><span class="section-number">41.74.2.6. </span>Task 6: Improving random forests<a class="headerlink" href="#task-6-improving-random-forests" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.74.2.6. </span>Task 6: Improving random forests<a class="headerlink" href="#task-6-improving-random-forests" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Random forest model parameters</span>
@@ -2226,7 +2226,7 @@ <h3><span class="section-number">41.74.2.6. </span>Task 6: Improving random fore
 <p>One way to improve the model can be to search more hyperparameters or adding more good data is always the best cure.</p>
 </section>
 <section id="task-7-evaluating-the-model-on-the-test-set">
-<h3><span class="section-number">41.74.2.7. </span>Task 7: Evaluating the model on the test set<a class="headerlink" href="#task-7-evaluating-the-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.74.2.7. </span>Task 7: Evaluating the model on the test set<a class="headerlink" href="#task-7-evaluating-the-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
 <p>Let us evaluate the model on the test set. But we need first run the label_encoder on the class feature as we did in the training labels. Note that we only transform (not fit_transform).</p>
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@@ -2262,7 +2262,7 @@ <h3><span class="section-number">41.74.2.7. </span>Task 7: Evaluating the model
 <p>As you can see the model is no longer overfitting. On the training set, the accuracy was 79%, which is a figure very similar to the test set. And the model never saw the test data. To improve the model in the case like this, if is often best to add more data if possible.</p>
 </section>
 <section id="task-8-feature-importance">
-<h3><span class="section-number">41.74.2.8. </span>Task 8: Feature importance<a class="headerlink" href="#task-8-feature-importance" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.74.2.8. </span>Task 8: Feature importance<a class="headerlink" href="#task-8-feature-importance" title="Permalink to this headline">#</a></h3>
 <p>Different to other machine learning models, random forests can show how each feature contributed to the model generalization. Let’s find it.</p>
 <p>The results are values between 0 and 1. The closer to 1, the good the feature was to the model.</p>
 <div class="cell docutils container">
@@ -2282,7 +2282,7 @@ <h3><span class="section-number">41.74.2.8. </span>Task 8: Feature importance<a
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.74.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.74.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Nyandwi for creating the open-source course <a class="reference external" href="https://github.com/Nyandwi/machine_learning_complete">Machine Learning complete</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2324,13 +2324,13 @@ <h2><span class="section-number">41.74.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.73. </span>Random forests intro and regression</p>
+            <p class="prev-next-title"><span class="section-number">40.73. </span>Random forests intro and regression</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="beyond-random-forests-more-ensemble-models.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">41.75. </span>Beyond random forests: more ensemble models</p>
+        <p class="prev-next-title"><span class="section-number">40.75. </span>Beyond random forests: more ensemble models</p>
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-     41.112. DQN On Foreign Exchange Market
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    </li>
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-     41.113. Art by gan
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-     41.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
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     <a class="reference internal" href="../../deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     41.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
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   <ul>
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-     42.1. Python programming introduction
+     41.1. Python programming introduction
     </a>
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-     42.4. Data Science introduction
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     </a>
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-     42.16. Build an machine learning web application
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-     42.20. Convolutional Neural Network
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@@ -1575,81 +1575,81 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#imports">
-   41.73.1. Imports
+   40.73.1. Imports
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-the-data">
-   41.73.2. Loading the data
+   40.73.2. Loading the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#tasks-and-roles">
-   41.73.3. Tasks and roles
+   40.73.3. Tasks and roles
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-exploratory-analysis">
-     41.73.3.1. Task 1: Exploratory analysis
+     40.73.3.1. Task 1: Exploratory analysis
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#part-1-the-histogram">
-       41.73.3.1.1. Part 1: The histogram
+       40.73.3.1.1. Part 1: The histogram
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#part-2-the-pairplot">
-       41.73.3.1.2. Part 2: The pairplot
+       40.73.3.1.2. Part 2: The pairplot
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#part-3-check-the-train-data">
-       41.73.3.1.3. Part 3: Check the train data
+       40.73.3.1.3. Part 3: Check the train data
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#part-4-look-the-correlation">
-       41.73.3.1.4. Part 4: Look the correlation
+       40.73.3.1.4. Part 4: Look the correlation
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-data-preprocessing">
-     41.73.3.2. Task 2: Data preprocessing
+     40.73.3.2. Task 2: Data preprocessing
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-training-random-forests-regressor">
-     41.73.3.3. Task 3: Training random forests regressor
+     40.73.3.3. Task 3: Training random forests regressor
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-evaluating-random-forests-regressor">
-     41.73.3.4. Task 4: Evaluating random forests regressor
+     40.73.3.4. Task 4: Evaluating random forests regressor
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-improving-random-forests">
-     41.73.3.5. Task 5: Improving random forests
+     40.73.3.5. Task 5: Improving random forests
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-6-feature-importance">
-     41.73.3.6. Task 6: Feature importance
+     40.73.3.6. Task 6: Feature importance
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-7-evaluating-the-model-on-the-test-set">
-     41.73.3.7. Task 7: Evaluating the Model on the Test Set
+     40.73.3.7. Task 7: Evaluating the Model on the Test Set
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.73.4. Acknowledgments
+   40.73.4. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1673,81 +1673,81 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#imports">
-   41.73.1. Imports
+   40.73.1. Imports
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-the-data">
-   41.73.2. Loading the data
+   40.73.2. Loading the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#tasks-and-roles">
-   41.73.3. Tasks and roles
+   40.73.3. Tasks and roles
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-exploratory-analysis">
-     41.73.3.1. Task 1: Exploratory analysis
+     40.73.3.1. Task 1: Exploratory analysis
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#part-1-the-histogram">
-       41.73.3.1.1. Part 1: The histogram
+       40.73.3.1.1. Part 1: The histogram
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#part-2-the-pairplot">
-       41.73.3.1.2. Part 2: The pairplot
+       40.73.3.1.2. Part 2: The pairplot
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#part-3-check-the-train-data">
-       41.73.3.1.3. Part 3: Check the train data
+       40.73.3.1.3. Part 3: Check the train data
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#part-4-look-the-correlation">
-       41.73.3.1.4. Part 4: Look the correlation
+       40.73.3.1.4. Part 4: Look the correlation
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-data-preprocessing">
-     41.73.3.2. Task 2: Data preprocessing
+     40.73.3.2. Task 2: Data preprocessing
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-training-random-forests-regressor">
-     41.73.3.3. Task 3: Training random forests regressor
+     40.73.3.3. Task 3: Training random forests regressor
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-evaluating-random-forests-regressor">
-     41.73.3.4. Task 4: Evaluating random forests regressor
+     40.73.3.4. Task 4: Evaluating random forests regressor
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-improving-random-forests">
-     41.73.3.5. Task 5: Improving random forests
+     40.73.3.5. Task 5: Improving random forests
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-6-feature-importance">
-     41.73.3.6. Task 6: Feature importance
+     40.73.3.6. Task 6: Feature importance
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-7-evaluating-the-model-on-the-test-set">
-     41.73.3.7. Task 7: Evaluating the Model on the Test Set
+     40.73.3.7. Task 7: Evaluating the Model on the Test Set
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.73.4. Acknowledgments
+   40.73.4. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1761,12 +1761,12 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="random-forests-intro-and-regression">
-<h1><span class="section-number">41.73. </span>Random forests intro and regression<a class="headerlink" href="#random-forests-intro-and-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.73. </span>Random forests intro and regression<a class="headerlink" href="#random-forests-intro-and-regression" title="Permalink to this headline">#</a></h1>
 <p>Random Forests are powerful machine learning algorithms used for supervised classification and regression. Random forests works by averaging the predictions of the multiple and randomized decision trees. Decision trees tends to overfit and so by combining multiple decision trees, the effect of overfitting can be minimized.</p>
 <p>Random Forests are type of ensemble models. More about ensembles models in the next notebook.</p>
 <p>Different to other learning algorithms, random forests provide a way to find the importance of each feature and this is implemented in Sklearn.</p>
 <section id="imports">
-<h2><span class="section-number">41.73.1. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.73.1. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -1786,7 +1786,7 @@ <h2><span class="section-number">41.73.1. </span>Imports<a class="headerlink" hr
 </div>
 </section>
 <section id="loading-the-data">
-<h2><span class="section-number">41.73.2. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.73.2. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
 <p>In this regression task with random forests, we will use the Machine CPU (Central Processing Unit) dataset which is available at <a class="reference external" href="https://www.openml.org/t/5492">OpenML</a>.</p>
 <p>If you are reading this, it’s very likely that you know CPU or you have once(or many times) thought about it when you were buying your computer. In this notebook, we will predict the relative performance of the CPU given the following data:</p>
 <ul class="simple">
@@ -1839,9 +1839,9 @@ <h2><span class="section-number">41.73.2. </span>Loading the data<a class="heade
 </div>
 </section>
 <section id="tasks-and-roles">
-<h2><span class="section-number">41.73.3. </span>Tasks and roles<a class="headerlink" href="#tasks-and-roles" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.73.3. </span>Tasks and roles<a class="headerlink" href="#tasks-and-roles" title="Permalink to this headline">#</a></h2>
 <section id="task-1-exploratory-analysis">
-<h3><span class="section-number">41.73.3.1. </span>Task 1: Exploratory analysis<a class="headerlink" href="#task-1-exploratory-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.73.3.1. </span>Task 1: Exploratory analysis<a class="headerlink" href="#task-1-exploratory-analysis" title="Permalink to this headline">#</a></h3>
 <p>Before doing exploratory analysis, let’s get the training and test data.</p>
 <div class="cell docutils container">
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@@ -1858,7 +1858,7 @@ <h3><span class="section-number">41.73.3.1. </span>Task 1: Exploratory analysis<
 </div>
 </div>
 <section id="part-1-the-histogram">
-<h4><span class="section-number">41.73.3.1.1. </span>Part 1: The histogram<a class="headerlink" href="#part-1-the-histogram" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.73.3.1.1. </span>Part 1: The histogram<a class="headerlink" href="#part-1-the-histogram" title="Permalink to this headline">#</a></h4>
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@@ -1931,7 +1931,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="part-2-the-pairplot">
-<h4><span class="section-number">41.73.3.1.2. </span>Part 2: The pairplot<a class="headerlink" href="#part-2-the-pairplot" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.73.3.1.2. </span>Part 2: The pairplot<a class="headerlink" href="#part-2-the-pairplot" title="Permalink to this headline">#</a></h4>
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@@ -2004,7 +2004,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="part-3-check-the-train-data">
-<h4><span class="section-number">41.73.3.1.3. </span>Part 3: Check the train data<a class="headerlink" href="#part-3-check-the-train-data" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.73.3.1.3. </span>Part 3: Check the train data<a class="headerlink" href="#part-3-check-the-train-data" title="Permalink to this headline">#</a></h4>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">df_desc</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
@@ -2044,7 +2044,7 @@ <h4><span class="section-number">41.73.3.1.3. </span>Part 3: Check the train dat
 <p>Great! We don’t have any missing values.</p>
 </section>
 <section id="part-4-look-the-correlation">
-<h4><span class="section-number">41.73.3.1.4. </span>Part 4: Look the correlation<a class="headerlink" href="#part-4-look-the-correlation" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.73.3.1.4. </span>Part 4: Look the correlation<a class="headerlink" href="#part-4-look-the-correlation" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">df_corr</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
@@ -2085,7 +2085,7 @@ <h4><span class="section-number">41.73.3.1.4. </span>Part 4: Look the correlatio
 </section>
 </section>
 <section id="task-2-data-preprocessing">
-<h3><span class="section-number">41.73.3.2. </span>Task 2: Data preprocessing<a class="headerlink" href="#task-2-data-preprocessing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.73.3.2. </span>Task 2: Data preprocessing<a class="headerlink" href="#task-2-data-preprocessing" title="Permalink to this headline">#</a></h3>
 <p>It is here that we prepare the data to be in the proper format for the machine learning model.
 Let’s set up a pipeline to scale features but before that, let’s take training input data and labels.</p>
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@@ -2109,7 +2109,7 @@ <h3><span class="section-number">41.73.3.2. </span>Task 2: Data preprocessing<a
 </div>
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 <section id="task-3-training-random-forests-regressor">
-<h3><span class="section-number">41.73.3.3. </span>Task 3: Training random forests regressor<a class="headerlink" href="#task-3-training-random-forests-regressor" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.73.3.3. </span>Task 3: Training random forests regressor<a class="headerlink" href="#task-3-training-random-forests-regressor" title="Permalink to this headline">#</a></h3>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestRegressor</span>
@@ -2125,7 +2125,7 @@ <h3><span class="section-number">41.73.3.3. </span>Task 3: Training random fores
 </div>
 </section>
 <section id="task-4-evaluating-random-forests-regressor">
-<h3><span class="section-number">41.73.3.4. </span>Task 4: Evaluating random forests regressor<a class="headerlink" href="#task-4-evaluating-random-forests-regressor" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.73.3.4. </span>Task 4: Evaluating random forests regressor<a class="headerlink" href="#task-4-evaluating-random-forests-regressor" title="Permalink to this headline">#</a></h3>
 <p>Let’s first check the root mean squarred errr on the training. It is not advised to evaluate the model on the test data since we haven’t improved it yet. we will make a function to make it easier and to avoid repetitions.</p>
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-<h3><span class="section-number">41.73.3.5. </span>Task 5: Improving random forests<a class="headerlink" href="#task-5-improving-random-forests" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.73.3.5. </span>Task 5: Improving random forests<a class="headerlink" href="#task-5-improving-random-forests" title="Permalink to this headline">#</a></h3>
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@@ -2221,7 +2221,7 @@ <h3><span class="section-number">41.73.3.5. </span>Task 5: Improving random fore
 <p>Surprisingly, by searching model hyperparameters, the model did not improve. Can you guess why? We can observe many things while running Grid Search and reading about the <a class="reference external" href="https://scikit-learn.org/stable/modules/ensemble.html#forests-of-randomized-trees">random forests</a>. If you can’t get good results, set the <code class="docutils literal notranslate"><span class="pre">bootstrap</span></code> to False. It is true by default, and that means that you are training on samples of the training set instead of the whole training set. Try going back to the orginal model and change it to True and note how the prediction changes. Also learn more about the other hyperparameters.</p>
 </section>
 <section id="task-6-feature-importance">
-<h3><span class="section-number">41.73.3.6. </span>Task 6: Feature importance<a class="headerlink" href="#task-6-feature-importance" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.73.3.6. </span>Task 6: Feature importance<a class="headerlink" href="#task-6-feature-importance" title="Permalink to this headline">#</a></h3>
 <p>Different to other machine learning models, random forests can show how each feature contributed to the model generalization. Let’s find it.
 The results are values between 0 and 1. The closer to 1, the good the feature was to the model.</p>
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@@ -2239,7 +2239,7 @@ <h3><span class="section-number">41.73.3.6. </span>Task 6: Feature importance<a
 <p>It makes sense that the model was able to find that out. Main memory (RAM, Read Only Memory) and cache memory (which stores frequently used information thus facilitating faster processing and quick retrieval of information) are the two most factors of the CPU performance and if you are going to buy a new computer, you want to have high RAM and cache memory in order to have a powerful machine that can process/compute and retrieve things faster.</p>
 </section>
 <section id="task-7-evaluating-the-model-on-the-test-set">
-<h3><span class="section-number">41.73.3.7. </span>Task 7: Evaluating the Model on the Test Set<a class="headerlink" href="#task-7-evaluating-the-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.73.3.7. </span>Task 7: Evaluating the Model on the Test Set<a class="headerlink" href="#task-7-evaluating-the-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
 <p>Let us evaluate the model on the test set. But we need first run the pipeline on the test data. Note that we only transform (not fit_transform).</p>
 <div class="cell docutils container">
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@@ -2270,7 +2270,7 @@ <h3><span class="section-number">41.73.3.7. </span>Task 7: Evaluating the Model
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 <section id="acknowledgments">
-<h2><span class="section-number">41.73.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.73.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Nyandwi for creating the open-source course <a class="reference external" href="https://github.com/Nyandwi/machine_learning_complete">Machine Learning complete</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2312,13 +2312,13 @@ <h2><span class="section-number">41.73.4. </span>Acknowledgments<a class="header
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-            <p class="prev-next-title"><span class="section-number">41.72. </span>Regularized Linear Models</p>
+            <p class="prev-next-title"><span class="section-number">40.72. </span>Regularized Linear Models</p>
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-        <p class="prev-next-title"><span class="section-number">41.74. </span>Random forests for classification</p>
+        <p class="prev-next-title"><span class="section-number">40.74. </span>Random forests for classification</p>
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diff --git a/assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html b/assignments/ml-advanced/gradient-boosting/boosting-with-tuning.html
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-    <link rel="prev" title="41.78. Gradient boosting" href="gradient-boosting-assignment.html" />
+    <link rel="next" title="40.80. Random Forest Classifier with Feature Importance" href="../ensemble-learning/random-forest-classifier-feature-importance.html" />
+    <link rel="prev" title="40.78. Gradient boosting" href="gradient-boosting-assignment.html" />
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       <a class="reference internal nav-link" href="#task-5-training-and-evaluation">
-       41.79.5.3.5. Task 5: Training and evaluation
+       40.79.5.3.5. Task 5: Training and evaluation
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-6-blending">
-       41.79.5.3.6. Task 6 Blending
+       40.79.5.3.6. Task 6 Blending
       </a>
      </li>
     </ul>
@@ -1792,7 +1792,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.79.6. Acknowledgments
+   40.79.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1816,59 +1816,59 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   41.79.1. Introduction
+   40.79.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#understanding-data">
-   41.79.2. Understanding data
+   40.79.2. Understanding data
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-imports-and-load-data">
-     41.79.2.1. Task 1: Imports and load data
+     40.79.2.1. Task 1: Imports and load data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-concatenate-the-train-and-test-together">
-     41.79.2.2. Task 2: Concatenate the train and test together
+     40.79.2.2. Task 2: Concatenate the train and test together
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-isolate-numerical-and-categorical-columns">
-     41.79.2.3. Task 3: Isolate numerical and categorical columns
+     40.79.2.3. Task 3: Isolate numerical and categorical columns
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#univariate-analysis">
-     41.79.2.4. Univariate Analysis
+     40.79.2.4. Univariate Analysis
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-1-numeric-features">
-       41.79.2.4.1. Task 1: Numeric features
+       40.79.2.4.1. Task 1: Numeric features
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-2-categorical-features">
-       41.79.2.4.2. Task 2: Categorical features
+       40.79.2.4.2. Task 2: Categorical features
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#bi-variate-analysis">
-     41.79.2.5. Bi-Variate Analysis
+     40.79.2.5. Bi-Variate Analysis
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-1-correlation-matrix">
-       41.79.2.5.1. Task 1: Correlation matrix
+       40.79.2.5.1. Task 1: Correlation matrix
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-2-identifying-relationship-between-numerical-predictor-and-target-saleprice">
-       41.79.2.5.2. Task 2: Identifying relationship between Numerical Predictor and Target (
+       40.79.2.5.2. Task 2: Identifying relationship between Numerical Predictor and Target (
        <em>
         SalePrice
        </em>
@@ -1877,7 +1877,7 @@ <h2> Contents </h2>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-3-scatterplot">
-       41.79.2.5.3. Task 3: Scatterplot
+       40.79.2.5.3. Task 3: Scatterplot
       </a>
      </li>
     </ul>
@@ -1886,69 +1886,69 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-processing">
-   41.79.3. Data Processing
+   40.79.3. Data Processing
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#removing-redundant-features">
-     41.79.3.1. Removing redundant features
+     40.79.3.1. Removing redundant features
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-1-features-with-multicollinearity">
-       41.79.3.1.1. Task 1：Features with multicollinearity
+       40.79.3.1.1. Task 1：Features with multicollinearity
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-2-features-with-a-lot-of-missing-values">
-       41.79.3.1.2. Task 2: Features with a lot of missing values
+       40.79.3.1.2. Task 2: Features with a lot of missing values
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-3-useless-features-in-predicting-saleprice">
-       41.79.3.1.3. Task 3: Useless features in predicting SalePrice
+       40.79.3.1.3. Task 3: Useless features in predicting SalePrice
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-4-removing-features-that-have-mostly-just-1-value">
-       41.79.3.1.4. Task 4: Removing features that have mostly just 1 value
+       40.79.3.1.4. Task 4: Removing features that have mostly just 1 value
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dealing-with-outliers">
-     41.79.3.2. Dealing with outliers
+     40.79.3.2. Dealing with outliers
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#filling-missing-values">
-     41.79.3.3. Filling Missing Values
+     40.79.3.3. Filling Missing Values
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-1-ordinal-features">
-       41.79.3.3.1. Task 1: Ordinal features
+       40.79.3.3.1. Task 1: Ordinal features
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       41.79.3.3.2. Task 2: Categorical features
+       40.79.3.3.2. Task 2: Categorical features
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-3-numerical-features">
-       41.79.3.3.3. Task 3: Numerical features
+       40.79.3.3.3. Task 3: Numerical features
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-4-changing-data-type">
-       41.79.3.3.4. Task 4: Changing Data Type
+       40.79.3.3.4. Task 4: Changing Data Type
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-5-mapping-ordinal-features">
-       41.79.3.3.5. Task 5: Mapping ordinal features
+       40.79.3.3.5. Task 5: Mapping ordinal features
       </a>
      </li>
     </ul>
@@ -1957,74 +1957,74 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#feature-engineering">
-   41.79.4. Feature engineering
+   40.79.4. Feature engineering
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-binay-columns">
-     41.79.4.1. Task 1: Binay columns
+     40.79.4.1. Task 1: Binay columns
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-converting-categorical-to-numerical">
-     41.79.4.2. Task 2: Converting categorical to numerical
+     40.79.4.2. Task 2: Converting categorical to numerical
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-saleprice-distribution">
-     41.79.4.3. Task 3: SalePrice distribution
+     40.79.4.3. Task 3: SalePrice distribution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#modeling">
-   41.79.5. Modeling
+   40.79.5. Modeling
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#split-into-train-validation-set">
-     41.79.5.1. Split into train-validation set
+     40.79.5.1. Split into train-validation set
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#scaling-of-data">
-     41.79.5.2. Scaling of Data
+     40.79.5.2. Scaling of Data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#ensemble-algorithms">
-     41.79.5.3. Ensemble Algorithms
+     40.79.5.3. Ensemble Algorithms
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-1-ensembling-models">
-       41.79.5.3.1. Task 1: Ensembling models
+       40.79.5.3.1. Task 1: Ensembling models
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-2-xgboost">
-       41.79.5.3.2. Task 2: XGBoost
+       40.79.5.3.2. Task 2: XGBoost
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-3-lightgbm">
-       41.79.5.3.3. Task 3: LightGBM
+       40.79.5.3.3. Task 3: LightGBM
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-4-catboost">
-       41.79.5.3.4. Task 4: Catboost
+       40.79.5.3.4. Task 4: Catboost
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-5-training-and-evaluation">
-       41.79.5.3.5. Task 5: Training and evaluation
+       40.79.5.3.5. Task 5: Training and evaluation
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#task-6-blending">
-       41.79.5.3.6. Task 6 Blending
+       40.79.5.3.6. Task 6 Blending
       </a>
      </li>
     </ul>
@@ -2033,7 +2033,7 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.79.6. Acknowledgments
+   40.79.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -2047,9 +2047,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="boosting-with-tuning">
-<h1><span class="section-number">41.79. </span>Boosting with tuning<a class="headerlink" href="#boosting-with-tuning" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.79. </span>Boosting with tuning<a class="headerlink" href="#boosting-with-tuning" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">41.79.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.79.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>In this assginment, we will take you through the step by step approach in solving a House Pricing regression problem. This notebook aims to:</p>
 <ol class="simple">
 <li><p>Provide insights on Housing Data</p></li>
@@ -2060,9 +2060,9 @@ <h2><span class="section-number">41.79.1. </span>Introduction<a class="headerlin
 <p>We hope that after reading this notebook, beginners will be more comfortable in tackling any learning problems and able to use the taught techniques to solve any problems from start to end. For non-beginners, hopefully you are able to get something out of it from this assignment and gain new insights and knowledge along the way.</p>
 </section>
 <section id="understanding-data">
-<h2><span class="section-number">41.79.2. </span>Understanding data<a class="headerlink" href="#understanding-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.79.2. </span>Understanding data<a class="headerlink" href="#understanding-data" title="Permalink to this headline">#</a></h2>
 <section id="task-1-imports-and-load-data">
-<h3><span class="section-number">41.79.2.1. </span>Task 1: Imports and load data<a class="headerlink" href="#task-1-imports-and-load-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.2.1. </span>Task 1: Imports and load data<a class="headerlink" href="#task-1-imports-and-load-data" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="c1"># linear algebra</span>
@@ -2095,7 +2095,7 @@ <h3><span class="section-number">41.79.2.1. </span>Task 1: Imports and load data
 <p>Just taking a quick glance at the top rows of the dataframe, we can see that there are some columns that are filled with <strong>NAN (Not a Number)</strong>. We will investigate this later on.</p>
 </section>
 <section id="task-2-concatenate-the-train-and-test-together">
-<h3><span class="section-number">41.79.2.2. </span>Task 2: Concatenate the train and test together<a class="headerlink" href="#task-2-concatenate-the-train-and-test-together" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.2.2. </span>Task 2: Concatenate the train and test together<a class="headerlink" href="#task-2-concatenate-the-train-and-test-together" title="Permalink to this headline">#</a></h3>
 <p>What we did here is first to concatenate the train and test together for extracting insights into the Housing Price data as a whole. It will also be more convenient for our preprocessing steps later on as we will only have 1 data reference</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2114,7 +2114,7 @@ <h3><span class="section-number">41.79.2.2. </span>Task 2: Concatenate the train
 </div>
 </section>
 <section id="task-3-isolate-numerical-and-categorical-columns">
-<h3><span class="section-number">41.79.2.3. </span>Task 3: Isolate numerical and categorical columns<a class="headerlink" href="#task-3-isolate-numerical-and-categorical-columns" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.2.3. </span>Task 3: Isolate numerical and categorical columns<a class="headerlink" href="#task-3-isolate-numerical-and-categorical-columns" title="Permalink to this headline">#</a></h3>
 <p>Lets isolate both the numerical and categorical columns since we will be applying different visualization techniques on them</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2148,9 +2148,9 @@ <h3><span class="section-number">41.79.2.3. </span>Task 3: Isolate numerical and
 </div>
 </section>
 <section id="univariate-analysis">
-<h3><span class="section-number">41.79.2.4. </span>Univariate Analysis<a class="headerlink" href="#univariate-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.2.4. </span>Univariate Analysis<a class="headerlink" href="#univariate-analysis" title="Permalink to this headline">#</a></h3>
 <section id="task-1-numeric-features">
-<h4><span class="section-number">41.79.2.4.1. </span>Task 1: Numeric features<a class="headerlink" href="#task-1-numeric-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.2.4.1. </span>Task 1: Numeric features<a class="headerlink" href="#task-1-numeric-features" title="Permalink to this headline">#</a></h4>
 <p>For numerical features, we are always concerned about the <strong>distribution</strong> of these features, including the <strong>statistical characteristics</strong> of these columns e.g mean, median, mode. Hence  we will usually use <strong>Distribution plot</strong> to visualize their data distribution. <strong>Boxplots</strong> are also commonly used to unearth the statistical characteristics of each feature. More often than not, we use it to look for any outliers that we might need to filter out later on during the preprocessing step.</p>
 <div class="cell docutils container">
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@@ -2198,7 +2198,7 @@ <h4><span class="section-number">41.79.2.4.1. </span>Task 1: Numeric features<a
 </div>
 </section>
 <section id="task-2-categorical-features">
-<h4><span class="section-number">41.79.2.4.2. </span>Task 2: Categorical features<a class="headerlink" href="#task-2-categorical-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.2.4.2. </span>Task 2: Categorical features<a class="headerlink" href="#task-2-categorical-features" title="Permalink to this headline">#</a></h4>
 <p>In the case of categorical features, we will often use countplots to visualize the count of each distinct value within each features. We can see that some categorical features like <strong>Utilities, Condition2</strong> consist of mainly just one value, which does not add any useful information. Thus, we will also remove them later on.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2216,11 +2216,11 @@ <h4><span class="section-number">41.79.2.4.2. </span>Task 2: Categorical feature
 </section>
 </section>
 <section id="bi-variate-analysis">
-<h3><span class="section-number">41.79.2.5. </span>Bi-Variate Analysis<a class="headerlink" href="#bi-variate-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.2.5. </span>Bi-Variate Analysis<a class="headerlink" href="#bi-variate-analysis" title="Permalink to this headline">#</a></h3>
 <p>Bi-variate analysis looks at 2 different features to identify any possible relationship or distinctive patterns between the 2 features. One of the commonly used technique is through the  <strong>Correlation Matrix</strong>. Correlation matrix is an effective tool to uncover linear relationship (Correlation) between any 2 continuous features. Correlation not only allow us to determine which features are important to Saleprice, but also as a mean to investigate any <strong>multicollinearity</strong> between our independent predictors.<br />
 Multicollinearity happens when 2 or more independent variables are highly correlated with one another. In such situation, it causes precision loss in our regression coefficients, affecting our ability to identify the most important features that are most useful to our model</p>
 <section id="task-1-correlation-matrix">
-<h4><span class="section-number">41.79.2.5.1. </span>Task 1: Correlation matrix<a class="headerlink" href="#task-1-correlation-matrix" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.2.5.1. </span>Task 1: Correlation matrix<a class="headerlink" href="#task-1-correlation-matrix" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span><span class="mi">12</span><span class="p">))</span>
@@ -2240,7 +2240,7 @@ <h4><span class="section-number">41.79.2.5.1. </span>Task 1: Correlation matrix<
 <p>From the correlation matrix we have identified the above variables which are highly correlated with each other. This finding will guide us in our preprocessing steps later on as we aim to remove highly correlated features to avoid performance loss in our model</p>
 </section>
 <section id="task-2-identifying-relationship-between-numerical-predictor-and-target-saleprice">
-<h4><span class="section-number">41.79.2.5.2. </span>Task 2: Identifying relationship between Numerical Predictor and Target (<em>SalePrice</em>)<a class="headerlink" href="#task-2-identifying-relationship-between-numerical-predictor-and-target-saleprice" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.2.5.2. </span>Task 2: Identifying relationship between Numerical Predictor and Target (<em>SalePrice</em>)<a class="headerlink" href="#task-2-identifying-relationship-between-numerical-predictor-and-target-saleprice" title="Permalink to this headline">#</a></h4>
 <p>Below, we sorted the strength of linear relationship between Saleprice and other features. OverallQual and GrLivArea has the strongest linear relationship with SalePrice. Hence, these 2 features will be important factor in predicting Housing Price</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2253,7 +2253,7 @@ <h4><span class="section-number">41.79.2.5.2. </span>Task 2: Identifying relatio
 </div>
 </section>
 <section id="task-3-scatterplot">
-<h4><span class="section-number">41.79.2.5.3. </span>Task 3: Scatterplot<a class="headerlink" href="#task-3-scatterplot" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.2.5.3. </span>Task 3: Scatterplot<a class="headerlink" href="#task-3-scatterplot" title="Permalink to this headline">#</a></h4>
 <p>Using scatterplot can also help us to identify potential linear relationship between Numerical features. Although scatterplot does not provide quantitative evidence on the strength of linear relationship between our features, it is useful in helping us to visualize any sort of relationship that correlation matrix could not calculate. E.g Quadratic, Exponential relationships.</p>
 <div class="cell docutils container">
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@@ -2270,7 +2270,7 @@ <h4><span class="section-number">41.79.2.5.3. </span>Task 3: Scatterplot<a class
 </section>
 </section>
 <section id="data-processing">
-<h2><span class="section-number">41.79.3. </span>Data Processing<a class="headerlink" href="#data-processing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.79.3. </span>Data Processing<a class="headerlink" href="#data-processing" title="Permalink to this headline">#</a></h2>
 <p>Now that we have more or less finished analysing our data and gaining insights through the various analysis and visualization, we will have to leverage on these insights to guide our preprocessing decision, so as to provide a clean and error-free data for our model to train on later on.</p>
 <p>We do not perform visualization and analysis just to create pretty graphs or for the sake of doing it, IT IS VITAL TO OUR PREPROCESSING!</p>
 <p>This section outlines the steps for Data Processing:</p>
@@ -2280,9 +2280,9 @@ <h2><span class="section-number">41.79.3. </span>Data Processing<a class="header
 <li><p>Filling in missing values</p></li>
 </ol>
 <section id="removing-redundant-features">
-<h3><span class="section-number">41.79.3.1. </span>Removing redundant features<a class="headerlink" href="#removing-redundant-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.3.1. </span>Removing redundant features<a class="headerlink" href="#removing-redundant-features" title="Permalink to this headline">#</a></h3>
 <section id="task-1-features-with-multicollinearity">
-<h4><span class="section-number">41.79.3.1.1. </span>Task 1：Features with multicollinearity<a class="headerlink" href="#task-1-features-with-multicollinearity" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.3.1.1. </span>Task 1：Features with multicollinearity<a class="headerlink" href="#task-1-features-with-multicollinearity" title="Permalink to this headline">#</a></h4>
 <p>From the above correlation matrix, we have pinpointed certain features that are highly correlated</p>
 <ul class="simple">
 <li><p>GarageYrBlt and YearBuilt</p></li>
@@ -2300,7 +2300,7 @@ <h4><span class="section-number">41.79.3.1.1. </span>Task 1：Features with mult
 </div>
 </section>
 <section id="task-2-features-with-a-lot-of-missing-values">
-<h4><span class="section-number">41.79.3.1.2. </span>Task 2: Features with a lot of missing values<a class="headerlink" href="#task-2-features-with-a-lot-of-missing-values" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.3.1.2. </span>Task 2: Features with a lot of missing values<a class="headerlink" href="#task-2-features-with-a-lot-of-missing-values" title="Permalink to this headline">#</a></h4>
 <p>Apart from these highly correlated features, we will also remove features that is not very useful in prediction due to many missing values. PoolQC, MiscFeature, Alley has too many missing values to provide any useful information</p>
 <div class="cell docutils container">
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@@ -2322,7 +2322,7 @@ <h4><span class="section-number">41.79.3.1.2. </span>Task 2: Features with a lot
 </div>
 </section>
 <section id="task-3-useless-features-in-predicting-saleprice">
-<h4><span class="section-number">41.79.3.1.3. </span>Task 3: Useless features in predicting SalePrice<a class="headerlink" href="#task-3-useless-features-in-predicting-saleprice" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.3.1.3. </span>Task 3: Useless features in predicting SalePrice<a class="headerlink" href="#task-3-useless-features-in-predicting-saleprice" title="Permalink to this headline">#</a></h4>
 <p>We will also remove features that does not have any linear relationship with target <em>SalePrice</em>. We can see from the plot below that the MoSold and YrSold does not have any impact on the price of the house sold.</p>
 <div class="cell docutils container">
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@@ -2345,7 +2345,7 @@ <h4><span class="section-number">41.79.3.1.3. </span>Task 3: Useless features in
 </div>
 </section>
 <section id="task-4-removing-features-that-have-mostly-just-1-value">
-<h4><span class="section-number">41.79.3.1.4. </span>Task 4: Removing features that have mostly just 1 value<a class="headerlink" href="#task-4-removing-features-that-have-mostly-just-1-value" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.3.1.4. </span>Task 4: Removing features that have mostly just 1 value<a class="headerlink" href="#task-4-removing-features-that-have-mostly-just-1-value" title="Permalink to this headline">#</a></h4>
 <p>Earlier during our Univariate analysis, we found that some features mostly consist of just a single value or 0s, which is not useful to us. Therefore, we set a user defined threshold at 96%. If a column has more than 96% of the same value, we will render the features to be useless and remove it, since there isnt much information we can extract from</p>
 <div class="cell docutils container">
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@@ -2390,7 +2390,7 @@ <h4><span class="section-number">41.79.3.1.4. </span>Task 4: Removing features t
 </section>
 </section>
 <section id="dealing-with-outliers">
-<h3><span class="section-number">41.79.3.2. </span>Dealing with outliers<a class="headerlink" href="#dealing-with-outliers" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.3.2. </span>Dealing with outliers<a class="headerlink" href="#dealing-with-outliers" title="Permalink to this headline">#</a></h3>
 <p>Removing outliers will prevent our models performance from being affected by extreme values.<br />
 From our boxplot earlier, we have pinpointed the following features with extreme outliers:</p>
 <ul class="simple">
@@ -2433,7 +2433,7 @@ <h3><span class="section-number">41.79.3.2. </span>Dealing with outliers<a class
 </div>
 </section>
 <section id="filling-missing-values">
-<h3><span class="section-number">41.79.3.3. </span>Filling Missing Values<a class="headerlink" href="#filling-missing-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.3.3. </span>Filling Missing Values<a class="headerlink" href="#filling-missing-values" title="Permalink to this headline">#</a></h3>
 <p>Our machine learning model is unable to deal with missing values, thus we need to deal with them based on our understanding of the features. These missing values are denoted <strong>NAN</strong> as we have seen earlier during our data exploration.</p>
 <div class="cell docutils container">
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@@ -2443,7 +2443,7 @@ <h3><span class="section-number">41.79.3.3. </span>Filling Missing Values<a clas
 </div>
 </div>
 <section id="task-1-ordinal-features">
-<h4><span class="section-number">41.79.3.3.1. </span>Task 1: Ordinal features<a class="headerlink" href="#task-1-ordinal-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.3.3.1. </span>Task 1: Ordinal features<a class="headerlink" href="#task-1-ordinal-features" title="Permalink to this headline">#</a></h4>
 <p>We will replace the ordinal missing values with NA, which will be mapped later on when we encode them into an ordered arrangement</p>
 <div class="cell docutils container">
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@@ -2458,7 +2458,7 @@ <h4><span class="section-number">41.79.3.3.1. </span>Task 1: Ordinal features<a
 </div>
 </section>
 <section id="id1">
-<h4><span class="section-number">41.79.3.3.2. </span>Task 2: Categorical features<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.3.3.2. </span>Task 2: Categorical features<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
 <p>We will replace the missing value of our categorical features with the most frequent occurrence (mode) of the individual features.</p>
 <div class="cell docutils container">
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@@ -2471,7 +2471,7 @@ <h4><span class="section-number">41.79.3.3.2. </span>Task 2: Categorical feature
 </div>
 </section>
 <section id="task-3-numerical-features">
-<h4><span class="section-number">41.79.3.3.3. </span>Task 3: Numerical features<a class="headerlink" href="#task-3-numerical-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.3.3.3. </span>Task 3: Numerical features<a class="headerlink" href="#task-3-numerical-features" title="Permalink to this headline">#</a></h4>
 <p>For <strong>Numerical Features</strong>, the common approach will be to replace the missing value with the mean of the feature distribution.<br />
 However, certain features like <em>LotFrontage</em> and <em>GarageArea</em> have wide variance in their distribution. Taking mean values across <em>Neighborhoods</em>, we will see that the mean varies alot from just taking the mean value of these individual column, since each neightborhood have different LotFrontage and GarageArea mean value. Hence, i decided to group these features by Neighborhoods to impute the respective mean values.</p>
 <p><strong>Note</strong>: My initial approach was based on the means of both train and test set. This exposed us to the issue of data leakage, where information from the test set is used to compute mean values. The right way to do it will be to impute solely based on the mean of the train data.</p>
@@ -2513,7 +2513,7 @@ <h4><span class="section-number">41.79.3.3.3. </span>Task 3: Numerical features<
 </div>
 </section>
 <section id="task-4-changing-data-type">
-<h4><span class="section-number">41.79.3.3.4. </span>Task 4: Changing Data Type<a class="headerlink" href="#task-4-changing-data-type" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.3.3.4. </span>Task 4: Changing Data Type<a class="headerlink" href="#task-4-changing-data-type" title="Permalink to this headline">#</a></h4>
 <p>Since <strong>MSSubClass</strong> is an integer column based on some mapped values in string notation, we change its data type to string value instead</p>
 <div class="cell docutils container">
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@@ -2524,7 +2524,7 @@ <h4><span class="section-number">41.79.3.3.4. </span>Task 4: Changing Data Type<
 </div>
 </section>
 <section id="task-5-mapping-ordinal-features">
-<h4><span class="section-number">41.79.3.3.5. </span>Task 5: Mapping ordinal features<a class="headerlink" href="#task-5-mapping-ordinal-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.3.3.5. </span>Task 5: Mapping ordinal features<a class="headerlink" href="#task-5-mapping-ordinal-features" title="Permalink to this headline">#</a></h4>
 <p>There are some columns which are ordinal by nature, which represents the quality or condition of certain housing features. In this case, we will map the respective strings to a value. The better the quality, the higher the value</p>
 <div class="cell docutils container">
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@@ -2557,7 +2557,7 @@ <h4><span class="section-number">41.79.3.3.5. </span>Task 5: Mapping ordinal fea
 </section>
 </section>
 <section id="feature-engineering">
-<h2><span class="section-number">41.79.4. </span>Feature engineering<a class="headerlink" href="#feature-engineering" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.79.4. </span>Feature engineering<a class="headerlink" href="#feature-engineering" title="Permalink to this headline">#</a></h2>
 <p>Feature Engineering is a technique by which we create new features that could potentially aid in predicting our target variable, which in this case, is <em>SalePrice</em>. In this notebook, we will create additional features based on our <strong>Domain Knowledge</strong> of the housing features</p>
 <p>Based on the current feature we have, the first additional featuire we can add would be <strong>TotalLot</strong>, which sums up both the <em>LotFrontage</em> and <em>LotArea</em> to identify the total area of land available as lot. We can also calculate the total number of surface area of the house, <strong>TotalSF</strong> by adding the area from basement and 2nd floor. <strong>TotalBath</strong> can also be used to tell us in total how many bathrooms are there in the house. We can also add all the different types of porches around the house and generalise into a total porch area, <strong>TotalPorch</strong>.</p>
 <ul class="simple">
@@ -2579,7 +2579,7 @@ <h2><span class="section-number">41.79.4. </span>Feature engineering<a class="he
 </div>
 </div>
 <section id="task-1-binay-columns">
-<h3><span class="section-number">41.79.4.1. </span>Task 1: Binay columns<a class="headerlink" href="#task-1-binay-columns" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.4.1. </span>Task 1: Binay columns<a class="headerlink" href="#task-1-binay-columns" title="Permalink to this headline">#</a></h3>
 <p>We also include simple feature engineering by creating binary columns for some features that can indicate the <strong>presence(1) / absence(0)</strong> of some features of the house</p>
 <div class="cell docutils container">
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@@ -2594,7 +2594,7 @@ <h3><span class="section-number">41.79.4.1. </span>Task 1: Binay columns<a class
 </div>
 </section>
 <section id="task-2-converting-categorical-to-numerical">
-<h3><span class="section-number">41.79.4.2. </span>Task 2: Converting categorical to numerical<a class="headerlink" href="#task-2-converting-categorical-to-numerical" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.4.2. </span>Task 2: Converting categorical to numerical<a class="headerlink" href="#task-2-converting-categorical-to-numerical" title="Permalink to this headline">#</a></h3>
 <p>Lastly, because machine learning only learns from data that is numerical in nature, we will convert the remaining categorical columns into one-hot features using the <em>get_dummies()</em> method into numerical columns that is suitable for feeding into our machine learning algorithm.</p>
 <div class="cell docutils container">
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@@ -2605,7 +2605,7 @@ <h3><span class="section-number">41.79.4.2. </span>Task 2: Converting categorica
 </div>
 </section>
 <section id="task-3-saleprice-distribution">
-<h3><span class="section-number">41.79.4.3. </span>Task 3: SalePrice distribution<a class="headerlink" href="#task-3-saleprice-distribution" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.4.3. </span>Task 3: SalePrice distribution<a class="headerlink" href="#task-3-saleprice-distribution" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
@@ -2637,10 +2637,10 @@ <h3><span class="section-number">41.79.4.3. </span>Task 3: SalePrice distributio
 </section>
 </section>
 <section id="modeling">
-<h2><span class="section-number">41.79.5. </span>Modeling<a class="headerlink" href="#modeling" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.79.5. </span>Modeling<a class="headerlink" href="#modeling" title="Permalink to this headline">#</a></h2>
 <p>This section will consist of scaling the data for better optimization in our training, and also introducing the varieties of ensembling methods that are used in this notebook for predicting the Housing price. We also try out hyperparameter tuning briefly, as i will be dedicating a new notebook that will explain more in details on the process of Hyperparameter Tuning as well as the mathematical aspect of the ensemble algorithms.</p>
 <section id="split-into-train-validation-set">
-<h3><span class="section-number">41.79.5.1. </span>Split into train-validation set<a class="headerlink" href="#split-into-train-validation-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.5.1. </span>Split into train-validation set<a class="headerlink" href="#split-into-train-validation-set" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">train</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
@@ -2652,7 +2652,7 @@ <h3><span class="section-number">41.79.5.1. </span>Split into train-validation s
 </div>
 </section>
 <section id="scaling-of-data">
-<h3><span class="section-number">41.79.5.2. </span>Scaling of Data<a class="headerlink" href="#scaling-of-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.5.2. </span>Scaling of Data<a class="headerlink" href="#scaling-of-data" title="Permalink to this headline">#</a></h3>
 <p><strong>RobustScaler</strong> is a transformation technique that removes the median and scales the data according to the quantile range (defaults to IQR: Interquartile Range). The IQR is the range between the 1st quartile (25th quantile) and the 3rd quartile (75th quantile). It is also robust to outliers, which makes it ideal for data where there are too many outliers that will drastically reduce the number of training data.</p>
 <p>Previously i fitted the RobustScaler on both Train and Test set, and that is a mistake on my side. By fitting the scaler on both train and testset, we exposed ourselves to the problem of <strong>Data Leakage</strong>. Data Leakage is a problem when information from outside the training dataset is used to create the model. If we fit the scaler on both training and test data, our training data characteristics will contain the distribution of our testset. As such, we are unknowningly passing in information about our test data into the final training data for training, which will not give us the opportunity to truly test our model on data it has never seen before.<br />
 <strong>Lesson Learnt</strong>: Fit the scaler just on training data, and then transforming it on both training and test data</p>
@@ -2670,7 +2670,7 @@ <h3><span class="section-number">41.79.5.2. </span>Scaling of Data<a class="head
 </div>
 </section>
 <section id="ensemble-algorithms">
-<h3><span class="section-number">41.79.5.3. </span>Ensemble Algorithms<a class="headerlink" href="#ensemble-algorithms" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.79.5.3. </span>Ensemble Algorithms<a class="headerlink" href="#ensemble-algorithms" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
@@ -2681,7 +2681,7 @@ <h3><span class="section-number">41.79.5.3. </span>Ensemble Algorithms<a class="
 </div>
 </div>
 <section id="task-1-ensembling-models">
-<h4><span class="section-number">41.79.5.3.1. </span>Task 1: Ensembling models<a class="headerlink" href="#task-1-ensembling-models" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.5.3.1. </span>Task 1: Ensembling models<a class="headerlink" href="#task-1-ensembling-models" title="Permalink to this headline">#</a></h4>
 <p>Ensembling methods are meta-algorithms which involves combining several machine learning models into one predictive model, aim at decreasing variance(reduce overfitting) and improving bias(improve accuracy).<br />
 The 3 main ensembling methods are <strong>Bagging, Boosting and Stacking</strong>.<br />
 In this notebook, we will focus mainly on <strong>Boosting</strong>, which is what we will be using for our prediction.</p>
@@ -2702,7 +2702,7 @@ <h4><span class="section-number">41.79.5.3.1. </span>Task 1: Ensembling models<a
 </div>
 </section>
 <section id="task-2-xgboost">
-<h4><span class="section-number">41.79.5.3.2. </span>Task 2: XGBoost<a class="headerlink" href="#task-2-xgboost" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.5.3.2. </span>Task 2: XGBoost<a class="headerlink" href="#task-2-xgboost" title="Permalink to this headline">#</a></h4>
 <p>Extreme Gradient Boost (XGB) is a boosting algorithm that uses the gradient boosting framework; where gradient descent algorithm is employed to minimize the errors in the sequential model. It improves on the gradient boosting framework with faster execution speed and improved performance.<br />
 <br/>
 <img src="https://miro.medium.com/max/1400/1*QJZ6W-Pck_W7RlIDwUIN9Q.jpeg" width=700/></p>
@@ -2742,7 +2742,7 @@ <h4><span class="section-number">41.79.5.3.2. </span>Task 2: XGBoost<a class="he
 </div>
 </section>
 <section id="task-3-lightgbm">
-<h4><span class="section-number">41.79.5.3.3. </span>Task 3: LightGBM<a class="headerlink" href="#task-3-lightgbm" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.5.3.3. </span>Task 3: LightGBM<a class="headerlink" href="#task-3-lightgbm" title="Permalink to this headline">#</a></h4>
 <p>LightBGM is another gradient boosting framework developed by Microsoft that is based on decision tree algorithm, designed to be efficient and distributed. Some of the advantages of implementing LightBGM compared to other boosting frameworks include:</p>
 <ol class="simple">
 <li><p>Faster training speed and higher efficiency (use histogram based algorithm i.e it buckets continuous feature values into discrete bins which fasten the training procedure)</p></li>
@@ -2794,7 +2794,7 @@ <h4><span class="section-number">41.79.5.3.3. </span>Task 3: LightGBM<a class="h
 </div>
 </section>
 <section id="task-4-catboost">
-<h4><span class="section-number">41.79.5.3.4. </span>Task 4: Catboost<a class="headerlink" href="#task-4-catboost" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.5.3.4. </span>Task 4: Catboost<a class="headerlink" href="#task-4-catboost" title="Permalink to this headline">#</a></h4>
 <p>Catboost is another alternative gradient boosting framework developed by Yandex. The word “Catboost” is derived from two words; “Category” and “Boosting”. This means that Catboost itself can deal with categorical features which usually has to be converted to numerical encodings in order to feed into traditional gradient boost frameworks and machine learning models.<br />
 The 2 critical features in Catboost algorithm is the use of <strong>ordered boosting</strong> and <strong>innovative algorithm for processing categorical features</strong>, which fight the prediction shift caused by a special kind of target leakage present in all existing implementations of gradient boosting algorithms.<br />
 Find out more <a class="reference external" href="https://towardsdatascience.com/https-medium-com-talperetz24-mastering-the-new-generation-of-gradient-boosting-db04062a7ea2">here</a></p>
@@ -2833,7 +2833,7 @@ <h4><span class="section-number">41.79.5.3.4. </span>Task 4: Catboost<a class="h
 </div>
 </section>
 <section id="task-5-training-and-evaluation">
-<h4><span class="section-number">41.79.5.3.5. </span>Task 5: Training and evaluation<a class="headerlink" href="#task-5-training-and-evaluation" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.5.3.5. </span>Task 5: Training and evaluation<a class="headerlink" href="#task-5-training-and-evaluation" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">mean_cross_val</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
@@ -2888,7 +2888,7 @@ <h4><span class="section-number">41.79.5.3.5. </span>Task 5: Training and evalua
 </div>
 </section>
 <section id="task-6-blending">
-<h4><span class="section-number">41.79.5.3.6. </span>Task 6 Blending<a class="headerlink" href="#task-6-blending" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.79.5.3.6. </span>Task 6 Blending<a class="headerlink" href="#task-6-blending" title="Permalink to this headline">#</a></h4>
 <p><strong>Blending</strong> is a technique that give different weightage to different algorithms that will affect their influence of the predictions. Such techniques can help to improve performance since it uses a variety of models as predictors. Special thanks to <a class="reference external" href="https://www.kaggle.com/itslek">&#64;itslek</a> for the implementation of blending. I have randomly chosen the weights for each models in this case, however, you can improve on this by futher tuning the weightage to be given to each model to achieve a better accuracy!</p>
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+<h2><span class="section-number">40.79.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/angqx95">aqx</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/angqx95/data-science-workflow-top-2-with-tuning">Data Science Workflow TOP 2% (with Tuning)</a>. It inspires the majority of the content in this chapter.</p>
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+   40.78.4. Acknowledgments
   </a>
  </li>
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               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="gradient-boosting">
-<h1><span class="section-number">41.78. </span>Gradient boosting<a class="headerlink" href="#gradient-boosting" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.78. </span>Gradient boosting<a class="headerlink" href="#gradient-boosting" title="Permalink to this headline">#</a></h1>
 <p>The logic of gradient boosting is very simple.
 One of the very basic assumption of linear regression is that it’s sum of residuals is 0. Although, tree based models are not based on any of such assumptions, but if we think logic behind these assumptions, we might argue that, if sum of residuals is not 0, then most probably there is some pattern in the residuals of our model which can be leveraged to make our model better.
 So, the intuition behind gradient boosting algorithm is to leverage the pattern in residuals and strenghten a weak prediction model, until our residuals become randomly distributed. Once we reach a stage that residuals do not have any pattern that could be modeled, we can stop modeling residuals. Algorithmically, we are minimizing our loss function, such that test loss reach it’s minima.</p>
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 <section id="data-simulation">
-<h2><span class="section-number">41.78.1. </span>Data simulation<a class="headerlink" href="#data-simulation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.78.1. </span>Data simulation<a class="headerlink" href="#data-simulation" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">50</span><span class="p">)</span>
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+<h2><span class="section-number">40.78.2. </span>Scatter plot of data<a class="headerlink" href="#scatter-plot-of-data" title="Permalink to this headline">#</a></h2>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y</span><span class="o">.</span><span class="n">shape</span>
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 <section id="gradient-boosting-decisiontrees-in-a-loop">
-<h2><span class="section-number">41.78.3. </span>Gradient Boosting (DecisionTrees in a loop)<a class="headerlink" href="#gradient-boosting-decisiontrees-in-a-loop" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.78.3. </span>Gradient Boosting (DecisionTrees in a loop)<a class="headerlink" href="#gradient-boosting-decisiontrees-in-a-loop" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">std_agg</span><span class="p">(</span><span class="n">cnt</span><span class="p">,</span> <span class="n">s1</span><span class="p">,</span> <span class="n">s2</span><span class="p">):</span> <span class="k">return</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">s2</span><span class="o">/</span><span class="n">cnt</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="n">s1</span><span class="o">/</span><span class="n">cnt</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
@@ -1858,7 +1858,7 @@ <h2><span class="section-number">41.78.3. </span>Gradient Boosting (DecisionTree
 </div>
 <p>Errors are not changing much after <code class="docutils literal notranslate"><span class="pre">20th</span> <span class="pre">iteration</span></code> and pattern in residuals is also removed. Residuals look distributed around the mean</p>
 <section id="maths-behind-this-logic">
-<h3><span class="section-number">41.78.3.1. </span>Maths behind this logic<a class="headerlink" href="#maths-behind-this-logic" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.78.3.1. </span>Maths behind this logic<a class="headerlink" href="#maths-behind-this-logic" title="Permalink to this headline">#</a></h3>
 <p><span class="math notranslate nohighlight">\( Predictions = y_i^p \)</span><br />
 <span class="math notranslate nohighlight">\( Loss = L(y_i, y_i^p) \)</span><br />
 <span class="math notranslate nohighlight">\( Loss = MSE = \sum {(y_i - y_i^p)}^2 \)</span><br />
@@ -1869,7 +1869,7 @@ <h3><span class="section-number">41.78.3.1. </span>Maths behind this logic<a cla
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.78.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.78.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/grroverpr">Prince Grover</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/grroverpr/gradient-boosting-simplified/notebook">gradient-boosting-simplified</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1911,13 +1911,13 @@ <h2><span class="section-number">41.78.4. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.77. </span>Hyperparameter tuning gradient boosting</p>
+            <p class="prev-next-title"><span class="section-number">40.77. </span>Hyperparameter tuning gradient boosting</p>
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-        <p class="prev-next-title"><span class="section-number">41.79. </span>Boosting with tuning</p>
+        <p class="prev-next-title"><span class="section-number">40.79. </span>Boosting with tuning</p>
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     <a class="reference internal" href="../../../slides/ml-fundamentals/logistic-regression.html">
-     42.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/logistic-regression-condensed.html">
-     42.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/neural-network.html">
-     42.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/build-an-ml-web-app.html">
-     42.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/unsupervised-learning.html">
-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/kernel-method.html">
-     42.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/model-selection.html">
-     42.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1575,227 +1575,227 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#context">
-   41.77.1. Context:
+   40.77.1. Context:
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#features">
-   41.77.2. Features
+   40.77.2. Features
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#plan">
-   41.77.3. Plan:
+   40.77.3. Plan:
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-preprocessing">
-     41.77.3.1. Data preprocessing:
+     40.77.3.1. Data preprocessing:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-visualization">
-     41.77.3.2. Data Visualization
+     40.77.3.2. Data Visualization
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#model-creation">
-     41.77.3.3. Model Creation:
+     40.77.3.3. Model Creation:
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#imports">
-   41.77.4. Imports¶
+   40.77.4. Imports¶
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-the-data">
-   41.77.5. Loading the data
+   40.77.5. Loading the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploratory-data-analysis">
-   41.77.6. Exploratory data analysis
+   40.77.6. Exploratory data analysis
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-city">
-     41.77.6.1. Task 1: City
+     40.77.6.1. Task 1: City
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task2-gender">
-     41.77.6.2. Task2: Gender
+     40.77.6.2. Task2: Gender
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-relevant-experience">
-     41.77.6.3. Task 3：Relevant experience
+     40.77.6.3. Task 3：Relevant experience
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#encoding">
-     41.77.6.4. Encoding:
+     40.77.6.4. Encoding:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-enrolled-university">
-     41.77.6.5. Task 4: Enrolled university
+     40.77.6.5. Task 4: Enrolled university
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-education-level">
-     41.77.6.6. Task 5: Education level
+     40.77.6.6. Task 5: Education level
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-6-major-discipline">
-     41.77.6.7. Task 6: Major discipline
+     40.77.6.7. Task 6: Major discipline
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-7-experience">
-     41.77.6.8. Task 7: Experience
+     40.77.6.8. Task 7: Experience
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-8-company-size">
-     41.77.6.9. Task 8:Company size
+     40.77.6.9. Task 8:Company size
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#we-can-see-that-most-candidates-work-in-small-companies-between-50-500">
-       41.77.6.9.1. We can see that most candidates work in small companies (between 50-500)
+       40.77.6.9.1. We can see that most candidates work in small companies (between 50-500)
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-9-company-type">
-     41.77.6.10. Task 9:Company type
+     40.77.6.10. Task 9:Company type
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-10-last-new-job">
-     41.77.6.11. Task 10: Last new job
+     40.77.6.11. Task 10: Last new job
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-11-pre-modeling-steps">
-     41.77.6.12. Task 11: Pre modeling steps
+     40.77.6.12. Task 11: Pre modeling steps
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#models-testing">
-   41.77.7. Models testing
+   40.77.7. Models testing
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#boosting">
-   41.77.8. Boosting:
+   40.77.8. Boosting:
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#gradient-boosting">
-   41.77.9. Gradient Boosting:
+   40.77.9. Gradient Boosting:
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#tree-specific-parameters">
-     41.77.9.1. Tree-specific parameters
+     40.77.9.1. Tree-specific parameters
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#boosting-parameters">
-     41.77.9.2. Boosting parameters:
+     40.77.9.2. Boosting parameters:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#miscellaneous-parameters">
-     41.77.9.3. Miscellaneous parameters
+     40.77.9.3. Miscellaneous parameters
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#hyperparameter-tuning-using-gridsearchcv">
-   41.77.10. Hyperparameter Tuning using GridSearchCV
+   40.77.10. Hyperparameter Tuning using GridSearchCV
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-initialize-model">
-     41.77.10.1. Task 1: Initialize model
+     40.77.10.1. Task 1: Initialize model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-remove-enrollee-id">
-     41.77.10.2. Task 2: Remove enrollee_id
+     40.77.10.2. Task 2: Remove enrollee_id
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-look-for-optimal-number">
-     41.77.10.3. Task 3: Look for optimal number
+     40.77.10.3. Task 3: Look for optimal number
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-find-learning-rate">
-     41.77.10.4. Task 4: Find learning rate
+     40.77.10.4. Task 4: Find learning rate
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-find-max-depth-and-min-samples-split">
-     41.77.10.5. Task 5: Find max_depth and min_samples_split
+     40.77.10.5. Task 5: Find max_depth and min_samples_split
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-6-find-min-samples-leaf">
-     41.77.10.6. Task 6: Find min_samples_leaf
+     40.77.10.6. Task 6: Find min_samples_leaf
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-7-auc">
-     41.77.10.7. Task 7: AUC
+     40.77.10.7. Task 7: AUC
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-8-find-max-features">
-     41.77.10.8. Task 8: Find max_features
+     40.77.10.8. Task 8: Find max_features
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-9-find-subsample-value">
-     41.77.10.9. Task 9: Find subsample value
+     40.77.10.9. Task 9: Find subsample value
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-10-improvement">
-     41.77.10.10. Task 10: Improvement
+     40.77.10.10. Task 10: Improvement
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-11-model-fitting">
-     41.77.10.11. Task 11: Model fitting
+     40.77.10.11. Task 11: Model fitting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-12-prediction">
-     41.77.10.12. Task 12: Prediction
+     40.77.10.12. Task 12: Prediction
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-13-result">
-     41.77.10.13. Task 13: Result
+     40.77.10.13. Task 13: Result
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.77.11. Acknowledgments
+   40.77.11. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1819,227 +1819,227 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#context">
-   41.77.1. Context:
+   40.77.1. Context:
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#features">
-   41.77.2. Features
+   40.77.2. Features
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#plan">
-   41.77.3. Plan:
+   40.77.3. Plan:
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-preprocessing">
-     41.77.3.1. Data preprocessing:
+     40.77.3.1. Data preprocessing:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-visualization">
-     41.77.3.2. Data Visualization
+     40.77.3.2. Data Visualization
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#model-creation">
-     41.77.3.3. Model Creation:
+     40.77.3.3. Model Creation:
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#imports">
-   41.77.4. Imports¶
+   40.77.4. Imports¶
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-the-data">
-   41.77.5. Loading the data
+   40.77.5. Loading the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploratory-data-analysis">
-   41.77.6. Exploratory data analysis
+   40.77.6. Exploratory data analysis
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-city">
-     41.77.6.1. Task 1: City
+     40.77.6.1. Task 1: City
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task2-gender">
-     41.77.6.2. Task2: Gender
+     40.77.6.2. Task2: Gender
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-relevant-experience">
-     41.77.6.3. Task 3：Relevant experience
+     40.77.6.3. Task 3：Relevant experience
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#encoding">
-     41.77.6.4. Encoding:
+     40.77.6.4. Encoding:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-enrolled-university">
-     41.77.6.5. Task 4: Enrolled university
+     40.77.6.5. Task 4: Enrolled university
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-education-level">
-     41.77.6.6. Task 5: Education level
+     40.77.6.6. Task 5: Education level
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-6-major-discipline">
-     41.77.6.7. Task 6: Major discipline
+     40.77.6.7. Task 6: Major discipline
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-7-experience">
-     41.77.6.8. Task 7: Experience
+     40.77.6.8. Task 7: Experience
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-8-company-size">
-     41.77.6.9. Task 8:Company size
+     40.77.6.9. Task 8:Company size
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#we-can-see-that-most-candidates-work-in-small-companies-between-50-500">
-       41.77.6.9.1. We can see that most candidates work in small companies (between 50-500)
+       40.77.6.9.1. We can see that most candidates work in small companies (between 50-500)
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-9-company-type">
-     41.77.6.10. Task 9:Company type
+     40.77.6.10. Task 9:Company type
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-10-last-new-job">
-     41.77.6.11. Task 10: Last new job
+     40.77.6.11. Task 10: Last new job
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-11-pre-modeling-steps">
-     41.77.6.12. Task 11: Pre modeling steps
+     40.77.6.12. Task 11: Pre modeling steps
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#models-testing">
-   41.77.7. Models testing
+   40.77.7. Models testing
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#boosting">
-   41.77.8. Boosting:
+   40.77.8. Boosting:
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#gradient-boosting">
-   41.77.9. Gradient Boosting:
+   40.77.9. Gradient Boosting:
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#tree-specific-parameters">
-     41.77.9.1. Tree-specific parameters
+     40.77.9.1. Tree-specific parameters
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#boosting-parameters">
-     41.77.9.2. Boosting parameters:
+     40.77.9.2. Boosting parameters:
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#miscellaneous-parameters">
-     41.77.9.3. Miscellaneous parameters
+     40.77.9.3. Miscellaneous parameters
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#hyperparameter-tuning-using-gridsearchcv">
-   41.77.10. Hyperparameter Tuning using GridSearchCV
+   40.77.10. Hyperparameter Tuning using GridSearchCV
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-initialize-model">
-     41.77.10.1. Task 1: Initialize model
+     40.77.10.1. Task 1: Initialize model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-2-remove-enrollee-id">
-     41.77.10.2. Task 2: Remove enrollee_id
+     40.77.10.2. Task 2: Remove enrollee_id
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-look-for-optimal-number">
-     41.77.10.3. Task 3: Look for optimal number
+     40.77.10.3. Task 3: Look for optimal number
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-4-find-learning-rate">
-     41.77.10.4. Task 4: Find learning rate
+     40.77.10.4. Task 4: Find learning rate
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-5-find-max-depth-and-min-samples-split">
-     41.77.10.5. Task 5: Find max_depth and min_samples_split
+     40.77.10.5. Task 5: Find max_depth and min_samples_split
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-6-find-min-samples-leaf">
-     41.77.10.6. Task 6: Find min_samples_leaf
+     40.77.10.6. Task 6: Find min_samples_leaf
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-7-auc">
-     41.77.10.7. Task 7: AUC
+     40.77.10.7. Task 7: AUC
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-8-find-max-features">
-     41.77.10.8. Task 8: Find max_features
+     40.77.10.8. Task 8: Find max_features
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-9-find-subsample-value">
-     41.77.10.9. Task 9: Find subsample value
+     40.77.10.9. Task 9: Find subsample value
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-10-improvement">
-     41.77.10.10. Task 10: Improvement
+     40.77.10.10. Task 10: Improvement
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-11-model-fitting">
-     41.77.10.11. Task 11: Model fitting
+     40.77.10.11. Task 11: Model fitting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-12-prediction">
-     41.77.10.12. Task 12: Prediction
+     40.77.10.12. Task 12: Prediction
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-13-result">
-     41.77.10.13. Task 13: Result
+     40.77.10.13. Task 13: Result
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.77.11. Acknowledgments
+   40.77.11. Acknowledgments
   </a>
  </li>
 </ul>
@@ -2053,13 +2053,13 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="hyperparameter-tuning-gradient-boosting">
-<h1><span class="section-number">41.77. </span>Hyperparameter tuning gradient boosting<a class="headerlink" href="#hyperparameter-tuning-gradient-boosting" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.77. </span>Hyperparameter tuning gradient boosting<a class="headerlink" href="#hyperparameter-tuning-gradient-boosting" title="Permalink to this headline">#</a></h1>
 <section id="context">
-<h2><span class="section-number">41.77.1. </span>Context:<a class="headerlink" href="#context" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.77.1. </span>Context:<a class="headerlink" href="#context" title="Permalink to this headline">#</a></h2>
 <p>A company which is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which are conducted by the company. Many people sign up for their training. Company wants to know which of these candidates really want to work for the company after training, because it helps reducing the cost and time as well as increasing the quality of training and the planning of the courses. Information related to demographics, education, experience are in hands from candidates signup and enrollment.</p>
 </section>
 <section id="features">
-<h2><span class="section-number">41.77.2. </span>Features<a class="headerlink" href="#features" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.77.2. </span>Features<a class="headerlink" href="#features" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>enrollee_id : Unique ID for candidate</p></li>
 <li><p>city: City code</p></li>
@@ -2078,20 +2078,20 @@ <h2><span class="section-number">41.77.2. </span>Features<a class="headerlink" h
 </ul>
 </section>
 <section id="plan">
-<h2><span class="section-number">41.77.3. </span>Plan:<a class="headerlink" href="#plan" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.77.3. </span>Plan:<a class="headerlink" href="#plan" title="Permalink to this headline">#</a></h2>
 <p>We will treat each column in two phases:</p>
 <section id="data-preprocessing">
-<h3><span class="section-number">41.77.3.1. </span>Data preprocessing:<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.3.1. </span>Data preprocessing:<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Imputing missing values.</p></li>
 <li><p>Handling categorical data.</p></li>
 </ul>
 </section>
 <section id="data-visualization">
-<h3><span class="section-number">41.77.3.2. </span>Data Visualization<a class="headerlink" href="#data-visualization" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.3.2. </span>Data Visualization<a class="headerlink" href="#data-visualization" title="Permalink to this headline">#</a></h3>
 </section>
 <section id="model-creation">
-<h3><span class="section-number">41.77.3.3. </span>Model Creation:<a class="headerlink" href="#model-creation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.3.3. </span>Model Creation:<a class="headerlink" href="#model-creation" title="Permalink to this headline">#</a></h3>
 <p>Then we will test several model and choose the one with the best performance:</p>
 <ul class="simple">
 <li><p>Models: Logistic Regression-SVM-Gradient Boosting-KNN-RandomForest-XGBoost classifier.</p></li>
@@ -2101,7 +2101,7 @@ <h3><span class="section-number">41.77.3.3. </span>Model Creation:<a class="head
 </section>
 </section>
 <section id="imports">
-<h2><span class="section-number">41.77.4. </span>Imports¶<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.77.4. </span>Imports¶<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <p>Lets get started, first lets import our typical libraries:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2124,7 +2124,7 @@ <h2><span class="section-number">41.77.4. </span>Imports¶<a class="headerlink"
 </div>
 </section>
 <section id="loading-the-data">
-<h2><span class="section-number">41.77.5. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.77.5. </span>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
 <p>Let’s import our train and test data.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2163,9 +2163,9 @@ <h2><span class="section-number">41.77.5. </span>Loading the data<a class="heade
 </div>
 </section>
 <section id="exploratory-data-analysis">
-<h2><span class="section-number">41.77.6. </span>Exploratory data analysis<a class="headerlink" href="#exploratory-data-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.77.6. </span>Exploratory data analysis<a class="headerlink" href="#exploratory-data-analysis" title="Permalink to this headline">#</a></h2>
 <section id="task-1-city">
-<h3><span class="section-number">41.77.6.1. </span>Task 1: City<a class="headerlink" href="#task-1-city" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.1. </span>Task 1: City<a class="headerlink" href="#task-1-city" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">[</span><span class="s2">&quot;city&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
@@ -2258,7 +2258,7 @@ <h3><span class="section-number">41.77.6.1. </span>Task 1: City<a class="headerl
 </div>
 </section>
 <section id="task2-gender">
-<h3><span class="section-number">41.77.6.2. </span>Task2: Gender<a class="headerlink" href="#task2-gender" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.2. </span>Task2: Gender<a class="headerlink" href="#task2-gender" title="Permalink to this headline">#</a></h3>
 <p>It is obvious we have to encode the caregorical data and take care of missing data, there are a lot of ways to handle gender missing values such as replacing them with most common gender in the dataset, deleting those rows…etc.
 But I prefer to fill the gender missing values with “Other” since we may have candidates identify as non-binary.
 First lets take a look at our gender column:</p>
@@ -2326,7 +2326,7 @@ <h3><span class="section-number">41.77.6.2. </span>Task2: Gender<a class="header
 </div>
 </section>
 <section id="task-3-relevant-experience">
-<h3><span class="section-number">41.77.6.3. </span>Task 3：Relevant experience<a class="headerlink" href="#task-3-relevant-experience" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.3. </span>Task 3：Relevant experience<a class="headerlink" href="#task-3-relevant-experience" title="Permalink to this headline">#</a></h3>
 <p>Before making any decision we have to look at the values of this column:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2346,7 +2346,7 @@ <h3><span class="section-number">41.77.6.3. </span>Task 3：Relevant experience<
 </div>
 </section>
 <section id="encoding">
-<h3><span class="section-number">41.77.6.4. </span>Encoding:<a class="headerlink" href="#encoding" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.4. </span>Encoding:<a class="headerlink" href="#encoding" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">[</span><span class="s2">&quot;relevent_experience&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">train</span><span class="p">[</span><span class="s2">&quot;relevent_experience&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">({</span><span class="s2">&quot;Has relevent experience&quot;</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;No relevent experience&quot;</span><span class="p">:</span><span class="mi">0</span><span class="p">})</span>
@@ -2356,7 +2356,7 @@ <h3><span class="section-number">41.77.6.4. </span>Encoding:<a class="headerlink
 </div>
 </section>
 <section id="task-4-enrolled-university">
-<h3><span class="section-number">41.77.6.5. </span>Task 4: Enrolled university<a class="headerlink" href="#task-4-enrolled-university" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.5. </span>Task 4: Enrolled university<a class="headerlink" href="#task-4-enrolled-university" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">[</span><span class="s1">&#39;enrolled_university&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
@@ -2402,7 +2402,7 @@ <h3><span class="section-number">41.77.6.5. </span>Task 4: Enrolled university<a
 </div>
 </section>
 <section id="task-5-education-level">
-<h3><span class="section-number">41.77.6.6. </span>Task 5: Education level<a class="headerlink" href="#task-5-education-level" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.6. </span>Task 5: Education level<a class="headerlink" href="#task-5-education-level" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">[</span><span class="s1">&#39;education_level&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
@@ -2444,7 +2444,7 @@ <h3><span class="section-number">41.77.6.6. </span>Task 5: Education level<a cla
 </div>
 </section>
 <section id="task-6-major-discipline">
-<h3><span class="section-number">41.77.6.7. </span>Task 6: Major discipline<a class="headerlink" href="#task-6-major-discipline" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.7. </span>Task 6: Major discipline<a class="headerlink" href="#task-6-major-discipline" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">[</span><span class="s1">&#39;major_discipline&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
@@ -2484,7 +2484,7 @@ <h3><span class="section-number">41.77.6.7. </span>Task 6: Major discipline<a cl
 </div>
 </section>
 <section id="task-7-experience">
-<h3><span class="section-number">41.77.6.8. </span>Task 7: Experience<a class="headerlink" href="#task-7-experience" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.8. </span>Task 7: Experience<a class="headerlink" href="#task-7-experience" title="Permalink to this headline">#</a></h3>
 <p>The experience variable is an object indicating the minimum or maximum years of experience a candidate had, so deleting the operators won’t make a big difference.</p>
 <p>First we have to convert the column to string.</p>
 <div class="cell docutils container">
@@ -2528,7 +2528,7 @@ <h3><span class="section-number">41.77.6.8. </span>Task 7: Experience<a class="h
 </div>
 </section>
 <section id="task-8-company-size">
-<h3><span class="section-number">41.77.6.9. </span>Task 8:Company size<a class="headerlink" href="#task-8-company-size" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.9. </span>Task 8:Company size<a class="headerlink" href="#task-8-company-size" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">[</span><span class="s1">&#39;company_size&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
@@ -2546,7 +2546,7 @@ <h3><span class="section-number">41.77.6.9. </span>Task 8:Company size<a class="
 </div>
 </div>
 <section id="we-can-see-that-most-candidates-work-in-small-companies-between-50-500">
-<h4><span class="section-number">41.77.6.9.1. </span>We can see that most candidates work in small companies (between 50-500)<a class="headerlink" href="#we-can-see-that-most-candidates-work-in-small-companies-between-50-500" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.77.6.9.1. </span>We can see that most candidates work in small companies (between 50-500)<a class="headerlink" href="#we-can-see-that-most-candidates-work-in-small-companies-between-50-500" title="Permalink to this headline">#</a></h4>
 <p>Identify each interval with a number:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2573,7 +2573,7 @@ <h4><span class="section-number">41.77.6.9.1. </span>We can see that most candid
 </section>
 </section>
 <section id="task-9-company-type">
-<h3><span class="section-number">41.77.6.10. </span>Task 9:Company type<a class="headerlink" href="#task-9-company-type" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.10. </span>Task 9:Company type<a class="headerlink" href="#task-9-company-type" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="s1">&#39;company_type&#39;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">)</span>
@@ -2610,7 +2610,7 @@ <h3><span class="section-number">41.77.6.10. </span>Task 9:Company type<a class=
 </div>
 </section>
 <section id="task-10-last-new-job">
-<h3><span class="section-number">41.77.6.11. </span>Task 10: Last new job<a class="headerlink" href="#task-10-last-new-job" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.11. </span>Task 10: Last new job<a class="headerlink" href="#task-10-last-new-job" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">[</span><span class="s1">&#39;last_new_job&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
@@ -2657,7 +2657,7 @@ <h3><span class="section-number">41.77.6.11. </span>Task 10: Last new job<a clas
 </div>
 </section>
 <section id="task-11-pre-modeling-steps">
-<h3><span class="section-number">41.77.6.12. </span>Task 11: Pre modeling steps<a class="headerlink" href="#task-11-pre-modeling-steps" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.6.12. </span>Task 11: Pre modeling steps<a class="headerlink" href="#task-11-pre-modeling-steps" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pred</span> <span class="o">=</span> <span class="n">train</span><span class="p">[</span><span class="s1">&#39;target&#39;</span><span class="p">]</span>
@@ -2682,7 +2682,7 @@ <h3><span class="section-number">41.77.6.12. </span>Task 11: Pre modeling steps<
 </section>
 </section>
 <section id="models-testing">
-<h2><span class="section-number">41.77.7. </span>Models testing<a class="headerlink" href="#models-testing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.77.7. </span>Models testing<a class="headerlink" href="#models-testing" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
@@ -2738,7 +2738,7 @@ <h2><span class="section-number">41.77.7. </span>Models testing<a class="headerl
 <p>Obviously,Gradient Boosting gives the best result.</p>
 </section>
 <section id="boosting">
-<h2><span class="section-number">41.77.8. </span>Boosting:<a class="headerlink" href="#boosting" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.77.8. </span>Boosting:<a class="headerlink" href="#boosting" title="Permalink to this headline">#</a></h2>
 <p><strong>Ensemble learning techniques:</strong> They combine the predictions of multiple machine learning models.</p>
 <p><strong>Boosting:</strong> Boosting algorithms play a crucial role in dealing with bias variance trade-off.  Unlike bagging algorithms, which only controls for high variance in a model, boosting controls both the aspects (bias &amp; variance), and is considered to be more effective.
 Boosting is an ensemble learning technique.</p>
@@ -2748,7 +2748,7 @@ <h2><span class="section-number">41.77.8. </span>Boosting:<a class="headerlink"
 <p><a href="https://ibb.co/PgstW8k"><img src="https://i.ibb.co/WfNsz1C/boosting.png" alt="boosting" border="0"></a></p>
 </section>
 <section id="gradient-boosting">
-<h2><span class="section-number">41.77.9. </span>Gradient Boosting:<a class="headerlink" href="#gradient-boosting" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.77.9. </span>Gradient Boosting:<a class="headerlink" href="#gradient-boosting" title="Permalink to this headline">#</a></h2>
 <p><img alt="image.png" src="../../../_images/boosting_visual_3.png" /></p>
 <p>The overall parameters of gradient boosting can be devided into three categories:</p>
 <ul class="simple">
@@ -2757,10 +2757,10 @@ <h2><span class="section-number">41.77.9. </span>Gradient Boosting:<a class="hea
 <li><p>Miscellaneous Parameters: Other parameters for overall functioning.</p></li>
 </ul>
 <section id="tree-specific-parameters">
-<h3><span class="section-number">41.77.9.1. </span>Tree-specific parameters<a class="headerlink" href="#tree-specific-parameters" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.9.1. </span>Tree-specific parameters<a class="headerlink" href="#tree-specific-parameters" title="Permalink to this headline">#</a></h3>
 </section>
 <section id="boosting-parameters">
-<h3><span class="section-number">41.77.9.2. </span>Boosting parameters:<a class="headerlink" href="#boosting-parameters" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.9.2. </span>Boosting parameters:<a class="headerlink" href="#boosting-parameters" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>learning_rate</p></li>
 <li><p>n_estimators</p></li>
@@ -2768,7 +2768,7 @@ <h3><span class="section-number">41.77.9.2. </span>Boosting parameters:<a class=
 </ul>
 </section>
 <section id="miscellaneous-parameters">
-<h3><span class="section-number">41.77.9.3. </span>Miscellaneous parameters<a class="headerlink" href="#miscellaneous-parameters" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.9.3. </span>Miscellaneous parameters<a class="headerlink" href="#miscellaneous-parameters" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>loss</p></li>
 <li><p>init</p></li>
@@ -2780,7 +2780,7 @@ <h3><span class="section-number">41.77.9.3. </span>Miscellaneous parameters<a cl
 </section>
 </section>
 <section id="hyperparameter-tuning-using-gridsearchcv">
-<h2><span class="section-number">41.77.10. </span>Hyperparameter Tuning using GridSearchCV<a class="headerlink" href="#hyperparameter-tuning-using-gridsearchcv" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.77.10. </span>Hyperparameter Tuning using GridSearchCV<a class="headerlink" href="#hyperparameter-tuning-using-gridsearchcv" title="Permalink to this headline">#</a></h2>
 <p><strong>GradientBoostingClassifier</strong>(*, loss=‘deviance’, learning_rate=0.1, n_estimators=100, subsample=1.0, criterion=‘friedman_mse’, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0, min_impurity_split=None, init=None, random_state=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, validation_fraction=0.1, n_iter_no_change=None, tol=0.0001, ccp_alpha=0.0)</p>
 <p>Grid search is best described as exhuastive guess and check. We have a problem: find the hyperparameters that result in the best cross validation score, and a set of values to try in the hyperparameter grid - the domain. The grid search method for finding the answer is to try all combinations of values in the domain and hope that the best combination is in the grid (in reality, we will never know if we found the best settings unless we have an infinite hyperparameter grid which would then require an infinite amount of time to run).</p>
 <p>Grid search suffers from one limiting problem: it is extremely computationally expensive because we have to perform cross validation with every single combination of hyperparameters in the grid!</p>
@@ -2796,7 +2796,7 @@ <h2><span class="section-number">41.77.10. </span>Hyperparameter Tuning using Gr
 <li><p>It is ‘uninformed’. Results of one model don’t help creating the next model.</p></li>
 </ul>
 <section id="task-1-initialize-model">
-<h3><span class="section-number">41.77.10.1. </span>Task 1: Initialize model<a class="headerlink" href="#task-1-initialize-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.1. </span>Task 1: Initialize model<a class="headerlink" href="#task-1-initialize-model" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">pred</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
@@ -2821,7 +2821,7 @@ <h3><span class="section-number">41.77.10.1. </span>Task 1: Initialize model<a c
 </div>
 </section>
 <section id="task-2-remove-enrollee-id">
-<h3><span class="section-number">41.77.10.2. </span>Task 2: Remove enrollee_id<a class="headerlink" href="#task-2-remove-enrollee-id" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.2. </span>Task 2: Remove enrollee_id<a class="headerlink" href="#task-2-remove-enrollee-id" title="Permalink to this headline">#</a></h3>
 <p>We shouldn’t use the enrollee_id since it is unique for each candidate.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2839,7 +2839,7 @@ <h3><span class="section-number">41.77.10.2. </span>Task 2: Remove enrollee_id<a
 </div>
 </section>
 <section id="task-3-look-for-optimal-number">
-<h3><span class="section-number">41.77.10.3. </span>Task 3: Look for optimal number<a class="headerlink" href="#task-3-look-for-optimal-number" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.3. </span>Task 3: Look for optimal number<a class="headerlink" href="#task-3-look-for-optimal-number" title="Permalink to this headline">#</a></h3>
 <p>First we have to look for an optimal number of estimators:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2875,7 +2875,7 @@ <h3><span class="section-number">41.77.10.3. </span>Task 3: Look for optimal num
 </div>
 </section>
 <section id="task-4-find-learning-rate">
-<h3><span class="section-number">41.77.10.4. </span>Task 4: Find learning rate<a class="headerlink" href="#task-4-find-learning-rate" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.4. </span>Task 4: Find learning rate<a class="headerlink" href="#task-4-find-learning-rate" title="Permalink to this headline">#</a></h3>
 <p>As you can see that here we got 90 as the optimal estimators for 0.1 learning rate, it is close to 100 so we will increase the learning rate to 0.2. (I tried working with number of estimators as 90 but when I increased the learning rate I got slightly better results, I won’t include the whole process because it will take a lot of time to run)</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2896,7 +2896,7 @@ <h3><span class="section-number">41.77.10.4. </span>Task 4: Find learning rate<a
 </div>
 </section>
 <section id="task-5-find-max-depth-and-min-samples-split">
-<h3><span class="section-number">41.77.10.5. </span>Task 5: Find max_depth and min_samples_split<a class="headerlink" href="#task-5-find-max-depth-and-min-samples-split" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.5. </span>Task 5: Find max_depth and min_samples_split<a class="headerlink" href="#task-5-find-max-depth-and-min-samples-split" title="Permalink to this headline">#</a></h3>
 <p>The next step is to find the max_depth and min_samples_split.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2912,7 +2912,7 @@ <h3><span class="section-number">41.77.10.5. </span>Task 5: Find max_depth and m
 <p>So we got a maximum depth of 7 and minimum samples split is 700.</p>
 </section>
 <section id="task-6-find-min-samples-leaf">
-<h3><span class="section-number">41.77.10.6. </span>Task 6: Find min_samples_leaf<a class="headerlink" href="#task-6-find-min-samples-leaf" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.6. </span>Task 6: Find min_samples_leaf<a class="headerlink" href="#task-6-find-min-samples-leaf" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">param_test3</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;min_samples_split&#39;</span><span class="p">:</span><span class="nb">range</span><span class="p">(</span><span class="mi">400</span><span class="p">,</span><span class="mi">1200</span><span class="p">,</span><span class="mi">100</span><span class="p">),</span> <span class="s1">&#39;min_samples_leaf&#39;</span><span class="p">:</span><span class="nb">range</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span><span class="mi">71</span><span class="p">,</span><span class="mi">10</span><span class="p">)}</span>
@@ -2926,7 +2926,7 @@ <h3><span class="section-number">41.77.10.6. </span>Task 6: Find min_samples_lea
 </div>
 </section>
 <section id="task-7-auc">
-<h3><span class="section-number">41.77.10.7. </span>Task 7: AUC<a class="headerlink" href="#task-7-auc" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.7. </span>Task 7: AUC<a class="headerlink" href="#task-7-auc" title="Permalink to this headline">#</a></h3>
 <p>Lets write a function that returns the accuracy, auc score and the importance of each variable:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2975,7 +2975,7 @@ <h3><span class="section-number">41.77.10.7. </span>Task 7: AUC<a class="headerl
 </div>
 </section>
 <section id="task-8-find-max-features">
-<h3><span class="section-number">41.77.10.8. </span>Task 8: Find max_features<a class="headerlink" href="#task-8-find-max-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.8. </span>Task 8: Find max_features<a class="headerlink" href="#task-8-find-max-features" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">param_test4</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;max_features&#39;</span><span class="p">:</span><span class="nb">range</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">20</span><span class="p">,</span><span class="mi">2</span><span class="p">)}</span>
@@ -2996,7 +2996,7 @@ <h3><span class="section-number">41.77.10.8. </span>Task 8: Find max_features<a
 </div>
 </section>
 <section id="task-9-find-subsample-value">
-<h3><span class="section-number">41.77.10.9. </span>Task 9: Find subsample value<a class="headerlink" href="#task-9-find-subsample-value" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.9. </span>Task 9: Find subsample value<a class="headerlink" href="#task-9-find-subsample-value" title="Permalink to this headline">#</a></h3>
 <p>The next step would be try different subsample values.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3012,7 +3012,7 @@ <h3><span class="section-number">41.77.10.9. </span>Task 9: Find subsample value
 <p>We got 0.85 as the optimum subsample value.</p>
 </section>
 <section id="task-10-improvement">
-<h3><span class="section-number">41.77.10.10. </span>Task 10: Improvement<a class="headerlink" href="#task-10-improvement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.10. </span>Task 10: Improvement<a class="headerlink" href="#task-10-improvement" title="Permalink to this headline">#</a></h3>
 <p>Now, we need to lower the learning rate and increase the number of estimators to see if we get better results.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3042,7 +3042,7 @@ <h3><span class="section-number">41.77.10.10. </span>Task 10: Improvement<a clas
 <p>increasing the number of estimators got us a slightly better model.</p>
 </section>
 <section id="task-11-model-fitting">
-<h3><span class="section-number">41.77.10.11. </span>Task 11: Model fitting<a class="headerlink" href="#task-11-model-fitting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.11. </span>Task 11: Model fitting<a class="headerlink" href="#task-11-model-fitting" title="Permalink to this headline">#</a></h3>
 <p>Lets fit the model.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3060,7 +3060,7 @@ <h3><span class="section-number">41.77.10.11. </span>Task 11: Model fitting<a cl
 </div>
 </section>
 <section id="task-12-prediction">
-<h3><span class="section-number">41.77.10.12. </span>Task 12: Prediction<a class="headerlink" href="#task-12-prediction" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.12. </span>Task 12: Prediction<a class="headerlink" href="#task-12-prediction" title="Permalink to this headline">#</a></h3>
 <p>Make predictions on test and training set:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3093,7 +3093,7 @@ <h3><span class="section-number">41.77.10.12. </span>Task 12: Prediction<a class
 </div>
 </section>
 <section id="task-13-result">
-<h3><span class="section-number">41.77.10.13. </span>Task 13: Result<a class="headerlink" href="#task-13-result" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.77.10.13. </span>Task 13: Result<a class="headerlink" href="#task-13-result" title="Permalink to this headline">#</a></h3>
 <p>Save results to a dataframe</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3112,7 +3112,7 @@ <h3><span class="section-number">41.77.10.13. </span>Task 13: Result<a class="he
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.77.11. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.77.11. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/elyousfiomar">Omar El Yousfi</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/elyousfiomar/hyperparameter-tuning-gradient-boosting/notebook#Relevant-experience:">Hyperparameter tuning - Gradient boosting</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -3154,13 +3154,13 @@ <h2><span class="section-number">41.77.11. </span>Acknowledgments<a class="heade
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.76. </span>Decision trees</p>
+            <p class="prev-next-title"><span class="section-number">40.76. </span>Decision trees</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="gradient-boosting-assignment.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">41.78. </span>Gradient boosting</p>
+        <p class="prev-next-title"><span class="section-number">40.78. </span>Gradient boosting</p>
     </div>
     <i class="fas fa-angle-right"></i>
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diff --git a/assignments/ml-advanced/kernel-method/decision_trees_for_classification.html b/assignments/ml-advanced/kernel-method/decision_trees_for_classification.html
index bad78471d..ff325d540 100644
--- a/assignments/ml-advanced/kernel-method/decision_trees_for_classification.html
+++ b/assignments/ml-advanced/kernel-method/decision_trees_for_classification.html
@@ -6,7 +6,7 @@
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>41.67. Decision Trees - Classification &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.67. Decision Trees - Classification &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="41.68. Model selection assignment 1" href="../model-selection/model-selection-assignment-1.html" />
-    <link rel="prev" title="41.66. Decision Trees - Intro and Regression" href="decision_trees_for_regression.html" />
+    <link rel="next" title="40.68. Model selection assignment 1" href="../model-selection/model-selection-assignment-1.html" />
+    <link rel="prev" title="40.66. Decision Trees - Intro and Regression" href="decision_trees_for_regression.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -159,17 +159,17 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   <a class="reference internal" href="../../../prerequisites/python-programming-introduction.html">
-   1. Python programming basics
+   1. Python programming introduction
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../prerequisites/python-programming-basics.html">
-   3. Python programming basics
+   2. Python programming basics
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   <a class="reference internal" href="../../../prerequisites/python-programming-advanced.html">
-   4. Python programming advanced
+   3. Python programming advanced
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   <a class="reference internal" href="../../../data-science/introduction/introduction.html">
-   5. Introduction
+   4. Introduction
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/introduction/defining-data-science.html">
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+     4.1. Defining data science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/introduction/data-science-ethics.html">
-     5.2. Data Science ethics
+     4.2. Data Science ethics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/introduction/defining-data.html">
-     5.3. Defining data
+     4.3. Defining data
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     <a class="reference internal" href="../../../data-science/introduction/introduction-to-statistics-and-probability.html">
-     5.4. Introduction to statistics and probability
+     4.4. Introduction to statistics and probability
     </a>
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   <a class="reference internal" href="../../../data-science/working-with-data/working-with-data.html">
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-     6.1. Relational databases
+     5.1. Relational databases
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     <a class="reference internal" href="../../../data-science/working-with-data/non-relational-data.html">
-     6.2. Non-relational data
+     5.2. Non-relational data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/working-with-data/numpy.html">
-     6.3. NumPy
+     5.3. NumPy
     </a>
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     <a class="reference internal" href="../../../data-science/working-with-data/pandas/pandas.html">
-     6.4. Pandas
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       <a class="reference internal" href="../../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
-       6.4.1. Introduction and Data Structures
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       </a>
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      <li class="toctree-l3">
       <a class="reference internal" href="../../../data-science/working-with-data/pandas/data-selection.html">
-       6.4.2. Data Selection
+       5.4.2. Data Selection
       </a>
      </li>
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       <a class="reference internal" href="../../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
-       6.4.3. Advanced Pandas Techniques
+       5.4.3. Advanced Pandas Techniques
       </a>
      </li>
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/working-with-data/data-preparation.html">
-     6.5. Data preparation
+     5.5. Data preparation
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/data-visualization/visualization-distributions.html">
-     7.1. Visualizing distributions
+     6.1. Visualizing distributions
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/data-visualization/visualization-proportions.html">
-     7.2. Visualizing proportions
+     6.2. Visualizing proportions
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/data-visualization/visualization-relationships.html">
-     7.3. Visualizing relationships: all about honey 🍯
+     6.3. Visualizing relationships: all about honey 🍯
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/data-visualization/meaningful-visualizations.html">
-     7.4. Making meaningful visualizations
+     6.4. Making meaningful visualizations
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     <a class="reference internal" href="../../../data-science/data-science-lifecycle/analyzing.html">
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-     8.3. Communication
+     7.3. Communication
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-     41.67.5.1. Handling Categorical Features
+     40.67.5.1. Handling Categorical Features
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-decision-tree-classifier">
-   41.67.6. 5 - Training Decision Tree Classifier
+   40.67.6. 5 - Training Decision Tree Classifier
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#evaluating-decision-trees">
-   41.67.7. 6 - Evaluating Decision Trees
+   40.67.7. 6 - Evaluating Decision Trees
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#improving-decision-trees">
-   41.67.8. 7 - Improving Decision Trees
+   40.67.8. 7 - Improving Decision Trees
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#evaluating-the-model-on-the-test-set">
-     41.67.8.1. Evaluating the model on the test set
+     40.67.8.1. Evaluating the model on the test set
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.67.9. Acknowledgments
+   40.67.9. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1682,90 +1682,90 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#decision-trees-for-classification">
-   41.67.1. Decision Trees for Classification
+   40.67.1. Decision Trees for Classification
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#contents">
-     41.67.1.1. Contents
+     40.67.1.1. Contents
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#imports">
-   41.67.2. 1 - Imports
+   40.67.2. 1 - Imports
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loading-the-data">
-   41.67.3. 2 - Loading the data
+   40.67.3. 2 - Loading the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploratory-analysis">
-   41.67.4. 3 - Exploratory Analysis
+   40.67.4. 3 - Exploratory Analysis
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#splitting-data-into-training-and-test-sets">
-     41.67.4.1. Splitting Data into Training and Test sets
+     40.67.4.1. Splitting Data into Training and Test sets
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-summary-statistics">
-     41.67.4.2. Checking Summary Statistics
+     40.67.4.2. Checking Summary Statistics
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-missing-values">
-     41.67.4.3. Checking Missing Values
+     40.67.4.3. Checking Missing Values
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-categorical-features">
-     41.67.4.4. Checking Categorical Features
+     40.67.4.4. Checking Categorical Features
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-preprocessing">
-   41.67.5. 4 - Data Preprocessing
+   40.67.5. 4 - Data Preprocessing
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#handling-categorical-features">
-     41.67.5.1. Handling Categorical Features
+     40.67.5.1. Handling Categorical Features
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-decision-tree-classifier">
-   41.67.6. 5 - Training Decision Tree Classifier
+   40.67.6. 5 - Training Decision Tree Classifier
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#evaluating-decision-trees">
-   41.67.7. 6 - Evaluating Decision Trees
+   40.67.7. 6 - Evaluating Decision Trees
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#improving-decision-trees">
-   41.67.8. 7 - Improving Decision Trees
+   40.67.8. 7 - Improving Decision Trees
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#evaluating-the-model-on-the-test-set">
-     41.67.8.1. Evaluating the model on the test set
+     40.67.8.1. Evaluating the model on the test set
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.67.9. Acknowledgments
+   40.67.9. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1779,7 +1779,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="decision-trees-classification">
-<h1><span class="section-number">41.67. </span>Decision Trees - Classification<a class="headerlink" href="#decision-trees-classification" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.67. </span>Decision Trees - Classification<a class="headerlink" href="#decision-trees-classification" title="Permalink to this headline">#</a></h1>
 <p>Decision Trees are supervised machine learning algorithms that are used for both regression and classification tasks. Trees are powerful algorithms that can handle complex datasets.</p>
 <p>In the previous notebook, we learnt to predict the continous values with decision trees (regression). This notebook is for classification.</p>
 <p>Different to other algorithms, here are two things that trees are very particular about:</p>
@@ -1798,9 +1798,9 @@ <h1><span class="section-number">41.67. </span>Decision Trees - Classification<a
 <p><img alt="Decision Trees.png" src="../../../_images/4XijLSXib.png" /></p>
 <p>A well-known downside of decision trees is that they tend to overfit the data easily(pretty much assumed they will always overfit at first). One way to overcome overfitting is to reduce the maximum depth of the decision tree (refered to as <code class="docutils literal notranslate"><span class="pre">max_depth</span></code>hyperparameter) in decision trees. We will see other techniques to avoid overfitting.</p>
 <section id="decision-trees-for-classification">
-<h2><span class="section-number">41.67.1. </span>Decision Trees for Classification<a class="headerlink" href="#decision-trees-for-classification" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.67.1. </span>Decision Trees for Classification<a class="headerlink" href="#decision-trees-for-classification" title="Permalink to this headline">#</a></h2>
 <section id="contents">
-<h3><span class="section-number">41.67.1.1. </span>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.67.1.1. </span>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>[1 - Imports]</p></li>
 <li><p>[2 - Loading the data]</p></li>
@@ -1813,7 +1813,7 @@ <h3><span class="section-number">41.67.1.1. </span>Contents<a class="headerlink"
 </section>
 </section>
 <section id="imports">
-<h2><span class="section-number">41.67.2. </span>1 - Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.67.2. </span>1 - Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -1828,7 +1828,7 @@ <h2><span class="section-number">41.67.2. </span>1 - Imports<a class="headerlink
 </div>
 </section>
 <section id="loading-the-data">
-<h2><span class="section-number">41.67.3. </span>2 - Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.67.3. </span>2 - Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
 <p>In this classification task with decision trees, we will use a car dataset that is avilable at <a class="reference external" href="https://www.openml.org/d/21">OpenML</a> to predict the car acceptability given the information about the car. We will load it with Sklearn <code class="docutils literal notranslate"><span class="pre">fetch_openml</span></code> function.</p>
 <p>The version of the data we are fetching is 2. In the version 1 of the dataset, the target class had 4 classes (unacc, acc, good, vgood) but in the second version, the majority class is Positive§ whereas the rest are Negative§. If you want to see version 1, you can change version parameter in a cell below.</p>
 <p>Here are the informations about the features:</p>
@@ -2048,10 +2048,10 @@ <h2><span class="section-number">41.67.3. </span>2 - Loading the data<a class="h
 </div>
 </section>
 <section id="exploratory-analysis">
-<h2><span class="section-number">41.67.4. </span>3 - Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.67.4. </span>3 - Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
 <p>Before doing exploratory analysis, let’s get the training and test data.</p>
 <section id="splitting-data-into-training-and-test-sets">
-<h3><span class="section-number">41.67.4.1. </span>Splitting Data into Training and Test sets<a class="headerlink" href="#splitting-data-into-training-and-test-sets" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.67.4.1. </span>Splitting Data into Training and Test sets<a class="headerlink" href="#splitting-data-into-training-and-test-sets" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
@@ -2071,7 +2071,7 @@ <h3><span class="section-number">41.67.4.1. </span>Splitting Data into Training
 </div>
 </section>
 <section id="checking-summary-statistics">
-<h3><span class="section-number">41.67.4.2. </span>Checking Summary Statistics<a class="headerlink" href="#checking-summary-statistics" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.67.4.2. </span>Checking Summary Statistics<a class="headerlink" href="#checking-summary-statistics" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Checking summary statistics</span>
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 </div>
 </section>
 <section id="checking-missing-values">
-<h3><span class="section-number">41.67.4.3. </span>Checking Missing Values<a class="headerlink" href="#checking-missing-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.67.4.3. </span>Checking Missing Values<a class="headerlink" href="#checking-missing-values" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Checking missing values</span>
@@ -2179,7 +2179,7 @@ <h3><span class="section-number">41.67.4.3. </span>Checking Missing Values<a cla
 <p>We don’t have any missing values.</p>
 </section>
 <section id="checking-categorical-features">
-<h3><span class="section-number">41.67.4.4. </span>Checking Categorical Features<a class="headerlink" href="#checking-categorical-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.67.4.4. </span>Checking Categorical Features<a class="headerlink" href="#checking-categorical-features" title="Permalink to this headline">#</a></h3>
 <p>Let’s inspect some categorical features that are in the dataset, almost all re. Let’s see that!</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2436,10 +2436,10 @@ <h3><span class="section-number">41.67.4.4. </span>Checking Categorical Features
 </section>
 </section>
 <section id="data-preprocessing">
-<h2><span class="section-number">41.67.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.67.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <p>It is here that we prepare the data to be in the proper format for the machine learning model.</p>
 <section id="handling-categorical-features">
-<h3><span class="section-number">41.67.5.1. </span>Handling Categorical Features<a class="headerlink" href="#handling-categorical-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.67.5.1. </span>Handling Categorical Features<a class="headerlink" href="#handling-categorical-features" title="Permalink to this headline">#</a></h3>
 <p>Decision trees don’t care if the features are scaled or not, and they can handle the categorical features. There is a <a class="reference external" href="https://scikit-learn.org/stable/modules/tree.html#tree">note on documentation</a> that the sklearn tree implementation doesn’t support categorical features, so I will go ahead and handle them. It’s fun anyways :)</p>
 <p>Before handling categorical features, let’s create a training input data and labels.</p>
 <div class="cell docutils container">
@@ -2489,7 +2489,7 @@ <h3><span class="section-number">41.67.5.1. </span>Handling Categorical Features
 </section>
 </section>
 <section id="training-decision-tree-classifier">
-<h2><span class="section-number">41.67.6. </span>5 - Training Decision Tree Classifier<a class="headerlink" href="#training-decision-tree-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.67.6. </span>5 - Training Decision Tree Classifier<a class="headerlink" href="#training-decision-tree-classifier" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <span class="n">DecisionTreeClassifier</span>
@@ -2520,7 +2520,7 @@ <h2><span class="section-number">41.67.6. </span>5 - Training Decision Tree Clas
 </div>
 </section>
 <section id="evaluating-decision-trees">
-<h2><span class="section-number">41.67.7. </span>6 - Evaluating Decision Trees<a class="headerlink" href="#evaluating-decision-trees" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.67.7. </span>6 - Evaluating Decision Trees<a class="headerlink" href="#evaluating-decision-trees" title="Permalink to this headline">#</a></h2>
 <p>Let’s build 3 functions to display accuracy, confusion matrix, and classification report. Classification report contains all useful metrics such as precision, recall, and f1 score.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2626,7 +2626,7 @@ <h2><span class="section-number">41.67.7. </span>6 - Evaluating Decision Trees<a
 <p>The model clearly overfitted the data. Let’s see how we can regularize it.</p>
 </section>
 <section id="improving-decision-trees">
-<h2><span class="section-number">41.67.8. </span>7 - Improving Decision Trees<a class="headerlink" href="#improving-decision-trees" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.67.8. </span>7 - Improving Decision Trees<a class="headerlink" href="#improving-decision-trees" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tree_clf</span><span class="o">.</span><span class="n">get_params</span><span class="p">()</span>
@@ -2829,7 +2829,7 @@ <h2><span class="section-number">41.67.8. </span>7 - Improving Decision Trees<a
 </ul>
 <p>The higher the precision and recall are, the higher the F1 score. But there is a tradeoff between them. Increasing precision will reduce recall, and vice versa. So it’s fair to say that it depends on the problem you’re trying to solve and the metrics you want to optimize for.</p>
 <section id="evaluating-the-model-on-the-test-set">
-<h3><span class="section-number">41.67.8.1. </span>Evaluating the model on the test set<a class="headerlink" href="#evaluating-the-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.67.8.1. </span>Evaluating the model on the test set<a class="headerlink" href="#evaluating-the-model-on-the-test-set" title="Permalink to this headline">#</a></h3>
 <p>It is only after we have improved the model that we can feed it to the test set. If we try to show the test set to the model while we are still training, we may cause a potential leak thus producing misleading predictions.</p>
 <p>Also, we will apply the same processing functions that we applied to the training set.</p>
 <div class="cell docutils container">
@@ -2909,7 +2909,7 @@ <h3><span class="section-number">41.67.8.1. </span>Evaluating the model on the t
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.67.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.67.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Jake VanderPlas for creating the open-source course <a class="reference external" href="https://github.com/jakevdp/PythonDataScienceHandbook"> Python Data Science Handbook </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2951,13 +2951,13 @@ <h2><span class="section-number">41.67.9. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.66. </span>Decision Trees - Intro and Regression</p>
+            <p class="prev-next-title"><span class="section-number">40.66. </span>Decision Trees - Intro and Regression</p>
         </div>
     </a>
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-        <p class="prev-next-title"><span class="section-number">41.68. </span>Model selection assignment 1</p>
+        <p class="prev-next-title"><span class="section-number">40.68. </span>Model selection assignment 1</p>
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-    <title>41.66. Decision Trees - Intro and Regression &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.66. Decision Trees - Intro and Regression &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="41.67. Decision Trees - Classification" href="decision_trees_for_classification.html" />
-    <link rel="prev" title="41.65. Support Vector Machines (SVM) - Classification" href="support_vector_machines_for_classification.html" />
+    <link rel="next" title="40.67. Decision Trees - Classification" href="decision_trees_for_classification.html" />
+    <link rel="prev" title="40.65. Support Vector Machines (SVM) - Classification" href="support_vector_machines_for_classification.html" />
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   <a class="reference internal" href="../../../prerequisites/python-programming-introduction.html">
-   1. Python programming basics
+   1. Python programming introduction
   </a>
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   <a class="reference internal" href="../../../prerequisites/python-programming-basics.html">
-   3. Python programming basics
+   2. Python programming basics
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   <a class="reference internal" href="../../../prerequisites/python-programming-advanced.html">
-   4. Python programming advanced
+   3. Python programming advanced
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@@ -181,7 +181,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   <a class="reference internal" href="../../../data-science/introduction/introduction.html">
-   5. Introduction
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-     5.1. Defining data science
+     4.1. Defining data science
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    </li>
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     <a class="reference internal" href="../../../data-science/introduction/data-science-ethics.html">
-     5.2. Data Science ethics
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               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="decision-trees-intro-and-regression">
-<h1><span class="section-number">41.66. </span>Decision Trees - Intro and Regression<a class="headerlink" href="#decision-trees-intro-and-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.66. </span>Decision Trees - Intro and Regression<a class="headerlink" href="#decision-trees-intro-and-regression" title="Permalink to this headline">#</a></h1>
 <p>Decision Trees are supervised machine learning algorithms that are used for both regression and classification tasks. Trees are powerful algorithms that can handle complex datasets.</p>
 <p>Here are 7 interesting facts about decision trees:</p>
 <ul class="simple">
@@ -1725,9 +1725,9 @@ <h1><span class="section-number">41.66. </span>Decision Trees - Intro and Regres
 <p>A well-known downside of decision trees is that they tend to overfit the data easily(pretty much assumed they will always overfit at first). One way to overcome overfitting is to reduce the maximum depth of the decision tree (refered to as <code class="docutils literal notranslate"><span class="pre">max_depth</span></code>hyperparameter) in decision trees. We will see other techniques to avoid overfitting.</p>
 <p>To motivate the superpower of decision trees, let’s use it for a regression task where instead of predicting class, we are predicting a continous value. In the next lab, we will use them for classification.</p>
 <section id="decision-trees-for-regression">
-<h2><span class="section-number">41.66.1. </span>Decision Trees for Regression<a class="headerlink" href="#decision-trees-for-regression" title="Permalink to this headline">#</a></h2>
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 <section id="contents">
-<h3><span class="section-number">41.66.1.1. </span>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.66.1.1. </span>Contents<a class="headerlink" href="#contents" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>[1 - Imports]</p></li>
 <li><p>[2 - Loading the data]</p></li>
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-<h2><span class="section-number">41.66.2. </span>1 - Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.66.2. </span>1 - Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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 </div>
 </section>
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-<h2><span class="section-number">41.66.3. </span>2 - Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.66.3. </span>2 - Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
 <p>In this regression task with decision trees, we will use the Machine CPU (Central Processing Unit) data which is avilable at <a class="reference external" href="https://www.openml.org/t/5492">OpenML</a>. We will load it with Sklearn <code class="docutils literal notranslate"><span class="pre">fetch_openml</span></code> function.</p>
 <p>If you are reading this, it’s very likely that you know CPU or you have once(or many times) thought about it when you were buying your computer. In this notebook, we will predict the relative performance of the CPU given the following data:</p>
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@@ -2043,7 +2043,7 @@ <h2><span class="section-number">41.66.3. </span>2 - Loading the data<a class="h
 </div>
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+<h2><span class="section-number">40.66.4. </span>3 - Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
 <p>Before doing exploratory analysis, let’s get the training and test data.</p>
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 <p>We don’t have any missing values.</p>
 </section>
 <section id="data-preprocessing">
-<h2><span class="section-number">41.66.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.66.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <p>It is here that we prepare the data to be in the proper format for the machine learning model.</p>
 <p>Decision trees don’t care if the features are scaled or not. Let’s set up a pipeline to scale features, and we will use it to verify that notion.</p>
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-<h2><span class="section-number">41.66.6. </span>5 - Training Decision Tree Regressor<a class="headerlink" href="#training-decision-tree-regressor" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.66.6. </span>5 - Training Decision Tree Regressor<a class="headerlink" href="#training-decision-tree-regressor" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <span class="n">DecisionTreeRegressor</span>
@@ -2271,7 +2271,7 @@ <h2><span class="section-number">41.66.6. </span>5 - Training Decision Tree Regr
 </div>
 </section>
 <section id="evaluating-decision-trees">
-<h2><span class="section-number">41.66.7. </span>6 - Evaluating Decision Trees<a class="headerlink" href="#evaluating-decision-trees" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.66.7. </span>6 - Evaluating Decision Trees<a class="headerlink" href="#evaluating-decision-trees" title="Permalink to this headline">#</a></h2>
 <p>Let’s first check the root mean squarred errr on the training. It is not advised to evaluate the model on the test data since we haven’t improved it yet. I will make a function to make it easier and to avoid repetitions.</p>
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 <p>Let’s try to improve the model.</p>
 </section>
 <section id="improving-decision-trees">
-<h2><span class="section-number">41.66.8. </span>7 - Improving Decision Trees<a class="headerlink" href="#improving-decision-trees" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.66.8. </span>7 - Improving Decision Trees<a class="headerlink" href="#improving-decision-trees" title="Permalink to this headline">#</a></h2>
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@@ -2448,7 +2448,7 @@ <h2><span class="section-number">41.66.8. </span>7 - Improving Decision Trees<a
 <p>This is the end of the notebook. We have learned the fundamental idea behind the decision trees, and used it to predict the CPU performance. In the next lab, we will use it for classification task and we will use a real world dataset so that we can practically improve the decision trees model.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.66.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.66.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Jake VanderPlas for creating the open-source course <a class="reference external" href="https://github.com/jakevdp/PythonDataScienceHandbook"> Python Data Science Handbook </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2490,13 +2490,13 @@ <h2><span class="section-number">41.66.9. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.65. </span>Support Vector Machines (SVM) - Classification</p>
+            <p class="prev-next-title"><span class="section-number">40.65. </span>Support Vector Machines (SVM) - Classification</p>
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-        <p class="prev-next-title"><span class="section-number">41.67. </span>Decision Trees - Classification</p>
+        <p class="prev-next-title"><span class="section-number">40.67. </span>Decision Trees - Classification</p>
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+    <link rel="prev" title="40.62. Explore classification methods" href="../../ml-fundamentals/explore-classification-methods.html" />
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-     42.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/model-selection.html">
-     42.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1575,74 +1575,74 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction-to-support-vector-machines">
-   41.63.1. Introduction to Support Vector Machines
+   40.63.1. Introduction to Support Vector Machines
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#support-vector-machines-intuition">
-   41.63.2. Support Vector Machines intuition
+   40.63.2. Support Vector Machines intuition
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#hyperplane">
-     41.63.2.1. Hyperplane
+     40.63.2.1. Hyperplane
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#support-vectors">
-     41.63.2.2. Support Vectors
+     40.63.2.2. Support Vectors
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#margin">
-     41.63.2.3. Margin
+     40.63.2.3. Margin
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#margin-in-svm">
-     41.63.2.4. Margin in SVM
+     40.63.2.4. Margin in SVM
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#svm-under-the-hood">
-     41.63.2.5. SVM Under the hood
+     40.63.2.5. SVM Under the hood
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#maximum-margin-hyperplane">
-     41.63.2.6. Maximum margin hyperplane
+     40.63.2.6. Maximum margin hyperplane
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#problem-with-dispersed-datasets">
-     41.63.2.7. Problem with dispersed datasets
+     40.63.2.7. Problem with dispersed datasets
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#kernel-trick-transformation-of-input-space-to-higher-dimensional-space">
-     41.63.2.8. Kernel trick - transformation of input space to higher dimensional space
+     40.63.2.8. Kernel trick - transformation of input space to higher dimensional space
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#kernel-trick">
-   41.63.3. Kernel trick
+   40.63.3. Kernel trick
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#kernel-function">
-     41.63.3.1. Kernel function
+     40.63.3.1. Kernel function
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#linear-kernel">
-     41.63.3.2. Linear kernel
+     40.63.3.2. Linear kernel
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       41.63.3.2.1. Linear Kernel
+       40.63.3.2.1. Linear Kernel
       </a>
      </li>
     </ul>
@@ -1651,27 +1651,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#polynomial-kernel">
-   41.63.4. Polynomial Kernel
+   40.63.4. Polynomial Kernel
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id2">
-     41.63.4.1. Polynomial Kernel
+     40.63.4.1. Polynomial Kernel
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#radial-basis-function-kernel">
-     41.63.4.2. Radial Basis Function Kernel
+     40.63.4.2. Radial Basis Function Kernel
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id3">
-       41.63.4.2.1. Radial Basis Function kernel
+       40.63.4.2.1. Radial Basis Function kernel
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#svm-classification-with-rbf-kernel">
-       41.63.4.2.2. SVM Classification with rbf kernel
+       40.63.4.2.2. SVM Classification with rbf kernel
       </a>
      </li>
     </ul>
@@ -1680,297 +1680,297 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#svm-scikit-learn-libraries">
-   41.63.5. SVM Scikit-Learn libraries
+   40.63.5. SVM Scikit-Learn libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dataset-description">
-   41.63.6. Dataset description
+   40.63.6. Dataset description
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#attribute-information">
-     41.63.6.1. Attribute Information:
+     40.63.6.1. Attribute Information:
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#import-libraries">
-   41.63.7. Import libraries
+   40.63.7. Import libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#import-dataset">
-   41.63.8. Import dataset
+   40.63.8. Import dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploratory-data-analysis">
-   41.63.9. Exploratory data analysis
+   40.63.9. Exploratory data analysis
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#explore-missing-values-in-variables">
-     41.63.9.1. Explore missing values in variables
+     40.63.9.1. Explore missing values in variables
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#summary-of-numerical-variables">
-     41.63.9.2. Summary of numerical variables
+     40.63.9.2. Summary of numerical variables
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#outliers-in-numerical-variables">
-     41.63.9.3. Outliers in numerical variables
+     40.63.9.3. Outliers in numerical variables
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#handle-outliers-with-svms">
-     41.63.9.4. Handle outliers with SVMs
+     40.63.9.4. Handle outliers with SVMs
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#check-the-distribution-of-variables">
-     41.63.9.5. Check the distribution of variables
+     40.63.9.5. Check the distribution of variables
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#declare-feature-vector-and-target-variable">
-   41.63.10. Declare feature vector and target variable
+   40.63.10. Declare feature vector and target variable
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#split-data-into-separate-training-and-test-set">
-   41.63.11. Split data into separate training and test set
+   40.63.11. Split data into separate training and test set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#feature-scaling">
-   41.63.12. Feature Scaling
+   40.63.12. Feature Scaling
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#run-svm-with-default-hyperparameters">
-   41.63.13. Run SVM with default hyperparameters
+   40.63.13. Run SVM with default hyperparameters
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-rbf-kernel-and-c-100-0">
-     41.63.13.1. Run SVM with rbf kernel and C=100.0
+     40.63.13.1. Run SVM with rbf kernel and C=100.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-rbf-kernel-and-c-1000-0">
-     41.63.13.2. Run SVM with rbf kernel and C=1000.0
+     40.63.13.2. Run SVM with rbf kernel and C=1000.0
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#run-svm-with-linear-kernel">
-   41.63.14. Run SVM with linear kernel
+   40.63.14. Run SVM with linear kernel
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-linear-kernel-and-c-1-0">
-     41.63.14.1. Run SVM with linear kernel and C=1.0
+     40.63.14.1. Run SVM with linear kernel and C=1.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-linear-kernel-and-c-100-0">
-     41.63.14.2. Run SVM with linear kernel and C=100.0
+     40.63.14.2. Run SVM with linear kernel and C=100.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-linear-kernel-and-c-1000-0">
-     41.63.14.3. Run SVM with linear kernel and C=1000.0
+     40.63.14.3. Run SVM with linear kernel and C=1000.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#compare-the-train-set-and-test-set-accuracy">
-     41.63.14.4. Compare the train-set and test-set accuracy
+     40.63.14.4. Compare the train-set and test-set accuracy
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#check-for-overfitting-and-underfitting">
-     41.63.14.5. Check for overfitting and underfitting
+     40.63.14.5. Check for overfitting and underfitting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#compare-model-accuracy-with-null-accuracy">
-     41.63.14.6. Compare model accuracy with null accuracy
+     40.63.14.6. Compare model accuracy with null accuracy
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#run-svm-with-polynomial-kernel">
-   41.63.15. Run SVM with polynomial kernel
+   40.63.15. Run SVM with polynomial kernel
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-polynomial-kernel-and-c-1-0">
-     41.63.15.1. Run SVM with polynomial kernel and C=1.0
+     40.63.15.1. Run SVM with polynomial kernel and C=1.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-polynomial-kernel-and-c-100-0">
-     41.63.15.2. Run SVM with polynomial kernel and C=100.0
+     40.63.15.2. Run SVM with polynomial kernel and C=100.0
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#run-svm-with-sigmoid-kernel">
-   41.63.16. Run SVM with sigmoid kernel
+   40.63.16. Run SVM with sigmoid kernel
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-sigmoid-kernel-and-c-1-0">
-     41.63.16.1. Run SVM with sigmoid kernel and C=1.0
+     40.63.16.1. Run SVM with sigmoid kernel and C=1.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-sigmoid-kernel-and-c-100-0">
-     41.63.16.2. Run SVM with sigmoid kernel and C=100.0
+     40.63.16.2. Run SVM with sigmoid kernel and C=100.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#comments">
-     41.63.16.3. Comments
+     40.63.16.3. Comments
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#confusion-matrix">
-   41.63.17. Confusion matrix
+   40.63.17. Confusion matrix
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#classification-metrices">
-   41.63.18. Classification metrices
+   40.63.18. Classification metrices
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-report">
-     41.63.18.1. Classification Report
+     40.63.18.1. Classification Report
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-accuracy">
-     41.63.18.2. Classification accuracy
+     40.63.18.2. Classification accuracy
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-error">
-     41.63.18.3. Classification error
+     40.63.18.3. Classification error
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#precision">
-     41.63.18.4. Precision
+     40.63.18.4. Precision
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#recall">
-     41.63.18.5. Recall
+     40.63.18.5. Recall
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#true-positive-rate">
-     41.63.18.6. True Positive Rate
+     40.63.18.6. True Positive Rate
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#false-positive-rate">
-     41.63.18.7. False Positive Rate
+     40.63.18.7. False Positive Rate
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#specificity">
-     41.63.18.8. Specificity
+     40.63.18.8. Specificity
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#f1-score">
-     41.63.18.9. f1-score
+     40.63.18.9. f1-score
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#support">
-     41.63.18.10. Support
+     40.63.18.10. Support
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#roc-auc">
-   41.63.19. ROC - AUC
+   40.63.19. ROC - AUC
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#roc-curve">
-     41.63.19.1. ROC Curve
+     40.63.19.1. ROC Curve
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id4">
-     41.63.19.2. ROC  AUC
+     40.63.19.2. ROC  AUC
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id5">
-     41.63.19.3. Comments
+     40.63.19.3. Comments
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#stratified-k-fold-cross-validation-with-shuffle-split">
-   41.63.20. Stratified k-fold Cross Validation with shuffle split
+   40.63.20. Stratified k-fold Cross Validation with shuffle split
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-linear-kernel">
-     41.63.20.1. Stratified k-Fold Cross Validation with shuffle split with  linear kernel
+     40.63.20.1. Stratified k-Fold Cross Validation with shuffle split with  linear kernel
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-rbf-kernel">
-     41.63.20.2. Stratified k-Fold Cross Validation with shuffle split with rbf kernel
+     40.63.20.2. Stratified k-Fold Cross Validation with shuffle split with rbf kernel
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id6">
-     41.63.20.3. Comments
+     40.63.20.3. Comments
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#hyperparameter-optimization-using-gridsearch-cv">
-   41.63.21. Hyperparameter Optimization using GridSearch CV
+   40.63.21. Hyperparameter Optimization using GridSearch CV
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id7">
-     41.63.21.1. Comments
+     40.63.21.1. Comments
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#results-and-conclusion">
-   41.63.22. Results and conclusion
+   40.63.22. Results and conclusion
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.63.23. Acknowledgments
+   40.63.23. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1994,74 +1994,74 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction-to-support-vector-machines">
-   41.63.1. Introduction to Support Vector Machines
+   40.63.1. Introduction to Support Vector Machines
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#support-vector-machines-intuition">
-   41.63.2. Support Vector Machines intuition
+   40.63.2. Support Vector Machines intuition
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#hyperplane">
-     41.63.2.1. Hyperplane
+     40.63.2.1. Hyperplane
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#support-vectors">
-     41.63.2.2. Support Vectors
+     40.63.2.2. Support Vectors
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#margin">
-     41.63.2.3. Margin
+     40.63.2.3. Margin
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#margin-in-svm">
-     41.63.2.4. Margin in SVM
+     40.63.2.4. Margin in SVM
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#svm-under-the-hood">
-     41.63.2.5. SVM Under the hood
+     40.63.2.5. SVM Under the hood
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#maximum-margin-hyperplane">
-     41.63.2.6. Maximum margin hyperplane
+     40.63.2.6. Maximum margin hyperplane
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#problem-with-dispersed-datasets">
-     41.63.2.7. Problem with dispersed datasets
+     40.63.2.7. Problem with dispersed datasets
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#kernel-trick-transformation-of-input-space-to-higher-dimensional-space">
-     41.63.2.8. Kernel trick - transformation of input space to higher dimensional space
+     40.63.2.8. Kernel trick - transformation of input space to higher dimensional space
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#kernel-trick">
-   41.63.3. Kernel trick
+   40.63.3. Kernel trick
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#kernel-function">
-     41.63.3.1. Kernel function
+     40.63.3.1. Kernel function
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#linear-kernel">
-     41.63.3.2. Linear kernel
+     40.63.3.2. Linear kernel
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       41.63.3.2.1. Linear Kernel
+       40.63.3.2.1. Linear Kernel
       </a>
      </li>
     </ul>
@@ -2070,27 +2070,27 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#polynomial-kernel">
-   41.63.4. Polynomial Kernel
+   40.63.4. Polynomial Kernel
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id2">
-     41.63.4.1. Polynomial Kernel
+     40.63.4.1. Polynomial Kernel
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#radial-basis-function-kernel">
-     41.63.4.2. Radial Basis Function Kernel
+     40.63.4.2. Radial Basis Function Kernel
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id3">
-       41.63.4.2.1. Radial Basis Function kernel
+       40.63.4.2.1. Radial Basis Function kernel
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#svm-classification-with-rbf-kernel">
-       41.63.4.2.2. SVM Classification with rbf kernel
+       40.63.4.2.2. SVM Classification with rbf kernel
       </a>
      </li>
     </ul>
@@ -2099,297 +2099,297 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#svm-scikit-learn-libraries">
-   41.63.5. SVM Scikit-Learn libraries
+   40.63.5. SVM Scikit-Learn libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dataset-description">
-   41.63.6. Dataset description
+   40.63.6. Dataset description
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#attribute-information">
-     41.63.6.1. Attribute Information:
+     40.63.6.1. Attribute Information:
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#import-libraries">
-   41.63.7. Import libraries
+   40.63.7. Import libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#import-dataset">
-   41.63.8. Import dataset
+   40.63.8. Import dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploratory-data-analysis">
-   41.63.9. Exploratory data analysis
+   40.63.9. Exploratory data analysis
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#explore-missing-values-in-variables">
-     41.63.9.1. Explore missing values in variables
+     40.63.9.1. Explore missing values in variables
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#summary-of-numerical-variables">
-     41.63.9.2. Summary of numerical variables
+     40.63.9.2. Summary of numerical variables
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#outliers-in-numerical-variables">
-     41.63.9.3. Outliers in numerical variables
+     40.63.9.3. Outliers in numerical variables
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#handle-outliers-with-svms">
-     41.63.9.4. Handle outliers with SVMs
+     40.63.9.4. Handle outliers with SVMs
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#check-the-distribution-of-variables">
-     41.63.9.5. Check the distribution of variables
+     40.63.9.5. Check the distribution of variables
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#declare-feature-vector-and-target-variable">
-   41.63.10. Declare feature vector and target variable
+   40.63.10. Declare feature vector and target variable
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#split-data-into-separate-training-and-test-set">
-   41.63.11. Split data into separate training and test set
+   40.63.11. Split data into separate training and test set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#feature-scaling">
-   41.63.12. Feature Scaling
+   40.63.12. Feature Scaling
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#run-svm-with-default-hyperparameters">
-   41.63.13. Run SVM with default hyperparameters
+   40.63.13. Run SVM with default hyperparameters
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-rbf-kernel-and-c-100-0">
-     41.63.13.1. Run SVM with rbf kernel and C=100.0
+     40.63.13.1. Run SVM with rbf kernel and C=100.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-rbf-kernel-and-c-1000-0">
-     41.63.13.2. Run SVM with rbf kernel and C=1000.0
+     40.63.13.2. Run SVM with rbf kernel and C=1000.0
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#run-svm-with-linear-kernel">
-   41.63.14. Run SVM with linear kernel
+   40.63.14. Run SVM with linear kernel
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-linear-kernel-and-c-1-0">
-     41.63.14.1. Run SVM with linear kernel and C=1.0
+     40.63.14.1. Run SVM with linear kernel and C=1.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-linear-kernel-and-c-100-0">
-     41.63.14.2. Run SVM with linear kernel and C=100.0
+     40.63.14.2. Run SVM with linear kernel and C=100.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-linear-kernel-and-c-1000-0">
-     41.63.14.3. Run SVM with linear kernel and C=1000.0
+     40.63.14.3. Run SVM with linear kernel and C=1000.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#compare-the-train-set-and-test-set-accuracy">
-     41.63.14.4. Compare the train-set and test-set accuracy
+     40.63.14.4. Compare the train-set and test-set accuracy
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#check-for-overfitting-and-underfitting">
-     41.63.14.5. Check for overfitting and underfitting
+     40.63.14.5. Check for overfitting and underfitting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#compare-model-accuracy-with-null-accuracy">
-     41.63.14.6. Compare model accuracy with null accuracy
+     40.63.14.6. Compare model accuracy with null accuracy
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#run-svm-with-polynomial-kernel">
-   41.63.15. Run SVM with polynomial kernel
+   40.63.15. Run SVM with polynomial kernel
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-polynomial-kernel-and-c-1-0">
-     41.63.15.1. Run SVM with polynomial kernel and C=1.0
+     40.63.15.1. Run SVM with polynomial kernel and C=1.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-polynomial-kernel-and-c-100-0">
-     41.63.15.2. Run SVM with polynomial kernel and C=100.0
+     40.63.15.2. Run SVM with polynomial kernel and C=100.0
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#run-svm-with-sigmoid-kernel">
-   41.63.16. Run SVM with sigmoid kernel
+   40.63.16. Run SVM with sigmoid kernel
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-sigmoid-kernel-and-c-1-0">
-     41.63.16.1. Run SVM with sigmoid kernel and C=1.0
+     40.63.16.1. Run SVM with sigmoid kernel and C=1.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#run-svm-with-sigmoid-kernel-and-c-100-0">
-     41.63.16.2. Run SVM with sigmoid kernel and C=100.0
+     40.63.16.2. Run SVM with sigmoid kernel and C=100.0
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#comments">
-     41.63.16.3. Comments
+     40.63.16.3. Comments
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#confusion-matrix">
-   41.63.17. Confusion matrix
+   40.63.17. Confusion matrix
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#classification-metrices">
-   41.63.18. Classification metrices
+   40.63.18. Classification metrices
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-report">
-     41.63.18.1. Classification Report
+     40.63.18.1. Classification Report
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-accuracy">
-     41.63.18.2. Classification accuracy
+     40.63.18.2. Classification accuracy
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-error">
-     41.63.18.3. Classification error
+     40.63.18.3. Classification error
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#precision">
-     41.63.18.4. Precision
+     40.63.18.4. Precision
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#recall">
-     41.63.18.5. Recall
+     40.63.18.5. Recall
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#true-positive-rate">
-     41.63.18.6. True Positive Rate
+     40.63.18.6. True Positive Rate
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#false-positive-rate">
-     41.63.18.7. False Positive Rate
+     40.63.18.7. False Positive Rate
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#specificity">
-     41.63.18.8. Specificity
+     40.63.18.8. Specificity
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#f1-score">
-     41.63.18.9. f1-score
+     40.63.18.9. f1-score
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#support">
-     41.63.18.10. Support
+     40.63.18.10. Support
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#roc-auc">
-   41.63.19. ROC - AUC
+   40.63.19. ROC - AUC
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#roc-curve">
-     41.63.19.1. ROC Curve
+     40.63.19.1. ROC Curve
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id4">
-     41.63.19.2. ROC  AUC
+     40.63.19.2. ROC  AUC
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id5">
-     41.63.19.3. Comments
+     40.63.19.3. Comments
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#stratified-k-fold-cross-validation-with-shuffle-split">
-   41.63.20. Stratified k-fold Cross Validation with shuffle split
+   40.63.20. Stratified k-fold Cross Validation with shuffle split
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-linear-kernel">
-     41.63.20.1. Stratified k-Fold Cross Validation with shuffle split with  linear kernel
+     40.63.20.1. Stratified k-Fold Cross Validation with shuffle split with  linear kernel
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-rbf-kernel">
-     41.63.20.2. Stratified k-Fold Cross Validation with shuffle split with rbf kernel
+     40.63.20.2. Stratified k-Fold Cross Validation with shuffle split with rbf kernel
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id6">
-     41.63.20.3. Comments
+     40.63.20.3. Comments
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#hyperparameter-optimization-using-gridsearch-cv">
-   41.63.21. Hyperparameter Optimization using GridSearch CV
+   40.63.21. Hyperparameter Optimization using GridSearch CV
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id7">
-     41.63.21.1. Comments
+     40.63.21.1. Comments
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#results-and-conclusion">
-   41.63.22. Results and conclusion
+   40.63.22. Results and conclusion
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.63.23. Acknowledgments
+   40.63.23. Acknowledgments
   </a>
  </li>
 </ul>
@@ -2403,36 +2403,36 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="kernel-method-assignment-1">
-<h1><span class="section-number">41.63. </span>Kernel method assignment 1<a class="headerlink" href="#kernel-method-assignment-1" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.63. </span>Kernel method assignment 1<a class="headerlink" href="#kernel-method-assignment-1" title="Permalink to this headline">#</a></h1>
 <p>Support Vector Machines (SVMs in short) are supervised machine learning algorithms that are used for classification and regression purposes. In this kernel, We build a Support Vector Machines classifier to classify a Pulsar star. We have used the <strong>Predicting a Pulsar Star</strong> dataset for this project.</p>
 <section id="introduction-to-support-vector-machines">
-<h2><span class="section-number">41.63.1. </span>Introduction to Support Vector Machines<a class="headerlink" href="#introduction-to-support-vector-machines" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.1. </span>Introduction to Support Vector Machines<a class="headerlink" href="#introduction-to-support-vector-machines" title="Permalink to this headline">#</a></h2>
 <p><strong>Support Vector Machines</strong> (SVMs in short) are machine learning algorithms that are used for classification and regression purposes. SVMs are one of the powerful machine learning algorithms for classification, regression and outlier detection purposes. An SVM classifier builds a model that assigns new data points to one of the given categories. Thus, it can be viewed as a non-probabilistic binary linear classifier.</p>
 <p>The original SVM algorithm was developed by Vladimir N Vapnik and Alexey Ya. Chervonenkis in 1963. At that time, the algorithm was in early stages. The only possibility is to draw hyperplanes for linear classifier. In 1992, Bernhard E. Boser, Isabelle M Guyon and Vladimir N Vapnik suggested a way to create non-linear classifiers by applying the kernel trick to maximum-margin hyperplanes. The current standard was proposed by Corinna Cortes and Vapnik in 1993 and published in 1995.</p>
 <p>SVMs can be used for linear classification purposes. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using the <strong>kernel trick</strong>. It enable us to implicitly map the inputs into high dimensional feature spaces.</p>
 </section>
 <section id="support-vector-machines-intuition">
-<h2><span class="section-number">41.63.2. </span>Support Vector Machines intuition<a class="headerlink" href="#support-vector-machines-intuition" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.2. </span>Support Vector Machines intuition<a class="headerlink" href="#support-vector-machines-intuition" title="Permalink to this headline">#</a></h2>
 <p>Now, we should be familiar with some SVM terminology.</p>
 <section id="hyperplane">
-<h3><span class="section-number">41.63.2.1. </span>Hyperplane<a class="headerlink" href="#hyperplane" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.2.1. </span>Hyperplane<a class="headerlink" href="#hyperplane" title="Permalink to this headline">#</a></h3>
 <p>A hyperplane is a decision boundary which separates between given set of data points having different class labels. The SVM classifier separates data points using a hyperplane with the maximum amount of margin. This hyperplane is known as the <code class="docutils literal notranslate"><span class="pre">maximum</span> <span class="pre">margin</span> <span class="pre">hyperplane</span></code> and the linear classifier it defines is known as the <code class="docutils literal notranslate"><span class="pre">maximum</span> <span class="pre">margin</span> <span class="pre">classifier</span></code>.</p>
 </section>
 <section id="support-vectors">
-<h3><span class="section-number">41.63.2.2. </span>Support Vectors<a class="headerlink" href="#support-vectors" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.2.2. </span>Support Vectors<a class="headerlink" href="#support-vectors" title="Permalink to this headline">#</a></h3>
 <p>Support vectors are the sample data points, which are closest to the hyperplane.  These data points will define the separating line or hyperplane better by calculating margins.</p>
 </section>
 <section id="margin">
-<h3><span class="section-number">41.63.2.3. </span>Margin<a class="headerlink" href="#margin" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.2.3. </span>Margin<a class="headerlink" href="#margin" title="Permalink to this headline">#</a></h3>
 <p>A margin is a separation gap between the two lines on the closest data points. It is calculated as the perpendicular distance from the line to support vectors or closest data points. In SVMs, we try to maximize this separation gap so that we get maximum margin.</p>
 <p>The following diagram illustrates these concepts visually.</p>
 </section>
 <section id="margin-in-svm">
-<h3><span class="section-number">41.63.2.4. </span>Margin in SVM<a class="headerlink" href="#margin-in-svm" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.2.4. </span>Margin in SVM<a class="headerlink" href="#margin-in-svm" title="Permalink to this headline">#</a></h3>
 <p><img alt="Margin in SVM" src="https://static.wixstatic.com/media/8f929f_7ecacdcf69d2450087cb4a898ef90837~mv2.png" /></p>
 </section>
 <section id="svm-under-the-hood">
-<h3><span class="section-number">41.63.2.5. </span>SVM Under the hood<a class="headerlink" href="#svm-under-the-hood" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.2.5. </span>SVM Under the hood<a class="headerlink" href="#svm-under-the-hood" title="Permalink to this headline">#</a></h3>
 <p>In SVMs, our main objective is to select a hyperplane with the maximum possible margin between support vectors in the given dataset. SVM searches for the maximum margin hyperplane in the following 2 step process –</p>
 <ol class="simple">
 <li><p>Generate hyperplanes which segregates the classes in the best possible way. There are many hyperplanes that might classify the data. We should look for the best hyperplane that represents the largest separation, or margin, between the two classes.</p></li>
@@ -2441,78 +2441,78 @@ <h3><span class="section-number">41.63.2.5. </span>SVM Under the hood<a class="h
 <p>The following diagram illustrates the concept of <strong>maximum margin</strong> and <strong>maximum margin hyperplane</strong> in a clear manner.</p>
 </section>
 <section id="maximum-margin-hyperplane">
-<h3><span class="section-number">41.63.2.6. </span>Maximum margin hyperplane<a class="headerlink" href="#maximum-margin-hyperplane" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.2.6. </span>Maximum margin hyperplane<a class="headerlink" href="#maximum-margin-hyperplane" title="Permalink to this headline">#</a></h3>
 <p><img alt="Maximum margin hyperplane" src="https://static.packt-cdn.com/products/9781783555130/graphics/3547_03_07.jpg" /></p>
 </section>
 <section id="problem-with-dispersed-datasets">
-<h3><span class="section-number">41.63.2.7. </span>Problem with dispersed datasets<a class="headerlink" href="#problem-with-dispersed-datasets" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.2.7. </span>Problem with dispersed datasets<a class="headerlink" href="#problem-with-dispersed-datasets" title="Permalink to this headline">#</a></h3>
 <p>Sometimes, the sample data points are so dispersed that it is not possible to separate them using a linear hyperplane.
 In such a situation, SVMs uses a <code class="docutils literal notranslate"><span class="pre">kernel</span> <span class="pre">trick</span></code> to transform the input space to a higher dimensional space as shown in the diagram below. It uses a mapping function to transform the 2-D input space into the 3-D input space. Now, we can easily segregate the data points using linear separation.</p>
 </section>
 <section id="kernel-trick-transformation-of-input-space-to-higher-dimensional-space">
-<h3><span class="section-number">41.63.2.8. </span>Kernel trick - transformation of input space to higher dimensional space<a class="headerlink" href="#kernel-trick-transformation-of-input-space-to-higher-dimensional-space" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.2.8. </span>Kernel trick - transformation of input space to higher dimensional space<a class="headerlink" href="#kernel-trick-transformation-of-input-space-to-higher-dimensional-space" title="Permalink to this headline">#</a></h3>
 <p><img alt="Kernel trick" src="http://www.aionlinecourse.com/uploads/tutorials/2019/07/11_21_kernel_svm_3.png" /></p>
 </section>
 </section>
 <section id="kernel-trick">
-<h2><span class="section-number">41.63.3. </span>Kernel trick<a class="headerlink" href="#kernel-trick" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.3. </span>Kernel trick<a class="headerlink" href="#kernel-trick" title="Permalink to this headline">#</a></h2>
 <p>In practice, SVM algorithm is implemented using a <code class="docutils literal notranslate"><span class="pre">kernel</span></code>. It uses a technique called the <code class="docutils literal notranslate"><span class="pre">kernel</span> <span class="pre">trick</span></code>. In simple words, a <code class="docutils literal notranslate"><span class="pre">kernel</span></code> is just a function that maps the data to a higher dimension where data is separable. A kernel transforms a low-dimensional input data space into a higher dimensional space. So, it converts non-linear separable problems to linear separable problems by adding more dimensions to it. Thus, the kernel trick helps us to build a more accurate classifier. Hence, it is useful in non-linear separation problems.</p>
 <p>We can define a kernel function as follows-</p>
 <section id="kernel-function">
-<h3><span class="section-number">41.63.3.1. </span>Kernel function<a class="headerlink" href="#kernel-function" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.3.1. </span>Kernel function<a class="headerlink" href="#kernel-function" title="Permalink to this headline">#</a></h3>
 <p><img alt="Kernel function" src="https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTodZptqcRor0LGo8Qn7_kJB9n9BACMt6jgIPZ4C3g_rgh_uSRZLQ&amp;s" /></p>
 <p>In the context of SVMs, there are 4 popular kernels – <code class="docutils literal notranslate"><span class="pre">Linear</span> <span class="pre">kernel</span></code>,<code class="docutils literal notranslate"><span class="pre">Polynomial</span> <span class="pre">kernel</span></code>,<code class="docutils literal notranslate"><span class="pre">Radial</span> <span class="pre">Basis</span> <span class="pre">Function</span> <span class="pre">(RBF)</span> <span class="pre">kernel</span></code> (also called Gaussian kernel) and <code class="docutils literal notranslate"><span class="pre">Sigmoid</span> <span class="pre">kernel</span></code>. These are described below -</p>
 </section>
 <section id="linear-kernel">
-<h3><span class="section-number">41.63.3.2. </span>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.3.2. </span>Linear kernel<a class="headerlink" href="#linear-kernel" title="Permalink to this headline">#</a></h3>
 <p>In linear kernel, the kernel function takes the form of a linear function as follows-</p>
 <p><strong>linear kernel : K(xi , xj ) = xiT xj</strong></p>
 <p>Linear kernel is used when the data is linearly separable. It means that data can be separated using a single line. It is one of the most common kernels to be used. It is mostly used when there are large number of features in a dataset. Linear kernel is often used for text classification purposes.</p>
 <p>Training with a linear kernel is usually faster, because we only need to optimize the C regularization parameter. When training with other kernels, we also need to optimize the γ parameter. So, performing a grid search will usually take more time.</p>
 <p>Linear kernel can be visualized with the following figure.</p>
 <section id="id1">
-<h4><span class="section-number">41.63.3.2.1. </span>Linear Kernel<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.63.3.2.1. </span>Linear Kernel<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
 <p><img alt="Linear Kernel" src="https://scikit-learn.org/stable/_images/sphx_glr_plot_svm_kernels_thumb.png" /></p>
 </section>
 </section>
 </section>
 <section id="polynomial-kernel">
-<h2><span class="section-number">41.63.4. </span>Polynomial Kernel<a class="headerlink" href="#polynomial-kernel" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.4. </span>Polynomial Kernel<a class="headerlink" href="#polynomial-kernel" title="Permalink to this headline">#</a></h2>
 <p>Polynomial kernel represents the similarity of vectors (training samples) in a feature space over polynomials of the original variables. The polynomial kernel looks not only at the given features of input samples to determine their similarity, but also combinations of the input samples.</p>
 <p>For degree-d polynomials, the polynomial kernel is defined as follows –</p>
 <p><strong>Polynomial kernel : K(xi , xj ) = (γxiT xj + r)d , γ &gt; 0</strong></p>
 <p>Polynomial kernel is very popular in Natural Language Processing. The most common degree is d = 2 (quadratic), since larger degrees tend to overfit on NLP problems. It can be visualized with the following diagram.</p>
 <section id="id2">
-<h3><span class="section-number">41.63.4.1. </span>Polynomial Kernel<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.4.1. </span>Polynomial Kernel<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <p><img alt="Polynomial Kernel" src="https://www.researchgate.net/profile/Cheng_Soon_Ong/publication/23442384/figure/fig12/AS:341444054274063&#64;1458418014823/The-effect-of-the-degree-of-a-polynomial-kernel-The-polynomial-kernel-of-degree-1-leads.png" /></p>
 </section>
 <section id="radial-basis-function-kernel">
-<h3><span class="section-number">41.63.4.2. </span>Radial Basis Function Kernel<a class="headerlink" href="#radial-basis-function-kernel" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.4.2. </span>Radial Basis Function Kernel<a class="headerlink" href="#radial-basis-function-kernel" title="Permalink to this headline">#</a></h3>
 <p>Radial basis function kernel is a general purpose kernel. It is used when we have no prior knowledge about the data. The RBF kernel on two samples x and y is defined by the following equation –</p>
 <section id="id3">
-<h4><span class="section-number">41.63.4.2.1. </span>Radial Basis Function kernel<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.63.4.2.1. </span>Radial Basis Function kernel<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h4>
 <p>![RBK Kernel](data:image/png;base64,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)</p>
 <p>The following diagram demonstrates the SVM classification with rbf kernel.</p>
 </section>
 <section id="svm-classification-with-rbf-kernel">
-<h4><span class="section-number">41.63.4.2.2. </span>SVM Classification with rbf kernel<a class="headerlink" href="#svm-classification-with-rbf-kernel" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.63.4.2.2. </span>SVM Classification with rbf kernel<a class="headerlink" href="#svm-classification-with-rbf-kernel" title="Permalink to this headline">#</a></h4>
 <p><img alt="SVM Classification with rbf kernel" src="https://www.researchgate.net/profile/Periklis_Gogas/publication/286180566/figure/fig5/AS:304327777374210&#64;1449568804246/An-example-of-an-SVM-classification-using-the-RBF-kernel-The-two-classes-are-separated.png" /></p>
 </section>
 </section>
 </section>
 <section id="svm-scikit-learn-libraries">
-<h2><span class="section-number">41.63.5. </span>SVM Scikit-Learn libraries<a class="headerlink" href="#svm-scikit-learn-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.5. </span>SVM Scikit-Learn libraries<a class="headerlink" href="#svm-scikit-learn-libraries" title="Permalink to this headline">#</a></h2>
 <p>Scikit-Learn provides useful libraries to implement Support Vector Machine algorithm on a dataset. There are many libraries that can help us to implement SVM smoothly. We just need to call the library with parameters that suit to our needs. In this project, I am dealing with a classification task. So, I will mention the Scikit-Learn libraries for SVM classification purposes.</p>
 <p>First, there is a <strong>LinearSVC()</strong> classifier. As the name suggests, this classifier uses only linear kernel. In LinearSVC() classifier, we don’t pass the value of kernel since it is used only for linear classification purposes.</p>
 <p>Scikit-Learn provides two other classifiers - <strong>SVC()</strong> and <strong>NuSVC()</strong> which are used for classification purposes. These classifiers are mostly similar with some difference in parameters. <strong>NuSVC()</strong> is similar to <strong>SVC()</strong> but uses a parameter to control the number of support vectors. We pass the values of kernel, gamma and C along with other parameters. By default kernel parameter uses rbf as its value but we can pass values like poly, linear, sigmoid or callable function.</p>
 </section>
 <section id="dataset-description">
-<h2><span class="section-number">41.63.6. </span>Dataset description<a class="headerlink" href="#dataset-description" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.6. </span>Dataset description<a class="headerlink" href="#dataset-description" title="Permalink to this headline">#</a></h2>
 <p>I have used the <strong>Predicting a Pulsar Star</strong> dataset for this project.</p>
 <p>Pulsars are a rare type of Neutron star that produce radio emission detectable here on Earth. They are of considerable scientific interest as probes of space-time, the inter-stellar medium, and states of matter. Classification algorithms in particular are being adopted, which treat the data sets as binary classification problems. Here the legitimate pulsar examples form  minority positive class and spurious examples form the majority negative class.</p>
 <p>The data set shared here contains 16,259 spurious examples caused by RFI/noise, and 1,639 real pulsar examples. Each row lists the variables first, and the class label is the final entry. The class labels used are 0 (negative) and 1 (positive).</p>
 <section id="attribute-information">
-<h3><span class="section-number">41.63.6.1. </span>Attribute Information:<a class="headerlink" href="#attribute-information" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.6.1. </span>Attribute Information:<a class="headerlink" href="#attribute-information" title="Permalink to this headline">#</a></h3>
 <p>Each candidate is described by 8 continuous variables, and a single class variable. The first four are simple statistics obtained from the integrated pulse profile. The remaining four variables are similarly obtained from the DM-SNR curve . These are summarised below:</p>
 <ol class="simple">
 <li><p>Mean of the integrated profile.</p></li>
@@ -2528,7 +2528,7 @@ <h3><span class="section-number">41.63.6.1. </span>Attribute Information:<a clas
 </section>
 </section>
 <section id="import-libraries">
-<h2><span class="section-number">41.63.7. </span>Import libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.7. </span>Import libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h2>
 <p>I will start off by importing the required Python libraries.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2546,7 +2546,7 @@ <h2><span class="section-number">41.63.7. </span>Import libraries<a class="heade
 </div>
 </section>
 <section id="import-dataset">
-<h2><span class="section-number">41.63.8. </span>Import dataset<a class="headerlink" href="#import-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.8. </span>Import dataset<a class="headerlink" href="#import-dataset" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="s1">&#39;../../../assets/data/pulsar_stars.csv&#39;</span>
@@ -2558,7 +2558,7 @@ <h2><span class="section-number">41.63.8. </span>Import dataset<a class="headerl
 </div>
 </section>
 <section id="exploratory-data-analysis">
-<h2><span class="section-number">41.63.9. </span>Exploratory data analysis<a class="headerlink" href="#exploratory-data-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.9. </span>Exploratory data analysis<a class="headerlink" href="#exploratory-data-analysis" title="Permalink to this headline">#</a></h2>
 <p>Now, we will explore the data to gain insights about the data.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2828,7 +2828,7 @@ <h2><span class="section-number">41.63.9. </span>Exploratory data analysis<a cla
 </div>
 <p>We can see that there are no missing values in the dataset and all the variables are numerical variables.</p>
 <section id="explore-missing-values-in-variables">
-<h3><span class="section-number">41.63.9.1. </span>Explore missing values in variables<a class="headerlink" href="#explore-missing-values-in-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.9.1. </span>Explore missing values in variables<a class="headerlink" href="#explore-missing-values-in-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># check for missing values in variables</span>
@@ -2855,7 +2855,7 @@ <h3><span class="section-number">41.63.9.1. </span>Explore missing values in var
 <p>We can see that there are no missing values in the dataset.</p>
 </section>
 <section id="summary-of-numerical-variables">
-<h3><span class="section-number">41.63.9.2. </span>Summary of numerical variables<a class="headerlink" href="#summary-of-numerical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.9.2. </span>Summary of numerical variables<a class="headerlink" href="#summary-of-numerical-variables" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>There are 9 numerical variables in the dataset.</p></li>
 <li><p>8 are continuous variables and 1 is discrete variable.</p></li>
@@ -2864,7 +2864,7 @@ <h3><span class="section-number">41.63.9.2. </span>Summary of numerical variable
 </ul>
 </section>
 <section id="outliers-in-numerical-variables">
-<h3><span class="section-number">41.63.9.3. </span>Outliers in numerical variables<a class="headerlink" href="#outliers-in-numerical-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.9.3. </span>Outliers in numerical variables<a class="headerlink" href="#outliers-in-numerical-variables" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># view summary statistics in numerical variables</span>
@@ -3072,7 +3072,7 @@ <h3><span class="section-number">41.63.9.3. </span>Outliers in numerical variabl
 <p>The above boxplots confirm that there are lot of outliers in these variables.</p>
 </section>
 <section id="handle-outliers-with-svms">
-<h3><span class="section-number">41.63.9.4. </span>Handle outliers with SVMs<a class="headerlink" href="#handle-outliers-with-svms" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.9.4. </span>Handle outliers with SVMs<a class="headerlink" href="#handle-outliers-with-svms" title="Permalink to this headline">#</a></h3>
 <p>There are 2 variants of SVMs. They are <code class="docutils literal notranslate"><span class="pre">hard-margin</span> <span class="pre">variant</span> <span class="pre">of</span> <span class="pre">SVM</span></code> and <code class="docutils literal notranslate"><span class="pre">soft-margin</span> <span class="pre">variant</span> <span class="pre">of</span> <span class="pre">SVM</span></code>.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">hard-margin</span> <span class="pre">variant</span> <span class="pre">of</span> <span class="pre">SVM</span></code> does not deal with outliers. In this case, we want to find the hyperplane with maximum margin such that every training point is correctly classified with margin at least 1. This technique does not handle outliers well.</p>
 <p>Another version of SVM is called <code class="docutils literal notranslate"><span class="pre">soft-margin</span> <span class="pre">variant</span> <span class="pre">of</span> <span class="pre">SVM</span></code>. In this case, we can have a few points incorrectly classified or
@@ -3080,7 +3080,7 @@ <h3><span class="section-number">41.63.9.4. </span>Handle outliers with SVMs<a c
 <p>The message is that since the dataset contains outliers, so the value of C should be high while training the model.</p>
 </section>
 <section id="check-the-distribution-of-variables">
-<h3><span class="section-number">41.63.9.5. </span>Check the distribution of variables<a class="headerlink" href="#check-the-distribution-of-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.9.5. </span>Check the distribution of variables<a class="headerlink" href="#check-the-distribution-of-variables" title="Permalink to this headline">#</a></h3>
 <p>Now, I will plot the histograms to check distributions to find out if they are normal or skewed.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3154,7 +3154,7 @@ <h3><span class="section-number">41.63.9.5. </span>Check the distribution of var
 </section>
 </section>
 <section id="declare-feature-vector-and-target-variable">
-<h2><span class="section-number">41.63.10. </span>Declare feature vector and target variable<a class="headerlink" href="#declare-feature-vector-and-target-variable" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.10. </span>Declare feature vector and target variable<a class="headerlink" href="#declare-feature-vector-and-target-variable" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">&#39;target_class&#39;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
@@ -3166,7 +3166,7 @@ <h2><span class="section-number">41.63.10. </span>Declare feature vector and tar
 </div>
 </section>
 <section id="split-data-into-separate-training-and-test-set">
-<h2><span class="section-number">41.63.11. </span>Split data into separate training and test set<a class="headerlink" href="#split-data-into-separate-training-and-test-set" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.11. </span>Split data into separate training and test set<a class="headerlink" href="#split-data-into-separate-training-and-test-set" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># split X and y into training and testing sets</span>
@@ -3194,7 +3194,7 @@ <h2><span class="section-number">41.63.11. </span>Split data into separate train
 </div>
 </section>
 <section id="feature-scaling">
-<h2><span class="section-number">41.63.12. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.12. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cols</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">columns</span>
@@ -3360,7 +3360,7 @@ <h2><span class="section-number">41.63.12. </span>Feature Scaling<a class="heade
 <p>We now have <code class="docutils literal notranslate"><span class="pre">X_train</span></code> dataset ready to be fed into the Logistic Regression classifier. I will do it as follows.</p>
 </section>
 <section id="run-svm-with-default-hyperparameters">
-<h2><span class="section-number">41.63.13. </span>Run SVM with default hyperparameters<a class="headerlink" href="#run-svm-with-default-hyperparameters" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.13. </span>Run SVM with default hyperparameters<a class="headerlink" href="#run-svm-with-default-hyperparameters" title="Permalink to this headline">#</a></h2>
 <p>Default hyperparameter means C=1.0,  kernel=<code class="docutils literal notranslate"><span class="pre">rbf</span></code> and gamma=<code class="docutils literal notranslate"><span class="pre">auto</span></code> among other parameters.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3396,7 +3396,7 @@ <h2><span class="section-number">41.63.13. </span>Run SVM with default hyperpara
 </div>
 </div>
 <section id="run-svm-with-rbf-kernel-and-c-100-0">
-<h3><span class="section-number">41.63.13.1. </span>Run SVM with rbf kernel and C=100.0<a class="headerlink" href="#run-svm-with-rbf-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.13.1. </span>Run SVM with rbf kernel and C=100.0<a class="headerlink" href="#run-svm-with-rbf-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
 <p>We have seen that there are outliers in our dataset. So, we should increase the value of C as higher C means fewer outliers.
 So, I will run SVM with kernel=<code class="docutils literal notranslate"><span class="pre">rbf</span></code> and C=100.0.</p>
 <div class="cell docutils container">
@@ -3428,7 +3428,7 @@ <h3><span class="section-number">41.63.13.1. </span>Run SVM with rbf kernel and
 <p>Now, I will further increase the value of C=1000.0 and check accuracy.</p>
 </section>
 <section id="run-svm-with-rbf-kernel-and-c-1000-0">
-<h3><span class="section-number">41.63.13.2. </span>Run SVM with rbf kernel and C=1000.0<a class="headerlink" href="#run-svm-with-rbf-kernel-and-c-1000-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.13.2. </span>Run SVM with rbf kernel and C=1000.0<a class="headerlink" href="#run-svm-with-rbf-kernel-and-c-1000-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with rbf kernel and C=1000</span>
@@ -3458,9 +3458,9 @@ <h3><span class="section-number">41.63.13.2. </span>Run SVM with rbf kernel and
 </section>
 </section>
 <section id="run-svm-with-linear-kernel">
-<h2><span class="section-number">41.63.14. </span>Run SVM with linear kernel<a class="headerlink" href="#run-svm-with-linear-kernel" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.14. </span>Run SVM with linear kernel<a class="headerlink" href="#run-svm-with-linear-kernel" title="Permalink to this headline">#</a></h2>
 <section id="run-svm-with-linear-kernel-and-c-1-0">
-<h3><span class="section-number">41.63.14.1. </span>Run SVM with linear kernel and C=1.0<a class="headerlink" href="#run-svm-with-linear-kernel-and-c-1-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.14.1. </span>Run SVM with linear kernel and C=1.0<a class="headerlink" href="#run-svm-with-linear-kernel-and-c-1-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with linear kernel and C=1.0</span>
@@ -3488,7 +3488,7 @@ <h3><span class="section-number">41.63.14.1. </span>Run SVM with linear kernel a
 </div>
 </section>
 <section id="run-svm-with-linear-kernel-and-c-100-0">
-<h3><span class="section-number">41.63.14.2. </span>Run SVM with linear kernel and C=100.0<a class="headerlink" href="#run-svm-with-linear-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.14.2. </span>Run SVM with linear kernel and C=100.0<a class="headerlink" href="#run-svm-with-linear-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with linear kernel and C=100.0</span>
@@ -3516,7 +3516,7 @@ <h3><span class="section-number">41.63.14.2. </span>Run SVM with linear kernel a
 </div>
 </section>
 <section id="run-svm-with-linear-kernel-and-c-1000-0">
-<h3><span class="section-number">41.63.14.3. </span>Run SVM with linear kernel and C=1000.0<a class="headerlink" href="#run-svm-with-linear-kernel-and-c-1000-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.14.3. </span>Run SVM with linear kernel and C=1000.0<a class="headerlink" href="#run-svm-with-linear-kernel-and-c-1000-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with linear kernel and C=1000.0</span>
@@ -3546,7 +3546,7 @@ <h3><span class="section-number">41.63.14.3. </span>Run SVM with linear kernel a
 <p>Here, <strong>y_test</strong> are the true class labels and <strong>y_pred</strong> are the predicted class labels in the test-set.</p>
 </section>
 <section id="compare-the-train-set-and-test-set-accuracy">
-<h3><span class="section-number">41.63.14.4. </span>Compare the train-set and test-set accuracy<a class="headerlink" href="#compare-the-train-set-and-test-set-accuracy" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.14.4. </span>Compare the train-set and test-set accuracy<a class="headerlink" href="#compare-the-train-set-and-test-set-accuracy" title="Permalink to this headline">#</a></h3>
 <p>Now, I will compare the train-set and test-set accuracy to check for overfitting.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3577,7 +3577,7 @@ <h3><span class="section-number">41.63.14.4. </span>Compare the train-set and te
 <p>We can see that the training set and test-set accuracy are very much comparable.</p>
 </section>
 <section id="check-for-overfitting-and-underfitting">
-<h3><span class="section-number">41.63.14.5. </span>Check for overfitting and underfitting<a class="headerlink" href="#check-for-overfitting-and-underfitting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.14.5. </span>Check for overfitting and underfitting<a class="headerlink" href="#check-for-overfitting-and-underfitting" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># print the scores on training and test set</span>
@@ -3598,7 +3598,7 @@ <h3><span class="section-number">41.63.14.5. </span>Check for overfitting and un
 <p>The training-set accuracy score is 0.9783 while the test-set accuracy to be 0.9830. These two values are quite comparable. So, there is no question of overfitting.</p>
 </section>
 <section id="compare-model-accuracy-with-null-accuracy">
-<h3><span class="section-number">41.63.14.6. </span>Compare model accuracy with null accuracy<a class="headerlink" href="#compare-model-accuracy-with-null-accuracy" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.14.6. </span>Compare model accuracy with null accuracy<a class="headerlink" href="#compare-model-accuracy-with-null-accuracy" title="Permalink to this headline">#</a></h3>
 <p>So, the model accuracy is 0.9832. But, we cannot say that our model is very good based on the above accuracy. We must compare it with the <strong>null accuracy</strong>. Null accuracy is the accuracy that could be achieved by always predicting the most frequent class.</p>
 <p>So, we should first check the class distribution in the test set.</p>
 <div class="cell docutils container">
@@ -3638,9 +3638,9 @@ <h3><span class="section-number">41.63.14.6. </span>Compare model accuracy with
 </section>
 </section>
 <section id="run-svm-with-polynomial-kernel">
-<h2><span class="section-number">41.63.15. </span>Run SVM with polynomial kernel<a class="headerlink" href="#run-svm-with-polynomial-kernel" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.15. </span>Run SVM with polynomial kernel<a class="headerlink" href="#run-svm-with-polynomial-kernel" title="Permalink to this headline">#</a></h2>
 <section id="run-svm-with-polynomial-kernel-and-c-1-0">
-<h3><span class="section-number">41.63.15.1. </span>Run SVM with polynomial kernel and C=1.0<a class="headerlink" href="#run-svm-with-polynomial-kernel-and-c-1-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.15.1. </span>Run SVM with polynomial kernel and C=1.0<a class="headerlink" href="#run-svm-with-polynomial-kernel-and-c-1-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with polynomial kernel and C=1.0</span>
@@ -3668,7 +3668,7 @@ <h3><span class="section-number">41.63.15.1. </span>Run SVM with polynomial kern
 </div>
 </section>
 <section id="run-svm-with-polynomial-kernel-and-c-100-0">
-<h3><span class="section-number">41.63.15.2. </span>Run SVM with polynomial kernel and C=100.0<a class="headerlink" href="#run-svm-with-polynomial-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.15.2. </span>Run SVM with polynomial kernel and C=100.0<a class="headerlink" href="#run-svm-with-polynomial-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with polynomial kernel and C=100.0</span>
@@ -3698,9 +3698,9 @@ <h3><span class="section-number">41.63.15.2. </span>Run SVM with polynomial kern
 </section>
 </section>
 <section id="run-svm-with-sigmoid-kernel">
-<h2><span class="section-number">41.63.16. </span>Run SVM with sigmoid kernel<a class="headerlink" href="#run-svm-with-sigmoid-kernel" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.16. </span>Run SVM with sigmoid kernel<a class="headerlink" href="#run-svm-with-sigmoid-kernel" title="Permalink to this headline">#</a></h2>
 <section id="run-svm-with-sigmoid-kernel-and-c-1-0">
-<h3><span class="section-number">41.63.16.1. </span>Run SVM with sigmoid kernel and C=1.0<a class="headerlink" href="#run-svm-with-sigmoid-kernel-and-c-1-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.16.1. </span>Run SVM with sigmoid kernel and C=1.0<a class="headerlink" href="#run-svm-with-sigmoid-kernel-and-c-1-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with sigmoid kernel and C=1.0</span>
@@ -3728,7 +3728,7 @@ <h3><span class="section-number">41.63.16.1. </span>Run SVM with sigmoid kernel
 </div>
 </section>
 <section id="run-svm-with-sigmoid-kernel-and-c-100-0">
-<h3><span class="section-number">41.63.16.2. </span>Run SVM with sigmoid kernel and C=100.0<a class="headerlink" href="#run-svm-with-sigmoid-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.16.2. </span>Run SVM with sigmoid kernel and C=100.0<a class="headerlink" href="#run-svm-with-sigmoid-kernel-and-c-100-0" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># instantiate classifier with sigmoid kernel and C=100.0</span>
@@ -3757,7 +3757,7 @@ <h3><span class="section-number">41.63.16.2. </span>Run SVM with sigmoid kernel
 <p>We can see that sigmoid kernel is also performing poorly just like with polynomial kernel.</p>
 </section>
 <section id="comments">
-<h3><span class="section-number">41.63.16.3. </span>Comments<a class="headerlink" href="#comments" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.16.3. </span>Comments<a class="headerlink" href="#comments" title="Permalink to this headline">#</a></h3>
 <p>We get maximum accuracy with <code class="docutils literal notranslate"><span class="pre">rbf</span></code> and <code class="docutils literal notranslate"><span class="pre">linear</span></code> kernel with C=100.0. and the accuracy is 0.9832. Based on the above analysis we can conclude that our classification model accuracy is very good. Our model is doing a very good job in terms of predicting the class labels.</p>
 <p>But, this is not true. Here, we have an imbalanced dataset. The problem is that accuracy is an inadequate measure for quantifying predictive performance in the imbalanced dataset problem.</p>
 <p>So, we must explore alternative metrices that provide better guidance in selecting models. In particular, we would like to know the underlying distribution of values and the type of errors our classifer is making.</p>
@@ -3765,7 +3765,7 @@ <h3><span class="section-number">41.63.16.3. </span>Comments<a class="headerlink
 </section>
 </section>
 <section id="confusion-matrix">
-<h2><span class="section-number">41.63.17. </span>Confusion matrix<a class="headerlink" href="#confusion-matrix" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.17. </span>Confusion matrix<a class="headerlink" href="#confusion-matrix" title="Permalink to this headline">#</a></h2>
 <p>A confusion matrix is a tool for summarizing the performance of a classification algorithm. A confusion matrix will give us a clear picture of classification model performance and the types of errors produced by the model. It gives us a summary of correct and incorrect predictions broken down by each category. The summary is represented in a tabular form.</p>
 <p>Four types of outcomes are possible while evaluating a classification model performance. These four outcomes are described below:-</p>
 <p><strong>True Positives (TP)</strong> – True Positives occur when we predict an observation belongs to a certain class and the observation actually belongs to that class.</p>
@@ -3838,9 +3838,9 @@ <h2><span class="section-number">41.63.17. </span>Confusion matrix<a class="head
 </div>
 </section>
 <section id="classification-metrices">
-<h2><span class="section-number">41.63.18. </span>Classification metrices<a class="headerlink" href="#classification-metrices" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.18. </span>Classification metrices<a class="headerlink" href="#classification-metrices" title="Permalink to this headline">#</a></h2>
 <section id="classification-report">
-<h3><span class="section-number">41.63.18.1. </span>Classification Report<a class="headerlink" href="#classification-report" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.18.1. </span>Classification Report<a class="headerlink" href="#classification-report" title="Permalink to this headline">#</a></h3>
 <p><strong>Classification report</strong> is another way to evaluate the classification model performance. It displays the  <strong>precision</strong>, <strong>recall</strong>, <strong>f1</strong> and <strong>support</strong> scores for the model. I have described these terms in later.</p>
 <p>We can print a classification report as follows:-</p>
 <div class="cell docutils container">
@@ -3866,7 +3866,7 @@ <h3><span class="section-number">41.63.18.1. </span>Classification Report<a clas
 </div>
 </section>
 <section id="classification-accuracy">
-<h3><span class="section-number">41.63.18.2. </span>Classification accuracy<a class="headerlink" href="#classification-accuracy" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.18.2. </span>Classification accuracy<a class="headerlink" href="#classification-accuracy" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">TP</span> <span class="o">=</span> <span class="n">cm</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span>
@@ -3895,7 +3895,7 @@ <h3><span class="section-number">41.63.18.2. </span>Classification accuracy<a cl
 </div>
 </section>
 <section id="classification-error">
-<h3><span class="section-number">41.63.18.3. </span>Classification error<a class="headerlink" href="#classification-error" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.18.3. </span>Classification error<a class="headerlink" href="#classification-error" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># print classification error</span>
@@ -3914,7 +3914,7 @@ <h3><span class="section-number">41.63.18.3. </span>Classification error<a class
 </div>
 </section>
 <section id="precision">
-<h3><span class="section-number">41.63.18.4. </span>Precision<a class="headerlink" href="#precision" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.18.4. </span>Precision<a class="headerlink" href="#precision" title="Permalink to this headline">#</a></h3>
 <p><strong>Precision</strong> can be defined as the percentage of correctly predicted positive outcomes out of all the predicted positive outcomes. It can be given as the ratio of true positives (TP) to the sum of true and false positives (TP + FP).</p>
 <p>So, <strong>Precision</strong> identifies the proportion of correctly predicted positive outcome. It is more concerned with the positive class than the negative class.</p>
 <p>Mathematically, precision can be defined as the ratio of <code class="docutils literal notranslate"><span class="pre">TP</span> <span class="pre">to</span> <span class="pre">(TP</span> <span class="pre">+</span> <span class="pre">FP)</span></code>.</p>
@@ -3937,7 +3937,7 @@ <h3><span class="section-number">41.63.18.4. </span>Precision<a class="headerlin
 </div>
 </section>
 <section id="recall">
-<h3><span class="section-number">41.63.18.5. </span>Recall<a class="headerlink" href="#recall" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.18.5. </span>Recall<a class="headerlink" href="#recall" title="Permalink to this headline">#</a></h3>
 <p>Recall can be defined as the percentage of correctly predicted positive outcomes out of all the actual positive outcomes.
 It can be given as the ratio of true positives (TP) to the sum of true positives and false negatives (TP + FN). <strong>Recall</strong> is also called <strong>Sensitivity</strong>.</p>
 <p><strong>Recall</strong> identifies the proportion of correctly predicted actual positives.</p>
@@ -3958,7 +3958,7 @@ <h3><span class="section-number">41.63.18.5. </span>Recall<a class="headerlink"
 </div>
 </section>
 <section id="true-positive-rate">
-<h3><span class="section-number">41.63.18.6. </span>True Positive Rate<a class="headerlink" href="#true-positive-rate" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.18.6. </span>True Positive Rate<a class="headerlink" href="#true-positive-rate" title="Permalink to this headline">#</a></h3>
 <p><strong>True Positive Rate</strong> is synonymous with <strong>Recall</strong>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3977,7 +3977,7 @@ <h3><span class="section-number">41.63.18.6. </span>True Positive Rate<a class="
 </div>
 </section>
 <section id="false-positive-rate">
-<h3><span class="section-number">41.63.18.7. </span>False Positive Rate<a class="headerlink" href="#false-positive-rate" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.18.7. </span>False Positive Rate<a class="headerlink" href="#false-positive-rate" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">false_positive_rate</span> <span class="o">=</span> <span class="n">FP</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">FP</span> <span class="o">+</span> <span class="n">TN</span><span class="p">)</span>
@@ -3995,7 +3995,7 @@ <h3><span class="section-number">41.63.18.7. </span>False Positive Rate<a class=
 </div>
 </section>
 <section id="specificity">
-<h3><span class="section-number">41.63.18.8. </span>Specificity<a class="headerlink" href="#specificity" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.18.8. </span>Specificity<a class="headerlink" href="#specificity" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">specificity</span> <span class="o">=</span> <span class="n">TN</span> <span class="o">/</span> <span class="p">(</span><span class="n">TN</span> <span class="o">+</span> <span class="n">FP</span><span class="p">)</span>
@@ -4012,20 +4012,20 @@ <h3><span class="section-number">41.63.18.8. </span>Specificity<a class="headerl
 </div>
 </section>
 <section id="f1-score">
-<h3><span class="section-number">41.63.18.9. </span>f1-score<a class="headerlink" href="#f1-score" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.18.9. </span>f1-score<a class="headerlink" href="#f1-score" title="Permalink to this headline">#</a></h3>
 <p><strong>f1-score</strong> is the weighted harmonic mean of precision and recall. The best possible <strong>f1-score</strong> would be 1.0 and the worst
 would be 0.0.  <strong>f1-score</strong> is the harmonic mean of precision and recall. So, <strong>f1-score</strong> is always lower than accuracy measures as they embed precision and recall into their computation. The weighted average of <code class="docutils literal notranslate"><span class="pre">f1-score</span></code> should be used to
 compare classifier models, not global accuracy.</p>
 </section>
 <section id="support">
-<h3><span class="section-number">41.63.18.10. </span>Support<a class="headerlink" href="#support" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.18.10. </span>Support<a class="headerlink" href="#support" title="Permalink to this headline">#</a></h3>
 <p><strong>Support</strong> is the actual number of occurrences of the class in our dataset.</p>
 </section>
 </section>
 <section id="roc-auc">
-<h2><span class="section-number">41.63.19. </span>ROC - AUC<a class="headerlink" href="#roc-auc" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.19. </span>ROC - AUC<a class="headerlink" href="#roc-auc" title="Permalink to this headline">#</a></h2>
 <section id="roc-curve">
-<h3><span class="section-number">41.63.19.1. </span>ROC Curve<a class="headerlink" href="#roc-curve" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.19.1. </span>ROC Curve<a class="headerlink" href="#roc-curve" title="Permalink to this headline">#</a></h3>
 <p>Another tool to measure the classification model performance visually is <strong>ROC Curve</strong>. ROC Curve stands for <strong>Receiver Operating Characteristic Curve</strong>. An <strong>ROC Curve</strong> is a plot which shows the performance of a classification model at various
 classification threshold levels.</p>
 <p>The <strong>ROC Curve</strong> plots the <strong>True Positive Rate (TPR)</strong> against the <strong>False Positive Rate (FPR)</strong> at various threshold levels.</p>
@@ -4065,7 +4065,7 @@ <h3><span class="section-number">41.63.19.1. </span>ROC Curve<a class="headerlin
 <p>ROC curve help us to choose a threshold level that balances sensitivity and specificity for a particular context.</p>
 </section>
 <section id="id4">
-<h3><span class="section-number">41.63.19.2. </span>ROC  AUC<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.19.2. </span>ROC  AUC<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
 <p><strong>ROC AUC</strong> stands for <strong>Receiver Operating Characteristic - Area Under Curve</strong>. It is a technique to compare classifier performance. In this technique, we measure the <code class="docutils literal notranslate"><span class="pre">area</span> <span class="pre">under</span> <span class="pre">the</span> <span class="pre">curve</span> <span class="pre">(AUC)</span></code>. A perfect classifier will have a ROC AUC equal to 1, whereas a purely random classifier will have a ROC AUC equal to 0.5.</p>
 <p>So, <strong>ROC AUC</strong> is the percentage of the ROC plot that is underneath the curve.</p>
 <div class="cell docutils container">
@@ -4088,7 +4088,7 @@ <h3><span class="section-number">41.63.19.2. </span>ROC  AUC<a class="headerlink
 </div>
 </section>
 <section id="id5">
-<h3><span class="section-number">41.63.19.3. </span>Comments<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.19.3. </span>Comments<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>ROC AUC is a single number summary of classifier performance. The higher the value, the better the classifier.</p></li>
 <li><p>ROC AUC of our model approaches towards 1. So, we can conclude that our classifier does a good job in classifying the pulsar star.</p></li>
@@ -4114,12 +4114,12 @@ <h3><span class="section-number">41.63.19.3. </span>Comments<a class="headerlink
 </section>
 </section>
 <section id="stratified-k-fold-cross-validation-with-shuffle-split">
-<h2><span class="section-number">41.63.20. </span>Stratified k-fold Cross Validation with shuffle split<a class="headerlink" href="#stratified-k-fold-cross-validation-with-shuffle-split" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.20. </span>Stratified k-fold Cross Validation with shuffle split<a class="headerlink" href="#stratified-k-fold-cross-validation-with-shuffle-split" title="Permalink to this headline">#</a></h2>
 <p>k-fold cross-validation is a very useful technique to evaluate model performance. But, it fails here because we have a imbalnced dataset. So, in the case of imbalanced dataset, I will use another technique to evaluate model performance. It is called <code class="docutils literal notranslate"><span class="pre">stratified</span> <span class="pre">k-fold</span> <span class="pre">cross-validation</span></code>.</p>
 <p>In <code class="docutils literal notranslate"><span class="pre">stratified</span> <span class="pre">k-fold</span> <span class="pre">cross-validation</span></code>, we split the data such that the proportions between classes are the same in each fold as they are in the whole dataset.</p>
 <p>Moreover, I will shuffle the data before splitting because shuffling yields much better result.</p>
 <section id="stratified-k-fold-cross-validation-with-shuffle-split-with-linear-kernel">
-<h3><span class="section-number">41.63.20.1. </span>Stratified k-Fold Cross Validation with shuffle split with  linear kernel<a class="headerlink" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-linear-kernel" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.20.1. </span>Stratified k-Fold Cross Validation with shuffle split with  linear kernel<a class="headerlink" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-linear-kernel" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">KFold</span>
@@ -4168,7 +4168,7 @@ <h3><span class="section-number">41.63.20.1. </span>Stratified k-Fold Cross Vali
 </div>
 </section>
 <section id="stratified-k-fold-cross-validation-with-shuffle-split-with-rbf-kernel">
-<h3><span class="section-number">41.63.20.2. </span>Stratified k-Fold Cross Validation with shuffle split with rbf kernel<a class="headerlink" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-rbf-kernel" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.20.2. </span>Stratified k-Fold Cross Validation with shuffle split with rbf kernel<a class="headerlink" href="#stratified-k-fold-cross-validation-with-shuffle-split-with-rbf-kernel" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">rbf_svc</span><span class="o">=</span><span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;rbf&#39;</span><span class="p">)</span>
@@ -4211,13 +4211,13 @@ <h3><span class="section-number">41.63.20.2. </span>Stratified k-Fold Cross Vali
 </div>
 </section>
 <section id="id6">
-<h3><span class="section-number">41.63.20.3. </span>Comments<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.20.3. </span>Comments<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h3>
 <p>I obtain higher average stratified k-fold cross-validation score of 0.9789 with linear kernel but the model accuracy is 0.9832.
 So, stratified cross-validation technique does not help to improve the model performance.</p>
 </section>
 </section>
 <section id="hyperparameter-optimization-using-gridsearch-cv">
-<h2><span class="section-number">41.63.21. </span>Hyperparameter Optimization using GridSearch CV<a class="headerlink" href="#hyperparameter-optimization-using-gridsearch-cv" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.21. </span>Hyperparameter Optimization using GridSearch CV<a class="headerlink" href="#hyperparameter-optimization-using-gridsearch-cv" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># import GridSearchCV</span>
@@ -4283,7 +4283,7 @@ <h2><span class="section-number">41.63.21. </span>Hyperparameter Optimization us
 </div>
 </div>
 <section id="id7">
-<h3><span class="section-number">41.63.21.1. </span>Comments<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.63.21.1. </span>Comments<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Our original model test accuracy is 0.9832 while GridSearch CV score on test-set is 0.9835.</p></li>
 <li><p>So, GridSearch CV helps to identify the parameters that will improve the performance for this particular model.</p></li>
@@ -4294,7 +4294,7 @@ <h3><span class="section-number">41.63.21.1. </span>Comments<a class="headerlink
 </section>
 </section>
 <section id="results-and-conclusion">
-<h2><span class="section-number">41.63.22. </span>Results and conclusion<a class="headerlink" href="#results-and-conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.22. </span>Results and conclusion<a class="headerlink" href="#results-and-conclusion" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p>There are outliers in our dataset. So, as we increase the value of C to limit fewer outliers, the accuracy increased. This is true with different kinds of kernels.</p></li>
 <li><p>We get maximum accuracy with <code class="docutils literal notranslate"><span class="pre">rbf</span></code> and <code class="docutils literal notranslate"><span class="pre">linear</span></code> kernel with C=100.0 and the accuracy is 0.9832. So, we can conclude that our model is doing a very good job in terms of predicting the class labels. But, this is not true. Here, we have an imbalanced dataset. Accuracy is an inadequate measure for quantifying predictive performance in the imbalanced dataset problem. So, we must explore <code class="docutils literal notranslate"><span class="pre">confusion</span> <span class="pre">matrix</span></code> that provide better guidance in selecting models.</p></li>
@@ -4304,7 +4304,7 @@ <h2><span class="section-number">41.63.22. </span>Results and conclusion<a class
 </ol>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.63.23. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.63.23. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to PRASHANT BANERJEE for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/prashant111/svm-classifier-tutorial"> SVM Classifier Tutorial </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -4346,13 +4346,13 @@ <h2><span class="section-number">41.63.23. </span>Acknowledgments<a class="heade
         <i class="fas fa-angle-left"></i>
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-            <p class="prev-next-title"><span class="section-number">41.62. </span>Explore classification methods</p>
+            <p class="prev-next-title"><span class="section-number">40.62. </span>Explore classification methods</p>
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+    <link rel="next" title="40.66. Decision Trees - Intro and Regression" href="decision_trees_for_regression.html" />
+    <link rel="prev" title="40.64. Support Vector Machines (SVM) - Intro and SVM for Regression" href="support_vector_machines_for_regression.html" />
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@@ -712,494 +712,494 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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 <section class="tex2jax_ignore mathjax_ignore" id="support-vector-machines-svm-classification">
-<h1><span class="section-number">41.65. </span>Support Vector Machines (SVM) - Classification<a class="headerlink" href="#support-vector-machines-svm-classification" title="Permalink to this headline">#</a></h1>
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 <p>Support Vector Machines are the type of supervised learning algorithms used for regression, classification and detecting outliers. SVMs are remarkably one of the powerful models in classical machine learning suited for handling complex and high dimensional datasets.</p>
 <p>With SVM supporting different kernels (linear, polynomial, Radial Basis Function(rbf), and sigmoid), SVM can tackle different kinds of datasets, both linear and non linear.</p>
 <p>While the maths behind the SVMs are beyond the scope of this notebook, here is the idea behind SVMs:</p>
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 <p>We will get Iris data from Sklearn datasets. Setting <code class="docutils literal notranslate"><span class="pre">as_frame</span></code> parameter to <code class="docutils literal notranslate"><span class="pre">True</span></code> will return data as a Pandas Dataframe.</p>
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 <p>Before exploring some insight in data, let’s split it into test and train set.</p>
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+<h2><span class="section-number">40.65.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <p>The features already have small values but let’s scale them to be between 0 and 1. SVM work well with scaled values. I will set up a pipeline to handle that.</p>
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-<h2><span class="section-number">41.65.6. </span>5 - Training Support Vector Classifier<a class="headerlink" href="#training-support-vector-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.65.6. </span>5 - Training Support Vector Classifier<a class="headerlink" href="#training-support-vector-classifier" title="Permalink to this headline">#</a></h2>
 <p>We are going to train two classifiers: Linear SVC and SVC that we can use different kernels. SVM supports <code class="docutils literal notranslate"><span class="pre">linear</span></code>, <code class="docutils literal notranslate"><span class="pre">polynomial</span></code>, <code class="docutils literal notranslate"><span class="pre">sigmoid</span></code> and <code class="docutils literal notranslate"><span class="pre">rbf</span></code> kernels.</p>
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 <section id="evaluating-support-vector-classifier">
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 <p>Let’s first check the accuracy on the training. For this step since we are trying to find model to improve further, we won’t touch test set yet.</p>
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 <section id="improving-support-vector-classifier">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchCV</span>
@@ -3621,7 +3621,7 @@ <h2><span class="section-number">41.65.8. </span>7 - Improving Support Vector Cl
 <p>This is the end of the lab which was all about using Support Vector Machines for classification task. As you can see, SVM is a robust algorithm given how it supports different kernels. These kernels are what make it suitable for both linear and non linear problems. In real world, many datasets are not linear. So when you can’t get good results with linear models, try things like SVM with polynomial kernel.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.65.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.65.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Jake VanderPlas for creating the open-source course <a class="reference external" href="https://github.com/jakevdp/PythonDataScienceHandbook"> Python Data Science Handbook </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -3663,13 +3663,13 @@ <h2><span class="section-number">41.65.9. </span>Acknowledgments<a class="header
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-            <p class="prev-next-title"><span class="section-number">41.64. </span>Support Vector Machines (SVM) - Intro and SVM for Regression</p>
+            <p class="prev-next-title"><span class="section-number">40.64. </span>Support Vector Machines (SVM) - Intro and SVM for Regression</p>
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-        <p class="prev-next-title"><span class="section-number">41.66. </span>Decision Trees - Intro and Regression</p>
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+    <link rel="next" title="40.65. Support Vector Machines (SVM) - Classification" href="support_vector_machines_for_classification.html" />
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@@ -1694,7 +1694,7 @@ <h2> Contents </h2>
                 
   <p><a name='0'></a></p>
 <section class="tex2jax_ignore mathjax_ignore" id="support-vector-machines-svm-intro-and-svm-for-regression">
-<h1><span class="section-number">41.64. </span>Support Vector Machines (SVM) - Intro and SVM for Regression<a class="headerlink" href="#support-vector-machines-svm-intro-and-svm-for-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.64. </span>Support Vector Machines (SVM) - Intro and SVM for Regression<a class="headerlink" href="#support-vector-machines-svm-intro-and-svm-for-regression" title="Permalink to this headline">#</a></h1>
 <p>Support Vector Machines are the type of supervised learning algorithms used for regression, classification and detecting outliers. SVMs are remarkably one of the powerful models in classical machine learning suited for handling complex and high dimensional datasets.</p>
 <p>With SVM supporting different kernels (linear, polynomial, Radial Basis Function(rbf), and sigmoid), SVM can tackle different kinds of datasets, both linear and non linear.</p>
 <p>While the maths behind the SVMs are beyond the scope of this notebook, here is the idea behind SVMs:</p>
@@ -1702,7 +1702,7 @@ <h1><span class="section-number">41.64. </span>Support Vector Machines (SVM) - I
 <p><img alt="SVM" src="../../../_images/1200px-Kernel_Machine.svg.png" /></p>
 <p>SMVs are widely used for classification. But to motivate that, let’s start with regression. For the purpose of examining how powerful this algorithm is, we will use the same dataset that we used in linear regression notebook, and hopefully the difference in performance will be notable.</p>
 <section id="contents">
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 <ul class="simple">
 <li><p><a class="reference external" href="#1">1 - Imports</a></p></li>
 <li><p><a class="reference external" href="#2">2 - Loading the data</a></p></li>
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 </section>
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-<h2><span class="section-number">41.64.2. </span>1 - Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.64.2. </span>1 - Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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-<h2><span class="section-number">41.64.3. </span>2 - Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">#</a></h2>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cal_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;../../assets/data/housing.csv&quot;</span><span class="p">)</span>
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+<h2><span class="section-number">40.64.4. </span>3 - Exploratory Analysis<a class="headerlink" href="#exploratory-analysis" title="Permalink to this headline">#</a></h2>
 <p>This is going to be a quick glance through the dataset. The full exploratory data analysis was done in the linear regression notebook[ADD LINK]. Before anything, let’s split the data into training and test set.</p>
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 </section>
 <section id="data-preprocessing">
-<h2><span class="section-number">41.64.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.64.5. </span>4 - Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <p>To do:</p>
 <ul class="simple">
 <li><p>Handle missing values</p></li>
@@ -2198,7 +2198,7 @@ <h2><span class="section-number">41.64.5. </span>4 - Data Preprocessing<a class=
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 </section>
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-<h2><span class="section-number">41.64.6. </span>5 - Training Support Vector Regressor<a class="headerlink" href="#training-support-vector-regressor" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.64.6. </span>5 - Training Support Vector Regressor<a class="headerlink" href="#training-support-vector-regressor" title="Permalink to this headline">#</a></h2>
 <p>In regression, instead of separating the classes with decision boundary  like in classification, SVR fits the training data points on the boundary margin but keep them off.</p>
 <p>We can implement it quite easily with Scikit-Learn.</p>
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-<h2><span class="section-number">41.64.7. </span>6 - Evaluating Support Vector Regressor<a class="headerlink" href="#evaluating-support-vector-regressor" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.64.7. </span>6 - Evaluating Support Vector Regressor<a class="headerlink" href="#evaluating-support-vector-regressor" title="Permalink to this headline">#</a></h2>
 <p>Let us evaluate the two models we created on the training set before evaluating on the test set. This is a good practice. Since finding a good model can take many iterations of improvements, it’s not advised to touch the test set until the model is good enough. Otherwise it would fail to make predictions on the new data.</p>
 <p>As always, we evaluate regression models with mean squarred error, but the commonly used one is root mean squarred error.</p>
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 <section id="improving-support-vector-regressor">
-<h2><span class="section-number">41.64.8. </span>7 - Improving Support Vector Regressor<a class="headerlink" href="#improving-support-vector-regressor" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.64.8. </span>7 - Improving Support Vector Regressor<a class="headerlink" href="#improving-support-vector-regressor" title="Permalink to this headline">#</a></h2>
 <p>Let use Randomized Search to improve the SVR model. Few notes about the parameters:</p>
 <ul class="simple">
 <li><p><em>Gamma(y)</em>: This is a regularization hyperparameter. When gamma is small, the model can underfit. It is too high, model can overfit.</p></li>
@@ -2405,7 +2405,7 @@ <h2><span class="section-number">41.64.8. </span>7 - Improving Support Vector Re
 <p>This is the end of the notebook. It was a practical introduction to using Support Vector Machines for regression. In the next lab, we will take a further step, where we will do classification with SVM.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.64.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.64.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Jake VanderPlas for creating the open-source course <a class="reference external" href="https://github.com/jakevdp/PythonDataScienceHandbook"> Python Data Science Handbook </a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2447,13 +2447,13 @@ <h2><span class="section-number">41.64.9. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
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-            <p class="prev-next-title"><span class="section-number">41.63. </span>Kernel method assignment 1</p>
+            <p class="prev-next-title"><span class="section-number">40.63. </span>Kernel method assignment 1</p>
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-        <p class="prev-next-title"><span class="section-number">41.65. </span>Support Vector Machines (SVM) - Classification</p>
+        <p class="prev-next-title"><span class="section-number">40.65. </span>Support Vector Machines (SVM) - Classification</p>
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+    <title>40.70. Dropout and Batch Normalization &#8212; Ocademy Open Machine Learning Book</title>
     
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+    <link rel="next" title="40.71. Lasso and Ridge Regression" href="lasso-and-ridge-regression.html" />
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@@ -1681,14 +1681,14 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="dropout-and-batch-normalization">
-<h1><span class="section-number">41.70. </span>Dropout and Batch Normalization<a class="headerlink" href="#dropout-and-batch-normalization" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.70. </span>Dropout and Batch Normalization<a class="headerlink" href="#dropout-and-batch-normalization" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">41.70.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.70.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>There’s more to the world of deep learning than just dense layers. There are dozens of kinds of layers you might add to a model. (Try browsing through the Keras docs for a sample!) Some are like dense layers and define connections between neurons, and others can do preprocessing or transformations of other sorts.</p>
 <p>In this lesson, we’ll learn about a two kinds of special layers, not containing any neurons themselves, but that add some functionality that can sometimes benefit a model in various ways. Both are commonly used in modern architectures.</p>
 </section>
 <section id="dropout">
-<h2><span class="section-number">41.70.2. </span>Dropout<a class="headerlink" href="#dropout" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.70.2. </span>Dropout<a class="headerlink" href="#dropout" title="Permalink to this headline">#</a></h2>
 <p>The first of these is the “dropout layer”, which can help correct overfitting.</p>
 <p>In the last lesson we talked about how overfitting is caused by the network learning spurious patterns in the training data. To recognize these spurious patterns a network will often rely on very a specific combinations of weight, a kind of “conspiracy” of weights. Being so specific, they tend to be fragile: remove one and the conspiracy falls apart.</p>
 <p>This is the idea behind <strong>dropout</strong>. To break up these conspiracies, we randomly drop out some fraction of a layer’s input units every step of training, making it much harder for the network to learn those spurious patterns in the training data. Instead, it has to search for broad, general patterns, whose weight patterns tend to be more robust.</p>
@@ -1696,7 +1696,7 @@ <h2><span class="section-number">41.70.2. </span>Dropout<a class="headerlink" hr
 <em>Here, 50% dropout has been added between the two hidden layers.</em></p>
 <p>You could also think about dropout as creating a kind of ensemble of networks. The predictions will no longer be made by one big network, but instead by a committee of smaller networks. Individuals in the committee tend to make different kinds of mistakes, but be right at the same time, making the committee as a whole better than any individual. (If you’re familiar with random forests as an ensemble of decision trees, it’s the same idea.)</p>
 <section id="adding-dropout">
-<h3><span class="section-number">41.70.2.1. </span>Adding Dropout<a class="headerlink" href="#adding-dropout" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.70.2.1. </span>Adding Dropout<a class="headerlink" href="#adding-dropout" title="Permalink to this headline">#</a></h3>
 <p>In Keras, the dropout rate argument <em>rate</em> defines what percentage of the input units to shut off. Put the <em>Dropout</em> layer just before the layer you want the dropout applied to:</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">keras</span><span class="o">.</span><span class="n">Sequential</span><span class="p">([</span>
     <span class="c1"># ...</span>
@@ -1709,13 +1709,13 @@ <h3><span class="section-number">41.70.2.1. </span>Adding Dropout<a class="heade
 </section>
 </section>
 <section id="batch-normalization">
-<h2><span class="section-number">41.70.3. </span>Batch Normalization<a class="headerlink" href="#batch-normalization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.70.3. </span>Batch Normalization<a class="headerlink" href="#batch-normalization" title="Permalink to this headline">#</a></h2>
 <p>The next special layer we’ll look at performs “batch normalization” (or “batchnorm”), which can help correct training that is slow or unstable.</p>
 <p>With neural networks, it’s generally a good idea to put all of your data on a common scale, perhaps with something like scikit-learn’s <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html">StandardScaler</a> or <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html">MinMaxScaler</a>. The reason is that SGD will shift the network weights in proportion to how large an activation the data produces. Features that tend to produce activations of very different sizes can make for unstable training behavior.</p>
 <p>Now, if it’s good to normalize the data before it goes into the network, maybe also normalizing inside the network would be better! In fact, we have a special kind of layer that can do this, the <strong>batch normalization layer</strong>. A batch normalization layer looks at each batch as it comes in, first normalizing the batch with its own mean and standard deviation, and then also putting the data on a new scale with two trainable rescaling parameters. Batchnorm, in effect, performs a kind of coordinated rescaling of its inputs.</p>
 <p>Most often, batchnorm is added as an aid to the optimization process (though it can sometimes also help prediction performance). Models with batchnorm tend to need fewer epochs to complete training. Moreover, batchnorm can also fix various problems that can cause the training to get “stuck”. Consider adding batch normalization to your models, especially if you’re having trouble during training.</p>
 <section id="adding-batch-normalization">
-<h3><span class="section-number">41.70.3.1. </span>Adding Batch Normalization<a class="headerlink" href="#adding-batch-normalization" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.70.3.1. </span>Adding Batch Normalization<a class="headerlink" href="#adding-batch-normalization" title="Permalink to this headline">#</a></h3>
 <p>It seems that batch normalization can be used at almost any point in a network. You can put it after a layer…</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">),</span>
 <span class="n">layers</span><span class="o">.</span><span class="n">BatchNormalization</span><span class="p">(),</span>
@@ -1731,7 +1731,7 @@ <h3><span class="section-number">41.70.3.1. </span>Adding Batch Normalization<a
 </section>
 </section>
 <section id="example-using-dropout-and-batch-normalization">
-<h2><span class="section-number">41.70.4. </span>Example - Using Dropout and Batch Normalization<a class="headerlink" href="#example-using-dropout-and-batch-normalization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.70.4. </span>Example - Using Dropout and Batch Normalization<a class="headerlink" href="#example-using-dropout-and-batch-normalization" title="Permalink to this headline">#</a></h2>
 <p>Let’s continue developing the Red Wine model. Now we’ll increase the capacity even more, but add dropout to control overfitting and batch normalization to speed up optimization. This time, we’ll also leave off standardizing the data, to demonstrate how batch normalization can stabalize the training.</p>
 <div class="cell docutils container">
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@@ -1818,7 +1818,7 @@ <h2><span class="section-number">41.70.4. </span>Example - Using Dropout and Bat
 <p>You’ll typically get better performance if you standardize your data before using it for training. That we were able to use the raw data at all, however, shows how effective batch normalization can be on more difficult datasets.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.70.5. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.70.5. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/ryanholbrook">Ryan Holbrook</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/ryanholbrook/dropout-and-batch-normalization">Learning Curve To Identify Overfit &amp; Underfit</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1860,13 +1860,13 @@ <h2><span class="section-number">41.70.5. </span>Acknowledgments<a class="header
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-            <p class="prev-next-title"><span class="section-number">41.69. </span>Learning Curve To Identify Overfit &amp; Underfit</p>
+            <p class="prev-next-title"><span class="section-number">40.69. </span>Learning Curve To Identify Overfit &amp; Underfit</p>
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-        <p class="prev-next-title"><span class="section-number">41.71. </span>Lasso and Ridge Regression</p>
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-    <title>41.71. Lasso and Ridge Regression &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.71. Lasso and Ridge Regression &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="next" title="41.72. Regularized Linear Models" href="regularized-linear-models.html" />
-    <link rel="prev" title="41.70. Dropout and Batch Normalization" href="dropout-and-batch-normalization.html" />
+    <link rel="next" title="40.72. Regularized Linear Models" href="regularized-linear-models.html" />
+    <link rel="prev" title="40.70. Dropout and Batch Normalization" href="dropout-and-batch-normalization.html" />
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 <p>The most of things common in Lasson and Ridge Regression. <br/>
 The only different between two regression is, what regularization it is using. <br/></p>
 <ul class="simple">
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@@ -2108,7 +2108,7 @@ <h2><span class="section-number">41.71.4. </span>Lasson Regression from scratch<
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 <section id="lasso-regression-using-skicit-learn">
-<h2><span class="section-number">41.71.5. </span>Lasso Regression using skicit-learn<a class="headerlink" href="#lasso-regression-using-skicit-learn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.71.5. </span>Lasso Regression using skicit-learn<a class="headerlink" href="#lasso-regression-using-skicit-learn" title="Permalink to this headline">#</a></h2>
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@@ -2151,11 +2151,11 @@ <h2><span class="section-number">41.71.5. </span>Lasso Regression using skicit-l
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+<h2><span class="section-number">40.71.6. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>Our model (from scratch) also works great as compared to skiti-learn model. Both the models are giving 0.99…% r2_socre which is good.</p>
 </section>
 <section id="ridge-regression-from-scratch">
-<h2><span class="section-number">41.71.7. </span>Ridge Regression from scratch<a class="headerlink" href="#ridge-regression-from-scratch" title="Permalink to this headline">#</a></h2>
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@@ -2237,7 +2237,7 @@ <h2><span class="section-number">41.71.7. </span>Ridge Regression from scratch<a
 </div>
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-<h2><span class="section-number">41.71.8. </span>Ridge Regression using scikit-learn<a class="headerlink" href="#ridge-regression-using-scikit-learn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.71.8. </span>Ridge Regression using scikit-learn<a class="headerlink" href="#ridge-regression-using-scikit-learn" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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@@ -2280,7 +2280,7 @@ <h2><span class="section-number">41.71.8. </span>Ridge Regression using scikit-l
 </div>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.71.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.71.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Pavithra Devi M for creating the Notebook <a class="reference external" href="https://www.kaggle.com/code/ninjaac/lasso-and-ridge-regression-from-scratch">Lasso and Ridge Regression from scratch</a>, lisensed under the <a class="reference external" href="https://www.apache.org/licenses/LICENSE-2.0">Apache 2.0</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2322,13 +2322,13 @@ <h2><span class="section-number">41.71.9. </span>Acknowledgments<a class="header
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+    <link rel="next" title="40.70. Dropout and Batch Normalization" href="dropout-and-batch-normalization.html" />
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+     40.69.7.1. Interpreting the training loss
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
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-     41.69.7.2. Interpreting the validation loss
+     40.69.7.2. Interpreting the validation loss
     </a>
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+   40.69.8. Learning Curve of an Underfit Model
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id3">
-     41.69.8.1. Interpreting the training loss
+     40.69.8.1. Interpreting the training loss
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id4">
-     41.69.8.2. Interpreting the validation loss
+     40.69.8.2. Interpreting the validation loss
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.69.9. Acknowledgments
+   40.69.9. Acknowledgments
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 </ul>
@@ -1809,9 +1809,9 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="learning-curve-to-identify-overfit-underfit">
-<h1><span class="section-number">41.69. </span>Learning Curve To Identify Overfit &amp; Underfit<a class="headerlink" href="#learning-curve-to-identify-overfit-underfit" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.69. </span>Learning Curve To Identify Overfit &amp; Underfit<a class="headerlink" href="#learning-curve-to-identify-overfit-underfit" title="Permalink to this headline">#</a></h1>
 <section id="imports">
-<h2><span class="section-number">41.69.1. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.69.1. </span>Imports<a class="headerlink" href="#imports" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -1831,24 +1831,24 @@ <h2><span class="section-number">41.69.1. </span>Imports<a class="headerlink" hr
 </div>
 </section>
 <section id="overfitting-and-underfitting-refresher">
-<h2><span class="section-number">41.69.2. </span>Overfitting and Underfitting Refresher<a class="headerlink" href="#overfitting-and-underfitting-refresher" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.69.2. </span>Overfitting and Underfitting Refresher<a class="headerlink" href="#overfitting-and-underfitting-refresher" title="Permalink to this headline">#</a></h2>
 <section id="overfitting-aka-variance">
-<h3><span class="section-number">41.69.2.1. </span>Overfitting (aka Variance):<a class="headerlink" href="#overfitting-aka-variance" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.69.2.1. </span>Overfitting (aka Variance):<a class="headerlink" href="#overfitting-aka-variance" title="Permalink to this headline">#</a></h3>
 <p>A model is said to be overfit if the model is overtrained on the data such that it even learns the noise from the data. In the figure below, the third image shows overfitting where the model has learnt each and every example so perfectly that it misclassifies an unseen/new example. For a model that’s overfit we have a perfect/close to perfect training set score while a poor test/validation score.</p>
 </section>
 <section id="reasons-for-overfitting">
-<h3><span class="section-number">41.69.2.2. </span>Reasons for overfitting<a class="headerlink" href="#reasons-for-overfitting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.69.2.2. </span>Reasons for overfitting<a class="headerlink" href="#reasons-for-overfitting" title="Permalink to this headline">#</a></h3>
 <ol class="simple">
 <li><p>Using a complex model for a simple problem which picks up the noise from the data. Example: Using a neural network on Iris dataset</p></li>
 <li><p>Small datasets, as the training set may not be a right representation of the universe</p></li>
 </ol>
 </section>
 <section id="underfitting-aka-bias">
-<h3><span class="section-number">41.69.2.3. </span>Underfitting (aka Bias):<a class="headerlink" href="#underfitting-aka-bias" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.69.2.3. </span>Underfitting (aka Bias):<a class="headerlink" href="#underfitting-aka-bias" title="Permalink to this headline">#</a></h3>
 <p>A model is said to be underfit if the model is unable to learn the patterns in the data properly. In the figure below, the first image shows overfitting where the model hasn’t fully learnt each and every example. In such cases we see a low score on both the training set and test/validation set</p>
 </section>
 <section id="reasons-for-underfitting">
-<h3><span class="section-number">41.69.2.4. </span>Reasons for underfitting<a class="headerlink" href="#reasons-for-underfitting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.69.2.4. </span>Reasons for underfitting<a class="headerlink" href="#reasons-for-underfitting" title="Permalink to this headline">#</a></h3>
 <ol class="simple">
 <li><p>Using a simple model for a complex problem which doesn’t pick up all the patterns from the data. Example: Using a logistic regression for image classification</p></li>
 <li><p>The underlying data has no inherent pattern. Example, trying to predict a student’s marks with his father’s weight.</p></li>
@@ -1856,7 +1856,7 @@ <h3><span class="section-number">41.69.2.4. </span>Reasons for underfitting<a cl
 </section>
 </section>
 <section id="modeling-iris-data-using-logistic-regression">
-<h2><span class="section-number">41.69.3. </span>Modeling (Iris data) using Logistic Regression<a class="headerlink" href="#modeling-iris-data-using-logistic-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.69.3. </span>Modeling (Iris data) using Logistic Regression<a class="headerlink" href="#modeling-iris-data-using-logistic-regression" title="Permalink to this headline">#</a></h2>
 <p>In this section we’ll fit a LogisticRegressor to the Iris dataset and identify underfitting, overfitting and good fit.</p>
 <div class="cell docutils container">
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@@ -2469,7 +2469,7 @@ <h2><span class="section-number">41.69.3. </span>Modeling (Iris data) using Logi
 </div>
 </section>
 <section id="identifying-and-handling-multicollinearity-using-vif">
-<h2><span class="section-number">41.69.4. </span>Identifying and handling Multicollinearity using VIF<a class="headerlink" href="#identifying-and-handling-multicollinearity-using-vif" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.69.4. </span>Identifying and handling Multicollinearity using VIF<a class="headerlink" href="#identifying-and-handling-multicollinearity-using-vif" title="Permalink to this headline">#</a></h2>
 <p>The correlation matrix below shows high multicollinearity among input features. VIF &gt; 5 or 10 implies multicollinearity.</p>
 <div class="cell docutils container">
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@@ -2585,11 +2585,11 @@ <h2><span class="section-number">41.69.4. </span>Identifying and handling Multic
 </div>
 </section>
 <section id="introduction-to-learning-curve">
-<h2><span class="section-number">41.69.5. </span>Introduction to Learning Curve<a class="headerlink" href="#introduction-to-learning-curve" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.69.5. </span>Introduction to Learning Curve<a class="headerlink" href="#introduction-to-learning-curve" title="Permalink to this headline">#</a></h2>
 <p>Learning curve is a plot that plots the training and validation loss for a sample of training examples by incrementally increasing them. Learning curves helps us in identifying if adding additional training examples could improve the validation score or not. If a model is overfit then adding additional training examples might improve the model performance on unseen data. Similarly, if a model is underfit then adding training examples doesn’t help</p>
 </section>
 <section id="learning-curve-of-a-good-fit-model">
-<h2><span class="section-number">41.69.6. </span>Learning Curve of a Good Fit Model<a class="headerlink" href="#learning-curve-of-a-good-fit-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.69.6. </span>Learning Curve of a Good Fit Model<a class="headerlink" href="#learning-curve-of-a-good-fit-model" title="Permalink to this headline">#</a></h2>
 <p>Below we’ll use the function created above to get a good fit model. We’ll set the inverse regularization variable/parameter ‘c’ to 1 (i.e. we are not performing any regularization)</p>
 <div class="cell docutils container">
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@@ -2627,11 +2627,11 @@ <h2><span class="section-number">41.69.6. </span>Learning Curve of a Good Fit Mo
 </div>
 <p>The cross validation accuracy and training accuracy calculated above are close to each other</p>
 <section id="interpreting-the-training-loss">
-<h3><span class="section-number">41.69.6.1. </span>Interpreting the training loss<a class="headerlink" href="#interpreting-the-training-loss" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.69.6.1. </span>Interpreting the training loss<a class="headerlink" href="#interpreting-the-training-loss" title="Permalink to this headline">#</a></h3>
 <p>Learning curve of a good fit model has a moderately high training loss at the beginning which gradually lowers upon adding training examples and flattens gradually indicating addition of more training examples doesn’t improve the model performance on training data</p>
 </section>
 <section id="interpreting-the-validation-loss">
-<h3><span class="section-number">41.69.6.2. </span>Interpreting the validation loss<a class="headerlink" href="#interpreting-the-validation-loss" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.69.6.2. </span>Interpreting the validation loss<a class="headerlink" href="#interpreting-the-validation-loss" title="Permalink to this headline">#</a></h3>
 <p>Learning curve of a good fit model has a high validation loss at the beginning which gradually lowers upon adding training examples and flattens gradually indicating addition of more training examples doesn’t improve the model performance on unseen data</p>
 <p>We can also see that upon adding a reasonable number of training examples, both the training and validation loss are close to each other</p>
 <p><strong>Typical Features</strong></p>
@@ -2642,7 +2642,7 @@ <h3><span class="section-number">41.69.6.2. </span>Interpreting the validation l
 </section>
 </section>
 <section id="learning-curve-of-an-overfit-model">
-<h2><span class="section-number">41.69.7. </span>Learning Curve of an Overfit Model<a class="headerlink" href="#learning-curve-of-an-overfit-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.69.7. </span>Learning Curve of an Overfit Model<a class="headerlink" href="#learning-curve-of-an-overfit-model" title="Permalink to this headline">#</a></h2>
 <p>Below we’ll use the function created above to get an overfit model. We’ll set the inverse regularization variable/parameter ‘c’ to 10000 (high value of ‘c’ causes overfitting)</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2680,11 +2680,11 @@ <h2><span class="section-number">41.69.7. </span>Learning Curve of an Overfit Mo
 </div>
 <p>The standard deviation in cross validation accuracy is high compared to underfit and good fit model. Training accuracy is higher than cross validation accuracy, typical to an overfit model, but not too high to detect overfitting. But Overfitting can be detected from the learning curve.</p>
 <section id="id1">
-<h3><span class="section-number">41.69.7.1. </span>Interpreting the training loss<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.69.7.1. </span>Interpreting the training loss<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <p>Learning curve of an overfit model has a very low training loss at the beginning which gradually increases very slightly upon adding training examples and doesn’t flatten.</p>
 </section>
 <section id="id2">
-<h3><span class="section-number">41.69.7.2. </span>Interpreting the validation loss<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.69.7.2. </span>Interpreting the validation loss<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <p>Learning curve of an overfit model has a high validation loss at the beginning which gradually lowers upon adding training examples and doesn’t flatten, indicating addition of more training examples can improve the model performance on unseen data</p>
 <p>We can also see that the training and validation losses are far away from each other, which may come close to each other upon adding additional training data</p>
 <p><strong>Typical Features</strong></p>
@@ -2696,7 +2696,7 @@ <h3><span class="section-number">41.69.7.2. </span>Interpreting the validation l
 </section>
 </section>
 <section id="learning-curve-of-an-underfit-model">
-<h2><span class="section-number">41.69.8. </span>Learning Curve of an Underfit Model<a class="headerlink" href="#learning-curve-of-an-underfit-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.69.8. </span>Learning Curve of an Underfit Model<a class="headerlink" href="#learning-curve-of-an-underfit-model" title="Permalink to this headline">#</a></h2>
 <p>Below we’ll use the function created above to get an underfit model. We’ll set the inverse regularization variable/parameter ‘c’ to 1/10000 (low value of ‘c’ causes underfitting)</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2734,11 +2734,11 @@ <h2><span class="section-number">41.69.8. </span>Learning Curve of an Underfit M
 </div>
 <p>The standard deviation in cross validation accuracy is low compared to overfit and good fit model. Underfitting can be detected from the learning curve.</p>
 <section id="id3">
-<h3><span class="section-number">41.69.8.1. </span>Interpreting the training loss<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.69.8.1. </span>Interpreting the training loss<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <p>Learning curve of an underfit model has a low training loss at the beginning which gradually increases upon adding training examples and suddenly falls to the minimum at the end.</p>
 </section>
 <section id="id4">
-<h3><span class="section-number">41.69.8.2. </span>Interpreting the validation loss<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.69.8.2. </span>Interpreting the validation loss<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
 <p>Learning curve of an underfit model has a high validation loss at the beginning which gradually lowers upon adding training examples and suddenly falls to the minimum at the end, indicating addition of more training examples can’t improve the model performance on unseen data</p>
 <p>We can also see that the training and validation losses are very close to each other at the end. Adding additional training examples doesn’t improve the performance on unseen data.</p>
 <p><strong>Typical Features</strong></p>
@@ -2771,7 +2771,7 @@ <h3><span class="section-number">41.69.8.2. </span>Interpreting the validation l
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.69.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.69.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/ksvmuralidhar">KSV MURALIDHAR</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/ksvmuralidhar/learning-curve-to-identify-overfit-underfit/notebook">Learning Curve To Identify Overfit &amp; Underfit</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2813,13 +2813,13 @@ <h2><span class="section-number">41.69.9. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.68. </span>Model selection assignment 1</p>
+            <p class="prev-next-title"><span class="section-number">40.68. </span>Model selection assignment 1</p>
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-        <p class="prev-next-title"><span class="section-number">41.70. </span>Dropout and Batch Normalization</p>
+        <p class="prev-next-title"><span class="section-number">40.70. </span>Dropout and Batch Normalization</p>
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     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="41.69. Learning Curve To Identify Overfit &amp; Underfit" href="learning-curve-to-identify-overfit-underfit.html" />
-    <link rel="prev" title="41.67. Decision Trees - Classification" href="../kernel-method/decision_trees_for_classification.html" />
+    <link rel="next" title="40.69. Learning Curve To Identify Overfit &amp; Underfit" href="learning-curve-to-identify-overfit-underfit.html" />
+    <link rel="prev" title="40.67. Decision Trees - Classification" href="../kernel-method/decision_trees_for_classification.html" />
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-     5.2. Data Science ethics
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 <p>The most of things common in Lasson and Ridge Regression. <br/>
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 <p>Our model (from scratch) also works great as compared to skiti-learn model. Both the models are giving 0.99…% r2_socre which is good.</p>
 </section>
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+<h2><span class="section-number">40.68.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Pavithra Devi M for creating the Notebook <a class="reference external" href="https://www.kaggle.com/code/ninjaac/lasso-and-ridge-regression-from-scratch">Lasso and Ridge Regression from scratch</a>, lisensed under the <a class="reference external" href="https://www.apache.org/licenses/LICENSE-2.0">Apache 2.0</a>. It inspires the majority of the content in this chapter.</p>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
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+<h2><span class="section-number">40.72.2. </span>Data preprocessing:<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <p>We’re not going to do anything fancy here:</p>
 <ul class="simple">
 <li><p>First I’ll transform the skewed numeric features by taking log(feature + 1) - this will make the features more normal</p></li>
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 <p>Now we are going to use regularized linear regression models from the scikit learn module. I’m going to try both l_1(Lasso) and l_2(Ridge) regularization. I’ll also define a function that returns the cross-validation rmse error so we can evaluate our models and pick the best tuning par</p>
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 <p>The residual plot looks pretty <a class="reference external" href="http://good.To">good.To</a> wrap it up let’s predict on the test set and submit on the leaderboard:</p>
 </section>
 <section id="adding-an-xgboost-model">
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+<h2><span class="section-number">40.72.4. </span>Adding an xgboost model:<a class="headerlink" href="#adding-an-xgboost-model" title="Permalink to this headline">#</a></h2>
 <p>Let’s add an xgboost model to our linear model to see if we can improve our score:</p>
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+<h2><span class="section-number">40.72.5. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Alexandru Papiu for creating the open-source project <a class="reference external" href="https://www.kaggle.com/code/apapiu/regularized-linear-models">Regularized Linear Models</a>. It inspires the majority of the content in this chapter.</p>
 </section>
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+<h2><span class="section-number">40.88.1. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
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+     15.2. Gradient descent
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+     40.3. First assignment
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-     41.5. Project Plan​ Template
+     40.5. Project Plan​ Template
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-     41.6. Python programming introduction
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-     41.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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-     41.10. Data Science scenarios
+     40.10. Data Science scenarios
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-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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-     41.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.15. Dive into the beehive
+     40.15. Dive into the beehive
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
+     40.17. Classifying datasets
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-     41.18. Small diabetes study
+     40.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
+     40.20. Displaying airport data
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-     41.21. Soda profits
+     40.21. Soda profits
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-     41.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
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-     41.23. Estimation of COVID-19 pandemic
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-     41.24. Data processing in Python
+     40.24. Data processing in Python
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-     41.26. Data preparation
+     40.26. Data preparation
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-     41.27. Analyzing data
+     40.27. Analyzing data
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-     41.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
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-     41.29. Matplotlib applied
+     40.29. Matplotlib applied
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-     41.35. Tell a story
+     40.35. Tell a story
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-     41.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
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-     41.37. Exploring for answers
+     40.37. Exploring for answers
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-     41.38. Market research
+     40.38. Market research
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-     41.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
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-     41.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
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    <li class="toctree-l2">
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-iris.html">
-     41.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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    </li>
    <li class="toctree-l2">
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-     41.43. Machine Learning overview - assignment 2
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   </ul>
  </li>
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+   40.87.7. Acknowledgments
   </a>
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                             <ul class="visible nav section-nav flex-column">
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   <a class="reference internal nav-link" href="#problem-formulation">
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+   40.87.1. 1. Problem Formulation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#finding-the-data">
-   41.87.2. 2. Finding the Data
+   40.87.2. 2. Finding the Data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exploring-insights-in-data-or-eda">
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+   40.87.3. 3. Exploring Insights in Data or EDA
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-summary-statistics">
-     41.87.3.1. Checking summary statistics
+     40.87.3.1. Checking summary statistics
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-missing-values">
-     41.87.3.2. Checking missing values
+     40.87.3.2. Checking missing values
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-correlation-between-features">
-     41.87.3.3. Checking Correlation Between Features
+     40.87.3.3. Checking Correlation Between Features
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#more-data-exploration">
-     41.87.3.4. More Data Exploration
+     40.87.3.4. More Data Exploration
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploring-categorical-features">
-     41.87.3.5. Exploring Categorical Features
+     40.87.3.5. Exploring Categorical Features
     </a>
    </li>
   </ul>
  </li>
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-   41.87.4. 4. Data Preprocessing
+   40.87.4. 4. Data Preprocessing
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#feature-scaling">
-     41.87.4.1. Feature Scaling
+     40.87.4.1. Feature Scaling
     </a>
    </li>
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  </li>
  <li class="toc-h2 nav-item toc-entry">
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-   41.87.5. 5. Creating and Training a Logistic Regression Model
+   40.87.5. 5. Creating and Training a Logistic Regression Model
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#trying-other-models">
-     41.87.5.1. Trying other models
+     40.87.5.1. Trying other models
     </a>
    </li>
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   <a class="reference internal nav-link" href="#model-evaluation">
-   41.87.6. 6.  Model Evaluation
+   40.87.6. 6.  Model Evaluation
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-performance-metrics">
-     41.87.6.1. Classification Performance Metrics
+     40.87.6.1. Classification Performance Metrics
     </a>
    </li>
   </ul>
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   </a>
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@@ -1769,19 +1769,19 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="build-classification-models">
-<h1><span class="section-number">41.87. </span>Build classification models<a class="headerlink" href="#build-classification-models" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.87. </span>Build classification models<a class="headerlink" href="#build-classification-models" title="Permalink to this headline">#</a></h1>
 <p><a name='0'></a></p>
 <p>In this lab, we will learn about classification where the task is to predict the class or category. Both regression and classification are the main two types of supervised learning.</p>
 <p><a name='1'></a></p>
 <section id="problem-formulation">
-<h2><span class="section-number">41.87.1. </span>1. Problem Formulation<a class="headerlink" href="#problem-formulation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.87.1. </span>1. Problem Formulation<a class="headerlink" href="#problem-formulation" title="Permalink to this headline">#</a></h2>
 <p>Let’s say you have an idea of a revolutionary mobile phone and you want to establish a start up, but you know little about the price of the mobile phones. You are interested in learning that!</p>
 <p>Fortunately, there is this mobile dataset <a class="reference external" href="https://www.kaggle.com/iabhishekofficial/mobile-price-classification?select=train.csv">on Kaggle</a> that you can use to learn about the price ranges of mobiles based on their features such as wifi &amp; bluetooth supports etc…</p>
 <p>So, to make it simple, you have a dataset containing the features of mobiles and the problem is to predict the price range, not the exact price.</p>
 <p><a name='2'></a></p>
 </section>
 <section id="finding-the-data">
-<h2><span class="section-number">41.87.2. </span>2. Finding the Data<a class="headerlink" href="#finding-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.87.2. </span>2. Finding the Data<a class="headerlink" href="#finding-the-data" title="Permalink to this headline">#</a></h2>
 <p>The data that we are going to use is found on Kaggle.</p>
 <p>Here are the details of the features. It is 21 features. The target feature is <code class="docutils literal notranslate"><span class="pre">price</span> <span class="pre">range</span></code> and it has four price ranges: <code class="docutils literal notranslate"><span class="pre">0(low</span> <span class="pre">cost),</span> <span class="pre">1(medium</span> <span class="pre">cost),</span> <span class="pre">2(high</span> <span class="pre">cost)</span> <span class="pre">and</span> <span class="pre">3(very</span> <span class="pre">high</span> <span class="pre">cost).</span></code></p>
 <ul class="simple">
@@ -1898,7 +1898,7 @@ <h2><span class="section-number">41.87.2. </span>2. Finding the Data<a class="he
 <p><a name='3'></a></p>
 </section>
 <section id="exploring-insights-in-data-or-eda">
-<h2><span class="section-number">41.87.3. </span>3. Exploring Insights in Data or EDA<a class="headerlink" href="#exploring-insights-in-data-or-eda" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.87.3. </span>3. Exploring Insights in Data or EDA<a class="headerlink" href="#exploring-insights-in-data-or-eda" title="Permalink to this headline">#</a></h2>
 <p>In this part, we are going to learn more about the dataset. Let’s start with the summary statistics, but before that, I will copy the training data in order to get it easily when we mess up down the road.</p>
 <div class="cell docutils container">
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 </div>
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 <section id="checking-summary-statistics">
-<h3><span class="section-number">41.87.3.1. </span>Checking summary statistics<a class="headerlink" href="#checking-summary-statistics" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.87.3.1. </span>Checking summary statistics<a class="headerlink" href="#checking-summary-statistics" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mobile_train</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
@@ -1918,7 +1918,7 @@ <h3><span class="section-number">41.87.3.1. </span>Checking summary statistics<a
 </div>
 </section>
 <section id="checking-missing-values">
-<h3><span class="section-number">41.87.3.2. </span>Checking missing values<a class="headerlink" href="#checking-missing-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.87.3.2. </span>Checking missing values<a class="headerlink" href="#checking-missing-values" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mobile_train</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
@@ -1929,7 +1929,7 @@ <h3><span class="section-number">41.87.3.2. </span>Checking missing values<a cla
 <p>We are lucky to not have missing values. If we had some, we would have to fill them with some strategies such as mean, remove them compleletly or leave them as they are. None of those 3 options is always the right choice for imputing missing values in all kinds of problems. it depends on the problem and the size of your dataset. Take an example, by removing a feature, you’re loosing data. And by filling the values, you’re adding noise or so. There is a trade off when it comes to imputing the missing values.</p>
 </section>
 <section id="checking-correlation-between-features">
-<h3><span class="section-number">41.87.3.3. </span>Checking Correlation Between Features<a class="headerlink" href="#checking-correlation-between-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.87.3.3. </span>Checking Correlation Between Features<a class="headerlink" href="#checking-correlation-between-features" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">correlation</span> <span class="o">=</span> <span class="n">mobile_train</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>
@@ -1958,7 +1958,7 @@ <h3><span class="section-number">41.87.3.3. </span>Checking Correlation Between
 <p>Let’s take that insights from words to visualization.</p>
 </section>
 <section id="more-data-exploration">
-<h3><span class="section-number">41.87.3.4. </span>More Data Exploration<a class="headerlink" href="#more-data-exploration" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.87.3.4. </span>More Data Exploration<a class="headerlink" href="#more-data-exploration" title="Permalink to this headline">#</a></h3>
 <p>Again, let’s see what we have in the price ranges.</p>
 <div class="cell docutils container">
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@@ -2076,7 +2076,7 @@ <h3><span class="section-number">41.87.3.4. </span>More Data Exploration<a class
 </div>
 </section>
 <section id="exploring-categorical-features">
-<h3><span class="section-number">41.87.3.5. </span>Exploring Categorical Features<a class="headerlink" href="#exploring-categorical-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.87.3.5. </span>Exploring Categorical Features<a class="headerlink" href="#exploring-categorical-features" title="Permalink to this headline">#</a></h3>
 <p>Even if all features have numerical values, but there some of them which are categorical. Let’s inspect them!! It might be that they are already encoded!</p>
 <div class="cell docutils container">
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@@ -2173,7 +2173,7 @@ <h3><span class="section-number">41.87.3.5. </span>Exploring Categorical Feature
 </section>
 </section>
 <section id="data-preprocessing">
-<h2><span class="section-number">41.87.4. </span>4. Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.87.4. </span>4. Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <p>In this part, we will prepare the data to be in proper format for ML models. We will do things like feature scaling. We would also had to encode categorical features but they are already encoded. Before we proceed with feature scaling, let’s take training labels from training data.</p>
 <div class="cell docutils container">
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@@ -2184,7 +2184,7 @@ <h2><span class="section-number">41.87.4. </span>4. Data Preprocessing<a class="
 </div>
 </div>
 <section id="feature-scaling">
-<h3><span class="section-number">41.87.4.1. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.87.4.1. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h3>
 <p>This is the only data processing type that we have to take care of here. Let’s make a function that can take handle that.</p>
 <p>We will normalize the data with sklearn <code class="docutils literal notranslate"><span class="pre">MinMaxScaler</span></code> where the numerical values will be scaled to values between 0 and 1.</p>
 <div class="cell docutils container">
@@ -2219,7 +2219,7 @@ <h3><span class="section-number">41.87.4.1. </span>Feature Scaling<a class="head
 </section>
 </section>
 <section id="creating-and-training-a-logistic-regression-model">
-<h2><span class="section-number">41.87.5. </span>5. Creating and Training a Logistic Regression Model<a class="headerlink" href="#creating-and-training-a-logistic-regression-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.87.5. </span>5. Creating and Training a Logistic Regression Model<a class="headerlink" href="#creating-and-training-a-logistic-regression-model" title="Permalink to this headline">#</a></h2>
 <p>We will start by importing <code class="docutils literal notranslate"><span class="pre">LogisticRegression</span></code> models available in Sklearn linear models and then proceed with model training.</p>
 <div class="cell docutils container">
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@@ -2242,7 +2242,7 @@ <h2><span class="section-number">41.87.5. </span>5. Creating and Training a Logi
 <p>Using <code class="docutils literal notranslate"><span class="pre">log_model.score()</span></code>, we can get an accuracy of the model confidence on the training data and labels. 94.7% is not that bad.</p>
 <p>Let’s train another linear classifier called SGD (Stockastic Gradient Descent) classifier on the same dataset.</p>
 <section id="trying-other-models">
-<h3><span class="section-number">41.87.5.1. </span>Trying other models<a class="headerlink" href="#trying-other-models" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.87.5.1. </span>Trying other models<a class="headerlink" href="#trying-other-models" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">SGDClassifier</span>
@@ -2283,7 +2283,7 @@ <h3><span class="section-number">41.87.5.1. </span>Trying other models<a class="
 </section>
 </section>
 <section id="model-evaluation">
-<h2><span class="section-number">41.87.6. </span>6.  Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.87.6. </span>6.  Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
 <p>Evaluating classification models is not as simple as for regression models. When you have skewed dataset or imbalances, you can get a high accuracy and you can think the model did well when in fact it didn’t.</p>
 <p>In this part, we are going to learn how to evaluate classification models. But first, let’s evaluate the model with cross validation.</p>
 <p>Here is an idea behind cross validation: We want to divide the training set into different training and validation subsets so that we can iteratively train and validate the models on those subsets.</p>
@@ -2390,7 +2390,7 @@ <h2><span class="section-number">41.87.6. </span>6.  Model Evaluation<a class="h
 </div>
 </div>
 <section id="classification-performance-metrics">
-<h3><span class="section-number">41.87.6.1. </span>Classification Performance Metrics<a class="headerlink" href="#classification-performance-metrics" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.87.6.1. </span>Classification Performance Metrics<a class="headerlink" href="#classification-performance-metrics" title="Permalink to this headline">#</a></h3>
 <p>Here are commom classification metrics:</p>
 <ul class="simple">
 <li><p>Accuracy</p></li>
@@ -2463,7 +2463,7 @@ <h3><span class="section-number">41.87.6.1. </span>Classification Performance Me
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.87.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.87.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2505,13 +2505,13 @@ <h2><span class="section-number">41.87.7. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.86. </span>Study the solvers</p>
+            <p class="prev-next-title"><span class="section-number">40.86. </span>Study the solvers</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="build-classification-model.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">41.88. </span>Build Classification Model</p>
+        <p class="prev-next-title"><span class="section-number">40.88. </span>Build Classification Model</p>
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+    <title>40.57. Create a regression model &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="41.58. Linear and polynomial regression" href="linear-and-polynomial-regression.html" />
-    <link rel="prev" title="41.56. Try a different model" href="try-a-different-model.html" />
+    <link rel="next" title="40.58. Linear and polynomial regression" href="linear-and-polynomial-regression.html" />
+    <link rel="prev" title="40.56. Try a different model" href="try-a-different-model.html" />
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-     5.2. Data Science ethics
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+     40.11. Write a data ethics case study
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+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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 <p>In this section you were shown how to build a model using both Linear and Polynomial Regression. Using this knowledge, find a dataset or use one of Scikit-learn’s built-in sets to build a fresh model. Explain in your notebook why you chose the technique you did, and demonstrate your model’s accuracy. If it is not accurate, explain why.</p>
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 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
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+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.40. Data Science project using Azure ML SDK
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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-     41.42. Machine Learning overview - assignment 1
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-     41.44. Regression with Scikit-learn
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-     41.45. Linear regression - California Housing
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-     41.46. Linear Regression Metrics
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-     41.47. Loss Function
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+     40.5. Project Plan​ Template
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-     41.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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-     41.10. Data Science scenarios
+     40.10. Data Science scenarios
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-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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-     41.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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+     40.15. Dive into the beehive
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
+     40.17. Classifying datasets
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-     41.18. Small diabetes study
+     40.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
+     40.20. Displaying airport data
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-     41.21. Soda profits
+     40.21. Soda profits
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-     41.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
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-     41.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
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-     41.24. Data processing in Python
+     40.24. Data processing in Python
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+     40.26. Data preparation
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-     41.27. Analyzing data
+     40.27. Analyzing data
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-     41.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
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-     41.29. Matplotlib applied
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-     41.35. Tell a story
+     40.35. Tell a story
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-     41.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
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-     41.37. Exploring for answers
+     40.37. Exploring for answers
     </a>
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-     41.38. Market research
+     40.38. Market research
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-     41.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
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-<h1><span class="section-number">41.62. </span>Explore classification methods<a class="headerlink" href="#explore-classification-methods" title="Permalink to this headline">#</a></h1>
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 <p>In <a class="reference external" href="https://scikit-learn.org/stable/supervised_learning.html">Scikit-learn documentation</a> you’ll find a large list of ways to classify data. Do a little scavenger hunt in these docs: your goals is to look for classification methods and match a dataset in this curriculum, a question you can ask of it, and a technique of classification. Create a spreadsheet or table in a .doc file and explain how the dataset would work with the classification algorithm.</p>
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+<h2><span class="section-number">40.62.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
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+     40.11. Write a data ethics case study
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+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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+     40.15. Dive into the beehive
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-     41.16. Build your own custom vis
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-     41.17. Classifying datasets
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-     41.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
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-     41.21. Soda profits
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-     41.22. Analyzing COVID-19 papers
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-     41.23. Estimation of COVID-19 pandemic
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-     41.24. Data processing in Python
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-     41.27. Analyzing data
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-     41.28. NYC taxi data in winter and summer
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-     41.29. Matplotlib applied
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-     41.35. Tell a story
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-     41.36. Explore a planetary computer dataset
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-     41.37. Exploring for answers
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-     41.38. Market research
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-     41.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
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-     41.40. Data Science project using Azure ML SDK
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
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    <li class="toctree-l2">
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-     41.42. Machine Learning overview - assignment 1
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-     41.43. Machine Learning overview - assignment 2
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-     41.44. Regression with Scikit-learn
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-     41.45. Linear regression - California Housing
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-     41.46. Linear Regression Metrics
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-     41.47. Loss Function
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-     41.48. Gradient descent
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-     41.49. Linear Regression Implementation from Scratch
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    <li class="toctree-l2">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/ml-overview.html">
-     42.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/linear-regression.html">
-     42.12. Linear Regression
+     41.12. Linear Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/logistic-regression.html">
-     42.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/logistic-regression-condensed.html">
-     42.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/neural-network.html">
-     42.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/build-an-ml-web-app.html">
-     42.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/unsupervised-learning.html">
-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/kernel-method.html">
-     42.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/model-selection.html">
-     42.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1575,116 +1575,116 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   41.45.1. Introduction
+   40.45.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#task">
-   41.45.2. Task
+   40.45.2. Task
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-exploratory-data-analysis">
-     41.45.2.1. Task 1: Exploratory data analysis
+     40.45.2.1. Task 1: Exploratory data analysis
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#split-the-data-set-into-a-training-set-and-a-test-set">
-       41.45.2.1.1. Split the data set into a training set and a test set
+       40.45.2.1.1. Split the data set into a training set and a test set
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#checking-data-statistics">
-       41.45.2.1.2. Checking data statistics
+       40.45.2.1.2. Checking data statistics
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#checking-missing-values">
-       41.45.2.1.3. Checking missing values
+       40.45.2.1.3. Checking missing values
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#checking-values-in-the-categorical-feature-s">
-       41.45.2.1.4. Checking values in the categorical feature(s)
+       40.45.2.1.4. Checking values in the categorical feature(s)
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#checking-correlation-between-features">
-       41.45.2.1.5. Checking Correlation Between Features
+       40.45.2.1.5. Checking Correlation Between Features
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#plotting-geographical-features">
-       41.45.2.1.6. Plotting geographical features
+       40.45.2.1.6. Plotting geographical features
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#exploring-relationship-between-individual-features">
-       41.45.2.1.7. Exploring Relationship Between Individual Features
+       40.45.2.1.7. Exploring Relationship Between Individual Features
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-data-preprocessing">
-     41.45.2.2. Task 3: Data preprocessing
+     40.45.2.2. Task 3: Data preprocessing
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#create-the-input-data-and-output-data-for-training-the-machine-learning-model">
-       41.45.2.2.1. Create the input data and output data for training the machine learning model
+       40.45.2.2.1. Create the input data and output data for training the machine learning model
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#handling-missing-values">
-       41.45.2.2.2. Handling missing values
+       40.45.2.2.2. Handling missing values
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#encoding-categorical-features">
-       41.45.2.2.3. Encoding categorical features
+       40.45.2.2.3. Encoding categorical features
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#mapping">
-         41.45.2.2.3.1. Mapping
+         40.45.2.2.3.1. Mapping
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#handling-categorical-features-with-sklearn">
-         41.45.2.2.3.2. Handling categorical features with Sklearn
+         40.45.2.2.3.2. Handling categorical features with Sklearn
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#one-hot-encoding">
-         41.45.2.2.3.3. One hot encoding
+         40.45.2.2.3.3. One hot encoding
         </a>
        </li>
       </ul>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#scaling-numerical">
-       41.45.2.2.4. Scaling numerical
+       40.45.2.2.4. Scaling numerical
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#putting-all-data-preprocessing-steps-into-a-single-pipeline">
-       41.45.2.2.5. Putting all data preprocessing steps into a single pipeline
+       40.45.2.2.5. Putting all data preprocessing steps into a single pipeline
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#creating-a-numerical-features-pipeline">
-         41.45.2.2.5.1. Creating a numerical features pipeline
+         40.45.2.2.5.1. Creating a numerical features pipeline
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#pipeline-for-transforming-categorical-features">
-         41.45.2.2.5.2. Pipeline for transforming categorical features
+         40.45.2.2.5.2. Pipeline for transforming categorical features
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#final-data-processing-pipeline">
-         41.45.2.2.5.3. Final data processing pipeline
+         40.45.2.2.5.3. Final data processing pipeline
         </a>
        </li>
       </ul>
@@ -1693,17 +1693,17 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#choosing-and-training-a-model">
-     41.45.2.3. Choosing and training a model
+     40.45.2.3. Choosing and training a model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#model-evaluation">
-     41.45.2.4. Model evaluation
+     40.45.2.4. Model evaluation
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#model-evaluation-with-cross-validation">
-       41.45.2.4.1. Model evaluation with cross validation
+       40.45.2.4.1. Model evaluation with cross validation
       </a>
      </li>
     </ul>
@@ -1712,7 +1712,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.45.3. Acknowledgments
+   40.45.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1736,116 +1736,116 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   41.45.1. Introduction
+   40.45.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#task">
-   41.45.2. Task
+   40.45.2. Task
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-1-exploratory-data-analysis">
-     41.45.2.1. Task 1: Exploratory data analysis
+     40.45.2.1. Task 1: Exploratory data analysis
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#split-the-data-set-into-a-training-set-and-a-test-set">
-       41.45.2.1.1. Split the data set into a training set and a test set
+       40.45.2.1.1. Split the data set into a training set and a test set
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#checking-data-statistics">
-       41.45.2.1.2. Checking data statistics
+       40.45.2.1.2. Checking data statistics
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#checking-missing-values">
-       41.45.2.1.3. Checking missing values
+       40.45.2.1.3. Checking missing values
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#checking-values-in-the-categorical-feature-s">
-       41.45.2.1.4. Checking values in the categorical feature(s)
+       40.45.2.1.4. Checking values in the categorical feature(s)
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#checking-correlation-between-features">
-       41.45.2.1.5. Checking Correlation Between Features
+       40.45.2.1.5. Checking Correlation Between Features
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#plotting-geographical-features">
-       41.45.2.1.6. Plotting geographical features
+       40.45.2.1.6. Plotting geographical features
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#exploring-relationship-between-individual-features">
-       41.45.2.1.7. Exploring Relationship Between Individual Features
+       40.45.2.1.7. Exploring Relationship Between Individual Features
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#task-3-data-preprocessing">
-     41.45.2.2. Task 3: Data preprocessing
+     40.45.2.2. Task 3: Data preprocessing
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#create-the-input-data-and-output-data-for-training-the-machine-learning-model">
-       41.45.2.2.1. Create the input data and output data for training the machine learning model
+       40.45.2.2.1. Create the input data and output data for training the machine learning model
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#handling-missing-values">
-       41.45.2.2.2. Handling missing values
+       40.45.2.2.2. Handling missing values
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#encoding-categorical-features">
-       41.45.2.2.3. Encoding categorical features
+       40.45.2.2.3. Encoding categorical features
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#mapping">
-         41.45.2.2.3.1. Mapping
+         40.45.2.2.3.1. Mapping
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#handling-categorical-features-with-sklearn">
-         41.45.2.2.3.2. Handling categorical features with Sklearn
+         40.45.2.2.3.2. Handling categorical features with Sklearn
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#one-hot-encoding">
-         41.45.2.2.3.3. One hot encoding
+         40.45.2.2.3.3. One hot encoding
         </a>
        </li>
       </ul>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#scaling-numerical">
-       41.45.2.2.4. Scaling numerical
+       40.45.2.2.4. Scaling numerical
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#putting-all-data-preprocessing-steps-into-a-single-pipeline">
-       41.45.2.2.5. Putting all data preprocessing steps into a single pipeline
+       40.45.2.2.5. Putting all data preprocessing steps into a single pipeline
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#creating-a-numerical-features-pipeline">
-         41.45.2.2.5.1. Creating a numerical features pipeline
+         40.45.2.2.5.1. Creating a numerical features pipeline
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#pipeline-for-transforming-categorical-features">
-         41.45.2.2.5.2. Pipeline for transforming categorical features
+         40.45.2.2.5.2. Pipeline for transforming categorical features
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#final-data-processing-pipeline">
-         41.45.2.2.5.3. Final data processing pipeline
+         40.45.2.2.5.3. Final data processing pipeline
         </a>
        </li>
       </ul>
@@ -1854,17 +1854,17 @@ <h2> Contents </h2>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#choosing-and-training-a-model">
-     41.45.2.3. Choosing and training a model
+     40.45.2.3. Choosing and training a model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#model-evaluation">
-     41.45.2.4. Model evaluation
+     40.45.2.4. Model evaluation
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#model-evaluation-with-cross-validation">
-       41.45.2.4.1. Model evaluation with cross validation
+       40.45.2.4.1. Model evaluation with cross validation
       </a>
      </li>
     </ul>
@@ -1873,7 +1873,7 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.45.3. Acknowledgments
+   40.45.3. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1887,10 +1887,10 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="linear-regression-california-housing">
-<h1><span class="section-number">41.45. </span>Linear regression - California Housing<a class="headerlink" href="#linear-regression-california-housing" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.45. </span>Linear regression - California Housing<a class="headerlink" href="#linear-regression-california-housing" title="Permalink to this headline">#</a></h1>
 <p>In this assignment, we will predict the price of a house by its region, size, number of bedrooms, etc.</p>
 <section id="introduction">
-<h2><span class="section-number">41.45.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.45.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -2225,11 +2225,11 @@ <h2><span class="section-number">41.45.1. </span>Introduction<a class="headerlin
 <p>So, we have 20640 data points and 10 features. In those 10 features, 9 features are input features and the feature <code class="docutils literal notranslate"><span class="pre">median_house_value</span></code> is the target variable/label.</p>
 </section>
 <section id="task">
-<h2><span class="section-number">41.45.2. </span>Task<a class="headerlink" href="#task" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.45.2. </span>Task<a class="headerlink" href="#task" title="Permalink to this headline">#</a></h2>
 <section id="task-1-exploratory-data-analysis">
-<h3><span class="section-number">41.45.2.1. </span>Task 1: Exploratory data analysis<a class="headerlink" href="#task-1-exploratory-data-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.45.2.1. </span>Task 1: Exploratory data analysis<a class="headerlink" href="#task-1-exploratory-data-analysis" title="Permalink to this headline">#</a></h3>
 <section id="split-the-data-set-into-a-training-set-and-a-test-set">
-<h4><span class="section-number">41.45.2.1.1. </span>Split the data set into a training set and a test set<a class="headerlink" href="#split-the-data-set-into-a-training-set-and-a-test-set" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.1.1. </span>Split the data set into a training set and a test set<a class="headerlink" href="#split-the-data-set-into-a-training-set-and-a-test-set" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_data</span><span class="p">,</span> <span class="n">test_data</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span><span class="n">random_state</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
@@ -2239,7 +2239,7 @@ <h4><span class="section-number">41.45.2.1.1. </span>Split the data set into a t
 </div>
 </section>
 <section id="checking-data-statistics">
-<h4><span class="section-number">41.45.2.1.2. </span>Checking data statistics<a class="headerlink" href="#checking-data-statistics" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.1.2. </span>Checking data statistics<a class="headerlink" href="#checking-data-statistics" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_data</span><span class="o">.</span><span class="n">describe</span><span class="p">(</span><span class="n">include</span><span class="o">=</span><span class="s1">&#39;all&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span>
@@ -2425,7 +2425,7 @@ <h4><span class="section-number">41.45.2.1.2. </span>Checking data statistics<a
 </div>
 </section>
 <section id="checking-missing-values">
-<h4><span class="section-number">41.45.2.1.3. </span>Checking missing values<a class="headerlink" href="#checking-missing-values" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.1.3. </span>Checking missing values<a class="headerlink" href="#checking-missing-values" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_data</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
@@ -2462,7 +2462,7 @@ <h4><span class="section-number">41.45.2.1.3. </span>Checking missing values<a c
 </div>
 </section>
 <section id="checking-values-in-the-categorical-feature-s">
-<h4><span class="section-number">41.45.2.1.4. </span>Checking values in the categorical feature(s)<a class="headerlink" href="#checking-values-in-the-categorical-feature-s" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.1.4. </span>Checking values in the categorical feature(s)<a class="headerlink" href="#checking-values-in-the-categorical-feature-s" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_data</span><span class="p">[</span><span class="s1">&#39;ocean_proximity&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
@@ -2495,7 +2495,7 @@ <h4><span class="section-number">41.45.2.1.4. </span>Checking values in the cate
 </div>
 </section>
 <section id="checking-correlation-between-features">
-<h4><span class="section-number">41.45.2.1.5. </span>Checking Correlation Between Features<a class="headerlink" href="#checking-correlation-between-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.1.5. </span>Checking Correlation Between Features<a class="headerlink" href="#checking-correlation-between-features" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">correlation</span> <span class="o">=</span> <span class="n">train_data</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>
@@ -2537,7 +2537,7 @@ <h4><span class="section-number">41.45.2.1.5. </span>Checking Correlation Betwee
 <p>The other interesting insights is that the <code class="docutils literal notranslate"><span class="pre">price</span> <span class="pre">of</span> <span class="pre">the</span> <span class="pre">house</span></code> is closely correlated with the <code class="docutils literal notranslate"><span class="pre">median</span> <span class="pre">income</span></code>, and that makes sense too. For many cases, you will resonably seek house that you will be able to afford based on your income.</p>
 </section>
 <section id="plotting-geographical-features">
-<h4><span class="section-number">41.45.2.1.6. </span>Plotting geographical features<a class="headerlink" href="#plotting-geographical-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.1.6. </span>Plotting geographical features<a class="headerlink" href="#plotting-geographical-features" title="Permalink to this headline">#</a></h4>
 <p>Since we have latitude and longitude, let’s plot it. It can help us to know the location of certain houses on the map and hopefully this will resemble California map.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2589,7 +2589,7 @@ <h4><span class="section-number">41.45.2.1.6. </span>Plotting geographical featu
 <p>Yup, all houses near the ocean are very expensive compared to other areas.</p>
 </section>
 <section id="exploring-relationship-between-individual-features">
-<h4><span class="section-number">41.45.2.1.7. </span>Exploring Relationship Between Individual Features<a class="headerlink" href="#exploring-relationship-between-individual-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.1.7. </span>Exploring Relationship Between Individual Features<a class="headerlink" href="#exploring-relationship-between-individual-features" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
@@ -2624,9 +2624,9 @@ <h4><span class="section-number">41.45.2.1.7. </span>Exploring Relationship Betw
 </section>
 </section>
 <section id="task-3-data-preprocessing">
-<h3><span class="section-number">41.45.2.2. </span>Task 3: Data preprocessing<a class="headerlink" href="#task-3-data-preprocessing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.45.2.2. </span>Task 3: Data preprocessing<a class="headerlink" href="#task-3-data-preprocessing" title="Permalink to this headline">#</a></h3>
 <section id="create-the-input-data-and-output-data-for-training-the-machine-learning-model">
-<h4><span class="section-number">41.45.2.2.1. </span>Create the input data and output data for training the machine learning model<a class="headerlink" href="#create-the-input-data-and-output-data-for-training-the-machine-learning-model" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.2.1. </span>Create the input data and output data for training the machine learning model<a class="headerlink" href="#create-the-input-data-and-output-data-for-training-the-machine-learning-model" title="Permalink to this headline">#</a></h4>
 <p>Since we are going to prepare the data for the ML model, let’s create an input training data and the training label, label being <code class="docutils literal notranslate"><span class="pre">median_house_value</span></code>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2756,7 +2756,7 @@ <h4><span class="section-number">41.45.2.2.1. </span>Create the input data and o
 </div>
 </section>
 <section id="handling-missing-values">
-<h4><span class="section-number">41.45.2.2.2. </span>Handling missing values<a class="headerlink" href="#handling-missing-values" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.2.2. </span>Handling missing values<a class="headerlink" href="#handling-missing-values" title="Permalink to this headline">#</a></h4>
 <p>In this example, we will fill the values with the mean of the concerned features.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2813,7 +2813,7 @@ <h4><span class="section-number">41.45.2.2.2. </span>Handling missing values<a c
 <p>The feature <code class="docutils literal notranslate"><span class="pre">total_bedroom</span></code> was the one having missing values. Looking above, we no longer have the missing values in whole dataframe.</p>
 </section>
 <section id="encoding-categorical-features">
-<h4><span class="section-number">41.45.2.2.3. </span>Encoding categorical features<a class="headerlink" href="#encoding-categorical-features" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.2.3. </span>Encoding categorical features<a class="headerlink" href="#encoding-categorical-features" title="Permalink to this headline">#</a></h4>
 <p>Categorical features are features which have categorical values. An example in our dataset is <code class="docutils literal notranslate"><span class="pre">ocean_proximity</span></code> that has the following values.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2834,7 +2834,7 @@ <h4><span class="section-number">41.45.2.2.3. </span>Encoding categorical featur
 </div>
 <p>So we have 5 categories: <code class="docutils literal notranslate"><span class="pre">&lt;1H</span> <span class="pre">OCEAN</span></code>, <code class="docutils literal notranslate"><span class="pre">INLAND</span></code>, <code class="docutils literal notranslate"><span class="pre">NEAR</span> <span class="pre">OCEAN</span></code>, <code class="docutils literal notranslate"><span class="pre">NEAR</span> <span class="pre">BAY</span></code>, <code class="docutils literal notranslate"><span class="pre">ISLAND</span></code>.</p>
 <section id="mapping">
-<h5><span class="section-number">41.45.2.2.3.1. </span>Mapping<a class="headerlink" href="#mapping" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">40.45.2.2.3.1. </span>Mapping<a class="headerlink" href="#mapping" title="Permalink to this headline">#</a></h5>
 <p>Mapping is simple. We create a dictionary of categorical values and their corresponding numerics. And after that, we map it to the categorical feature.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2889,7 +2889,7 @@ <h5><span class="section-number">41.45.2.2.3.1. </span>Mapping<a class="headerli
 <p>Cool, all categories were mapped to their corresponding numerals. That is actually encoding. We are converting the categories (in text) into numbers, typically because ML models expect numeric inputs.</p>
 </section>
 <section id="handling-categorical-features-with-sklearn">
-<h5><span class="section-number">41.45.2.2.3.2. </span>Handling categorical features with Sklearn<a class="headerlink" href="#handling-categorical-features-with-sklearn" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">40.45.2.2.3.2. </span>Handling categorical features with Sklearn<a class="headerlink" href="#handling-categorical-features-with-sklearn" title="Permalink to this headline">#</a></h5>
 <p>Sklearn has many preprocessing functions to handle categorical features. Ordinary Encoder is one of them. It does the same as what we did before with mapping. The only difference is implementation.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2926,7 +2926,7 @@ <h5><span class="section-number">41.45.2.2.3.2. </span>Handling categorical feat
 </div>
 </section>
 <section id="one-hot-encoding">
-<h5><span class="section-number">41.45.2.2.3.3. </span>One hot encoding<a class="headerlink" href="#one-hot-encoding" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">40.45.2.2.3.3. </span>One hot encoding<a class="headerlink" href="#one-hot-encoding" title="Permalink to this headline">#</a></h5>
 <p>One hot encoding is most preferred when the categories are not in any order and that is exactly how our categorical feature is. This is what I mean by saying unordered categories: If you have 3 cities and encode them with numbers (1,2,3) respectively, a machine learning model may learn that city 1 is close to city 2 and to city 3. As that is a false assumption to make, the model will likely give incorrect predictions if the city feature plays an important role in the analysis.</p>
 <p>On the flip side, if you have the feature of ordered ranges like low, medium, and high, then numbers can be an effective way because you want to keep the sequence of these ranges.</p>
 <p>In our case, the ocean proximity feature is not in any order. By using one hot, The categories will be converted into binary representation (1s or 0s), and the orginal categorical feature will be splitted into more features, equivalent to the number of categories.</p>
@@ -2973,7 +2973,7 @@ <h5><span class="section-number">41.45.2.2.3.3. </span>One hot encoding<a class=
 </section>
 </section>
 <section id="scaling-numerical">
-<h4><span class="section-number">41.45.2.2.4. </span>Scaling numerical<a class="headerlink" href="#scaling-numerical" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.2.4. </span>Scaling numerical<a class="headerlink" href="#scaling-numerical" title="Permalink to this headline">#</a></h4>
 <p>Most machine learning models will work well when given small input values, and best if they are in the same range. For that reason, there are two most techniques to scale features:</p>
 <ul class="simple">
 <li><p>Normalization where the features are scaled to values between 0 and 1. And</p></li>
@@ -3043,7 +3043,7 @@ <h4><span class="section-number">41.45.2.2.4. </span>Scaling numerical<a class="
 </div>
 </section>
 <section id="putting-all-data-preprocessing-steps-into-a-single-pipeline">
-<h4><span class="section-number">41.45.2.2.5. </span>Putting all data preprocessing steps into a single pipeline<a class="headerlink" href="#putting-all-data-preprocessing-steps-into-a-single-pipeline" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.2.5. </span>Putting all data preprocessing steps into a single pipeline<a class="headerlink" href="#putting-all-data-preprocessing-steps-into-a-single-pipeline" title="Permalink to this headline">#</a></h4>
 <p>We are going to do three things:</p>
 <ul class="simple">
 <li><p>Creating a numerical pipeline having all numerical preprocessing steps (handling missing values and standardization)</p></li>
@@ -3051,7 +3051,7 @@ <h4><span class="section-number">41.45.2.2.5. </span>Putting all data preprocess
 <li><p>Combining both pipelines into one pipeline.</p></li>
 </ul>
 <section id="creating-a-numerical-features-pipeline">
-<h5><span class="section-number">41.45.2.2.5.1. </span>Creating a numerical features pipeline<a class="headerlink" href="#creating-a-numerical-features-pipeline" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">40.45.2.2.5.1. </span>Creating a numerical features pipeline<a class="headerlink" href="#creating-a-numerical-features-pipeline" title="Permalink to this headline">#</a></h5>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
@@ -3111,7 +3111,7 @@ <h5><span class="section-number">41.45.2.2.5.1. </span>Creating a numerical feat
 </div>
 </section>
 <section id="pipeline-for-transforming-categorical-features">
-<h5><span class="section-number">41.45.2.2.5.2. </span>Pipeline for transforming categorical features<a class="headerlink" href="#pipeline-for-transforming-categorical-features" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">40.45.2.2.5.2. </span>Pipeline for transforming categorical features<a class="headerlink" href="#pipeline-for-transforming-categorical-features" title="Permalink to this headline">#</a></h5>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cat_feats_pipe</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">([</span>
@@ -3137,7 +3137,7 @@ <h5><span class="section-number">41.45.2.2.5.2. </span>Pipeline for transforming
 </div>
 </section>
 <section id="final-data-processing-pipeline">
-<h5><span class="section-number">41.45.2.2.5.3. </span>Final data processing pipeline<a class="headerlink" href="#final-data-processing-pipeline" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">40.45.2.2.5.3. </span>Final data processing pipeline<a class="headerlink" href="#final-data-processing-pipeline" title="Permalink to this headline">#</a></h5>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.compose</span> <span class="kn">import</span> <span class="n">ColumnTransformer</span>
@@ -3198,7 +3198,7 @@ <h5><span class="section-number">41.45.2.2.5.3. </span>Final data processing pip
 </section>
 </section>
 <section id="choosing-and-training-a-model">
-<h3><span class="section-number">41.45.2.3. </span>Choosing and training a model<a class="headerlink" href="#choosing-and-training-a-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.45.2.3. </span>Choosing and training a model<a class="headerlink" href="#choosing-and-training-a-model" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
@@ -3256,7 +3256,7 @@ <h3><span class="section-number">41.45.2.3. </span>Choosing and training a model
 </div>
 </section>
 <section id="model-evaluation">
-<h3><span class="section-number">41.45.2.4. </span>Model evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.45.2.4. </span>Model evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">mean_squared_error</span>
@@ -3294,7 +3294,7 @@ <h3><span class="section-number">41.45.2.4. </span>Model evaluation<a class="hea
 </div>
 </div>
 <section id="model-evaluation-with-cross-validation">
-<h4><span class="section-number">41.45.2.4.1. </span>Model evaluation with cross validation<a class="headerlink" href="#model-evaluation-with-cross-validation" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">40.45.2.4.1. </span>Model evaluation with cross validation<a class="headerlink" href="#model-evaluation-with-cross-validation" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_val_score</span>
@@ -3376,7 +3376,7 @@ <h4><span class="section-number">41.45.2.4.1. </span>Model evaluation with cross
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.45.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.45.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Nyandwi for creating the open-source course <a class="reference external" href="https://github.com/Nyandwi/machine_learning_complete/blob/main/6_classical_machine_learning_with_scikit-learn/1_linear_models_for_regression.ipynb">Linear Models for regression</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -3418,13 +3418,13 @@ <h2><span class="section-number">41.45.3. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.44. </span>Regression with Scikit-learn</p>
+            <p class="prev-next-title"><span class="section-number">40.44. </span>Regression with Scikit-learn</p>
         </div>
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-        <p class="prev-next-title"><span class="section-number">41.46. </span>Linear Regression Metrics</p>
+        <p class="prev-next-title"><span class="section-number">40.46. </span>Linear Regression Metrics</p>
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diff --git a/assignments/ml-fundamentals/linear-regression/gradient-descent.html b/assignments/ml-fundamentals/linear-regression/gradient-descent.html
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@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>41.48. Gradient descent &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.48. Gradient descent &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
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     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="41.49. Linear Regression Implementation from Scratch" href="linear-regression-from-scratch.html" />
-    <link rel="prev" title="41.47. Loss Function" href="loss-function.html" />
+    <link rel="next" title="40.49. Linear Regression Implementation from Scratch" href="linear-regression-from-scratch.html" />
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 <ul class="nav bd-sidenav">
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   <a class="reference internal" href="../../../prerequisites/python-programming-introduction.html">
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+   1. Python programming introduction
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+   2. Python programming basics
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+   3. Python programming advanced
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-     5.1. Defining data science
+     4.1. Defining data science
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/introduction/data-science-ethics.html">
-     5.2. Data Science ethics
+     4.2. Data Science ethics
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../data-science/introduction/defining-data.html">
-     5.3. Defining data
+     4.3. Defining data
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     <a class="reference internal" href="../../../data-science/introduction/introduction-to-statistics-and-probability.html">
-     5.4. Introduction to statistics and probability
+     4.4. Introduction to statistics and probability
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   <ul>
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-     6.1. Relational databases
+     5.1. Relational databases
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               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="gradient-descent">
-<h1><span class="section-number">41.48. </span>Gradient descent<a class="headerlink" href="#gradient-descent" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.48. </span>Gradient descent<a class="headerlink" href="#gradient-descent" title="Permalink to this headline">#</a></h1>
 <section id="session-objective">
-<h2><span class="section-number">41.48.1. </span>Session Objective<a class="headerlink" href="#session-objective" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.48.1. </span>Session Objective<a class="headerlink" href="#session-objective" title="Permalink to this headline">#</a></h2>
 <p>In previous sessions, we’ve delved into the application of Linear Regression and Logistic Regression models. You may find the code relatively straightforward and intuitive at this point. However, you might be pondering questions like:</p>
 <ul class="simple">
 <li><p>What exactly occurs when we invoke the <code class="docutils literal notranslate"><span class="pre">.fit()</span></code> function?</p></li>
@@ -1760,7 +1760,7 @@ <h2><span class="section-number">41.48.1. </span>Session Objective<a class="head
 <p>This session is designed to provide insight into the functionality of the <code class="docutils literal notranslate"><span class="pre">.fit()</span></code> method, which is responsible for training machine learning models and fine-tuning model parameters. The underlying technique at play here is known as “Gradient Descent.”</p>
 </section>
 <section id="lets-explore-and-gain-intuition">
-<h2><span class="section-number">41.48.2. </span>Let’s Explore and Gain Intuition<a class="headerlink" href="#lets-explore-and-gain-intuition" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.48.2. </span>Let’s Explore and Gain Intuition<a class="headerlink" href="#lets-explore-and-gain-intuition" title="Permalink to this headline">#</a></h2>
 <p>To further enhance your understanding and gain a playful insight into Gradient Descent, you can explore the following resources:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://github.com/lilipads/gradient_descent_viz">Gradient Descent Visualization</a>: This GitHub repository offers a visualization of the Gradient Descent algorithm, which can be a valuable resource for understanding the optimization process.</p></li>
@@ -1768,7 +1768,7 @@ <h2><span class="section-number">41.48.2. </span>Let’s Explore and Gain Intuit
 </ul>
 <p>These resources will help you build an intuitive grasp of Gradient Descent and its role in training machine learning models. Enjoy your exploration!</p>
 <section id="math-feel-free-to-skip-if-you-find-it-difficult">
-<h3><span class="section-number">41.48.2.1. </span>Math (feel free to skip if you find it difficult)<a class="headerlink" href="#math-feel-free-to-skip-if-you-find-it-difficult" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.48.2.1. </span>Math (feel free to skip if you find it difficult)<a class="headerlink" href="#math-feel-free-to-skip-if-you-find-it-difficult" title="Permalink to this headline">#</a></h3>
 <p>The fundamental concept behind gradient descent is rather straightforward: it involves the gradual adjustment of parameters, such as the slope (<span class="math notranslate nohighlight">\(m\)</span>) and the intercept (<span class="math notranslate nohighlight">\(b\)</span>) in our regression equation $y = mx + b, with the aim of minimizing a cost function. This cost function is typically a metric that quantifies the disparity between our model’s predicted results and the actual values. In regression scenarios, the widely employed cost function is the <code class="docutils literal notranslate"><span class="pre">mean</span> <span class="pre">squared</span> <span class="pre">error</span></code> (MSE). When dealing with classification problems, a different set of parameters must be fine-tuned.</p>
 <p>The MSE (Mean Squared Error) is mathematically expressed as:</p>
 <div class="math notranslate nohighlight">
@@ -1779,7 +1779,7 @@ <h3><span class="section-number">41.48.2.1. </span>Math (feel free to skip if yo
 <p>Our primary challenge is to determine the optimal adjustments to parameters <span class="math notranslate nohighlight">\(m\)</span> and $b” to minimize the MSE effectively.</p>
 </section>
 <section id="partial-derivatives">
-<h3><span class="section-number">41.48.2.2. </span>Partial Derivatives<a class="headerlink" href="#partial-derivatives" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.48.2.2. </span>Partial Derivatives<a class="headerlink" href="#partial-derivatives" title="Permalink to this headline">#</a></h3>
 <p>In our pursuit of minimizing the Mean Squared Error (MSE), we turn to partial derivatives to understand how each individual parameter influences the MSE. The term “partial” signifies that we are taking derivatives with respect to individual parameters, in this case, <span class="math notranslate nohighlight">\(m\)</span> and $b, separately.</p>
 <p>Consider the following formula, which closely resembles the MSE, but now we’ve introduced the function <span class="math notranslate nohighlight">\(f(m, b)\)</span> into it. The addition of this function doesn’t significantly alter the essence of the calculation, but it allows us to input specific values for <span class="math notranslate nohighlight">\(m\)</span> and <span class="math notranslate nohighlight">\(b\)</span> to compute the result.</p>
 <div class="math notranslate nohighlight">
@@ -1787,7 +1787,7 @@ <h3><span class="section-number">41.48.2.2. </span>Partial Derivatives<a class="
 <p>For the purposes of calculating partial derivatives, we can temporarily disregard the summation and the terms preceding it, focusing solely on the expression <span class="math notranslate nohighlight">\(y - (mx + b)^2\)</span>. This expression serves as a better starting point for the subsequent partial derivative calculations.</p>
 </section>
 <section id="partial-derivative-with-respect-to-m">
-<h3><span class="section-number">41.48.2.3. </span>Partial Derivative with Respect to <span class="math notranslate nohighlight">\(m\)</span><a class="headerlink" href="#partial-derivative-with-respect-to-m" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.48.2.3. </span>Partial Derivative with Respect to <span class="math notranslate nohighlight">\(m\)</span><a class="headerlink" href="#partial-derivative-with-respect-to-m" title="Permalink to this headline">#</a></h3>
 <p>When we calculate the partial derivative with respect to the parameter <span class="math notranslate nohighlight">\(m,&quot; we isolate the parameter \)</span>m” and treat <span class="math notranslate nohighlight">\(b\)</span> as a constant (effectively setting it to 0 for differentiation purposes). To compute this derivative, we utilize the chain rule, which is a fundamental concept in calculus.</p>
 <p>The chain rule is expressed as follows:</p>
 <div class="math notranslate nohighlight">
@@ -1812,7 +1812,7 @@ <h3><span class="section-number">41.48.2.3. </span>Partial Derivative with Respe
 <p>Here, <span class="math notranslate nohighlight">\(\frac{df}{dm}\)</span> signifies the partial derivative of function <span class="math notranslate nohighlight">\(f\)</span> (as previously defined) with respect to the parameter <span class="math notranslate nohighlight">\(m\)</span>. We can now insert this derivative into our summation to complete the calculation.</p>
 </section>
 <section id="partial-derivative-with-respect-to-b">
-<h3><span class="section-number">41.48.2.4. </span>Partial Derivative with Respect to <span class="math notranslate nohighlight">\(b\)</span><a class="headerlink" href="#partial-derivative-with-respect-to-b" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.48.2.4. </span>Partial Derivative with Respect to <span class="math notranslate nohighlight">\(b\)</span><a class="headerlink" href="#partial-derivative-with-respect-to-b" title="Permalink to this headline">#</a></h3>
 <p>The process for computing the partial derivative with respect to the parameter <span class="math notranslate nohighlight">\(b&quot; is analogous to our previous derivation with respect to \)</span>m. We still apply the same rules and utilize the chain rule:</p>
 <ol class="simple">
 <li><p>The derivative of <span class="math notranslate nohighlight">\(()^2\)</span> is <span class="math notranslate nohighlight">\(2()\)</span>, which corresponds to how <span class="math notranslate nohighlight">\(x^2\)</span> becomes <span class="math notranslate nohighlight">\(2x\)</span>.</p></li>
@@ -1831,7 +1831,7 @@ <h3><span class="section-number">41.48.2.4. </span>Partial Derivative with Respe
 <p>Similarly to the previous case, <span class="math notranslate nohighlight">\(\frac{df}{db}\)</span> represents the partial derivative of function <span class="math notranslate nohighlight">\(f\)</span> with respect to the parameter $b”. Inserting this derivative into our summation concludes the computation.</p>
 </section>
 <section id="the-final-function">
-<h3><span class="section-number">41.48.2.5. </span>The Final Function<a class="headerlink" href="#the-final-function" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.48.2.5. </span>The Final Function<a class="headerlink" href="#the-final-function" title="Permalink to this headline">#</a></h3>
 <p>Before delving into the code, there are a few essential details to address:</p>
 <ol class="simple">
 <li><p>Gradient descent is an iterative process, and with each iteration (referred to as an “epoch”), we incrementally reduce the Mean Squared Error (MSE). At each iteration, we apply our derived functions to update the values of parameters <span class="math notranslate nohighlight">\(m\)</span> and <span class="math notranslate nohighlight">\(b\)</span>.</p></li>
@@ -1842,7 +1842,7 @@ <h3><span class="section-number">41.48.2.5. </span>The Final Function<a class="h
 </section>
 </section>
 <section id="time-to-code">
-<h2><span class="section-number">41.48.3. </span>Time to code!<a class="headerlink" href="#time-to-code" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.48.3. </span>Time to code!<a class="headerlink" href="#time-to-code" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
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 </div>
 </section>
 <section id="one-dimensional-gradient-descent">
-<h2><span class="section-number">41.48.4. </span>1. One-dimensional gradient descent<a class="headerlink" href="#one-dimensional-gradient-descent" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.48.4. </span>1. One-dimensional gradient descent<a class="headerlink" href="#one-dimensional-gradient-descent" title="Permalink to this headline">#</a></h2>
 <p>One-dimensional gradient descent is a simple optimization algorithm used to find the minimum (or maximum) of a univariate function.</p>
 <p>Here are the main steps:</p>
 <blockquote>
@@ -1918,7 +1918,7 @@ <h2><span class="section-number">41.48.4. </span>1. One-dimensional gradient des
 </div>
 </div>
 <section id="lets-try">
-<h3><span class="section-number">41.48.4.1. </span>Let’s try!<a class="headerlink" href="#lets-try" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.48.4.1. </span>Let’s try!<a class="headerlink" href="#lets-try" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define the target function</span>
@@ -1988,7 +1988,7 @@ <h3><span class="section-number">41.48.4.1. </span>Let’s try!<a class="headerl
 </section>
 </section>
 <section id="two-dimensional-gradient-descent">
-<h2><span class="section-number">41.48.5. </span>2. Two-dimensional gradient descent<a class="headerlink" href="#two-dimensional-gradient-descent" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.48.5. </span>2. Two-dimensional gradient descent<a class="headerlink" href="#two-dimensional-gradient-descent" title="Permalink to this headline">#</a></h2>
 <p>Two-dimensional gradient descent is an optimization algorithm used to find the minimum (or maximum) of a function with two input variables.</p>
 <p>Here are the main steps:</p>
 <blockquote>
@@ -2055,7 +2055,7 @@ <h2><span class="section-number">41.48.5. </span>2. Two-dimensional gradient des
 </div>
 </div>
 <section id="id1">
-<h3><span class="section-number">41.48.5.1. </span>Let’s try!<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.48.5.1. </span>Let’s try!<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define the target function</span>
@@ -2172,13 +2172,13 @@ <h3><span class="section-number">41.48.5.1. </span>Let’s try!<a class="headerl
         <i class="fas fa-angle-left"></i>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.47. </span>Loss Function</p>
+            <p class="prev-next-title"><span class="section-number">40.47. </span>Loss Function</p>
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-        <p class="prev-next-title"><span class="section-number">41.49. </span>Linear Regression Implementation from Scratch</p>
+        <p class="prev-next-title"><span class="section-number">40.49. </span>Linear Regression Implementation from Scratch</p>
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-    <title>41.49. Linear Regression Implementation from Scratch &#8212; Ocademy Open Machine Learning Book</title>
+    <title>40.49. Linear Regression Implementation from Scratch &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../../genindex.html" />
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   <section class="tex2jax_ignore mathjax_ignore" id="linear-regression-implementation-from-scratch">
-<h1><span class="section-number">41.49. </span>Linear Regression Implementation from Scratch<a class="headerlink" href="#linear-regression-implementation-from-scratch" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.49. </span>Linear Regression Implementation from Scratch<a class="headerlink" href="#linear-regression-implementation-from-scratch" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">41.49.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.49.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>In previous sections, we have gained some understanding of linear regression, gradient descent, evaluation metrics, and the role of the loss function in this regression technique. In summary, linear regression utilizes gradient descent to optimize the model’s parameters by minimizing the loss function. This enables it to establish a linear relationship between the input features and the target variable, making it a powerful algorithm for predicting continuous values.</p>
 <p>Linear regression is a widely used method in data analysis to describe the relationship between independent variables and a dependent variable using a linear equation. It aims to minimize the error between predicted and actual values by finding the best-fit line or surface. Linear regression can be used for predicting trends, exploring relationships, and identifying patterns in the data.</p>
 <p>This chapter will apply the previously learnt knowledge to implement a linear regression model from scratch. The chapter includes steps for data preparation, model development, and model evaluation, and ultimately summarises the process of developing and evaluating linear regression models.</p>
@@ -1720,7 +1720,7 @@ <h2><span class="section-number">41.49.1. </span>Introduction<a class="headerlin
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-<h2><span class="section-number">41.49.2. </span>Data Preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.49.2. </span>Data Preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">#</a></h2>
 <p>Before we can start developing the linear regression model, it is important to prepare the data appropriately. This involves importing the necessary libraries, loading the dataset, performing exploratory data analysis, and cleaning and preprocessing the data.</p>
 <p>Let’s take a look at the code snippet below to understand how we can perform these steps:</p>
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+<h2><span class="section-number">40.49.3. </span>Model Development<a class="headerlink" href="#model-development" title="Permalink to this headline">#</a></h2>
 <p>Once we have prepared the data, we can proceed with developing the linear regression model. This involves deriving the mathematical formula for linear regression, implementing the formula in code, defining a cost function, and implementing the gradient descent algorithm to train the model.</p>
 <p>Let’s take a look at the code snippet below to understand how we can develop the linear regression model:</p>
 <section id="define-loss-function">
-<h3><span class="section-number">41.49.3.1. </span>Define loss function<a class="headerlink" href="#define-loss-function" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.49.3.1. </span>Define loss function<a class="headerlink" href="#define-loss-function" title="Permalink to this headline">#</a></h3>
 <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>Calculate the loss between the true target variable and the predicted target variable.
 
 Parameters:
@@ -1803,7 +1803,7 @@ <h3><span class="section-number">41.49.3.1. </span>Define loss function<a class=
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+<h3><span class="section-number">40.49.3.2. </span>Use gradient descent to train the model<a class="headerlink" href="#use-gradient-descent-to-train-the-model" title="Permalink to this headline">#</a></h3>
 <p>The <code class="docutils literal notranslate"><span class="pre">gradient_descent</span></code> function is responsible for performing gradient descent to optimize the coefficients of the linear regression model. Here is a breakdown of the steps involved:</p>
 <ol class="simple">
 <li><p>Scale the data: The input features (<code class="docutils literal notranslate"><span class="pre">X_train</span></code> and <code class="docutils literal notranslate"><span class="pre">X_test</span></code>) are scaled using the <code class="docutils literal notranslate"><span class="pre">StandardScaler</span></code> from scikit-learn. This ensures that all features have a similar scale, which can improve the performance of the gradient descent algorithm.</p></li>
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 </section>
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 <section id="model-evaluation">
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+<h2><span class="section-number">40.49.4. </span>Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
 <p>After training the linear regression model, it is important to evaluate its performance to assess how well it is able to make predictions. In this section, we will discuss some commonly used evaluation metrics for regression models.</p>
 <p>When evaluating a machine learning model, we often use certain metrics to measure its performance. Here are some commonly used metrics and plotting methods:</p>
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+<h2><span class="section-number">40.49.5. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>In this assignment, we implemented a simple linear regression model and trained and predicted using the from-scratch method. Through this implementation process, we gained a deeper understanding of the linear regression model and learned how to use gradient descent algorithm to minimize the loss function.</p>
 <p>We evaluated the performance of the model and used several common evaluation metrics to measure the accuracy of the model’s predictions. Based on our results, the linear regression model performed well. The values of mean absolute error and root mean squared error were relatively small, indicating that the errors between the model’s predictions and actual results were small. And the value of R-squared was close to 1, indicating that the model was able to explain the variability in the data well.</p>
 <p>Although our model performed well on this task, we also need to be aware of its limitations. Linear regression models assume a linear relationship between input features and target variables. If the data has a nonlinear relationship, the model may not fit the data well. In addition, it may also be affected by problems such as outliers and multicollinearity.</p>
@@ -2998,13 +2998,13 @@ <h2><span class="section-number">41.49.5. </span>Conclusion<a class="headerlink"
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+            <p class="prev-next-title"><span class="section-number">40.48. </span>Gradient descent</p>
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-        <p class="prev-next-title"><span class="section-number">41.50. </span>ML logistic regression - assignment 1</p>
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   <a class="reference internal nav-link" href="#root-mean-squared-error-rmse">
-   41.46.2. Root Mean Squared Error (RMSE)
+   40.46.2. Root Mean Squared Error (RMSE)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     41.46.2.1. The Formula
+     40.46.2.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id2">
-     41.46.2.2. Python Implementation
+     40.46.2.2. Python Implementation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#r-squared-r2-score">
-   41.46.3. R-squared (R2) Score
+   40.46.3. R-squared (R2) Score
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id3">
-     41.46.3.1. The Formula
+     40.46.3.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id4">
-     41.46.3.2. Python Implementation
+     40.46.3.2. Python Implementation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#residual-analysis">
-   41.46.4. Residual Analysis
+   40.46.4. Residual Analysis
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id5">
-     41.46.4.1. Python Implementation
+     40.46.4.1. Python Implementation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id6">
-     41.46.4.2. Interpretation
+     40.46.4.2. Interpretation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#mean-absolute-error-mae">
-   41.46.5. Mean Absolute Error (MAE)
+   40.46.5. Mean Absolute Error (MAE)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id7">
-     41.46.5.1. The Formula
+     40.46.5.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id8">
-     41.46.5.2. Python Implementation
+     40.46.5.2. Python Implementation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id9">
-     41.46.5.3. Interpretation
+     40.46.5.3. Interpretation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#mean-absolute-percentage-error-mape">
-   41.46.6. Mean Absolute Percentage Error (MAPE)
+   40.46.6. Mean Absolute Percentage Error (MAPE)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id10">
-     41.46.6.1. The Formula
+     40.46.6.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id11">
-     41.46.6.2. Interpretation
+     40.46.6.2. Interpretation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion-and-recap">
-   41.46.7. Conclusion and Recap
+   40.46.7. Conclusion and Recap
   </a>
  </li>
 </ul>
@@ -1713,119 +1713,119 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#mean-squared-error-mse">
-   41.46.1. Mean Squared Error (MSE)
+   40.46.1. Mean Squared Error (MSE)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-formula">
-     41.46.1.1. The Formula
+     40.46.1.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#interpretation">
-     41.46.1.2. Interpretation
+     40.46.1.2. Interpretation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#python-implementation">
-     41.46.1.3. Python Implementation
+     40.46.1.3. Python Implementation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#root-mean-squared-error-rmse">
-   41.46.2. Root Mean Squared Error (RMSE)
+   40.46.2. Root Mean Squared Error (RMSE)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     41.46.2.1. The Formula
+     40.46.2.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id2">
-     41.46.2.2. Python Implementation
+     40.46.2.2. Python Implementation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#r-squared-r2-score">
-   41.46.3. R-squared (R2) Score
+   40.46.3. R-squared (R2) Score
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id3">
-     41.46.3.1. The Formula
+     40.46.3.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id4">
-     41.46.3.2. Python Implementation
+     40.46.3.2. Python Implementation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#residual-analysis">
-   41.46.4. Residual Analysis
+   40.46.4. Residual Analysis
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id5">
-     41.46.4.1. Python Implementation
+     40.46.4.1. Python Implementation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id6">
-     41.46.4.2. Interpretation
+     40.46.4.2. Interpretation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#mean-absolute-error-mae">
-   41.46.5. Mean Absolute Error (MAE)
+   40.46.5. Mean Absolute Error (MAE)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id7">
-     41.46.5.1. The Formula
+     40.46.5.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id8">
-     41.46.5.2. Python Implementation
+     40.46.5.2. Python Implementation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id9">
-     41.46.5.3. Interpretation
+     40.46.5.3. Interpretation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#mean-absolute-percentage-error-mape">
-   41.46.6. Mean Absolute Percentage Error (MAPE)
+   40.46.6. Mean Absolute Percentage Error (MAPE)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id10">
-     41.46.6.1. The Formula
+     40.46.6.1. The Formula
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id11">
-     41.46.6.2. Interpretation
+     40.46.6.2. Interpretation
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion-and-recap">
-   41.46.7. Conclusion and Recap
+   40.46.7. Conclusion and Recap
   </a>
  </li>
 </ul>
@@ -1839,16 +1839,16 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="linear-regression-metrics">
-<h1><span class="section-number">41.46. </span>Linear Regression Metrics<a class="headerlink" href="#linear-regression-metrics" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.46. </span>Linear Regression Metrics<a class="headerlink" href="#linear-regression-metrics" title="Permalink to this headline">#</a></h1>
 <p>Linear regression is a fundamental and widely used technique in machine learning and statistics for predicting continuous values based on input variables. It finds its application in various domains, from finance and economics to healthcare and engineering. When using linear regression, it’s essential to assess the model’s performance accurately. This is where linear regression metrics come into play.</p>
 <p>In this tutorial, we will delve into the world of linear regression metrics, exploring the key evaluation measures that allow us to gauge how well a linear regression model fits the data and makes predictions. These metrics provide valuable insights into the model’s accuracy, precision, and ability to capture the underlying relationships between variables.</p>
 <p>We will cover essential concepts such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R2) score, and Mean Absolute Error (MAE). Understanding these metrics is crucial for data scientists, machine learning practitioners, and anyone looking to harness the power of linear regression for predictive modeling.</p>
 <p>Whether you are building models for price predictions, sales forecasts, or any other regression task, mastering these metrics will empower you to make informed decisions and fine-tune your models for optimal performance. Let’s embark on this journey to explore the intricacies of linear regression metrics and enhance our ability to assess and improve regression models.</p>
 <section id="mean-squared-error-mse">
-<h2><span class="section-number">41.46.1. </span>Mean Squared Error (MSE)<a class="headerlink" href="#mean-squared-error-mse" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.46.1. </span>Mean Squared Error (MSE)<a class="headerlink" href="#mean-squared-error-mse" title="Permalink to this headline">#</a></h2>
 <p>In the realm of linear regression metrics, one fundamental measure of model performance is the <strong>Mean Squared Error (MSE)</strong>. MSE serves as a valuable indicator of how well your linear regression model aligns its predictions with the actual data points. This metric quantifies the average of the squared differences between predicted values and observed values.</p>
 <section id="the-formula">
-<h3><span class="section-number">41.46.1.1. </span>The Formula<a class="headerlink" href="#the-formula" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.1.1. </span>The Formula<a class="headerlink" href="#the-formula" title="Permalink to this headline">#</a></h3>
 <p>Mathematically, the MSE is computed using the following formula:</p>
 <div class="math notranslate nohighlight">
 \[ MSE = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2 \]</div>
@@ -1860,11 +1860,11 @@ <h3><span class="section-number">41.46.1.1. </span>The Formula<a class="headerli
 </ul>
 </section>
 <section id="interpretation">
-<h3><span class="section-number">41.46.1.2. </span>Interpretation<a class="headerlink" href="#interpretation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.1.2. </span>Interpretation<a class="headerlink" href="#interpretation" title="Permalink to this headline">#</a></h3>
 <p>A lower MSE value indicates that the model’s predictions are closer to the actual values, signifying better model performance. Conversely, a higher MSE suggests that the model’s predictions deviate more from the true values, indicating poorer performance.</p>
 </section>
 <section id="python-implementation">
-<h3><span class="section-number">41.46.1.3. </span>Python Implementation<a class="headerlink" href="#python-implementation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.1.3. </span>Python Implementation<a class="headerlink" href="#python-implementation" title="Permalink to this headline">#</a></h3>
 <p>Let’s take a look at how to calculate MSE in Python. We’ll use a simple example with sample data:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1895,10 +1895,10 @@ <h3><span class="section-number">41.46.1.3. </span>Python Implementation<a class
 </section>
 </section>
 <section id="root-mean-squared-error-rmse">
-<h2><span class="section-number">41.46.2. </span>Root Mean Squared Error (RMSE)<a class="headerlink" href="#root-mean-squared-error-rmse" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.46.2. </span>Root Mean Squared Error (RMSE)<a class="headerlink" href="#root-mean-squared-error-rmse" title="Permalink to this headline">#</a></h2>
 <p>The Root Mean Squared Error (RMSE) is another important metric for evaluating the performance of a linear regression model. It is the square root of the Mean Squared Error (MSE) and provides a measure of the average magnitude of error between actual and predicted values.</p>
 <section id="id1">
-<h3><span class="section-number">41.46.2.1. </span>The Formula<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.2.1. </span>The Formula<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <p>The formula for RMSE is:</p>
 <div class="math notranslate nohighlight">
 \[ RMSE = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2} \]</div>
@@ -1910,7 +1910,7 @@ <h3><span class="section-number">41.46.2.1. </span>The Formula<a class="headerli
 </ul>
 </section>
 <section id="id2">
-<h3><span class="section-number">41.46.2.2. </span>Python Implementation<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.2.2. </span>Python Implementation<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Calculate RMSE</span>
@@ -1930,10 +1930,10 @@ <h3><span class="section-number">41.46.2.2. </span>Python Implementation<a class
 </section>
 </section>
 <section id="r-squared-r2-score">
-<h2><span class="section-number">41.46.3. </span>R-squared (R2) Score<a class="headerlink" href="#r-squared-r2-score" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.46.3. </span>R-squared (R2) Score<a class="headerlink" href="#r-squared-r2-score" title="Permalink to this headline">#</a></h2>
 <p>The R-squared (R2) score, also known as the coefficient of determination, is a measure that indicates the proportion of the variance in the dependent variable that is predictable from the independent variables. It provides insight into how well the model is performing compared to a simple mean.</p>
 <section id="id3">
-<h3><span class="section-number">41.46.3.1. </span>The Formula<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.3.1. </span>The Formula<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <p>The formula for R2 score is:</p>
 <div class="math notranslate nohighlight">
 \[ R^2 = 1 - \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{\sum_{i=1}^{n} (y_i - \bar{y})^2} \]</div>
@@ -1946,7 +1946,7 @@ <h3><span class="section-number">41.46.3.1. </span>The Formula<a class="headerli
 </ul>
 </section>
 <section id="id4">
-<h3><span class="section-number">41.46.3.2. </span>Python Implementation<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.3.2. </span>Python Implementation<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Calculate the mean of actual values</span>
@@ -1975,10 +1975,10 @@ <h3><span class="section-number">41.46.3.2. </span>Python Implementation<a class
 </section>
 </section>
 <section id="residual-analysis">
-<h2><span class="section-number">41.46.4. </span>Residual Analysis<a class="headerlink" href="#residual-analysis" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.46.4. </span>Residual Analysis<a class="headerlink" href="#residual-analysis" title="Permalink to this headline">#</a></h2>
 <p>Residual analysis is a critical step in evaluating the performance of a linear regression model. It involves examining the differences between the observed and predicted values (i.e., the residuals). This helps us understand if there are any patterns or trends that the model might have missed.</p>
 <section id="id5">
-<h3><span class="section-number">41.46.4.1. </span>Python Implementation<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.4.1. </span>Python Implementation<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
@@ -2005,17 +2005,17 @@ <h3><span class="section-number">41.46.4.1. </span>Python Implementation<a class
 </div>
 </section>
 <section id="id6">
-<h3><span class="section-number">41.46.4.2. </span>Interpretation<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.4.2. </span>Interpretation<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h3>
 <p>If the residuals are randomly scattered around the red line, it suggests that the model is capturing the underlying patterns in the data well.
 If there’s a clear pattern in the residuals (e.g., a curve or a trend), it indicates that the model might be missing some important features.
 Residual analysis is a crucial step in understanding the limitations of the model and can provide insights into potential areas of improvement.</p>
 </section>
 </section>
 <section id="mean-absolute-error-mae">
-<h2><span class="section-number">41.46.5. </span>Mean Absolute Error (MAE)<a class="headerlink" href="#mean-absolute-error-mae" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.46.5. </span>Mean Absolute Error (MAE)<a class="headerlink" href="#mean-absolute-error-mae" title="Permalink to this headline">#</a></h2>
 <p>The Mean Absolute Error (MAE) is a metric that measures the average absolute differences between actual and predicted values. Unlike MSE, MAE does not square the differences, which makes it less sensitive to outliers.</p>
 <section id="id7">
-<h3><span class="section-number">41.46.5.1. </span>The Formula<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.5.1. </span>The Formula<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h3>
 <p>The formula for Mean Absolute Errord is:
 $<span class="math notranslate nohighlight">\( MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i| \)</span>$
 Where:</p>
@@ -2026,7 +2026,7 @@ <h3><span class="section-number">41.46.5.1. </span>The Formula<a class="headerli
 </ul>
 </section>
 <section id="id8">
-<h3><span class="section-number">41.46.5.2. </span>Python Implementation<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.5.2. </span>Python Implementation<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Calculate absolute differences between actual and predicted values</span>
@@ -2048,15 +2048,15 @@ <h3><span class="section-number">41.46.5.2. </span>Python Implementation<a class
 </div>
 </section>
 <section id="id9">
-<h3><span class="section-number">41.46.5.3. </span>Interpretation<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.5.3. </span>Interpretation<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h3>
 <p>The MAE gives us an average of how far off our predictions are from the actual values. It provides a more intuitive understanding of error compared to MSE.</p>
 </section>
 </section>
 <section id="mean-absolute-percentage-error-mape">
-<h2><span class="section-number">41.46.6. </span>Mean Absolute Percentage Error (MAPE)<a class="headerlink" href="#mean-absolute-percentage-error-mape" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.46.6. </span>Mean Absolute Percentage Error (MAPE)<a class="headerlink" href="#mean-absolute-percentage-error-mape" title="Permalink to this headline">#</a></h2>
 <p>The Mean Absolute Percentage Error (MAPE) is a metric used to evaluate the accuracy of a model’s predictions in percentage terms. It measures the average absolute percentage difference between actual and predicted values.</p>
 <section id="id10">
-<h3><span class="section-number">41.46.6.1. </span>The Formula<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.46.6.1. </span>The Formula<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h3>
 <p>The formula for MAPE is:</p>
 <div class="math notranslate nohighlight">
 \[ MAPE = \frac{1}{n} \sum_{i=1}^{n} \left| \frac{y_i - \hat{y}_i}{y_i} \right| \times 100 \]</div>
@@ -2068,7 +2068,7 @@ <h3><span class="section-number">41.46.6.1. </span>The Formula<a class="headerli
 </ul>
 </section>
 <section id="id11">
-<h3><span class="section-number">41.46.6.2. </span>Interpretation<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h3>
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   <section class="tex2jax_ignore mathjax_ignore" id="loss-function">
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 <section id="objective-of-this-section">
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 <p>We have already learned math and code for “Gradient Descent”, as well as other optimization techniques.</p>
 <p>In this section, we will learn more about loss functions.</p>
 <p>As a learner, you can focus on learning the L1, L2 loss, and Classification Losses in the regression loss function in this section. And learn about other loss functions.</p>
 </section>
 <section id="concept-of-loss-function">
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 <ul class="simple">
 <li><p>A loss function gauges the disparity between the model’s predictions and the actual values. Simply put, it indicates how “off” our model is. By optimizing this function, our objective is to identify parameters that bring the model’s predictions as close as possible to the true values.</p></li>
 <li><p>The function we want to minimize or maximize is called the objective function or criterion. When we are minimizing it, we may also call it the cost function, loss function, or error function.
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 <section id="regression-losses">
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 <p>These are employed when the objective is to predict a continuous outcome.</p>
 <ul class="simple">
 <li><p>Mean Squared Error (MSE): Measures the average squared discrepancies between predictions and actual values, emphasizing larger errors.</p></li>
@@ -1753,7 +1753,7 @@ <h3><span class="section-number">41.47.3.1. </span>Regression Losses<a class="he
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 <section id="classification-losses">
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 <p>Utilized for tasks requiring the prediction of discrete categories.</p>
 <ul class="simple">
 <li><p>Cross Entropy Loss: Quantifies the dissimilarity between the predicted probability distribution and the actual class distribution.</p></li>
@@ -1763,7 +1763,7 @@ <h3><span class="section-number">41.47.3.2. </span>Classification Losses<a class
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 <section id="structured-losses">
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 <p>Tailored for intricate tasks involving structured data patterns.</p>
 <ul class="simple">
 <li><p>Sequence Generation Loss: Emblematic examples include the CTC (Connectionist Temporal Classification) designed for undertakings such as speech and text identification.</p></li>
@@ -1771,7 +1771,7 @@ <h3><span class="section-number">41.47.3.3. </span>Structured Losses<a class="he
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 </section>
 <section id="regularization-losses">
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 <p>Rather than directly influencing the model’s predictions, these losses are integrated into the objective function to counteract excessive model complexity.</p>
 <ul class="simple">
 <li><p>L1 Regularization (Lasso): Enforces sparsity by compelling certain model coefficients to be exactly zero.</p></li>
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 </section>
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 <section id="regression-loss-functions">
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 <ol class="simple">
 <li><p><strong>Mean Squared Error, MSE</strong></p></li>
 </ol>
@@ -1858,7 +1858,7 @@ <h3><span class="section-number">41.47.4.1. </span>Regression Loss Functions<a c
 <p>Corresponds to MSE.</p>
 </section>
 <section id="classification-loss-functions">
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 <ol class="simple">
 <li><p><strong>Hinge Loss</strong></p></li>
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+<h2><span class="section-number">40.47.5. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>Loss functions hold a pivotal role in machine learning. By minimizing the loss, we enhance the accuracy of our model’s predictions. A deep understanding of various loss functions aids in selecting the most appropriate optimization technique for specific challenges.</p>
 </section>
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@@ -1923,13 +1923,13 @@ <h2><span class="section-number">41.47.5. </span>Conclusion<a class="headerlink"
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-     41.95. Intro to TensorFlow for Deep Learning
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  </li>
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.40. Data Science project using Azure ML SDK
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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-     41.42. Machine Learning overview - assignment 1
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-     41.44. Regression with Scikit-learn
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-     41.45. Linear regression - California Housing
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-     41.46. Linear Regression Metrics
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-     41.47. Loss Function
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-     41.48. Gradient descent
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-     41.58. Linear and polynomial regression
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+     40.50.4.3. Checking Correlation Between Features
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+   40.50.1. Build classification models：Predict the price range
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+   40.50.4. 3. Exploring Insights in Data or EDA
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+     40.50.4.1. Checking summary statistics
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+     40.50.4.2. Checking missing values
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     <a class="reference internal nav-link" href="#checking-correlation-between-features">
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+     40.50.4.3. Checking Correlation Between Features
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     <a class="reference internal nav-link" href="#more-data-exploration">
-     41.50.4.4. More Data Exploration
+     40.50.4.4. More Data Exploration
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     <a class="reference internal nav-link" href="#exploring-categorical-features">
-     41.50.4.5. Exploring Categorical Features
+     40.50.4.5. Exploring Categorical Features
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+   40.50.5. 4. Data Preprocessing
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+     40.50.5.1. Feature Scaling
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+   40.50.6. 5. Creating and Training a Logistic Regression Model
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+     40.50.6.1. Trying other models
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+   40.50.7. 6.  Model Evaluation
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+     40.50.7.1. Classification Performance Metrics
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+   40.50.8. Acknowledgments
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@@ -1779,21 +1779,21 @@ <h2> Contents </h2>
               <div>
                 
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-<h1><span class="section-number">41.50. </span>ML logistic regression - assignment 1<a class="headerlink" href="#ml-logistic-regression-assignment-1" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.50. </span>ML logistic regression - assignment 1<a class="headerlink" href="#ml-logistic-regression-assignment-1" title="Permalink to this headline">#</a></h1>
 <section id="build-classification-models-predict-the-price-range">
-<h2><span class="section-number">41.50.1. </span>Build classification models：Predict the price range<a class="headerlink" href="#build-classification-models-predict-the-price-range" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.50.1. </span>Build classification models：Predict the price range<a class="headerlink" href="#build-classification-models-predict-the-price-range" title="Permalink to this headline">#</a></h2>
 <p>In this lab, we will learn about classification where the task is to predict the class or category. Both regression and classification are the main two types of supervised learning.</p>
 <p><a name='1'></a></p>
 </section>
 <section id="problem-formulation">
-<h2><span class="section-number">41.50.2. </span>1. Problem Formulation<a class="headerlink" href="#problem-formulation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.50.2. </span>1. Problem Formulation<a class="headerlink" href="#problem-formulation" title="Permalink to this headline">#</a></h2>
 <p>Let’s say you have an idea of a revolutionary mobile phone and you want to establish a start up, but you know little about the price of the mobile phones. You are interested in learning that!</p>
 <p>Fortunately, there is this mobile dataset <a class="reference external" href="https://www.kaggle.com/iabhishekofficial/mobile-price-classification?select=train.csv">on Kaggle</a> that you can use to learn about the price ranges of mobiles based on their features such as wifi &amp; bluetooth supports etc…</p>
 <p>So, to make it simple, you have a dataset containing the features of mobiles and the problem is to predict the price range, not the exact price.</p>
 <p><a name='2'></a></p>
 </section>
 <section id="finding-the-data">
-<h2><span class="section-number">41.50.3. </span>2. Finding the Data<a class="headerlink" href="#finding-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.50.3. </span>2. Finding the Data<a class="headerlink" href="#finding-the-data" title="Permalink to this headline">#</a></h2>
 <p>The data that we are going to use is found on Kaggle.</p>
 <p>Here are the details of the features. It is 21 features. The target feature is <code class="docutils literal notranslate"><span class="pre">price</span> <span class="pre">range</span></code> and it has four price ranges: <code class="docutils literal notranslate"><span class="pre">0(low</span> <span class="pre">cost),</span> <span class="pre">1(medium</span> <span class="pre">cost),</span> <span class="pre">2(high</span> <span class="pre">cost)</span> <span class="pre">and</span> <span class="pre">3(very</span> <span class="pre">high</span> <span class="pre">cost).</span></code></p>
 <ul class="simple">
@@ -1915,7 +1915,7 @@ <h2><span class="section-number">41.50.3. </span>2. Finding the Data<a class="he
 <p><a name='3'></a></p>
 </section>
 <section id="exploring-insights-in-data-or-eda">
-<h2><span class="section-number">41.50.4. </span>3. Exploring Insights in Data or EDA<a class="headerlink" href="#exploring-insights-in-data-or-eda" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.50.4. </span>3. Exploring Insights in Data or EDA<a class="headerlink" href="#exploring-insights-in-data-or-eda" title="Permalink to this headline">#</a></h2>
 <p>In this part, we are going to learn more about the dataset. Let’s start with the summary statistics, but before that, I will copy the training data in order to get it easily when we mess up down the road.</p>
 <div class="cell docutils container">
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@@ -1925,7 +1925,7 @@ <h2><span class="section-number">41.50.4. </span>3. Exploring Insights in Data o
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 <section id="checking-summary-statistics">
-<h3><span class="section-number">41.50.4.1. </span>Checking summary statistics<a class="headerlink" href="#checking-summary-statistics" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.50.4.1. </span>Checking summary statistics<a class="headerlink" href="#checking-summary-statistics" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mobile_train</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
@@ -1935,7 +1935,7 @@ <h3><span class="section-number">41.50.4.1. </span>Checking summary statistics<a
 </div>
 </section>
 <section id="checking-missing-values">
-<h3><span class="section-number">41.50.4.2. </span>Checking missing values<a class="headerlink" href="#checking-missing-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.50.4.2. </span>Checking missing values<a class="headerlink" href="#checking-missing-values" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mobile_train</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
@@ -1946,7 +1946,7 @@ <h3><span class="section-number">41.50.4.2. </span>Checking missing values<a cla
 <p>We are lucky to not have missing values. If we had some, we would have to fill them with some strategies such as mean, remove them compleletly or leave them as they are. None of those 3 options is always the right choice for imputing missing values in all kinds of problems. it depends on the problem and the size of your dataset. Take an example, by removing a feature, you’re loosing data. And by filling the values, you’re adding noise or so. There is a trade off when it comes to imputing the missing values.</p>
 </section>
 <section id="checking-correlation-between-features">
-<h3><span class="section-number">41.50.4.3. </span>Checking Correlation Between Features<a class="headerlink" href="#checking-correlation-between-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.50.4.3. </span>Checking Correlation Between Features<a class="headerlink" href="#checking-correlation-between-features" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">correlation</span> <span class="o">=</span> <span class="n">mobile_train</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>
@@ -1975,7 +1975,7 @@ <h3><span class="section-number">41.50.4.3. </span>Checking Correlation Between
 <p>Let’s take that insights from words to visualization.</p>
 </section>
 <section id="more-data-exploration">
-<h3><span class="section-number">41.50.4.4. </span>More Data Exploration<a class="headerlink" href="#more-data-exploration" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.50.4.4. </span>More Data Exploration<a class="headerlink" href="#more-data-exploration" title="Permalink to this headline">#</a></h3>
 <p>Again, let’s see what we have in the price ranges.</p>
 <div class="cell docutils container">
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@@ -2093,7 +2093,7 @@ <h3><span class="section-number">41.50.4.4. </span>More Data Exploration<a class
 </div>
 </section>
 <section id="exploring-categorical-features">
-<h3><span class="section-number">41.50.4.5. </span>Exploring Categorical Features<a class="headerlink" href="#exploring-categorical-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.50.4.5. </span>Exploring Categorical Features<a class="headerlink" href="#exploring-categorical-features" title="Permalink to this headline">#</a></h3>
 <p>Even if all features have numerical values, but there some of them which are categorical. Let’s inspect them!! It might be that they are already encoded!</p>
 <div class="cell docutils container">
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@@ -2183,7 +2183,7 @@ <h3><span class="section-number">41.50.4.5. </span>Exploring Categorical Feature
 </section>
 </section>
 <section id="data-preprocessing">
-<h2><span class="section-number">41.50.5. </span>4. Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.50.5. </span>4. Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <p>In this part, we will prepare the data to be in proper format for ML models. We will do things like feature scaling. We would also had to encode categorical features but they are already encoded. Before we proceed with feature scaling, let’s take training labels from training data.</p>
 <div class="cell docutils container">
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@@ -2194,7 +2194,7 @@ <h2><span class="section-number">41.50.5. </span>4. Data Preprocessing<a class="
 </div>
 </div>
 <section id="feature-scaling">
-<h3><span class="section-number">41.50.5.1. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.50.5.1. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h3>
 <p>This is the only data processing type that we have to take care of here. Let’s make a function that can take handle that.</p>
 <p>We will normalize the data with sklearn <code class="docutils literal notranslate"><span class="pre">MinMaxScaler</span></code> where the numerical values will be scaled to values between 0 and 1.</p>
 <div class="cell docutils container">
@@ -2229,7 +2229,7 @@ <h3><span class="section-number">41.50.5.1. </span>Feature Scaling<a class="head
 </section>
 </section>
 <section id="creating-and-training-a-logistic-regression-model">
-<h2><span class="section-number">41.50.6. </span>5. Creating and Training a Logistic Regression Model<a class="headerlink" href="#creating-and-training-a-logistic-regression-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.50.6. </span>5. Creating and Training a Logistic Regression Model<a class="headerlink" href="#creating-and-training-a-logistic-regression-model" title="Permalink to this headline">#</a></h2>
 <p>We will start by importing <code class="docutils literal notranslate"><span class="pre">LogisticRegression</span></code> models available in Sklearn linear models and then proceed with model training.</p>
 <div class="cell docutils container">
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@@ -2252,7 +2252,7 @@ <h2><span class="section-number">41.50.6. </span>5. Creating and Training a Logi
 <p>Using <code class="docutils literal notranslate"><span class="pre">log_model.score()</span></code>, we can get an accuracy of the model confidence on the training data and labels. 94.7% is not that bad.</p>
 <p>Let’s train another linear classifier called SGD (Stockastic Gradient Descent) classifier on the same dataset.</p>
 <section id="trying-other-models">
-<h3><span class="section-number">41.50.6.1. </span>Trying other models<a class="headerlink" href="#trying-other-models" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.50.6.1. </span>Trying other models<a class="headerlink" href="#trying-other-models" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">SGDClassifier</span>
@@ -2293,7 +2293,7 @@ <h3><span class="section-number">41.50.6.1. </span>Trying other models<a class="
 </section>
 </section>
 <section id="model-evaluation">
-<h2><span class="section-number">41.50.7. </span>6.  Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.50.7. </span>6.  Model Evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
 <p>Evaluating classification models is not as simple as for regression models. When you have skewed dataset or imbalances, you can get a high accuracy and you can think the model did well when in fact it didn’t.</p>
 <p>In this part, we are going to learn how to evaluate classification models. But first, let’s evaluate the model with cross validation.</p>
 <p>Here is an idea behind cross validation: We want to divide the training set into different training and validation subsets so that we can iteratively train and validate the models on those subsets.</p>
@@ -2400,7 +2400,7 @@ <h2><span class="section-number">41.50.7. </span>6.  Model Evaluation<a class="h
 </div>
 </div>
 <section id="classification-performance-metrics">
-<h3><span class="section-number">41.50.7.1. </span>Classification Performance Metrics<a class="headerlink" href="#classification-performance-metrics" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.50.7.1. </span>Classification Performance Metrics<a class="headerlink" href="#classification-performance-metrics" title="Permalink to this headline">#</a></h3>
 <p>Here are commom classification metrics:</p>
 <ul class="simple">
 <li><p>Accuracy</p></li>
@@ -2473,7 +2473,7 @@ <h3><span class="section-number">41.50.7.1. </span>Classification Performance Me
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.50.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.50.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Jean de Dieu Nyandwi for creating the open source course <a class="reference external" href="https://github.com/Nyandwi/machine_learning_complete">machine_learning_complete</a> . It inspires the majority of the content in this chapter.Original Notebook source from <a class="reference external" href="https://github.com/Nyandwi/machine_learning_complete/blob/main/6_classical_machine_learning_with_scikit-learn/2_linear_models_for_classification.ipynb">Linear Models for Classification</a></p>
 </section>
 </section>
@@ -2515,13 +2515,13 @@ <h2><span class="section-number">41.50.8. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.49. </span>Linear Regression Implementation from Scratch</p>
+            <p class="prev-next-title"><span class="section-number">40.49. </span>Linear Regression Implementation from Scratch</p>
         </div>
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-        <p class="prev-next-title"><span class="section-number">41.51. </span>ML logistic regression - assignment 2</p>
+        <p class="prev-next-title"><span class="section-number">40.51. </span>ML logistic regression - assignment 2</p>
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     <link rel="index" title="Index" href="../../genindex.html" />
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+     40.5. Project Plan​ Template
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+     40.9. Analyzing text about Data Science
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-     41.10. Data Science scenarios
+     40.10. Data Science scenarios
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-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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-     41.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.15. Dive into the beehive
+     40.15. Dive into the beehive
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
+     40.17. Classifying datasets
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-     41.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
+     40.20. Displaying airport data
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-     41.21. Soda profits
+     40.21. Soda profits
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-     41.22. Analyzing COVID-19 papers
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-     41.23. Estimation of COVID-19 pandemic
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-     41.24. Data processing in Python
+     40.24. Data processing in Python
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+     40.26. Data preparation
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-     41.27. Analyzing data
+     40.27. Analyzing data
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-     41.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
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-     41.29. Matplotlib applied
+     40.29. Matplotlib applied
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-     41.35. Tell a story
+     40.35. Tell a story
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-     41.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
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-     41.37. Exploring for answers
+     40.37. Exploring for answers
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-     41.38. Market research
+     40.38. Market research
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-     41.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
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-     41.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
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    <li class="toctree-l2">
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview-iris.html">
-     41.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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   <section class="tex2jax_ignore mathjax_ignore" id="ml-neural-network-assignment-1">
-<h1><span class="section-number">41.52. </span>ML neural network - Assignment 1<a class="headerlink" href="#ml-neural-network-assignment-1" title="Permalink to this headline">#</a></h1>
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 <p>In this notebook, we’re going to build a neural network using naught but pure numpy and your willpower. It’s going to be fun, I promise!</p>
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 <p>We’re going to build a neural network that classifies MNIST digits. To do so, we’ll need a few building blocks:</p>
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 <p>Let’s approach them one at a time.</p>
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 <p>A dense layer applies affine transformation. In a vectorized form, it can be described as:
 $<span class="math notranslate nohighlight">\(f(X)= X \cdot W + \vec b \)</span>$</p>
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 <p>Here we have a few tests to make sure your dense layer works properly. You can just run them, get 3 “well done”s and forget they ever existed.</p>
 <p>… or not get 3 “well done”s and go fix stuff. If that is the case, here are some tips for you:</p>
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 <p>Since we want to predict probabilities, it would be logical for us to define softmax nonlinearity on top of our network and compute loss given predicted probabilities. However, there is a better way to do so.</p>
 <p>If you write down the expression for crossentropy as a function of softmax logits (a), you’ll see:</p>
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 </section>
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 <p>Thanks to <a class="reference external" href="https://github.com/yandexdataschool">yandexdataschool</a> for creating the open-source course <a class="reference external" href="https://github.com/yandexdataschool/Practical_DL"> Practical_DL</a>. It inspires the majority of the content in this assignment.</p>
 </section>
 </section>
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+     40.5. Project Plan​ Template
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+     40.10. Data Science scenarios
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-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
+     40.17. Classifying datasets
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     <a class="reference internal" href="../../slides/ml-advanced/unsupervised-learning.html">
-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/kernel-method.html">
-     42.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/model-selection.html">
-     42.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1575,111 +1575,111 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#what-the-assignment-is-for">
-   41.42.1. What the assignment is for
+   40.42.1. What the assignment is for
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#import-libraries">
-   41.42.2. Import libraries
+   40.42.2. Import libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#load-dataset">
-   41.42.3. Load Dataset
+   40.42.3. Load Dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dimension-shape-of-dataset">
-   41.42.4. Dimension (shape) of Dataset
+   40.42.4. Dimension (shape) of Dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#show-the-first-5-rows-of-the-data">
-   41.42.5. Show the first 5 rows of the data
+   40.42.5. Show the first 5 rows of the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#get-a-global-description-of-the-dataset">
-   41.42.6. Get a global description of the dataset
+   40.42.6. Get a global description of the dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#group-data">
-   41.42.7. Group data
+   40.42.7. Group data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dividing-data-into-features-and-labels">
-   41.42.8. Dividing data into features and labels
+   40.42.8. Dividing data into features and labels
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#label-encoding">
-   41.42.9. Label encoding
+   40.42.9. Label encoding
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#spliting-dataset-into-training-set-and-test-set">
-   41.42.10. Spliting dataset into training set and test set
+   40.42.10. Spliting dataset into training set and test set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#feature-scaling">
-   41.42.11. Feature scaling
+   40.42.11. Feature scaling
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-visualization">
-   41.42.12. Data Visualization
+   40.42.12. Data Visualization
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#pairplot">
-     41.42.12.1. pairplot
+     40.42.12.1. pairplot
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#boxplots">
-     41.42.12.2. Boxplots
+     40.42.12.2. Boxplots
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#d-visualization">
-     41.42.12.3. 3D visualization
+     40.42.12.3. 3D visualization
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#using-knn-for-classification">
-   41.42.13. Using KNN for classification
+   40.42.13. Using KNN for classification
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#train-the-model">
-     41.42.13.1. Train the model
+     40.42.13.1. Train the model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#predict-with-test-dataset">
-     41.42.13.2. Predict, with test dataset
+     40.42.13.2. Predict, with test dataset
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#evaluating-predictions">
-     41.42.13.3. Evaluating predictions
+     40.42.13.3. Evaluating predictions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#using-cross-validation-for-parameter-tuning">
-     41.42.13.4. Using cross-validation for parameter tuning
+     40.42.13.4. Using cross-validation for parameter tuning
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.42.14. Acknowledgments
+   40.42.14. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1703,111 +1703,111 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#what-the-assignment-is-for">
-   41.42.1. What the assignment is for
+   40.42.1. What the assignment is for
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#import-libraries">
-   41.42.2. Import libraries
+   40.42.2. Import libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#load-dataset">
-   41.42.3. Load Dataset
+   40.42.3. Load Dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dimension-shape-of-dataset">
-   41.42.4. Dimension (shape) of Dataset
+   40.42.4. Dimension (shape) of Dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#show-the-first-5-rows-of-the-data">
-   41.42.5. Show the first 5 rows of the data
+   40.42.5. Show the first 5 rows of the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#get-a-global-description-of-the-dataset">
-   41.42.6. Get a global description of the dataset
+   40.42.6. Get a global description of the dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#group-data">
-   41.42.7. Group data
+   40.42.7. Group data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dividing-data-into-features-and-labels">
-   41.42.8. Dividing data into features and labels
+   40.42.8. Dividing data into features and labels
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#label-encoding">
-   41.42.9. Label encoding
+   40.42.9. Label encoding
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#spliting-dataset-into-training-set-and-test-set">
-   41.42.10. Spliting dataset into training set and test set
+   40.42.10. Spliting dataset into training set and test set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#feature-scaling">
-   41.42.11. Feature scaling
+   40.42.11. Feature scaling
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-visualization">
-   41.42.12. Data Visualization
+   40.42.12. Data Visualization
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#pairplot">
-     41.42.12.1. pairplot
+     40.42.12.1. pairplot
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#boxplots">
-     41.42.12.2. Boxplots
+     40.42.12.2. Boxplots
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#d-visualization">
-     41.42.12.3. 3D visualization
+     40.42.12.3. 3D visualization
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#using-knn-for-classification">
-   41.42.13. Using KNN for classification
+   40.42.13. Using KNN for classification
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#train-the-model">
-     41.42.13.1. Train the model
+     40.42.13.1. Train the model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#predict-with-test-dataset">
-     41.42.13.2. Predict, with test dataset
+     40.42.13.2. Predict, with test dataset
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#evaluating-predictions">
-     41.42.13.3. Evaluating predictions
+     40.42.13.3. Evaluating predictions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#using-cross-validation-for-parameter-tuning">
-     41.42.13.4. Using cross-validation for parameter tuning
+     40.42.13.4. Using cross-validation for parameter tuning
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.42.14. Acknowledgments
+   40.42.14. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1821,14 +1821,14 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="machine-learning-overview-assignment-1">
-<h1><span class="section-number">41.42. </span>Machine Learning overview - assignment 1<a class="headerlink" href="#machine-learning-overview-assignment-1" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.42. </span>Machine Learning overview - assignment 1<a class="headerlink" href="#machine-learning-overview-assignment-1" title="Permalink to this headline">#</a></h1>
 <section id="what-the-assignment-is-for">
-<h2><span class="section-number">41.42.1. </span>What the assignment is for<a class="headerlink" href="#what-the-assignment-is-for" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.1. </span>What the assignment is for<a class="headerlink" href="#what-the-assignment-is-for" title="Permalink to this headline">#</a></h2>
 <p>In this assignment, we will explore the very classic iris dataset and use the very simple KNN (K-Nearest Neighbors) algorithm for classification.</p>
 <p><strong>You should pay attention to how this assignment follows the Machine Learning Workflow in our lecture session!</strong></p>
 </section>
 <section id="import-libraries">
-<h2><span class="section-number">41.42.2. </span>Import libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.2. </span>Import libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -1841,7 +1841,7 @@ <h2><span class="section-number">41.42.2. </span>Import libraries<a class="heade
 </div>
 </section>
 <section id="load-dataset">
-<h2><span class="section-number">41.42.3. </span>Load Dataset<a class="headerlink" href="#load-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.3. </span>Load Dataset<a class="headerlink" href="#load-dataset" title="Permalink to this headline">#</a></h2>
 <p>NOTE: Iris dataset includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1852,7 +1852,7 @@ <h2><span class="section-number">41.42.3. </span>Load Dataset<a class="headerlin
 </div>
 </section>
 <section id="dimension-shape-of-dataset">
-<h2><span class="section-number">41.42.4. </span>Dimension (shape) of Dataset<a class="headerlink" href="#dimension-shape-of-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.4. </span>Dimension (shape) of Dataset<a class="headerlink" href="#dimension-shape-of-dataset" title="Permalink to this headline">#</a></h2>
 <p>We can get a quick idea of how many instances (rows) and how many attributes (columns) the data contains with the shape property.</p>
 <div class="cell docutils container">
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@@ -1863,7 +1863,7 @@ <h2><span class="section-number">41.42.4. </span>Dimension (shape) of Dataset<a
 </div>
 </section>
 <section id="show-the-first-5-rows-of-the-data">
-<h2><span class="section-number">41.42.5. </span>Show the first 5 rows of the data<a class="headerlink" href="#show-the-first-5-rows-of-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.5. </span>Show the first 5 rows of the data<a class="headerlink" href="#show-the-first-5-rows-of-the-data" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
@@ -1873,7 +1873,7 @@ <h2><span class="section-number">41.42.5. </span>Show the first 5 rows of the da
 </div>
 </section>
 <section id="get-a-global-description-of-the-dataset">
-<h2><span class="section-number">41.42.6. </span>Get a global description of the dataset<a class="headerlink" href="#get-a-global-description-of-the-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.6. </span>Get a global description of the dataset<a class="headerlink" href="#get-a-global-description-of-the-dataset" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">describe</span><span class="p">())</span>
@@ -1883,7 +1883,7 @@ <h2><span class="section-number">41.42.6. </span>Get a global description of the
 </div>
 </section>
 <section id="group-data">
-<h2><span class="section-number">41.42.7. </span>Group data<a class="headerlink" href="#group-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.7. </span>Group data<a class="headerlink" href="#group-data" title="Permalink to this headline">#</a></h2>
 <p>Let’s now take a look at the number of instances (rows) that belong to each class. We can view this as an absolute count.</p>
 <div class="cell docutils container">
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@@ -1894,7 +1894,7 @@ <h2><span class="section-number">41.42.7. </span>Group data<a class="headerlink"
 </div>
 </section>
 <section id="dividing-data-into-features-and-labels">
-<h2><span class="section-number">41.42.8. </span>Dividing data into features and labels<a class="headerlink" href="#dividing-data-into-features-and-labels" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.8. </span>Dividing data into features and labels<a class="headerlink" href="#dividing-data-into-features-and-labels" title="Permalink to this headline">#</a></h2>
 <p>As we can see dataset contain five columns: SepalLength, SepalWidth, PetalLength, PetalWidth and Name. The actual features are described by columns 0-3. Last column contains labels of samples. Firstly we need to split data into two arrays: X (features) and y (labels).</p>
 <div class="cell docutils container">
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@@ -1921,7 +1921,7 @@ <h2><span class="section-number">41.42.8. </span>Dividing data into features and
 </div>
 </section>
 <section id="label-encoding">
-<h2><span class="section-number">41.42.9. </span>Label encoding<a class="headerlink" href="#label-encoding" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.9. </span>Label encoding<a class="headerlink" href="#label-encoding" title="Permalink to this headline">#</a></h2>
 <p>As we can see labels are categorical. KNeighborsClassifier does not accept string labels. We need to use LabelEncoder to transform them into numbers. Iris-setosa correspond to 0, Iris-versicolor correspond to 1 and Iris-virginica correspond to 2.</p>
 <div class="cell docutils container">
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@@ -1935,7 +1935,7 @@ <h2><span class="section-number">41.42.9. </span>Label encoding<a class="headerl
 </div>
 </section>
 <section id="spliting-dataset-into-training-set-and-test-set">
-<h2><span class="section-number">41.42.10. </span>Spliting dataset into training set and test set<a class="headerlink" href="#spliting-dataset-into-training-set-and-test-set" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.10. </span>Spliting dataset into training set and test set<a class="headerlink" href="#spliting-dataset-into-training-set-and-test-set" title="Permalink to this headline">#</a></h2>
 <p>Let’s split dataset into training set and test set, to check later on whether or not our classifier works correctly.</p>
 <div class="cell docutils container">
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@@ -1948,13 +1948,13 @@ <h2><span class="section-number">41.42.10. </span>Spliting dataset into training
 </div>
 </section>
 <section id="feature-scaling">
-<h2><span class="section-number">41.42.11. </span>Feature scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.11. </span>Feature scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
 <p>Because features values are in the same order of magnitude, there is no need for feature scaling. Nevertheless in other sercostamses it is extremly important to apply feature scaling before running classification algorythms.</p>
 </section>
 <section id="data-visualization">
-<h2><span class="section-number">41.42.12. </span>Data Visualization<a class="headerlink" href="#data-visualization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.12. </span>Data Visualization<a class="headerlink" href="#data-visualization" title="Permalink to this headline">#</a></h2>
 <section id="pairplot">
-<h3><span class="section-number">41.42.12.1. </span>pairplot<a class="headerlink" href="#pairplot" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.42.12.1. </span>pairplot<a class="headerlink" href="#pairplot" title="Permalink to this headline">#</a></h3>
 <p>Pairwise is useful when you want to visualize the distribution of a variable or the relationship between multiple variables separately within subsets of your dataset.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1969,7 +1969,7 @@ <h3><span class="section-number">41.42.12.1. </span>pairplot<a class="headerlink
 </div>
 </section>
 <section id="boxplots">
-<h3><span class="section-number">41.42.12.2. </span>Boxplots<a class="headerlink" href="#boxplots" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.42.12.2. </span>Boxplots<a class="headerlink" href="#boxplots" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
@@ -1981,7 +1981,7 @@ <h3><span class="section-number">41.42.12.2. </span>Boxplots<a class="headerlink
 </div>
 </section>
 <section id="d-visualization">
-<h3><span class="section-number">41.42.12.3. </span>3D visualization<a class="headerlink" href="#d-visualization" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.42.12.3. </span>3D visualization<a class="headerlink" href="#d-visualization" title="Permalink to this headline">#</a></h3>
 <p>You can also try to visualize high-dimensional datasets in 3D using color, shape, size and other properties of 3D and 2D objects. In this plot we use marks sizes to visualize fourth dimenssion which is Petal Width.</p>
 <div class="cell docutils container">
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@@ -2014,9 +2014,9 @@ <h3><span class="section-number">41.42.12.3. </span>3D visualization<a class="he
 </section>
 </section>
 <section id="using-knn-for-classification">
-<h2><span class="section-number">41.42.13. </span>Using KNN for classification<a class="headerlink" href="#using-knn-for-classification" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.13. </span>Using KNN for classification<a class="headerlink" href="#using-knn-for-classification" title="Permalink to this headline">#</a></h2>
 <section id="train-the-model">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
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 </div>
 </section>
 <section id="using-cross-validation-for-parameter-tuning">
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+<h3><span class="section-number">40.42.13.4. </span>Using cross-validation for parameter tuning<a class="headerlink" href="#using-cross-validation-for-parameter-tuning" title="Permalink to this headline">#</a></h3>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_val_score</span>
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 </section>
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 <section id="acknowledgments">
-<h2><span class="section-number">41.42.14. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.42.14. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to SkalskiP for creating the open-source <a class="reference external" href="https://www.kaggle.com/code/skalskip/iris-data-visualization-and-knn-classification">Kaggle jupyter notebook</a>, licensed under Apache 2.0. It inspires the majority of the content of this assignment.</p>
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+            <p class="prev-next-title"><span class="section-number">40.41. </span>Data Science in the cloud: The “Azure ML SDK” way</p>
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-        <p class="prev-next-title"><span class="section-number">41.43. </span>Machine Learning overview - assignment 2</p>
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-    <link rel="prev" title="41.42. Machine Learning overview - assignment 1" href="ml-overview-iris.html" />
+    <link rel="next" title="40.44. Regression with Scikit-learn" href="regression-with-scikit-learn.html" />
+    <link rel="prev" title="40.42. Machine Learning overview - assignment 1" href="ml-overview-iris.html" />
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+     40.14. Try it in Excel
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-     41.58. Linear and polynomial regression
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-     41.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     41.70. Dropout and Batch Normalization
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 <p>Although this is a computer vision problem, we employ here a simple model using <strong>K-Nearest Neighbors</strong> algorithm in this notebook to be a good starting point. We use the <strong>GridSearchCV</strong> to fine tune the hyperparameters such as <em>“n_neighbors”, and “weights”</em>. Furthermore, we use <strong>Data Augmentation</strong> or <strong>Artificial Data Synthesis</strong> technique in this notebook to boost the model’s performance on the test set.</p>
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 <p><strong>Note:</strong> With <strong>Data Augmentation</strong> the accuracy jumped from 91.6% to 95.3% on the test data.</p>
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 <p>Thanks to SkalskiP for creating the open-source <a class="reference external" href="https://www.kaggle.com/code/gauthampughazh/digit-recognition-using-knn">Kaggle jupyter notebook</a>, licensed under Apache 2.0. It inspires the majority of the content of this assignment.</p>
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+     40.10. Data Science scenarios
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-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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+     40.12. Lines, scatters and bars
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+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
+     40.17. Classifying datasets
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-     41.18. Small diabetes study
+     40.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
+     40.20. Displaying airport data
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-     41.21. Soda profits
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-     41.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
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-     41.24. Data processing in Python
+     40.24. Data processing in Python
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-     41.28. NYC taxi data in winter and summer
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 <section id="instructions">
-<h2><span class="section-number">41.89.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.89.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>There are a lot of parameters that are set by default when working with these classifiers. Intellisense in VS Code can help you dig into them. Adopt one of the ML Classification Techniques in this section and retrain models tweaking various parameter values. Build a notebook explaining why some changes help the model quality while others degrade it. Be detailed in your answer.</p>
 </section>
 <section id="rubric">
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+<h2><span class="section-number">40.89.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
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 <p>Load up required libraries and dataset. Convert the data to a dataframe containing a subset of the data:</p>
 <p>Let’s look at the relationship between color and variety</p>
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 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
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+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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+     40.17. Classifying datasets
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-     41.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
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-     41.24. Data processing in Python
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+     40.36. Explore a planetary computer dataset
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+     40.37. Exploring for answers
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-     41.38. Market research
+     40.38. Market research
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+     40.39. Low code/no code Data Science project on Azure ML
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-     41.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
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-     41.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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-     41.44. Regression with Scikit-learn
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-     41.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
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-     41.46. Linear Regression Metrics
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-     41.47. Loss Function
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 <p>Take a look at the <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_linnerud.html#sklearn.datasets.load_linnerud">Linnerud dataset</a> in Scikit-learn. This dataset has multiple <a class="reference external" href="https://scikit-learn.org/stable/datasets/toy_dataset.html#linnerrud-dataset">targets</a>: ‘It consists of three exercise (data) and three physiological (target) variables collected from twenty middle-aged men in a fitness club’.</p>
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 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
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-     41.14. Try it in Excel
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-     41.40. Data Science project using Azure ML SDK
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
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-     41.42. Machine Learning overview - assignment 1
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-     41.44. Regression with Scikit-learn
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-     41.45. Linear regression - California Housing
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-     41.46. Linear Regression Metrics
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-     41.47. Loss Function
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-     41.48. Gradient descent
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-     41.49. Linear Regression Implementation from Scratch
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-     41.50. ML logistic regression - assignment 1
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 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
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+     40.11. Write a data ethics case study
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-     41.13. Apply your skills
+     40.13. Apply your skills
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 <p>In this section you learned about the various solvers that pair algorithms with a machine learning process to create an accurate model. Walk through the solvers listed in the section and pick two. In your own words, compare and contrast these two solvers. What kind of problem do they address? How do they work with various data structures? Why would you pick one over another?</p>
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+<h2><span class="section-number">40.86.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
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-     41.45. Linear regression - California Housing
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-     41.46. Linear Regression Metrics
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 <section id="instructions">
-<h2><span class="section-number">41.56.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.56.1. </span>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">#</a></h2>
 <p>Now that you have built one web app using a trained Regression model, use one of the models from an earlier Regression section to redo this web app. You can keep the style or design it differently to reflect the pumpkin data. Be careful to change the inputs to reflect your model’s training method.</p>
 </section>
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+<h2><span class="section-number">40.56.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
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@@ -1635,13 +1635,13 @@ <h2><span class="section-number">41.56.3. </span>Acknowledgments<a class="header
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-            <p class="prev-next-title"><span class="section-number">41.55. </span>Exploring visualizations</p>
+            <p class="prev-next-title"><span class="section-number">40.55. </span>Exploring visualizations</p>
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+     40.8. Python programming advanced
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+     40.10. Data Science scenarios
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-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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+     40.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.16. Build your own custom vis
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-     41.17. Classifying datasets
+     40.17. Classifying datasets
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-     41.18. Small diabetes study
+     40.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
+     40.20. Displaying airport data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/model-selection.html">
-     42.19. Model Selection
+     41.19. Model Selection
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
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         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conditionals">
-   41.8.1. Conditionals
+   40.8.1. Conditionals
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#if-elif-else">
-     41.8.1.1. if-elif-else
+     40.8.1.1. if-elif-else
     </a>
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   </ul>
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-   41.8.2. For loops
+   40.8.2. For loops
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     <a class="reference internal nav-link" href="#fill-the-missing-pieces">
-     41.8.2.1. Fill the missing pieces
+     40.8.2.1. Fill the missing pieces
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#range">
-     41.8.2.2. range()
+     40.8.2.2. range()
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#looping-dictionaries">
-     41.8.2.3. Looping dictionaries
+     40.8.2.3. Looping dictionaries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#calculate-the-sum-of-dict-values">
-     41.8.2.4. Calculate the sum of dict values
+     40.8.2.4. Calculate the sum of dict values
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#create-a-list-of-strings-based-on-a-list-of-numbers">
-     41.8.2.5. Create a list of strings based on a list of numbers
+     40.8.2.5. Create a list of strings based on a list of numbers
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#while-loops">
-   41.8.3. While loops
+   40.8.3. While loops
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     41.8.3.1. Fill the missing pieces
+     40.8.3.1. Fill the missing pieces
     </a>
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   </ul>
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+   40.8.4. Break
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+     40.8.4.1. Fill the missing pieces using
      <code class="docutils literal notranslate">
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@@ -1649,12 +1649,12 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-   41.8.5. Continue
+   40.8.5. Continue
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-     41.8.5.1. Fill the missing pieces using
+     40.8.5.1. Fill the missing pieces using
      <code class="docutils literal notranslate">
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@@ -1667,12 +1667,12 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#functions">
-   41.8.6. Functions
+   40.8.6. Functions
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#fill-the-missing-pieces-of-the-gcd-function">
-     41.8.6.1. Fill the missing pieces of the
+     40.8.6.1. Fill the missing pieces of the
      <code class="docutils literal notranslate">
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        gcd
@@ -1683,36 +1683,36 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    </li>
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     <a class="reference internal nav-link" href="#searching-for-wanted-people">
-     41.8.6.2. Searching for wanted people
+     40.8.6.2. Searching for wanted people
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#counting-average-length-of-words-in-a-sentence">
-     41.8.6.3. Counting average length of words in a sentence
+     40.8.6.3. Counting average length of words in a sentence
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#lambda">
-   41.8.7. Lambda
+   40.8.7. Lambda
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id2">
-     41.8.7.1. Fill the missing pieces
+     40.8.7.1. Fill the missing pieces
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#classes">
-   41.8.8. Classes
+   40.8.8. Classes
   </a>
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    <li class="toc-h3 nav-item toc-entry">
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-     41.8.8.1. Fill the missing pieces of the
+     40.8.8.1. Fill the missing pieces of the
      <code class="docutils literal notranslate">
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    </li>
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     <a class="reference internal nav-link" href="#fill-the-missing-pieces-of-the-complex-class">
-     41.8.8.2. Fill the missing pieces of the
+     40.8.8.2. Fill the missing pieces of the
      <code class="docutils literal notranslate">
       <span class="pre">
        Complex
@@ -1736,24 +1736,24 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exceptions">
-   41.8.9. Exceptions
+   40.8.9. Exceptions
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dealing-with-exceptions">
-     41.8.9.1. Dealing with exceptions
+     40.8.9.1. Dealing with exceptions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id3">
-     41.8.9.2. Dealing with exceptions
+     40.8.9.2. Dealing with exceptions
     </a>
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-   41.8.10. Acknowledgments
+   40.8.10. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1777,68 +1777,68 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
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-   41.8.1. Conditionals
+   40.8.1. Conditionals
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#if-elif-else">
-     41.8.1.1. if-elif-else
+     40.8.1.1. if-elif-else
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#for-loops">
-   41.8.2. For loops
+   40.8.2. For loops
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    <li class="toc-h3 nav-item toc-entry">
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+     40.8.2.1. Fill the missing pieces
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#range">
-     41.8.2.2. range()
+     40.8.2.2. range()
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#looping-dictionaries">
-     41.8.2.3. Looping dictionaries
+     40.8.2.3. Looping dictionaries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#calculate-the-sum-of-dict-values">
-     41.8.2.4. Calculate the sum of dict values
+     40.8.2.4. Calculate the sum of dict values
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#create-a-list-of-strings-based-on-a-list-of-numbers">
-     41.8.2.5. Create a list of strings based on a list of numbers
+     40.8.2.5. Create a list of strings based on a list of numbers
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#while-loops">
-   41.8.3. While loops
+   40.8.3. While loops
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     41.8.3.1. Fill the missing pieces
+     40.8.3.1. Fill the missing pieces
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
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-   41.8.4. Break
+   40.8.4. Break
   </a>
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    <li class="toc-h3 nav-item toc-entry">
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-     41.8.4.1. Fill the missing pieces using
+     40.8.4.1. Fill the missing pieces using
      <code class="docutils literal notranslate">
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@@ -1851,12 +1851,12 @@ <h2> Contents </h2>
  </li>
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-   41.8.5. Continue
+   40.8.5. Continue
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    <li class="toc-h3 nav-item toc-entry">
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+     40.8.5.1. Fill the missing pieces using
      <code class="docutils literal notranslate">
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@@ -1869,12 +1869,12 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
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-   41.8.6. Functions
+   40.8.6. Functions
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#fill-the-missing-pieces-of-the-gcd-function">
-     41.8.6.1. Fill the missing pieces of the
+     40.8.6.1. Fill the missing pieces of the
      <code class="docutils literal notranslate">
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        gcd
@@ -1885,36 +1885,36 @@ <h2> Contents </h2>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#searching-for-wanted-people">
-     41.8.6.2. Searching for wanted people
+     40.8.6.2. Searching for wanted people
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#counting-average-length-of-words-in-a-sentence">
-     41.8.6.3. Counting average length of words in a sentence
+     40.8.6.3. Counting average length of words in a sentence
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#lambda">
-   41.8.7. Lambda
+   40.8.7. Lambda
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id2">
-     41.8.7.1. Fill the missing pieces
+     40.8.7.1. Fill the missing pieces
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#classes">
-   41.8.8. Classes
+   40.8.8. Classes
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#fill-the-missing-pieces-of-the-stack-class">
-     41.8.8.1. Fill the missing pieces of the
+     40.8.8.1. Fill the missing pieces of the
      <code class="docutils literal notranslate">
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    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#fill-the-missing-pieces-of-the-complex-class">
-     41.8.8.2. Fill the missing pieces of the
+     40.8.8.2. Fill the missing pieces of the
      <code class="docutils literal notranslate">
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        Complex
@@ -1938,24 +1938,24 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exceptions">
-   41.8.9. Exceptions
+   40.8.9. Exceptions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dealing-with-exceptions">
-     41.8.9.1. Dealing with exceptions
+     40.8.9.1. Dealing with exceptions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id3">
-     41.8.9.2. Dealing with exceptions
+     40.8.9.2. Dealing with exceptions
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.8.10. Acknowledgments
+   40.8.10. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1989,7 +1989,7 @@ <h2> Contents </h2>
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.</p>
 </details><section class="tex2jax_ignore mathjax_ignore" id="python-programming-advanced">
-<h1><span class="section-number">41.8. </span>Python programming advanced<a class="headerlink" href="#python-programming-advanced" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.8. </span>Python programming advanced<a class="headerlink" href="#python-programming-advanced" title="Permalink to this headline">#</a></h1>
 <div class="cell docutils container">
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@@ -2004,9 +2004,9 @@ <h1><span class="section-number">41.8. </span>Python programming advanced<a clas
 </div>
 </div>
 <section id="conditionals">
-<h2><span class="section-number">41.8.1. </span>Conditionals<a class="headerlink" href="#conditionals" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.8.1. </span>Conditionals<a class="headerlink" href="#conditionals" title="Permalink to this headline">#</a></h2>
 <section id="if-elif-else">
-<h3><span class="section-number">41.8.1.1. </span>if-elif-else<a class="headerlink" href="#if-elif-else" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.1.1. </span>if-elif-else<a class="headerlink" href="#if-elif-else" title="Permalink to this headline">#</a></h3>
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 <section id="for-loops">
-<h2><span class="section-number">41.8.2. </span>For loops<a class="headerlink" href="#for-loops" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.8.2. </span>For loops<a class="headerlink" href="#for-loops" title="Permalink to this headline">#</a></h2>
 <section id="fill-the-missing-pieces">
-<h3><span class="section-number">41.8.2.1. </span>Fill the missing pieces<a class="headerlink" href="#fill-the-missing-pieces" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.2.1. </span>Fill the missing pieces<a class="headerlink" href="#fill-the-missing-pieces" title="Permalink to this headline">#</a></h3>
 <p>Fill the <code class="docutils literal notranslate"><span class="pre">____</span></code> parts in the code below.</p>
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 </div></section>
 <section id="range">
-<h3><span class="section-number">41.8.2.2. </span>range()<a class="headerlink" href="#range" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.2.2. </span>range()<a class="headerlink" href="#range" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">calculate_sum</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
@@ -2202,7 +2202,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="looping-dictionaries">
-<h3><span class="section-number">41.8.2.3. </span>Looping dictionaries<a class="headerlink" href="#looping-dictionaries" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.2.3. </span>Looping dictionaries<a class="headerlink" href="#looping-dictionaries" title="Permalink to this headline">#</a></h3>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">my_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;hacker&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">&#39;age&#39;</span><span class="p">:</span> <span class="mi">72</span><span class="p">,</span> <span class="s1">&#39;name&#39;</span><span class="p">:</span> <span class="s1">&#39;John Doe&#39;</span><span class="p">}</span>
@@ -2222,7 +2222,7 @@ <h3><span class="section-number">41.8.2.3. </span>Looping dictionaries<a class="
 </div>
 </section>
 <section id="calculate-the-sum-of-dict-values">
-<h3><span class="section-number">41.8.2.4. </span>Calculate the sum of dict values<a class="headerlink" href="#calculate-the-sum-of-dict-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.2.4. </span>Calculate the sum of dict values<a class="headerlink" href="#calculate-the-sum-of-dict-values" title="Permalink to this headline">#</a></h3>
 <p>Calculate the sum of the values in <code class="docutils literal notranslate"><span class="pre">magic_dict</span></code> by taking only into account numeric values (hint: see <a class="reference external" href="https://docs.python.org/3/library/functions.html#isinstance">isinstance</a>).</p>
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-<h3><span class="section-number">41.8.2.5. </span>Create a list of strings based on a list of numbers<a class="headerlink" href="#create-a-list-of-strings-based-on-a-list-of-numbers" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.2.5. </span>Create a list of strings based on a list of numbers<a class="headerlink" href="#create-a-list-of-strings-based-on-a-list-of-numbers" title="Permalink to this headline">#</a></h3>
 <p>The rules:</p>
 <ul class="simple">
 <li><p>If the number is a multiple of five and odd, the string should be <code class="docutils literal notranslate"><span class="pre">'five</span> <span class="pre">odd'</span></code></p></li>
@@ -2281,9 +2281,9 @@ <h3><span class="section-number">41.8.2.5. </span>Create a list of strings based
 </section>
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 <section id="while-loops">
-<h2><span class="section-number">41.8.3. </span>While loops<a class="headerlink" href="#while-loops" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.8.3. </span>While loops<a class="headerlink" href="#while-loops" title="Permalink to this headline">#</a></h2>
 <section id="id1">
-<h3><span class="section-number">41.8.3.1. </span>Fill the missing pieces<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.3.1. </span>Fill the missing pieces<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 <p>Fill the <code class="docutils literal notranslate"><span class="pre">____</span></code> parts in the code below.</p>
 <div class="cell docutils container">
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 <section id="break">
-<h2><span class="section-number">41.8.4. </span>Break<a class="headerlink" href="#break" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.8.4. </span>Break<a class="headerlink" href="#break" title="Permalink to this headline">#</a></h2>
 <section id="fill-the-missing-pieces-using-break-statement">
-<h3><span class="section-number">41.8.4.1. </span>Fill the missing pieces using <code class="docutils literal notranslate"><span class="pre">break</span></code> statement<a class="headerlink" href="#fill-the-missing-pieces-using-break-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.4.1. </span>Fill the missing pieces using <code class="docutils literal notranslate"><span class="pre">break</span></code> statement<a class="headerlink" href="#fill-the-missing-pieces-using-break-statement" title="Permalink to this headline">#</a></h3>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">find_prime_factors</span><span class="p">(</span><span class="n">number</span><span class="p">):</span>
@@ -2418,9 +2418,9 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </div></section>
 </section>
 <section id="continue">
-<h2><span class="section-number">41.8.5. </span>Continue<a class="headerlink" href="#continue" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.8.5. </span>Continue<a class="headerlink" href="#continue" title="Permalink to this headline">#</a></h2>
 <section id="fill-the-missing-pieces-using-continue-statement">
-<h3><span class="section-number">41.8.5.1. </span>Fill the missing pieces using  <code class="docutils literal notranslate"><span class="pre">continue</span></code> statement<a class="headerlink" href="#fill-the-missing-pieces-using-continue-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.5.1. </span>Fill the missing pieces using  <code class="docutils literal notranslate"><span class="pre">continue</span></code> statement<a class="headerlink" href="#fill-the-missing-pieces-using-continue-statement" title="Permalink to this headline">#</a></h3>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">sieve_of_eratosthenes</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
@@ -2508,9 +2508,9 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </div></section>
 </section>
 <section id="functions">
-<h2><span class="section-number">41.8.6. </span>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.8.6. </span>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">#</a></h2>
 <section id="fill-the-missing-pieces-of-the-gcd-function">
-<h3><span class="section-number">41.8.6.1. </span>Fill the missing pieces of the <code class="docutils literal notranslate"><span class="pre">gcd</span></code> function<a class="headerlink" href="#fill-the-missing-pieces-of-the-gcd-function" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.6.1. </span>Fill the missing pieces of the <code class="docutils literal notranslate"><span class="pre">gcd</span></code> function<a class="headerlink" href="#fill-the-missing-pieces-of-the-gcd-function" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">gcd</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
@@ -2592,7 +2592,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="searching-for-wanted-people">
-<h3><span class="section-number">41.8.6.2. </span>Searching for wanted people<a class="headerlink" href="#searching-for-wanted-people" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.6.2. </span>Searching for wanted people<a class="headerlink" href="#searching-for-wanted-people" title="Permalink to this headline">#</a></h3>
 <p>Implement <code class="docutils literal notranslate"><span class="pre">find_wanted_people</span></code> function which takes a list of names (strings) as argument. The function should return a list of names which are present both in <code class="docutils literal notranslate"><span class="pre">WANTED_PEOPLE</span></code> and in the name list given as argument to the function.</p>
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 <section id="counting-average-length-of-words-in-a-sentence">
-<h3><span class="section-number">41.8.6.3. </span>Counting average length of words in a sentence<a class="headerlink" href="#counting-average-length-of-words-in-a-sentence" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.6.3. </span>Counting average length of words in a sentence<a class="headerlink" href="#counting-average-length-of-words-in-a-sentence" title="Permalink to this headline">#</a></h3>
 <p>Create a function <code class="docutils literal notranslate"><span class="pre">average_length_of_words</span></code> which takes a string as an argument and returns the average length of the words in the string. You can assume that there is a single space between each word and that the input does not have punctuation. The result should be rounded to one decimal place (hint: see <a class="reference external" href="https://docs.python.org/3/library/functions.html#round"><code class="docutils literal notranslate"><span class="pre">round</span></code></a>).</p>
 <div class="cell docutils container">
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@@ -2650,9 +2650,9 @@ <h3><span class="section-number">41.8.6.3. </span>Counting average length of wor
 </section>
 </section>
 <section id="lambda">
-<h2><span class="section-number">41.8.7. </span>Lambda<a class="headerlink" href="#lambda" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.8.7. </span>Lambda<a class="headerlink" href="#lambda" title="Permalink to this headline">#</a></h2>
 <section id="id2">
-<h3><span class="section-number">41.8.7.1. </span>Fill the missing pieces<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.7.1. </span>Fill the missing pieces<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">map_function</span><span class="p">(</span><span class="n">nums</span><span class="p">,</span> <span class="n">func</span><span class="p">):</span>
@@ -2716,9 +2716,9 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </div></section>
 </section>
 <section id="classes">
-<h2><span class="section-number">41.8.8. </span>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.8.8. </span>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">#</a></h2>
 <section id="fill-the-missing-pieces-of-the-stack-class">
-<h3><span class="section-number">41.8.8.1. </span>Fill the missing pieces of the <code class="docutils literal notranslate"><span class="pre">Stack</span></code> class<a class="headerlink" href="#fill-the-missing-pieces-of-the-stack-class" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.8.1. </span>Fill the missing pieces of the <code class="docutils literal notranslate"><span class="pre">Stack</span></code> class<a class="headerlink" href="#fill-the-missing-pieces-of-the-stack-class" title="Permalink to this headline">#</a></h3>
 <p>Fill <code class="docutils literal notranslate"><span class="pre">____</span></code> pieces of the <code class="docutils literal notranslate"><span class="pre">Stack</span></code> implemention in order to pass the assertions.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2800,7 +2800,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </details>
 </div></section>
 <section id="fill-the-missing-pieces-of-the-complex-class">
-<h3><span class="section-number">41.8.8.2. </span>Fill the missing pieces of the <code class="docutils literal notranslate"><span class="pre">Complex</span></code> class<a class="headerlink" href="#fill-the-missing-pieces-of-the-complex-class" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.8.2. </span>Fill the missing pieces of the <code class="docutils literal notranslate"><span class="pre">Complex</span></code> class<a class="headerlink" href="#fill-the-missing-pieces-of-the-complex-class" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Complex</span><span class="p">:</span>
@@ -2941,9 +2941,9 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </div></section>
 </section>
 <section id="exceptions">
-<h2><span class="section-number">41.8.9. </span>Exceptions<a class="headerlink" href="#exceptions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.8.9. </span>Exceptions<a class="headerlink" href="#exceptions" title="Permalink to this headline">#</a></h2>
 <section id="dealing-with-exceptions">
-<h3><span class="section-number">41.8.9.1. </span>Dealing with exceptions<a class="headerlink" href="#dealing-with-exceptions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.9.1. </span>Dealing with exceptions<a class="headerlink" href="#dealing-with-exceptions" title="Permalink to this headline">#</a></h3>
 <p>Fill <code class="docutils literal notranslate"><span class="pre">____</span></code> parts of the implementation below. <code class="docutils literal notranslate"><span class="pre">sum_of_list</span></code> function takes a list as argument and calculates the sum of values in the list. If some element in the list can not be converted to a numeric value, it should be ignored from the sum.</p>
 <div class="cell docutils container">
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@@ -2984,7 +2984,7 @@ <h3><span class="section-number">41.8.9.1. </span>Dealing with exceptions<a clas
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 </section>
 <section id="id3">
-<h3><span class="section-number">41.8.9.2. </span>Dealing with exceptions<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.8.9.2. </span>Dealing with exceptions<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">square_root</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
@@ -3068,7 +3068,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5><div cla
 </div></section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.8.10. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.8.10. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to below awesome open source projects for Python learning, which inspire this chapter.</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://github.com/trekhleb/learn-python">learn-python</a> and <a class="reference external" href="https://github.com/trekhleb">Oleksii Trekhleb</a></p></li>
@@ -3116,13 +3116,13 @@ <h2><span class="section-number">41.8.10. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.7. </span>Python programming basics</p>
+            <p class="prev-next-title"><span class="section-number">40.7. </span>Python programming basics</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../data-science/analyzing-text-about-data-science.html" title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">41.9. </span>Analyzing text about Data Science</p>
+        <p class="prev-next-title"><span class="section-number">40.9. </span>Analyzing text about Data Science</p>
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diff --git a/assignments/prerequisites/python-programming-basics.html b/assignments/prerequisites/python-programming-basics.html
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+    <title>40.7. Python programming basics &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="41.8. Python programming advanced" href="python-programming-advanced.html" />
-    <link rel="prev" title="41.6. Python programming introduction" href="python-programming-introduction.html" />
+    <link rel="next" title="40.8. Python programming advanced" href="python-programming-advanced.html" />
+    <link rel="prev" title="40.6. Python programming introduction" href="python-programming-introduction.html" />
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+   1. Python programming introduction
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-     5.1. Defining data science
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-     5.2. Data Science ethics
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+     5.3. NumPy
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+       5.4.2. Data Selection
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-     7.1. Visualizing distributions
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+     6.2. Visualizing proportions
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+     6.3. Visualizing relationships: all about honey 🍯
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+     8.3. Data Science in the cloud: The “Azure ML SDK” way
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-     41.5. Project Plan​ Template
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-     41.8. Python programming advanced
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-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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+     40.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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+     40.15. Dive into the beehive
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
+     40.17. Classifying datasets
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-     41.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
+     40.20. Displaying airport data
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-     41.21. Soda profits
+     40.21. Soda profits
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-     41.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
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-     41.23. Estimation of COVID-19 pandemic
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-     41.24. Data processing in Python
+     40.24. Data processing in Python
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-     41.26. Data preparation
+     40.26. Data preparation
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-     41.27. Analyzing data
+     40.27. Analyzing data
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-     41.28. NYC taxi data in winter and summer
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-     41.29. Matplotlib applied
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-     41.35. Tell a story
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-     41.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
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-     41.37. Exploring for answers
+     40.37. Exploring for answers
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-     41.38. Market research
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-     41.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
     </a>
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-     41.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
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+     40.7.1.1. Implement a function to generate the Fibonacci sequence up to a specified number of terms.
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-     41.7.1.3. Implement a function to calculate the square root of a number using the Babylonian method.
+     40.7.1.3. Implement a function to calculate the square root of a number using the Babylonian method.
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    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#str-format-str-join">
-     41.7.2.4. str.format str.join
+     40.7.2.4. str.format str.join
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#str-split">
-     41.7.2.5. str.split
+     40.7.2.5. str.split
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#numbers">
-   41.7.3. Numbers
+   40.7.3. Numbers
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#creating-formulas">
-     41.7.3.1. Creating formulas
+     40.7.3.1. Creating formulas
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#lists">
-   41.7.4. Lists
+   40.7.4. Lists
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#list-append-list-remove-mutable">
-     41.7.4.1. list.append, list.remove, mutable
+     40.7.4.1. list.append, list.remove, mutable
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#slice">
-     41.7.4.2. Slice
+     40.7.4.2. Slice
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#list-extend">
-     41.7.4.3. list.extend
+     40.7.4.3. list.extend
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dictionaries">
-   41.7.5. Dictionaries
+   40.7.5. Dictionaries
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#populating-a-dictionary">
-     41.7.5.1. Populating a dictionary
+     40.7.5.1. Populating a dictionary
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#removing-duplicate-values-from-a-dictionary-using-del">
-     41.7.5.2. Removing Duplicate Values from a Dictionary using ‘del’
+     40.7.5.2. Removing Duplicate Values from a Dictionary using ‘del’
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#counting-occurrences-of-elements-using-dict-get">
-     41.7.5.3. Counting Occurrences of Elements using dict.get()
+     40.7.5.3. Counting Occurrences of Elements using dict.get()
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#grouping-items-by-category-using-dict-setdefault">
-     41.7.5.4. Grouping Items by Category using dict.setdefault()
+     40.7.5.4. Grouping Items by Category using dict.setdefault()
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#accessing-and-merging-dictionaries-into-a-single-dictionary">
-     41.7.5.5. Accessing and Merging Dictionaries into a Single Dictionary
+     40.7.5.5. Accessing and Merging Dictionaries into a Single Dictionary
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.7.6. Acknowledgments
+   40.7.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1721,127 +1721,127 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#operators">
-   41.7.1. Operators
+   40.7.1. Operators
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#implement-a-function-to-generate-the-fibonacci-sequence-up-to-a-specified-number-of-terms">
-     41.7.1.1. Implement a function to generate the Fibonacci sequence up to a specified number of terms.
+     40.7.1.1. Implement a function to generate the Fibonacci sequence up to a specified number of terms.
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#implement-a-function-for-insertion-sort-to-sort-a-list-in-ascending-order">
-     41.7.1.2. Implement a function for insertion sort to sort a list in ascending order.
+     40.7.1.2. Implement a function for insertion sort to sort a list in ascending order.
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#implement-a-function-to-calculate-the-square-root-of-a-number-using-the-babylonian-method">
-     41.7.1.3. Implement a function to calculate the square root of a number using the Babylonian method.
+     40.7.1.3. Implement a function to calculate the square root of a number using the Babylonian method.
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#string">
-   41.7.2. String
+   40.7.2. String
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#str-upper">
-     41.7.2.1. str.upper
+     40.7.2.1. str.upper
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#str-title">
-     41.7.2.2. str.title
+     40.7.2.2. str.title
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#str-replace">
-     41.7.2.3. str.replace
+     40.7.2.3. str.replace
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#str-format-str-join">
-     41.7.2.4. str.format str.join
+     40.7.2.4. str.format str.join
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#str-split">
-     41.7.2.5. str.split
+     40.7.2.5. str.split
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#numbers">
-   41.7.3. Numbers
+   40.7.3. Numbers
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#creating-formulas">
-     41.7.3.1. Creating formulas
+     40.7.3.1. Creating formulas
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#lists">
-   41.7.4. Lists
+   40.7.4. Lists
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#list-append-list-remove-mutable">
-     41.7.4.1. list.append, list.remove, mutable
+     40.7.4.1. list.append, list.remove, mutable
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#slice">
-     41.7.4.2. Slice
+     40.7.4.2. Slice
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#list-extend">
-     41.7.4.3. list.extend
+     40.7.4.3. list.extend
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dictionaries">
-   41.7.5. Dictionaries
+   40.7.5. Dictionaries
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#populating-a-dictionary">
-     41.7.5.1. Populating a dictionary
+     40.7.5.1. Populating a dictionary
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#removing-duplicate-values-from-a-dictionary-using-del">
-     41.7.5.2. Removing Duplicate Values from a Dictionary using ‘del’
+     40.7.5.2. Removing Duplicate Values from a Dictionary using ‘del’
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#counting-occurrences-of-elements-using-dict-get">
-     41.7.5.3. Counting Occurrences of Elements using dict.get()
+     40.7.5.3. Counting Occurrences of Elements using dict.get()
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#grouping-items-by-category-using-dict-setdefault">
-     41.7.5.4. Grouping Items by Category using dict.setdefault()
+     40.7.5.4. Grouping Items by Category using dict.setdefault()
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#accessing-and-merging-dictionaries-into-a-single-dictionary">
-     41.7.5.5. Accessing and Merging Dictionaries into a Single Dictionary
+     40.7.5.5. Accessing and Merging Dictionaries into a Single Dictionary
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   41.7.6. Acknowledgments
+   40.7.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1873,7 +1873,7 @@ <h2> Contents </h2>
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.</p>
 </details><section class="tex2jax_ignore mathjax_ignore" id="python-programming-basics">
-<h1><span class="section-number">41.7. </span>Python programming basics<a class="headerlink" href="#python-programming-basics" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">40.7. </span>Python programming basics<a class="headerlink" href="#python-programming-basics" title="Permalink to this headline">#</a></h1>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># set up the env</span>
@@ -1888,9 +1888,9 @@ <h1><span class="section-number">41.7. </span>Python programming basics<a class=
 </div>
 </div>
 <section id="operators">
-<h2><span class="section-number">41.7.1. </span>Operators<a class="headerlink" href="#operators" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.7.1. </span>Operators<a class="headerlink" href="#operators" title="Permalink to this headline">#</a></h2>
 <section id="implement-a-function-to-generate-the-fibonacci-sequence-up-to-a-specified-number-of-terms">
-<h3><span class="section-number">41.7.1.1. </span>Implement a function to generate the Fibonacci sequence up to a specified number of terms.<a class="headerlink" href="#implement-a-function-to-generate-the-fibonacci-sequence-up-to-a-specified-number-of-terms" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.1.1. </span>Implement a function to generate the Fibonacci sequence up to a specified number of terms.<a class="headerlink" href="#implement-a-function-to-generate-the-fibonacci-sequence-up-to-a-specified-number-of-terms" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">fibonacci</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
@@ -1966,7 +1966,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="implement-a-function-for-insertion-sort-to-sort-a-list-in-ascending-order">
-<h3><span class="section-number">41.7.1.2. </span>Implement a function for insertion sort to sort a list in ascending order.<a class="headerlink" href="#implement-a-function-for-insertion-sort-to-sort-a-list-in-ascending-order" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.1.2. </span>Implement a function for insertion sort to sort a list in ascending order.<a class="headerlink" href="#implement-a-function-for-insertion-sort-to-sort-a-list-in-ascending-order" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">insertion_sort</span><span class="p">(</span><span class="n">arr</span><span class="p">):</span>
@@ -2056,7 +2056,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="implement-a-function-to-calculate-the-square-root-of-a-number-using-the-babylonian-method">
-<h3><span class="section-number">41.7.1.3. </span>Implement a function to calculate the square root of a number using the Babylonian method.<a class="headerlink" href="#implement-a-function-to-calculate-the-square-root-of-a-number-using-the-babylonian-method" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.1.3. </span>Implement a function to calculate the square root of a number using the Babylonian method.<a class="headerlink" href="#implement-a-function-to-calculate-the-square-root-of-a-number-using-the-babylonian-method" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Inspired by SICP http://mitpress.mit.edu/sicp/full-text/book/book-Z-H-10.html#%_sec_1.1.7</span>
@@ -2147,9 +2147,9 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </div></section>
 </section>
 <section id="string">
-<h2><span class="section-number">41.7.2. </span>String<a class="headerlink" href="#string" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.7.2. </span>String<a class="headerlink" href="#string" title="Permalink to this headline">#</a></h2>
 <section id="str-upper">
-<h3><span class="section-number">41.7.2.1. </span>str.upper<a class="headerlink" href="#str-upper" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.2.1. </span>str.upper<a class="headerlink" href="#str-upper" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">capitalize_first_letter</span><span class="p">(</span><span class="n">string</span><span class="p">):</span>
@@ -2219,7 +2219,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="str-title">
-<h3><span class="section-number">41.7.2.2. </span>str.title<a class="headerlink" href="#str-title" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.2.2. </span>str.title<a class="headerlink" href="#str-title" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">capitalize_words</span><span class="p">(</span><span class="n">sentence</span><span class="p">,</span> <span class="n">exclude_words</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
@@ -2297,7 +2297,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="str-replace">
-<h3><span class="section-number">41.7.2.3. </span>str.replace<a class="headerlink" href="#str-replace" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.2.3. </span>str.replace<a class="headerlink" href="#str-replace" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">censor_words</span><span class="p">(</span><span class="n">sentence</span><span class="p">,</span> <span class="n">words</span><span class="p">):</span>
@@ -2376,7 +2376,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="str-format-str-join">
-<h3><span class="section-number">41.7.2.4. </span>str.format str.join<a class="headerlink" href="#str-format-str-join" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.2.4. </span>str.format str.join<a class="headerlink" href="#str-format-str-join" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">format_person_info</span><span class="p">(</span><span class="n">people_info</span><span class="p">):</span>
@@ -2461,7 +2461,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="str-split">
-<h3><span class="section-number">41.7.2.5. </span>str.split<a class="headerlink" href="#str-split" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.2.5. </span>str.split<a class="headerlink" href="#str-split" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">string</span>
@@ -2572,9 +2572,9 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </div></section>
 </section>
 <section id="numbers">
-<h2><span class="section-number">41.7.3. </span>Numbers<a class="headerlink" href="#numbers" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.7.3. </span>Numbers<a class="headerlink" href="#numbers" title="Permalink to this headline">#</a></h2>
 <section id="creating-formulas">
-<h3><span class="section-number">41.7.3.1. </span>Creating formulas<a class="headerlink" href="#creating-formulas" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.3.1. </span>Creating formulas<a class="headerlink" href="#creating-formulas" title="Permalink to this headline">#</a></h3>
 <p>Write the following mathematical formula in Python:</p>
 <p><span class="math notranslate nohighlight">\(result = 6a^3 - \frac{8b^2 }{4c} + 11\)</span></p>
 <div class="cell docutils container">
@@ -2657,9 +2657,9 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </section>
 </section>
 <section id="lists">
-<h2><span class="section-number">41.7.4. </span>Lists<a class="headerlink" href="#lists" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.7.4. </span>Lists<a class="headerlink" href="#lists" title="Permalink to this headline">#</a></h2>
 <section id="list-append-list-remove-mutable">
-<h3><span class="section-number">41.7.4.1. </span>list.append, list.remove, mutable<a class="headerlink" href="#list-append-list-remove-mutable" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.4.1. </span>list.append, list.remove, mutable<a class="headerlink" href="#list-append-list-remove-mutable" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">permutations</span><span class="p">(</span><span class="n">elements</span><span class="p">):</span>
@@ -2810,7 +2810,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="slice">
-<h3><span class="section-number">41.7.4.2. </span>Slice<a class="headerlink" href="#slice" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.4.2. </span>Slice<a class="headerlink" href="#slice" title="Permalink to this headline">#</a></h3>
 <p>Create a new list without modifiying the original one.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2837,7 +2837,7 @@ <h3><span class="section-number">41.7.4.2. </span>Slice<a class="headerlink" hre
 </div>
 </section>
 <section id="list-extend">
-<h3><span class="section-number">41.7.4.3. </span>list.extend<a class="headerlink" href="#list-extend" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.4.3. </span>list.extend<a class="headerlink" href="#list-extend" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">flatten_nested_lists</span><span class="p">(</span><span class="n">nested_lists</span><span class="p">):</span>
@@ -2912,9 +2912,9 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </div></section>
 </section>
 <section id="dictionaries">
-<h2><span class="section-number">41.7.5. </span>Dictionaries<a class="headerlink" href="#dictionaries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.7.5. </span>Dictionaries<a class="headerlink" href="#dictionaries" title="Permalink to this headline">#</a></h2>
 <section id="populating-a-dictionary">
-<h3><span class="section-number">41.7.5.1. </span>Populating a dictionary<a class="headerlink" href="#populating-a-dictionary" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.5.1. </span>Populating a dictionary<a class="headerlink" href="#populating-a-dictionary" title="Permalink to this headline">#</a></h3>
 <p>Create a dictionary by using all the given variables.</p>
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-<h3><span class="section-number">41.7.5.2. </span>Removing Duplicate Values from a Dictionary using ‘del’<a class="headerlink" href="#removing-duplicate-values-from-a-dictionary-using-del" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.5.2. </span>Removing Duplicate Values from a Dictionary using ‘del’<a class="headerlink" href="#removing-duplicate-values-from-a-dictionary-using-del" title="Permalink to this headline">#</a></h3>
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 </details>
 </div></section>
 <section id="counting-occurrences-of-elements-using-dict-get">
-<h3><span class="section-number">41.7.5.3. </span>Counting Occurrences of Elements using dict.get()<a class="headerlink" href="#counting-occurrences-of-elements-using-dict-get" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.5.3. </span>Counting Occurrences of Elements using dict.get()<a class="headerlink" href="#counting-occurrences-of-elements-using-dict-get" title="Permalink to this headline">#</a></h3>
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 </details>
 </div></section>
 <section id="grouping-items-by-category-using-dict-setdefault">
-<h3><span class="section-number">41.7.5.4. </span>Grouping Items by Category using dict.setdefault()<a class="headerlink" href="#grouping-items-by-category-using-dict-setdefault" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.5.4. </span>Grouping Items by Category using dict.setdefault()<a class="headerlink" href="#grouping-items-by-category-using-dict-setdefault" title="Permalink to this headline">#</a></h3>
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@@ -3315,7 +3315,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </details>
 </div></section>
 <section id="accessing-and-merging-dictionaries-into-a-single-dictionary">
-<h3><span class="section-number">41.7.5.5. </span>Accessing and Merging Dictionaries into a Single Dictionary<a class="headerlink" href="#accessing-and-merging-dictionaries-into-a-single-dictionary" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">40.7.5.5. </span>Accessing and Merging Dictionaries into a Single Dictionary<a class="headerlink" href="#accessing-and-merging-dictionaries-into-a-single-dictionary" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">merge_dicts</span><span class="p">(</span><span class="o">*</span><span class="n">dicts</span><span class="p">):</span>
@@ -3417,7 +3417,7 @@ <h5><font color=blue>Check result by executing below... 📝</font></h5>
 </div></section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">41.7.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">40.7.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to below awesome open source projects for Python learning, which inspire this chapter.</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://github.com/trekhleb/learn-python">learn-python</a> and <a class="reference external" href="https://github.com/trekhleb">Oleksii Trekhleb</a></p></li>
@@ -3465,13 +3465,13 @@ <h2><span class="section-number">41.7.6. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">41.6. </span>Python programming introduction</p>
+            <p class="prev-next-title"><span class="section-number">40.6. </span>Python programming introduction</p>
         </div>
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-        <p class="prev-next-title"><span class="section-number">41.8. </span>Python programming advanced</p>
+        <p class="prev-next-title"><span class="section-number">40.8. </span>Python programming advanced</p>
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diff --git a/assignments/prerequisites/python-programming-introduction.html b/assignments/prerequisites/python-programming-introduction.html
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+    <title>40.6. Python programming introduction &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="prev" title="41.5. Project Plan​ Template" href="../project-plan-template.html" />
+    <link rel="next" title="40.7. Python programming basics" href="python-programming-basics.html" />
+    <link rel="prev" title="40.5. Project Plan​ Template" href="../project-plan-template.html" />
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+<h2><span class="section-number">40.6.1. </span>Merge two ascending lists into a new ascending list and returns.<a class="headerlink" href="#merge-two-ascending-lists-into-a-new-ascending-list-and-returns" title="Permalink to this headline">#</a></h2>
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+     8.2. The “low code/no code” way
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+     8.3. Data Science in the cloud: The “Azure ML SDK” way
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+     14.3. Yet other classifiers
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-     15.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
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+     15.1. Loss function
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+     15.2. Gradient descent
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+     17.1. Bagging
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+     18.1. Gradient Boosting
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-     19.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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+   19. Unsupervised learning
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+   21. Unsupervised learning: PCA and clustering
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+   32. Image segmentation
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+   34. Object detection
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+     40.3. First assignment
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+     40.4. Second assignment
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+     40.5. Project Plan​ Template
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+     40.6. Python programming introduction
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+     40.7. Python programming basics
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+     40.9. Analyzing text about Data Science
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+     40.10. Data Science scenarios
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-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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-     41.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
+     40.17. Classifying datasets
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-     41.18. Small diabetes study
+     40.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
+     40.20. Displaying airport data
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-     41.21. Soda profits
+     40.21. Soda profits
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-     41.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
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-     41.24. Data processing in Python
+     40.24. Data processing in Python
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-     41.29. Matplotlib applied
+     40.29. Matplotlib applied
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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-     41.42. Machine Learning overview - assignment 1
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+<h1><span class="section-number">40.5. </span>Project Plan​ Template<a class="headerlink" href="#project-plan-template" title="Permalink to this headline">#</a></h1>
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         <i class="fas fa-angle-left"></i>
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-     41.42. Machine Learning overview - assignment 1
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-     41.45. Linear regression - California Housing
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-     41.46. Linear Regression Metrics
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-     41.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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+<h1><span class="section-number">40.4. </span>Second assignment<a class="headerlink" href="#second-assignment" title="Permalink to this headline">#</a></h1>
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+<h2><span class="section-number">40.4.1. </span><code class="docutils literal notranslate"><span class="pre">str.upper()</span></code>, <code class="docutils literal notranslate"><span class="pre">str.lower()</span></code>, <code class="docutils literal notranslate"><span class="pre">str.title()</span></code><a class="headerlink" href="#str-upper-str-lower-str-title" title="Permalink to this headline">#</a></h2>
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+            <p class="prev-next-title"><span class="section-number">40.3. </span>First assignment</p>
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-    <link rel="prev" title="8.3. Communication" href="../data-science-lifecycle/communication.html" />
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+     6.3. Visualizing relationships: all about honey 🍯
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+     8.2. The “low code/no code” way
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+     8.3. Data Science in the cloud: The “Azure ML SDK” way
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+            <p class="prev-next-title"><span class="section-number">7.3. </span>Communication</p>
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 </ul>
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 </div>
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 <section class="tex2jax_ignore mathjax_ignore" id="introduction">
-<h1><span class="section-number">9.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">8.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h1>
 <p>In this section, you will learn the fundamental principles of the cloud, then you will see why it can be interesting for you to use cloud services to run your data science projects and we’ll look at some examples of data science projects run in the cloud.</p>
 <section id="what-is-the-cloud">
-<h2><span class="section-number">9.1.1. </span>What is the cloud?<a class="headerlink" href="#what-is-the-cloud" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">8.1.1. </span>What is the cloud?<a class="headerlink" href="#what-is-the-cloud" title="Permalink to this headline">#</a></h2>
 <p>The Cloud, or Cloud Computing, is the delivery of a wide range of pay-as-you-go computing services hosted on infrastructure over the internet. Services include solutions such as storage, databases, networking, software, analytics, and intelligent services.</p>
 <p>We usually differentiate the Public, Private and Hybrid clouds as follows:</p>
 <ul class="simple">
@@ -1718,7 +1718,7 @@ <h2><span class="section-number">9.1.1. </span>What is the cloud?<a class="heade
 <p>Some of the largest Cloud providers are Amazon Web Services, Google Cloud Platform and Microsoft Azure.</p>
 </section>
 <section id="why-choose-the-cloud-for-data-science">
-<h2><span class="section-number">9.1.2. </span>Why choose the cloud for Data Science?<a class="headerlink" href="#why-choose-the-cloud-for-data-science" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">8.1.2. </span>Why choose the cloud for Data Science?<a class="headerlink" href="#why-choose-the-cloud-for-data-science" title="Permalink to this headline">#</a></h2>
 <p>Developers and IT professionals chose to work with the Cloud for many reasons, including the following:</p>
 <ul class="simple">
 <li><p>Innovation: you can power your applications by integrating innovative services created by Cloud providers directly into your apps.</p></li>
@@ -1739,10 +1739,10 @@ <h2><span class="section-number">9.1.2. </span>Why choose the cloud for Data Sci
 </ul>
 </section>
 <section id="examples-of-data-science-in-the-cloud">
-<h2><span class="section-number">9.1.3. </span>Examples of Data Science in the cloud<a class="headerlink" href="#examples-of-data-science-in-the-cloud" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">8.1.3. </span>Examples of Data Science in the cloud<a class="headerlink" href="#examples-of-data-science-in-the-cloud" title="Permalink to this headline">#</a></h2>
 <p>Let’s make this more tangible by looking at a couple of scenarios.</p>
 <section id="real-time-social-media-sentiment-analysis">
-<h3><span class="section-number">9.1.3.1. </span>Real-time social media sentiment analysis<a class="headerlink" href="#real-time-social-media-sentiment-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">8.1.3.1. </span>Real-time social media sentiment analysis<a class="headerlink" href="#real-time-social-media-sentiment-analysis" title="Permalink to this headline">#</a></h3>
 <p>We’ll start with a scenario commonly studied by people who start with machine learning: social media sentiment analysis in real time.</p>
 <p>Let’s say you run a news media website and you want to leverage live data to understand what content your readers could be interested in. To know more about that, you can build a program that performs real-time sentiment analysis of data from Twitter publications, on topics that are relevant to your readers.</p>
 <p>The key indicators you will look at are the volume of tweets on specific topics (hashtags) and sentiment, which is established using analytics tools that perform sentiment analysis around the specified topics.</p>
@@ -1758,7 +1758,7 @@ <h3><span class="section-number">9.1.3.1. </span>Real-time social media sentimen
 <p>To view the full process, check out the <a class="reference external" href="https://docs.microsoft.com/azure/stream-analytics/stream-analytics-twitter-sentiment-analysis-trends?WT.mc_id=academic-77958-bethanycheum&amp;ocid=AID30411099">documentation</a>.</p>
 </section>
 <section id="scientific-papers-analysis">
-<h3><span class="section-number">9.1.3.2. </span>Scientific papers analysis<a class="headerlink" href="#scientific-papers-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">8.1.3.2. </span>Scientific papers analysis<a class="headerlink" href="#scientific-papers-analysis" title="Permalink to this headline">#</a></h3>
 <p>Let’s take another example of a project created by <a class="reference external" href="http://soshnikov.com">Dmitry Soshnikov</a>, one of the authors of this curriculum.</p>
 <p>Dmitry created a tool that analyses COVID papers. By reviewing this project, you will see how you can create a tool that extracts knowledge from scientific papers, gains insights and helps researchers navigate through large collections of papers in an efficient way.</p>
 <p>Let’s see the different steps used for this:</p>
@@ -1773,7 +1773,7 @@ <h3><span class="section-number">9.1.3.2. </span>Scientific papers analysis<a cl
 </section>
 </section>
 <section id="self-study">
-<h2><span class="section-number">9.1.4. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">8.1.4. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="https://azure.microsoft.com/overview/what-is-cloud-computing?ocid=AID3041109">What Is Cloud Computing? A Beginner’s Guide | Microsoft Azure</a></p></li>
 <li><p><a class="reference external" href="https://docs.microsoft.com/azure/stream-analytics/stream-analytics-twitter-sentiment-analysis-trends?ocid=AID3041109">Social media analysis with Azure Stream Analytics | Microsoft Learn</a></p></li>
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 </ul>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">9.1.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">8.1.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p><a class="reference external" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/assignments/data-science/market-research.md">Market research</a></p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">9.1.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">8.1.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1827,13 +1827,13 @@ <h2><span class="section-number">9.1.6. </span>Acknowledgments<a class="headerli
         <i class="fas fa-angle-left"></i>
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-            <p class="prev-next-title"><span class="section-number">9. </span>Data Science in the cloud</p>
+            <p class="prev-next-title"><span class="section-number">8. </span>Data Science in the cloud</p>
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-        <p class="prev-next-title"><span class="section-number">9.2. </span>The “low code/no code” way</p>
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-     41.103. Fun with Variational Autoencoders
+     40.103. Fun with Variational Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/time-series-forecasting-assignment.html">
-     41.104. Time Series Forecasting Assignment
+     40.104. Time Series Forecasting Assignment
     </a>
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     <a class="reference internal" href="../../assignments/deep-learning/nn-for-classification-assignment.html">
-     41.106. Neural Networks for Classification with TensorFlow
+     40.106. Neural Networks for Classification with TensorFlow
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
-     41.107. NN Classify 15 Fruits Assignment
+     40.107. NN Classify 15 Fruits Assignment
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     41.112. DQN On Foreign Exchange Market
+     40.112. DQN On Foreign Exchange Market
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/gan/art-by-gan.html">
-     41.113. Art by gan
+     40.113. Art by gan
     </a>
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     <a class="reference internal" href="../../assignments/deep-learning/gan/gan-introduction.html">
-     41.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     41.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
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+   41. Slides
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@@ -1209,107 +1209,107 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/python-programming/python-programming-introduction.html">
-     42.1. Python programming introduction
+     41.1. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/python-programming/python-programming-basics.html">
-     42.2. Python programming basics
+     41.2. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/python-programming/python-programming-advanced.html">
-     42.3. Python programming advanced
+     41.3. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-introduction.html">
-     42.4. Data Science introduction
+     41.4. Data Science introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/relational-vs-non-relational-database.html">
-     42.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/numpy-and-pandas.html">
-     42.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-visualization.html">
-     42.7. Data visualization
+     41.7. Data visualization
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-lifecycle.html">
-     42.8. Data Science lifecycle
+     41.8. Data Science lifecycle
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-in-the-cloud.html">
-     42.9. Data Science in the cloud
+     41.9. Data Science in the cloud
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-in-real-world.html">
-     42.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/ml-overview.html">
-     42.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/linear-regression.html">
-     42.12. Linear Regression
+     41.12. Linear Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression.html">
-     42.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression-condensed.html">
-     42.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/neural-network.html">
-     42.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/build-an-ml-web-app.html">
-     42.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/unsupervised-learning.html">
-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/kernel-method.html">
-     42.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/model-selection.html">
-     42.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1575,59 +1575,59 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   9.3.1. Introduction
+   8.3.1. Introduction
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#what-is-azure-ml-sdk">
-     9.3.1.1. What is Azure ML SDK?
+     8.3.1.1. What is Azure ML SDK?
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#heart-failure-prediction-project-and-dataset-introduction">
-     9.3.1.2. Heart failure prediction project and dataset introduction
+     8.3.1.2. Heart failure prediction project and dataset introduction
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-a-model-with-the-azure-ml-sdk">
-   9.3.2. Training a model with the Azure ML SDK
+   8.3.2. Training a model with the Azure ML SDK
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#create-an-azure-ml-workspace">
-     9.3.2.1. Create an Azure ML workspace
+     8.3.2.1. Create an Azure ML workspace
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#create-a-compute-instance">
-     9.3.2.2. Create a compute instance
+     8.3.2.2. Create a compute instance
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#loading-the-dataset">
-     9.3.2.3. Loading the dataset
+     8.3.2.3. Loading the dataset
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#creating-notebooks">
-     9.3.2.4. Creating Notebooks
+     8.3.2.4. Creating Notebooks
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#training-a-model">
-     9.3.2.5. Training a model
+     8.3.2.5. Training a model
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#setup-workspace-experiment-compute-cluster-and-dataset">
-       9.3.2.5.1. Setup Workspace, experiment, compute cluster and dataset
+       8.3.2.5.1. Setup Workspace, experiment, compute cluster and dataset
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#automl-configuration-and-training">
-       9.3.2.5.2. AutoML configuration and training
+       8.3.2.5.2. AutoML configuration and training
       </a>
      </li>
     </ul>
@@ -1636,39 +1636,39 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#model-deployment-and-endpoint-consumption-with-the-azure-ml-sdk">
-   9.3.3. Model deployment and endpoint consumption with the Azure ML SDK
+   8.3.3. Model deployment and endpoint consumption with the Azure ML SDK
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#saving-the-best-model">
-     9.3.3.1. Saving the best model
+     8.3.3.1. Saving the best model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#model-deployment">
-     9.3.3.2. Model deployment
+     8.3.3.2. Model deployment
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#endpoint-consumption">
-     9.3.3.3. Endpoint consumption
+     8.3.3.3. Endpoint consumption
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   9.3.4. Your turn! 🚀
+   8.3.4. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   9.3.5. Self study
+   8.3.5. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   9.3.6. Acknowledgments
+   8.3.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1692,59 +1692,59 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   9.3.1. Introduction
+   8.3.1. Introduction
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#what-is-azure-ml-sdk">
-     9.3.1.1. What is Azure ML SDK?
+     8.3.1.1. What is Azure ML SDK?
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#heart-failure-prediction-project-and-dataset-introduction">
-     9.3.1.2. Heart failure prediction project and dataset introduction
+     8.3.1.2. Heart failure prediction project and dataset introduction
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-a-model-with-the-azure-ml-sdk">
-   9.3.2. Training a model with the Azure ML SDK
+   8.3.2. Training a model with the Azure ML SDK
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#create-an-azure-ml-workspace">
-     9.3.2.1. Create an Azure ML workspace
+     8.3.2.1. Create an Azure ML workspace
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#create-a-compute-instance">
-     9.3.2.2. Create a compute instance
+     8.3.2.2. Create a compute instance
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#loading-the-dataset">
-     9.3.2.3. Loading the dataset
+     8.3.2.3. Loading the dataset
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#creating-notebooks">
-     9.3.2.4. Creating Notebooks
+     8.3.2.4. Creating Notebooks
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#training-a-model">
-     9.3.2.5. Training a model
+     8.3.2.5. Training a model
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#setup-workspace-experiment-compute-cluster-and-dataset">
-       9.3.2.5.1. Setup Workspace, experiment, compute cluster and dataset
+       8.3.2.5.1. Setup Workspace, experiment, compute cluster and dataset
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#automl-configuration-and-training">
-       9.3.2.5.2. AutoML configuration and training
+       8.3.2.5.2. AutoML configuration and training
       </a>
      </li>
     </ul>
@@ -1753,39 +1753,39 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#model-deployment-and-endpoint-consumption-with-the-azure-ml-sdk">
-   9.3.3. Model deployment and endpoint consumption with the Azure ML SDK
+   8.3.3. Model deployment and endpoint consumption with the Azure ML SDK
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#saving-the-best-model">
-     9.3.3.1. Saving the best model
+     8.3.3.1. Saving the best model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#model-deployment">
-     9.3.3.2. Model deployment
+     8.3.3.2. Model deployment
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#endpoint-consumption">
-     9.3.3.3. Endpoint consumption
+     8.3.3.3. Endpoint consumption
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   9.3.4. Your turn! 🚀
+   8.3.4. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   9.3.5. Self study
+   8.3.5. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   9.3.6. Acknowledgments
+   8.3.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1810,11 +1810,11 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="data-science-in-the-cloud-the-azure-ml-sdk-way">
-<h1><span class="section-number">9.3. </span>Data Science in the cloud: The “Azure ML SDK” way<a class="headerlink" href="#data-science-in-the-cloud-the-azure-ml-sdk-way" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">8.3. </span>Data Science in the cloud: The “Azure ML SDK” way<a class="headerlink" href="#data-science-in-the-cloud-the-azure-ml-sdk-way" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">9.3.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">8.3.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <section id="what-is-azure-ml-sdk">
-<h3><span class="section-number">9.3.1.1. </span>What is Azure ML SDK?<a class="headerlink" href="#what-is-azure-ml-sdk" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">8.3.1.1. </span>What is Azure ML SDK?<a class="headerlink" href="#what-is-azure-ml-sdk" title="Permalink to this headline">#</a></h3>
 <p>Data scientists and AI developers use the Azure Machine Learning SDK to build and run Machine Learning workflows with the Azure Machine Learning service. You can interact with the service in any Python environment, including Jupyter Notebooks, Visual Studio Code, or your favorite Python IDE.</p>
 <p>Key areas of the SDK include:</p>
 <ul class="simple">
@@ -1829,19 +1829,19 @@ <h3><span class="section-number">9.3.1.1. </span>What is Azure ML SDK?<a class="
 <p><img alt="project-schema" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/project-schema.png" /></p>
 </section>
 <section id="heart-failure-prediction-project-and-dataset-introduction">
-<h3><span class="section-number">9.3.1.2. </span>Heart failure prediction project and dataset introduction<a class="headerlink" href="#heart-failure-prediction-project-and-dataset-introduction" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">8.3.1.2. </span>Heart failure prediction project and dataset introduction<a class="headerlink" href="#heart-failure-prediction-project-and-dataset-introduction" title="Permalink to this headline">#</a></h3>
 <p>Check <a class="reference internal" href="the-low-code-no-code-way.html"><span class="doc std std-doc">here</span></a> the Heart failure prediction project and dataset introduction.</p>
 </section>
 </section>
 <section id="training-a-model-with-the-azure-ml-sdk">
-<h2><span class="section-number">9.3.2. </span>Training a model with the Azure ML SDK<a class="headerlink" href="#training-a-model-with-the-azure-ml-sdk" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">8.3.2. </span>Training a model with the Azure ML SDK<a class="headerlink" href="#training-a-model-with-the-azure-ml-sdk" title="Permalink to this headline">#</a></h2>
 <section id="create-an-azure-ml-workspace">
-<h3><span class="section-number">9.3.2.1. </span>Create an Azure ML workspace<a class="headerlink" href="#create-an-azure-ml-workspace" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">8.3.2.1. </span>Create an Azure ML workspace<a class="headerlink" href="#create-an-azure-ml-workspace" title="Permalink to this headline">#</a></h3>
 <p>For simplicity, we are going to work on a Jupyter Notebook. This implies that you already have a Workspace and a compute instance. If you already have a Workspace, you can directly jump to section 2.3 Notebook creation.</p>
 <p>If not, please follow the instructions in section <strong>2.1 Create an Azure ML workspace</strong> in the <a class="reference internal" href="the-low-code-no-code-way.html"><span class="doc std std-doc">previous section</span></a> to create a workspace.</p>
 </section>
 <section id="create-a-compute-instance">
-<h3><span class="section-number">9.3.2.2. </span>Create a compute instance<a class="headerlink" href="#create-a-compute-instance" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">8.3.2.2. </span>Create a compute instance<a class="headerlink" href="#create-a-compute-instance" title="Permalink to this headline">#</a></h3>
 <p>In the <a class="reference external" href="https://ml.azure.com/">Azure ML workspace</a> that we created earlier, go to the compute menu and you will see the different compute resources available</p>
 <p><img alt="compute-instance-1" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/compute-instance-1.PNG" /></p>
 <p>Let’s create a compute instance to provision a Jupyter Notebook.</p>
@@ -1854,11 +1854,11 @@ <h3><span class="section-number">9.3.2.2. </span>Create a compute instance<a cla
 <p>Congratulations, you have just created a compute instance! We will use this compute instance to create a Notebook in the <a class="reference external" href="#23-creating-notebooks">Creating Notebooks section</a>.</p>
 </section>
 <section id="loading-the-dataset">
-<h3><span class="section-number">9.3.2.3. </span>Loading the dataset<a class="headerlink" href="#loading-the-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">8.3.2.3. </span>Loading the dataset<a class="headerlink" href="#loading-the-dataset" title="Permalink to this headline">#</a></h3>
 <p>Refer to the <a class="reference internal" href="the-low-code-no-code-way.html"><span class="doc std std-doc">previous section</span></a> in the section <a class="reference external" href="#loading-the-dataset">Loading the dataset</a> if you have not uploaded the dataset yet.</p>
 </section>
 <section id="creating-notebooks">
-<h3><span class="section-number">9.3.2.4. </span>Creating Notebooks<a class="headerlink" href="#creating-notebooks" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">8.3.2.4. </span>Creating Notebooks<a class="headerlink" href="#creating-notebooks" title="Permalink to this headline">#</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>For the next step you can either create a new notebook from scratch, or you can upload the <a class="reference external" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.ipynb">notebook we created</a> in you Azure ML Studio. To upload it, simply click on the “Notebook” menu and upload the notebook.</p>
@@ -1875,10 +1875,10 @@ <h3><span class="section-number">9.3.2.4. </span>Creating Notebooks<a class="hea
 <p>Now that we have a Notebook, we can start training the model with Azure ML SDK.</p>
 </section>
 <section id="training-a-model">
-<h3><span class="section-number">9.3.2.5. </span>Training a model<a class="headerlink" href="#training-a-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">8.3.2.5. </span>Training a model<a class="headerlink" href="#training-a-model" title="Permalink to this headline">#</a></h3>
 <p>First of all, if you ever have a doubt, refer to the <a class="reference external" href="https://docs.microsoft.com/python/api/overview/azure/ml?WT.mc_id=academic-77958-bethanycheum&amp;ocid=AID3041109">Azure ML SDK documentation</a>. It contains all the necessary information to understand the modules we are going to see in this section.</p>
 <section id="setup-workspace-experiment-compute-cluster-and-dataset">
-<h4><span class="section-number">9.3.2.5.1. </span>Setup Workspace, experiment, compute cluster and dataset<a class="headerlink" href="#setup-workspace-experiment-compute-cluster-and-dataset" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">8.3.2.5.1. </span>Setup Workspace, experiment, compute cluster and dataset<a class="headerlink" href="#setup-workspace-experiment-compute-cluster-and-dataset" title="Permalink to this headline">#</a></h4>
 <p>You need to load the <code class="docutils literal notranslate"><span class="pre">workspace</span></code> from the configuration file using the following code:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1932,7 +1932,7 @@ <h4><span class="section-number">9.3.2.5.1. </span>Setup Workspace, experiment,
 </div>
 </section>
 <section id="automl-configuration-and-training">
-<h4><span class="section-number">9.3.2.5.2. </span>AutoML configuration and training<a class="headerlink" href="#automl-configuration-and-training" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">8.3.2.5.2. </span>AutoML configuration and training<a class="headerlink" href="#automl-configuration-and-training" title="Permalink to this headline">#</a></h4>
 <p>To set the AutoML configuration, use the <a class="reference external" href="https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig(class)?WT.mc_id=academic-77958-bethanycheum&amp;ocid=AID3041109">AutoMLConfig class</a>.</p>
 <p>As described in the doc, there are a lot of parameters with which you can play with. For this project, we will use the following parameters:</p>
 <ul class="simple">
@@ -1995,9 +1995,9 @@ <h4><span class="section-number">9.3.2.5.2. </span>AutoML configuration and trai
 </section>
 </section>
 <section id="model-deployment-and-endpoint-consumption-with-the-azure-ml-sdk">
-<h2><span class="section-number">9.3.3. </span>Model deployment and endpoint consumption with the Azure ML SDK<a class="headerlink" href="#model-deployment-and-endpoint-consumption-with-the-azure-ml-sdk" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">8.3.3. </span>Model deployment and endpoint consumption with the Azure ML SDK<a class="headerlink" href="#model-deployment-and-endpoint-consumption-with-the-azure-ml-sdk" title="Permalink to this headline">#</a></h2>
 <section id="saving-the-best-model">
-<h3><span class="section-number">9.3.3.1. </span>Saving the best model<a class="headerlink" href="#saving-the-best-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">8.3.3.1. </span>Saving the best model<a class="headerlink" href="#saving-the-best-model" title="Permalink to this headline">#</a></h3>
 <p>The <code class="docutils literal notranslate"><span class="pre">remote_run</span></code> is an object of type <a class="reference external" href="https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.run.automlrun?WT.mc_id=academic-77958-bethanycheum&amp;ocid=AID3041109">AutoMLRun</a>. This object contains the method <code class="docutils literal notranslate"><span class="pre">get_output()</span></code> which returns the best run and the corresponding fitted model.</p>
 <div class="cell docutils container">
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@@ -2033,7 +2033,7 @@ <h3><span class="section-number">9.3.3.1. </span>Saving the best model<a class="
 </div>
 </section>
 <section id="model-deployment">
-<h3><span class="section-number">9.3.3.2. </span>Model deployment<a class="headerlink" href="#model-deployment" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">8.3.3.2. </span>Model deployment<a class="headerlink" href="#model-deployment" title="Permalink to this headline">#</a></h3>
 <p>Once the best model is saved, we can deploy it with the <a class="reference external" href="https://docs.microsoft.com/python/api/azureml-core/azureml.core.model.inferenceconfig?view=azure-ml-py?ocid=AID3041109">InferenceConfig</a> class. InferenceConfig represents the configuration settings for a custom environment used for deployment. The <a class="reference external" href="https://docs.microsoft.com/python/api/azureml-core/azureml.core.webservice.aciwebservice?view=azure-ml-py">AciWebservice</a> class represents a Machine Learning model deployed as a web service endpoint on Azure Container Instances. A deployed service is created from a model, script, and associated files. The resulting web service is a load-balanced, HTTP endpoint with a REST API. You can send data to this API and receive the prediction returned by the model.</p>
 <p>The model is deployed using the <a class="reference external" href="https://docs.microsoft.com/python/api/azureml-core/azureml.core.model(class)?view=azure-ml-py#deploy-workspace--name--models--inference-config-none--deployment-config-none--deployment-target-none--overwrite-false--show-output-false-?WT.mc_id=academic-77958-bethanycheum&amp;ocid=AID3041109">deploy</a> method.</p>
 <div class="cell docutils container">
@@ -2061,7 +2061,7 @@ <h3><span class="section-number">9.3.3.2. </span>Model deployment<a class="heade
 <p>This step should take a few minutes.</p>
 </section>
 <section id="endpoint-consumption">
-<h3><span class="section-number">9.3.3.3. </span>Endpoint consumption<a class="headerlink" href="#endpoint-consumption" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">8.3.3.3. </span>Endpoint consumption<a class="headerlink" href="#endpoint-consumption" title="Permalink to this headline">#</a></h3>
 <p>You consume your endpoint by creating a sample input:</p>
 <div class="cell docutils container">
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@@ -2108,7 +2108,7 @@ <h3><span class="section-number">9.3.3.3. </span>Endpoint consumption<a class="h
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">9.3.4. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">8.3.4. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>There are many other things you can do through the SDK, unfortunately, we can not view them all in this section. But good news, learning how to skim through the SDK documentation can take you a long way on your own. Have a look at the Azure ML SDK documentation and find the <code class="docutils literal notranslate"><span class="pre">Pipeline</span></code> class that allows you to create pipelines. A Pipeline is a collection of steps which can be executed as a workflow.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -2117,11 +2117,11 @@ <h2><span class="section-number">9.3.4. </span>Your turn! 🚀<a class="headerli
 <p>Assignment - <a class="reference external" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/assignments/data-science/data-science-project-using-azure-ml-sdk.md">Data Science project using Azure ML SDK</a></p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">9.3.5. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">8.3.5. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>In this section, you learned how to train, deploy and consume a model to predict heart failure risk with the Azure ML SDK in the cloud. Check this <a class="reference external" href="https://docs.microsoft.com/python/api/overview/azure/ml/?view=azure-ml-py?WT.mc_id=academic-77958-bethanycheum&amp;ocid=AID3041109">documentation</a> for further information about the Azure ML SDK. Try to create your own model with the Azure ML SDK.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">9.3.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">8.3.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2163,13 +2163,13 @@ <h2><span class="section-number">9.3.6. </span>Acknowledgments<a class="headerli
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-            <p class="prev-next-title"><span class="section-number">9.2. </span>The “low code/no code” way</p>
+            <p class="prev-next-title"><span class="section-number">8.2. </span>The “low code/no code” way</p>
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-        <p class="prev-next-title"><span class="section-number">10. </span>Data Science in the real world</p>
+        <p class="prev-next-title"><span class="section-number">9. </span>Data Science in the real world</p>
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-    <title>9.2. The “low code/no code” way &#8212; Ocademy Open Machine Learning Book</title>
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-    <link rel="prev" title="9.1. Introduction" href="introduction.html" />
+    <link rel="next" title="8.3. Data Science in the cloud: The “Azure ML SDK” way" href="the-azure-ml-sdk-way.html" />
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+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.36. Explore a planetary computer dataset
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-     41.39. Low code/no code Data Science project on Azure ML
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-     41.45. Linear regression - California Housing
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-     41.47. Loss Function
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-     41.112. DQN On Foreign Exchange Market
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-     41.115. Generative Adversarial Networks (GANs)
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-     41.116. Basic classification: Classify images of clothing
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-     42.2. Python programming basics
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-     42.4. Data Science introduction
+     41.4. Data Science introduction
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+     41.11. Machine Learning overview
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+   8.2.1. What is Azure Machine Learning(ML)?
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+   8.2.2. The heart failure prediction project
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+   8.2.3. The heart failure dataset
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+   8.2.4. Low code/no code training of a model in Azure ML Studio
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    <li class="toc-h3 nav-item toc-entry">
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+     8.2.4.1. Create an Azure ML workspace
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#compute-resources">
-     9.2.4.2. Compute resources
+     8.2.4.2. Compute resources
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     <a class="reference internal nav-link" href="#choosing-the-right-options-for-your-compute-resources">
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+     8.2.4.3. Choosing the right options for your compute resources
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+     8.2.4.4. Creating a compute cluster
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+     8.2.4.5. Loading the dataset
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+   8.2.5. Low code/no code training with AutoML
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+   8.2.6. Low code/no code model deployment and endpoint consumption
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+     8.2.6.1. Model deployment
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+   8.2.7. Your turn! 🚀
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 </ul>
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                             <ul class="visible nav section-nav flex-column">
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   <a class="reference internal nav-link" href="#what-is-azure-machine-learning-ml">
-   9.2.1. What is Azure Machine Learning(ML)?
+   8.2.1. What is Azure Machine Learning(ML)?
   </a>
  </li>
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   <a class="reference internal nav-link" href="#the-heart-failure-prediction-project">
-   9.2.2. The heart failure prediction project
+   8.2.2. The heart failure prediction project
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#the-heart-failure-dataset">
-   9.2.3. The heart failure dataset
+   8.2.3. The heart failure dataset
   </a>
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   <a class="reference internal nav-link" href="#low-code-no-code-training-of-a-model-in-azure-ml-studio">
-   9.2.4. Low code/no code training of a model in Azure ML Studio
+   8.2.4. Low code/no code training of a model in Azure ML Studio
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#create-an-azure-ml-workspace">
-     9.2.4.1. Create an Azure ML workspace
+     8.2.4.1. Create an Azure ML workspace
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#compute-resources">
-     9.2.4.2. Compute resources
+     8.2.4.2. Compute resources
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#choosing-the-right-options-for-your-compute-resources">
-     9.2.4.3. Choosing the right options for your compute resources
+     8.2.4.3. Choosing the right options for your compute resources
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#creating-a-compute-cluster">
-     9.2.4.4. Creating a compute cluster
+     8.2.4.4. Creating a compute cluster
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#loading-the-dataset">
-     9.2.4.5. Loading the dataset
+     8.2.4.5. Loading the dataset
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#low-code-no-code-training-with-automl">
-   9.2.5. Low code/no code training with AutoML
+   8.2.5. Low code/no code training with AutoML
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#low-code-no-code-model-deployment-and-endpoint-consumption">
-   9.2.6. Low code/no code model deployment and endpoint consumption
+   8.2.6. Low code/no code model deployment and endpoint consumption
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#model-deployment">
-     9.2.6.1. Model deployment
+     8.2.6.1. Model deployment
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#endpoint-consumption">
-     9.2.6.2. Endpoint consumption
+     8.2.6.2. Endpoint consumption
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   9.2.7. Your turn! 🚀
+   8.2.7. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   9.2.8. Self Study
+   8.2.8. Self Study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   9.2.9. Acknowledgments
+   8.2.9. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1794,9 +1794,9 @@ <h2> Contents </h2>
 </ul>
 <hr class="docutils" />
 <section class="tex2jax_ignore mathjax_ignore" id="the-low-code-no-code-way">
-<h1><span class="section-number">9.2. </span>The “low code/no code” way<a class="headerlink" href="#the-low-code-no-code-way" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">8.2. </span>The “low code/no code” way<a class="headerlink" href="#the-low-code-no-code-way" title="Permalink to this headline">#</a></h1>
 <section id="what-is-azure-machine-learning-ml">
-<h2><span class="section-number">9.2.1. </span>What is Azure Machine Learning(ML)?<a class="headerlink" href="#what-is-azure-machine-learning-ml" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">8.2.1. </span>What is Azure Machine Learning(ML)?<a class="headerlink" href="#what-is-azure-machine-learning-ml" title="Permalink to this headline">#</a></h2>
 <p>The Azure cloud platform is more than 200 products and cloud services designed to help you bring new solutions to life. Data scientists expend a lot of effort exploring and pre-processing data and trying various types of model-training algorithms to produce accurate models. These tasks are time-consuming and often make inefficient use of expensive compute hardware.</p>
 <p><a class="reference external" href="https://docs.microsoft.com/azure/machine-learning/overview-what-is-azure-machine-learning?WT.mc_id=academic-77958-bethanycheum&amp;ocid=AID3041109">Azure ML</a> is a cloud-based platform for building and operating Machine Learning solutions in Azure. It includes a wide range of features and capabilities that help data scientists prepare data, train models, publish predictive services, and monitor their usage. Most importantly, it helps them to increase their efficiency by automating many of the time-consuming tasks associated with training models; and it enables them to use cloud-based compute resources that scale effectively, to handle large volumes of data while incurring costs only when actually used.</p>
 <p>Azure ML provides all the tools developers and data scientists need for their Machine Learning workflows. These include:</p>
@@ -1813,7 +1813,7 @@ <h2><span class="section-number">9.2.1. </span>What is Azure Machine Learning(ML
 </ul>
 </section>
 <section id="the-heart-failure-prediction-project">
-<h2><span class="section-number">9.2.2. </span>The heart failure prediction project<a class="headerlink" href="#the-heart-failure-prediction-project" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">8.2.2. </span>The heart failure prediction project<a class="headerlink" href="#the-heart-failure-prediction-project" title="Permalink to this headline">#</a></h2>
 <p>There is no doubt that making and building projects are the best way to put your skills and knowledge to the test. In this section, we are going to explore two different ways of building a data science project for the prediction of heart failure attacks in Azure ML Studio, through Low code/No code and through the Azure ML SDK as shown in the following schema:</p>
 <p><img alt="project-schema" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/project-schema.png" /></p>
 <p>Each way has its own pros and cons. The Low code/No code way is easier to start with as it involves interacting with a GUI (Graphical User Interface), with no prior knowledge of code required. This method enables quick testing of the project’s viability and to create POC (Proof Of Concept). However, as the project grows and things need to be production ready, it is not feasible to create resources through GUI. We need to programmatically automate everything, from the creation of resources to the deployment of a model. This is where knowing how to use the Azure ML SDK becomes crucial.</p>
@@ -1841,7 +1841,7 @@ <h2><span class="section-number">9.2.2. </span>The heart failure prediction proj
 </table>
 </section>
 <section id="the-heart-failure-dataset">
-<h2><span class="section-number">9.2.3. </span>The heart failure dataset<a class="headerlink" href="#the-heart-failure-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">8.2.3. </span>The heart failure dataset<a class="headerlink" href="#the-heart-failure-dataset" title="Permalink to this headline">#</a></h2>
 <p>Cardiovascular diseases (CVDs) are the number 1 cause of death globally, accounting for 31% of all deaths worldwide. Environmental and behavioral risk factors such as use of tobacco, unhealthy diet and obesity, physical inactivity and harmful use of alcohol could be used as features for estimation models. Being able to estimate the probability of the development of a CVD could be of great use to prevent attacks in high-risk people.</p>
 <p>Kaggle has made a <a class="reference external" href="https://www.kaggle.com/andrewmvd/heart-failure-clinical-data">Heart Failure dataset</a> publicly available, that we are going to use for this project. You can download the dataset now. This is a tabular dataset with 13 columns (12 features and 1 target variable) and 299 rows.</p>
 <table class="colwidths-auto table">
@@ -1943,9 +1943,9 @@ <h2><span class="section-number">9.2.3. </span>The heart failure dataset<a class
 <p>Once you have the dataset, we can start the project in Azure.</p>
 </section>
 <section id="low-code-no-code-training-of-a-model-in-azure-ml-studio">
-<h2><span class="section-number">9.2.4. </span>Low code/no code training of a model in Azure ML Studio<a class="headerlink" href="#low-code-no-code-training-of-a-model-in-azure-ml-studio" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">8.2.4. </span>Low code/no code training of a model in Azure ML Studio<a class="headerlink" href="#low-code-no-code-training-of-a-model-in-azure-ml-studio" title="Permalink to this headline">#</a></h2>
 <section id="create-an-azure-ml-workspace">
-<h3><span class="section-number">9.2.4.1. </span>Create an Azure ML workspace<a class="headerlink" href="#create-an-azure-ml-workspace" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">8.2.4.1. </span>Create an Azure ML workspace<a class="headerlink" href="#create-an-azure-ml-workspace" title="Permalink to this headline">#</a></h3>
 <p>To train a model in Azure ML you first need to create an Azure ML workspace. The workspace is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. The workspace keeps a history of all training runs, including logs, metrics, output, and a snapshot of your scripts. You use this information to determine which training run produces the best model. <a class="reference external" href="https://docs.microsoft.com/azure/machine-learning/concept-workspace?WT.mc_id=academic-77958-bethanycheum&amp;ocid=AID3041109">Learn more</a>.</p>
 <p>It is recommended to use the most up-to-date browser that’s compatible with your operating system. The following browsers are supported:</p>
 <ul class="simple">
@@ -1987,7 +1987,7 @@ <h3><span class="section-number">9.2.4.1. </span>Create an Azure ML workspace<a
 <p>You can manage your workspace using the Azure portal, but for data scientists and Machine Learning operations engineers, Azure Machine Learning Studio provides a more focused user interface for managing workspace resources.</p>
 </section>
 <section id="compute-resources">
-<h3><span class="section-number">9.2.4.2. </span>Compute resources<a class="headerlink" href="#compute-resources" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">8.2.4.2. </span>Compute resources<a class="headerlink" href="#compute-resources" title="Permalink to this headline">#</a></h3>
 <p>Compute Resources are cloud-based resources on which you can run model training and data exploration processes. There are four kinds of compute resource you can create:</p>
 <ul class="simple">
 <li><p><strong>Compute Instances</strong>: Development workstations that data scientists can use to work with data and models. This involves the creation of a Virtual Machine (VM) and launching a notebook instance. You can then train a model by calling a computer cluster from the notebook.</p></li>
@@ -1997,7 +1997,7 @@ <h3><span class="section-number">9.2.4.2. </span>Compute resources<a class="head
 </ul>
 </section>
 <section id="choosing-the-right-options-for-your-compute-resources">
-<h3><span class="section-number">9.2.4.3. </span>Choosing the right options for your compute resources<a class="headerlink" href="#choosing-the-right-options-for-your-compute-resources" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">8.2.4.3. </span>Choosing the right options for your compute resources<a class="headerlink" href="#choosing-the-right-options-for-your-compute-resources" title="Permalink to this headline">#</a></h3>
 <p>Some key factors are to consider when creating a compute resource and those choices can be critical decisions to make.</p>
 <p><strong>Do you need CPU or GPU?</strong></p>
 <p>A CPU (Central Processing Unit) is the electronic circuitry that executes instructions comprising a computer program. A GPU (Graphics Processing Unit) is a specialized electronic circuit that can execute graphics-related code at a very high rate.</p>
@@ -2029,7 +2029,7 @@ <h3><span class="section-number">9.2.4.3. </span>Choosing the right options for
 This is another consideration of time vs money, since interruptible instances are less expensive than dedicated ones.</p>
 </section>
 <section id="creating-a-compute-cluster">
-<h3><span class="section-number">9.2.4.4. </span>Creating a compute cluster<a class="headerlink" href="#creating-a-compute-cluster" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">8.2.4.4. </span>Creating a compute cluster<a class="headerlink" href="#creating-a-compute-cluster" title="Permalink to this headline">#</a></h3>
 <p>In the <a class="reference external" href="https://ml.azure.com/">Azure ML workspace</a> that we created earlier, go to compute and you will be able to see the different compute resources we just discussed (i.e compute instances, compute clusters, inference clusters and attached compute). For this project, we are going to need a compute cluster for model training. In the Studio, Click on the “Compute” menu, then the “Compute cluster” tab and click on the “+ New” button to create a compute cluster.</p>
 <p><img alt="22" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/cluster-1.PNG" /></p>
 <p>6. Choose your options: Dedicated vs Low priority, CPU or GPU, VM size and core number (you can keep the default settings for this project).</p>
@@ -2042,7 +2042,7 @@ <h3><span class="section-number">9.2.4.4. </span>Creating a compute cluster<a cl
 <p>Awesome! Now that we have a Compute cluster, we need to load the data to Azure ML Studio.</p>
 </section>
 <section id="loading-the-dataset">
-<h3><span class="section-number">9.2.4.5. </span>Loading the dataset<a class="headerlink" href="#loading-the-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">8.2.4.5. </span>Loading the dataset<a class="headerlink" href="#loading-the-dataset" title="Permalink to this headline">#</a></h3>
 <p>11. In the <a class="reference external" href="https://ml.azure.com/">Azure ML workspace</a> that we created earlier, click on “Datasets” in the left menu and click on the “+ Create dataset” button to create a dataset. Choose the “From local files” option and select the Kaggle dataset we downloaded earlier.
 <img alt="24" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/dataset-1.PNG" /></p>
 <p>12. Give your dataset a name, a type and a description. Click Next. Upload the data from files. Click Next.
@@ -2053,7 +2053,7 @@ <h3><span class="section-number">9.2.4.5. </span>Loading the dataset<a class="he
 </section>
 </section>
 <section id="low-code-no-code-training-with-automl">
-<h2><span class="section-number">9.2.5. </span>Low code/no code training with AutoML<a class="headerlink" href="#low-code-no-code-training-with-automl" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">8.2.5. </span>Low code/no code training with AutoML<a class="headerlink" href="#low-code-no-code-training-with-automl" title="Permalink to this headline">#</a></h2>
 <p>Traditional Machine Learning model development is resource-intensive, requires significant domain knowledge and time to produce and compare dozens of models.
 Automated Machine Learning (AutoML), is the process of automating the time-consuming, iterative tasks of Machine Learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity, all while sustaining model quality. It reduces the time it takes to get production-ready ML models, with great ease and efficiency. <a class="reference external" href="https://docs.microsoft.com/azure/machine-learning/concept-automated-ml?WT.mc_id=academic-77958-bethanycheum&amp;ocid=AID3041109">Learn more</a></p>
 <p>14. In the <a class="reference external" href="https://ml.azure.com/">Azure ML workspace</a> that we created earlier click on “Automated ML” in the left menu and select the dataset you just uploaded. Click Next.
@@ -2067,9 +2067,9 @@ <h2><span class="section-number">9.2.5. </span>Low code/no code training with Au
 <p>Here you can see a detailed description of the best model that AutoML generated. You can also explore other modes generated in the Models tab. Take a few minutes to explore the models in the Explanations (preview button). Once you have chosen the model you want to use (here we will choose the best model selected by autoML), we will see how we can deploy it.</p>
 </section>
 <section id="low-code-no-code-model-deployment-and-endpoint-consumption">
-<h2><span class="section-number">9.2.6. </span>Low code/no code model deployment and endpoint consumption<a class="headerlink" href="#low-code-no-code-model-deployment-and-endpoint-consumption" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">8.2.6. </span>Low code/no code model deployment and endpoint consumption<a class="headerlink" href="#low-code-no-code-model-deployment-and-endpoint-consumption" title="Permalink to this headline">#</a></h2>
 <section id="model-deployment">
-<h3><span class="section-number">9.2.6.1. </span>Model deployment<a class="headerlink" href="#model-deployment" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">8.2.6.1. </span>Model deployment<a class="headerlink" href="#model-deployment" title="Permalink to this headline">#</a></h3>
 <p>The automated Machine Learning interface allows you to deploy the best model as a web service in a few steps. Deployment is the integration of the model so that it can make predictions based on new data and identify potential areas of opportunity. For this project, deployment to a web service means that medical applications will be able to consume the model to be able to make live predictions of their patient’s risk to get a heart attack.</p>
 <p>In the best model description, click on the “Deploy” button.</p>
 <p><img alt="deploy-1" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deploy-1.PNG" /></p>
@@ -2080,7 +2080,7 @@ <h3><span class="section-number">9.2.6.1. </span>Model deployment<a class="heade
 <p>Amazing! Now that we have a model deployed, we can start the consumption of the endpoint.</p>
 </section>
 <section id="endpoint-consumption">
-<h3><span class="section-number">9.2.6.2. </span>Endpoint consumption<a class="headerlink" href="#endpoint-consumption" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">8.2.6.2. </span>Endpoint consumption<a class="headerlink" href="#endpoint-consumption" title="Permalink to this headline">#</a></h3>
 <p>Click on the “Consume” tab. Here you can find the REST endpoint and a python script in the consumption option. Take some time to read the python code.</p>
 <p>This script can be run directly from your local machine and will consume your endpoint.</p>
 <p><img alt="35" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/consumption-1.PNG" /></p>
@@ -2168,17 +2168,17 @@ <h3><span class="section-number">9.2.6.2. </span>Endpoint consumption<a class="h
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">9.2.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">8.2.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Look closely at the model explanations and details that AutoML generated for the top models. Try to understand why the best model is better than the other ones. What algorithms were compared? What are the differences between them? Why is the best one performing better in this case?</p>
 <p>Assignment - <a class="reference external" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.md">Low code/no code Data Science project on Azure ML</a></p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">9.2.8. </span>Self Study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">8.2.8. </span>Self Study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>In this section, you learned how to train, deploy and consume a model to predict heart failure risk in a low code/no code fashion in the cloud. If you have not done it yet, dive deeper into the model explanations that AutoML generated for the top models and try to understand why the best model is better than others.</p>
 <p>You can go further into Low code/No code AutoML by reading this <a class="reference external" href="https://docs.microsoft.com/azure/machine-learning/tutorial-first-experiment-automated-ml?WT.mc_id=academic-77958-bethanycheum&amp;ocid=AID3041109">documentation</a>.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">9.2.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">8.2.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 <p>Data for the Heart Failure Prediction project is sourced from <a class="reference external" href="https://www.kaggle.com/andrewmvd">Larxel</a> on <a class="reference external" href="https://www.kaggle.com/andrewmvd/heart-failure-clinical-data">Kaggle</a>. It is licensed under the <a class="reference external" href="https://creativecommons.org/licenses/by/4.0/">Attribution 4.0 International (CC BY 4.0)</a></p>
 </section>
@@ -2221,13 +2221,13 @@ <h2><span class="section-number">9.2.9. </span>Acknowledgments<a class="headerli
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-            <p class="prev-next-title"><span class="section-number">9.1. </span>Introduction</p>
+            <p class="prev-next-title"><span class="section-number">8.1. </span>Introduction</p>
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-        <p class="prev-next-title"><span class="section-number">9.3. </span>Data Science in the cloud: The “Azure ML SDK” way</p>
+        <p class="prev-next-title"><span class="section-number">8.3. </span>Data Science in the cloud: The “Azure ML SDK” way</p>
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-    <title>10. Data Science in the real world &#8212; Ocademy Open Machine Learning Book</title>
+    <title>9. Data Science in the real world &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="next" title="11. Machine Learning overview" href="../ml-fundamentals/ml-overview.html" />
-    <link rel="prev" title="9.3. Data Science in the cloud: The “Azure ML SDK” way" href="data-science-in-the-cloud/the-azure-ml-sdk-way.html" />
+    <link rel="next" title="10. Machine Learning overview" href="../ml-fundamentals/ml-overview.html" />
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+     6.3. Visualizing relationships: all about honey 🍯
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+<h1><span class="section-number">9. </span>Data Science in the real world<a class="headerlink" href="#data-science-in-the-real-world" title="Permalink to this headline">#</a></h1>
 <p>We’re almost at the end of this learning journey!</p>
 <p>We started with definitions of data science and ethics, explored various tools &amp; techniques for data analysis and visualization, reviewed the data science lifecycle, and looked at scaling and automating data science workflows with cloud computing services. So, you’re probably wondering: <em>“How exactly do I map all these learnings to real-world contexts?”</em></p>
 <p>In this section, we’ll explore real-world applications of data science across industries and dive into specific examples in the research, digital humanities, and sustainability, contexts. We’ll look at student project opportunities and conclude with useful resources to help you continue your learning journey!</p>
 <section id="data-science-industry">
-<h2><span class="section-number">10.1. </span>Data Science + industry<a class="headerlink" href="#data-science-industry" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">9.1. </span>Data Science + industry<a class="headerlink" href="#data-science-industry" title="Permalink to this headline">#</a></h2>
 <p>Thanks to the democratization of AI, developers are now finding it easier to design and integrate AI-driven decision-making and data-driven insights into user experiences and development workflows. Here are a few examples of how data science is “applied” to real-world applications across the industry:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://www.wired.com/2015/10/can-learn-epic-failure-google-flu-trends/">Google Flu Trends</a> used data science to correlate search terms with flu trends. While the approach had flaws, it raised awareness of the possibilities (and challenges) of data-driven healthcare predictions.</p></li>
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 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/data-science-applications.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/data-science-applications.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 10.1 </span><span class="caption-text"><a class="reference external" href="https://data-flair.training/blogs/data-science-applications/">Data Science applications in the real world</a></span><a class="headerlink" href="#data-science-applications-in-the-real-world" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 9.1 </span><span class="caption-text"><a class="reference external" href="https://data-flair.training/blogs/data-science-applications/">Data Science applications in the real world</a></span><a class="headerlink" href="#data-science-applications-in-the-real-world" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The figure shows other domains and examples for applying data science techniques. Want to explore other applications? Check out the <a class="reference external" href="#self-study">Self study</a> section below.</p>
 </section>
 <section id="data-science-research">
-<h2><span class="section-number">10.2. </span>Data Science + research<a class="headerlink" href="#data-science-research" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">9.2. </span>Data Science + research<a class="headerlink" href="#data-science-research" title="Permalink to this headline">#</a></h2>
 <p>While real-world applications often focus on industry use cases at scale, <em>research</em> applications and projects can be useful from two perspectives:</p>
 <ul class="simple">
 <li><p><em>innovation opportunities</em> - explore rapid prototyping of advanced concepts and testing of user experiences for next-generation applications.</p></li>
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 </ul>
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 <section id="data-science-humanities">
-<h2><span class="section-number">10.3. </span>Data Science + humanities<a class="headerlink" href="#data-science-humanities" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">9.3. </span>Data Science + humanities<a class="headerlink" href="#data-science-humanities" title="Permalink to this headline">#</a></h2>
 <p>Digital Humanities <a class="reference external" href="https://digitalhumanities.stanford.edu/about-dh-stanford">has been defined</a> as “a collection of practices and approaches combining computational methods with the humanistic inquiry”. <a class="reference external" href="https://digitalhumanities.stanford.edu/projects">Stanford projects</a> like <em>“rebooting history”</em> and <em>“poetic thinking”</em> illustrate the linkage between <a class="reference external" href="https://digitalhumanities.stanford.edu/digital-humanities-and-data-science">Digital Humanities and Data Science</a> - emphasizing techniques like network analysis, information visualization, and spatial and text analysis that can help us revisit historical and literary data sets to derive new insights and perspectives.</p>
 <p><strong>Want to explore and extend a project in this space?</strong></p>
 <p>Check out <a class="reference external" href="https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671">“Emily Dickinson and the Meter of Mood”</a> - a great example from <a class="reference external" href="https://twitter.com/jenlooper">Jen Looper</a> that asks how we can use data science to revisit familiar poetry and re-evaluate its meaning and the contributions of its author in new contexts. For instance, <em>can we predict the season in which a poem was authored by analyzing its tone or sentiment</em> - and what does this tell us about the author’s state of mind over the relevant period?</p>
@@ -1694,7 +1694,7 @@ <h2><span class="section-number">10.3. </span>Data Science + humanities<a class=
 </div>
 </section>
 <section id="data-science-sustainability">
-<h2><span class="section-number">10.4. </span>Data Science + sustainability<a class="headerlink" href="#data-science-sustainability" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">9.4. </span>Data Science + sustainability<a class="headerlink" href="#data-science-sustainability" title="Permalink to this headline">#</a></h2>
 <p>The <a class="reference external" href="https://sdgs.un.org/2030agenda">2030 Agenda For Sustainable Development</a> - adopted by all United Nations members in 2015 - identifies 17 goals including ones that focus on <strong>Protecting the Planet</strong> from degradation and the impact of climate change. The <a class="reference external" href="https://www.microsoft.com/en-us/sustainability">Microsoft Sustainability</a> initiative supports these goals by exploring ways in which technology solutions can support and build more sustainable futures with a <a class="reference external" href="https://dev.to/azure/a-visual-guide-to-sustainable-software-engineering-53hh">focus on 4 goals</a> - being carbon negative, water positive, zero waste, and bio-diverse by 2030.</p>
 <p>Tackling these challenges in a scalable and timely manner requires cloud-scale thinking - and large-scale data. The <a class="reference external" href="https://planetarycomputer.microsoft.com/">Planetary Computer</a> initiative provides 4 components to help data scientists and developers in this effort:</p>
 <ul class="simple">
@@ -1712,7 +1712,7 @@ <h2><span class="section-number">10.4. </span>Data Science + sustainability<a cl
 <p>Think about how you can use data visualization to expose or amplify relevant insights into areas like climate change and deforestation. Or think about how insights can be used to create new user experiences that motivate behavioral changes for more sustainable living.</p>
 </section>
 <section id="data-science-students">
-<h2><span class="section-number">10.5. </span>Data Science + students<a class="headerlink" href="#data-science-students" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">9.5. </span>Data Science + students<a class="headerlink" href="#data-science-students" title="Permalink to this headline">#</a></h2>
 <p>We’ve talked about real-world applications in industry and research, and explored data science application examples in digital humanities and sustainability. So how can you build your skills and share your expertise as a data science beginner?</p>
 <p>Here are some examples of data science student projects to inspire you.</p>
 <ul class="simple">
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 <section id="self-study">
-<h2><span class="section-number">10.6. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">9.6. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>Want to explore more use cases? Here are a few relevant articles:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://builtin.com/data-science/data-science-applications-examples">17 Data Science Applications and Examples</a> - Jul 2021</p></li>
@@ -1736,12 +1736,12 @@ <h2><span class="section-number">10.6. </span>Self study<a class="headerlink" hr
 </ul>
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 <section id="your-turn">
-<h2><span class="section-number">10.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">9.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Search for articles that recommend data science projects that are beginner friendly - like <a class="reference external" href="https://www.upgrad.com/blog/data-science-project-ideas-topics-beginners/">these 50 topic areas</a> or <a class="reference external" href="https://www.intellspot.com/data-science-project-ideas">these 21 project ideas</a> or <a class="reference external" href="https://data-flair.training/blogs/data-science-project-ideas/">these 16 projects with source code</a> that you can deconstruct and remix. And don’t forget to blog about your learning journeys and share your insights with all of us.</p>
 <p>Assignment - <a class="reference internal" href="../assignments/data-science/explore-a-planetary-computer-dataset.html"><span class="doc std std-doc">Explore A Planetary Computer Dataset</span></a></p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">10.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">9.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
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-    <link rel="next" title="8.3. Communication" href="communication.html" />
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+     6.3. Visualizing relationships: all about honey 🍯
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-     9.3. Data Science in the cloud: The “Azure ML SDK” way
+     8.3. Data Science in the cloud: The “Azure ML SDK” way
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+<h1><span class="section-number">7.2. </span>Analyzing<a class="headerlink" href="#analyzing" title="Permalink to this headline">#</a></h1>
 <p>Analyzing the data lifecycle confirms that the data can answer the questions that are proposed or solve a particular problem. This step can also focus on confirming a model is correctly addressing these questions and problems. This section is focused on Exploratory Data Analysis or EDA, which are techniques for defining features and relationships within the data and can be used to prepare the data for modeling.</p>
 <p>We’ll be using an example dataset from <a class="reference external" href="https://www.kaggle.com/balaka18/email-spam-classification-dataset-csv/version/1">Kaggle</a> to show how this can be applied with Python and the Pandas library. This dataset contains a count of some common words found in emails, the sources of these emails are anonymous. Use the <a class="reference external" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/data-science/nyc-taxi-data-in-winter-and-summer.ipynb">notebook</a> in this directory to follow along.</p>
 <section id="exploratory-data-analysis">
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+<h2><span class="section-number">7.2.1. </span>Exploratory data analysis<a class="headerlink" href="#exploratory-data-analysis" title="Permalink to this headline">#</a></h2>
 <p>The capture phase of the lifecycle is where the data is acquired as well as the problems and questions at hand, but how do we know the data can help support the end result?
 Recall that a data scientist may ask the following questions when they acquire the data:</p>
 <ul class="simple">
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 <p>Exploratory Data Analysis is the process of getting to know that data can be used to answer these questions, as well as identify the challenges of working with the dataset. Let’s focus on some of the techniques used to achieve this.</p>
 </section>
 <section id="data-profiling-descriptive-statistics-and-pandas">
-<h2><span class="section-number">8.2.2. </span>Data profiling, descriptive statistics, and Pandas<a class="headerlink" href="#data-profiling-descriptive-statistics-and-pandas" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">7.2.2. </span>Data profiling, descriptive statistics, and Pandas<a class="headerlink" href="#data-profiling-descriptive-statistics-and-pandas" title="Permalink to this headline">#</a></h2>
 <p>How do we evaluate if we have enough data to solve this problem? Data profiling can summarize and gather some general overall information about our dataset through techniques of descriptive statistics. Data profiling helps us understand what is available to us, and descriptive statistics help us understand how many things are available to us.</p>
 <p>In a few of the previous sections, we used Pandas to provide some descriptive statistics with the <a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.describe.html"><code class="docutils literal notranslate"><span class="pre">describe()</span></code> function</a>. It provides the count, max &amp; min values, mean, standard deviation, and quantiles on the numerical data. Using descriptive statistics like the <code class="docutils literal notranslate"><span class="pre">describe()</span></code> function can help you assess how much you have and if you need more.</p>
 </section>
 <section id="sampling-and-querying">
-<h2><span class="section-number">8.2.3. </span>Sampling and querying<a class="headerlink" href="#sampling-and-querying" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">7.2.3. </span>Sampling and querying<a class="headerlink" href="#sampling-and-querying" title="Permalink to this headline">#</a></h2>
 <p>Exploring everything in a large dataset can be very time consuming and a task that’s usually left up to a computer to do. However, sampling is a helpful tool in the understanding of the data and allows us to have a better understanding of what’s in the dataset and what it represents. With a sample, you can apply probability and statistics to come to some general conclusions about your data. While there’s no defined rule on how much data you should sample it’s important to note that the more data you sample, the more precise of a generalization you can make about data.</p>
 <p>Pandas has the <a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sample.html"><code class="docutils literal notranslate"><span class="pre">sample()</span></code> function in its library</a> where you can pass an argument of how many random samples you’d like to receive and use.</p>
 <p>General querying of the data can help you answer some general questions and theories you may have. In contrast to sampling, queries allow you to have control and focus on specific parts of the data you have questions about.</p>
 <p>The <a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html"><code class="docutils literal notranslate"><span class="pre">query()</span></code> function</a> in the Pandas library allows you to select columns and receive simple answers about the data through the rows retrieved.</p>
 </section>
 <section id="exploring-with-visualizations">
-<h2><span class="section-number">8.2.4. </span>Exploring with visualizations<a class="headerlink" href="#exploring-with-visualizations" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">7.2.4. </span>Exploring with visualizations<a class="headerlink" href="#exploring-with-visualizations" title="Permalink to this headline">#</a></h2>
 <p>You don’t have to wait until the data is thoroughly cleaned and analyzed to start creating visualizations. In fact, having a visual representation while exploring can help identify patterns, relationships, and problems in the data. Furthermore, visualizations provide a means of communication with those who are not involved with managing the data and can be an opportunity to share and clarify additional questions that were not addressed in the capture stage. Refer to the section on <a class="reference external" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/data-science/data-visualization.ipynb">Visualizations</a> to learn more about some popular ways to explore visually.</p>
 </section>
 <section id="exploring-to-identify-inconsistencies">
-<h2><span class="section-number">8.2.5. </span>Exploring to identify inconsistencies<a class="headerlink" href="#exploring-to-identify-inconsistencies" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">7.2.5. </span>Exploring to identify inconsistencies<a class="headerlink" href="#exploring-to-identify-inconsistencies" title="Permalink to this headline">#</a></h2>
 <p>All the topics in this section can help identify missing or inconsistent values, but Pandas provides functions to check for some of these. <a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.isna.html"><code class="docutils literal notranslate"><span class="pre">isna()</span></code> or <code class="docutils literal notranslate"><span class="pre">isnull()</span></code></a> can check for missing values. One important piece of exploring these values within your data is to explore why they ended up that way in the first place. This can help you decide on what <a class="reference external" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/data-science/data-preparation.ipynb">actions to take to resolve them</a>.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">8.2.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">7.2.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p><a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html"><span class="doc std std-doc">Exploring for answers</span></a></p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">8.2.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">7.2.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
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-            <p class="prev-next-title"><span class="section-number">8.1. </span>Introduction to the Data Science lifecycle</p>
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+    <link rel="next" title="8. Data Science in the cloud" href="../data-science-in-the-cloud/data-science-in-the-cloud.html" />
+    <link rel="prev" title="7.2. Analyzing" href="analyzing.html" />
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+     40.14. Try it in Excel
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-     41.36. Explore a planetary computer dataset
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-     41.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal nav-link" href="#understand-your-audience-your-channel-your-communication-method">
-     8.3.2.2. 1. Understand your audience, your channel &amp; your communication method
+     7.3.2.2. 1. Understand your audience, your channel &amp; your communication method
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
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+     7.3.2.3. 2. Begin with the end in mind
     </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#approach-it-like-an-actual-story">
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     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#use-meaningful-words-phrases">
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+     7.3.2.5. 4. Use meaningful words &amp; phrases
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#use-emotion">
-     8.3.2.6. 5. Use emotion
+     7.3.2.6. 5. Use emotion
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#communication-case-study">
-   8.3.3. Communication case study
+   7.3.3. Communication case study
   </a>
  </li>
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   <a class="reference internal nav-link" href="#conclusion">
-   8.3.4. Conclusion
+   7.3.4. Conclusion
   </a>
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   <a class="reference internal nav-link" href="#self-study">
-   8.3.5. Self study
+   7.3.5. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   8.3.6. Your turn! 🚀
+   7.3.6. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   8.3.7. Acknowledgments
+   7.3.7. Acknowledgments
   </a>
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 </ul>
@@ -1613,86 +1613,86 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#what-is-data-communication">
-     8.3.1.1. What is data communication?
+     7.3.1.1. What is data communication?
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-communication-storytelling">
-     8.3.1.2. Data communication &amp; storytelling
+     7.3.1.2. Data communication &amp; storytelling
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#types-of-communication">
-     8.3.1.3. Types of communication
+     7.3.1.3. Types of communication
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#effective-communication">
-   8.3.2. Effective communication
+   7.3.2. Effective communication
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#your-responsibilities-as-a-communicator">
-     8.3.2.1. Your responsibilities as a communicator
+     7.3.2.1. Your responsibilities as a communicator
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#understand-your-audience-your-channel-your-communication-method">
-     8.3.2.2. 1. Understand your audience, your channel &amp; your communication method
+     7.3.2.2. 1. Understand your audience, your channel &amp; your communication method
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#begin-with-the-end-in-mind">
-     8.3.2.3. 2. Begin with the end in mind
+     7.3.2.3. 2. Begin with the end in mind
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#approach-it-like-an-actual-story">
-     8.3.2.4. 3. Approach it like an actual story
+     7.3.2.4. 3. Approach it like an actual story
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#use-meaningful-words-phrases">
-     8.3.2.5. 4. Use meaningful words &amp; phrases
+     7.3.2.5. 4. Use meaningful words &amp; phrases
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#use-emotion">
-     8.3.2.6. 5. Use emotion
+     7.3.2.6. 5. Use emotion
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#communication-case-study">
-   8.3.3. Communication case study
+   7.3.3. Communication case study
   </a>
  </li>
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   <a class="reference internal nav-link" href="#conclusion">
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+   7.3.4. Conclusion
   </a>
  </li>
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+   7.3.5. Self study
   </a>
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   <a class="reference internal nav-link" href="#your-turn">
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+   7.3.6. Your turn! 🚀
   </a>
  </li>
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   <a class="reference internal nav-link" href="#acknowledgments">
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+   7.3.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1716,19 +1716,19 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="communication">
-<h1><span class="section-number">8.3. </span>Communication<a class="headerlink" href="#communication" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">7.3. </span>Communication<a class="headerlink" href="#communication" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">8.3.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">7.3.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <section id="what-is-data-communication">
-<h3><span class="section-number">8.3.1.1. </span>What is data communication?<a class="headerlink" href="#what-is-data-communication" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">7.3.1.1. </span>What is data communication?<a class="headerlink" href="#what-is-data-communication" title="Permalink to this headline">#</a></h3>
 <p>Let’s start this section by defining what it means to communicate. <strong>To communicate is to convey or exchange information.</strong> Information can be ideas, thoughts, feelings, messages, covert signals, data – anything that a <strong><em>sender</em></strong> (someone sending information) wants a <strong><em>receiver</em></strong> (someone receiving information) to understand. In this section, we will refer to senders as communicators and receivers as the audience.</p>
 </section>
 <section id="data-communication-storytelling">
-<h3><span class="section-number">8.3.1.2. </span>Data communication &amp; storytelling<a class="headerlink" href="#data-communication-storytelling" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">7.3.1.2. </span>Data communication &amp; storytelling<a class="headerlink" href="#data-communication-storytelling" title="Permalink to this headline">#</a></h3>
 <p>We understand that when communicating, the aim is to convey or exchange information. But when communicating data, your aim shouldn’t be to simply pass along numbers to your audience. Your aim should be to communicate a story that is informed by your data - effective data communication and storytelling go hand-in-hand. Your audience is more likely to remember a story you tell than a number you give. Later in this section, we will go over a few ways that you can use storytelling to communicate your data more effectively.</p>
 </section>
 <section id="types-of-communication">
-<h3><span class="section-number">8.3.1.3. </span>Types of communication<a class="headerlink" href="#types-of-communication" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">7.3.1.3. </span>Types of communication<a class="headerlink" href="#types-of-communication" title="Permalink to this headline">#</a></h3>
 <p>Throughout this section two different types of communication will be discussed, One-Way Communication and Two-Way Communication.</p>
 <p><strong>One way communication</strong> happens is when a sender sends information to a receiver, without any feedback or response. We see examples of one-way communication every day – in bulk/mass emails, when the news delivers the most recent stories, or even when a television commercial comes on and informs you about why their product is great. In each of these instances, the sender is not seeking an exchange of information. They are only seeking to convey or deliver information.</p>
 <p><strong>Two-way communication</strong> happens when all involved parties act as both senders and receivers. A sender will begin by communicating with a receiver, and the receiver will provide feedback or a response. Two-way communication is what we traditionally think of when we talk about communication. We usually think of people engaged in a conversation - either in person, or over a phone call, social media, or text message.</p>
@@ -1736,9 +1736,9 @@ <h3><span class="section-number">8.3.1.3. </span>Types of communication<a class=
 </section>
 </section>
 <section id="effective-communication">
-<h2><span class="section-number">8.3.2. </span>Effective communication<a class="headerlink" href="#effective-communication" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">7.3.2. </span>Effective communication<a class="headerlink" href="#effective-communication" title="Permalink to this headline">#</a></h2>
 <section id="your-responsibilities-as-a-communicator">
-<h3><span class="section-number">8.3.2.1. </span>Your responsibilities as a communicator<a class="headerlink" href="#your-responsibilities-as-a-communicator" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">7.3.2.1. </span>Your responsibilities as a communicator<a class="headerlink" href="#your-responsibilities-as-a-communicator" title="Permalink to this headline">#</a></h3>
 <p>When communicating, it is your job to make sure that your receiver(s) are taking away the information that you want them to take away. When you’re communicating data, you don’t just want your receivers to takeaway numbers, you want your receivers to takeaway a story that’s informed by your data. A good data communicator is a good storyteller.</p>
 <p>How do you tell a story with data? There are infinite ways – but below are 6 that we will talk about in this section.</p>
 <ol class="simple">
@@ -1751,7 +1751,7 @@ <h3><span class="section-number">8.3.2.1. </span>Your responsibilities as a comm
 <p>Each of these strategies is explained in greater detail below.</p>
 </section>
 <section id="understand-your-audience-your-channel-your-communication-method">
-<h3><span class="section-number">8.3.2.2. </span>1. Understand your audience, your channel &amp; your communication method<a class="headerlink" href="#understand-your-audience-your-channel-your-communication-method" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">7.3.2.2. </span>1. Understand your audience, your channel &amp; your communication method<a class="headerlink" href="#understand-your-audience-your-channel-your-communication-method" title="Permalink to this headline">#</a></h3>
 <p>The way you communicate with family members is likely different than the way you communicate with your friends. You probably use different words and phrases that the people you’re speaking to are more likely to understand. You should take the same approach when communicating data. Think about who you’re communicating with. Think about their goals and the context that they have around the situation that you’re explaining to them.</p>
 <p>You can likely group the majority of your audience within a category. In a <em>Harvard Business Review</em> article, “<a class="reference external" href="http://blogs.hbr.org/2013/04/how-to-tell-a-story-with-data/">How to Tell a Story with Data</a>,” Dell Executive Strategist Jim Stikeleather identifies five categories of audiences.</p>
 <ul class="simple">
@@ -1768,19 +1768,19 @@ <h3><span class="section-number">8.3.2.2. </span>1. Understand your audience, yo
 <p>If you are communicating with a majority Managerial audience and you’re using two-way communication, you likely won’t need to educate your audience or provide them with much context. You may be able to jump straight into discussing the data that you’ve collected and why it matters. In this scenario though, you should be focused on timing and controlling your presentation. When using two-way communication (especially with a Managerial audience who is seeking an “actionable understanding of intricacies and interrelationships with access to detail”) questions may pop up during your interaction that may take the discussion in a direction that doesn’t relate to the story that you’re trying to tell. When this happens, you can take action and move the discussion back on track with your story.</p>
 </section>
 <section id="begin-with-the-end-in-mind">
-<h3><span class="section-number">8.3.2.3. </span>2. Begin with the end in mind<a class="headerlink" href="#begin-with-the-end-in-mind" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">7.3.2.3. </span>2. Begin with the end in mind<a class="headerlink" href="#begin-with-the-end-in-mind" title="Permalink to this headline">#</a></h3>
 <p>Beginning with the end in mind means understanding your intended takeaways for your audience before you start communicating with them. Being thoughtful about what you want your audience to take away ahead of time can help you craft a story that your audience can follow. Beginning with the end in mind is appropriate for both one-way communication and two-way communication.</p>
 <p>How do you begin with the end in mind? Before communicating your data, write down your key takeaways. Then, every step of the way as you’re preparing the story that you want to tell with your data, ask yourself, “How does this integrate into the story I’m telling?”</p>
 <p>Be Aware – While starting with the end in mind is ideal, you don’t want to communicate only the data that supports your intended takeaways. Doing this is called Cherry-Picking, which happens when a communicator only communicates data that supports the point they are trying to make and ignores all other data.</p>
 <p>If all the data that you collected clearly supports your intended takeaways, great. But if there is data that you collected that doesn’t support your takeaways, or even supports an argument against your key takeaways, you should communicate that data as well. If this happens, be upfront with your audience and let them know why you’re choosing to stick with your story even though all the data doesn’t necessarily support it.</p>
 </section>
 <section id="approach-it-like-an-actual-story">
-<h3><span class="section-number">8.3.2.4. </span>3. Approach it like an actual story<a class="headerlink" href="#approach-it-like-an-actual-story" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">7.3.2.4. </span>3. Approach it like an actual story<a class="headerlink" href="#approach-it-like-an-actual-story" title="Permalink to this headline">#</a></h3>
 <p>A traditional story happens in 5 Phases. You may have heard these phases expressed as Exposition, Rising Action, Climax, Falling Action, and Denouncement. Or the easier to remember Context, Conflict, Climax, Closure, and Conclusion. When communicating your data and your story, you can take a similar approach.</p>
 <p>You can begin with context, set the stage, and make sure your audience is all on the same page. Then introduce the conflict. Why did you need to collect this data? What problems were you seeking to solve? After that, the climax. What is the data? What does the data mean? What solutions does the data tell us we need? Then you get to the closure, where you can reiterate the problem, and the proposed solution(s). Lastly, we come to the conclusion, where you can summarize your key takeaways and the next steps you recommend the team takes.</p>
 </section>
 <section id="use-meaningful-words-phrases">
-<h3><span class="section-number">8.3.2.5. </span>4. Use meaningful words &amp; phrases<a class="headerlink" href="#use-meaningful-words-phrases" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">7.3.2.5. </span>4. Use meaningful words &amp; phrases<a class="headerlink" href="#use-meaningful-words-phrases" title="Permalink to this headline">#</a></h3>
 <p>If you and I were working together on a product, and I said to you “Our users take a long time to onboard onto our platform,” how long would you estimate that “long time” to be? An hour? A week? It’s hard to know. What if I said that to an entire audience? Everyone in the audience may end up with a different idea of how long users take to onboard onto our platform.</p>
 <p>Instead, what if I said, “Out users take, on average, 3 minutes to sign up and onboard onto our platform.”</p>
 <p>That messaging is more clear. When communicating data, it can be easy to think that everyone in your audience is thinking just like you. But that is not always the case. Driving clarity around your data and what it means is one of your responsibilities as a communicator.  If the data or your story is not clear, your audience will have a hard time following, and it is less likely that they will understand your key takeaways.</p>
@@ -1805,7 +1805,7 @@ <h3><span class="section-number">8.3.2.5. </span>4. Use meaningful words &amp; p
 <p>Using vague words could be useful as an introduction to more data that’s coming, or as a summary of the story that you’ve just told. But consider ensuring that every part of your presentation is clear for your audience.</p>
 </section>
 <section id="use-emotion">
-<h3><span class="section-number">8.3.2.6. </span>5. Use emotion<a class="headerlink" href="#use-emotion" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">7.3.2.6. </span>5. Use emotion<a class="headerlink" href="#use-emotion" title="Permalink to this headline">#</a></h3>
 <p>Emotion is key in storytelling. It’s even more important when you’re telling a story with data. When you’re communicating data, everything is focused on the takeaways you want your audience to have. When you evoke emotion in an audience it helps them empathize and makes them more likely to take action. Emotion also increases the likelihood that an audience will remember your message.</p>
 <p>You may have encountered this before with TV commercials. Some commercials are very somber and use sad emotions to connect with their audience and make the data that they’re presenting really stand out. Or, some commercials that are very upbeat and happy may make you associate their data with a happy feeling.</p>
 <p>How do you use emotion when communicating data? Below are a couple of ways.</p>
@@ -1836,7 +1836,7 @@ <h3><span class="section-number">8.3.2.6. </span>5. Use emotion<a class="headerl
 </section>
 </section>
 <section id="communication-case-study">
-<h2><span class="section-number">8.3.3. </span>Communication case study<a class="headerlink" href="#communication-case-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">7.3.3. </span>Communication case study<a class="headerlink" href="#communication-case-study" title="Permalink to this headline">#</a></h2>
 <p>Emerson is a Product Manager for a mobile app. Emerson has noticed that customers submit 42% more complaints and bug reports on the weekends. Emerson also noticed that customers who submit a complaint that goes unanswered after 48 hours are 32% more likely to give the app a rating of 1 or 2 in the app store.</p>
 <p>After doing research, Emerson has a couple of solutions that will address the issue. Emerson sets up a 30-minute meeting with the 3 company leads to communicate the data and the proposed solutions.</p>
 <p>During this meeting, Emerson’s goal is to have the company leads understand that the 2 solutions below can improve the app’s rating, which will likely translate into higher revenue.</p>
@@ -1858,7 +1858,7 @@ <h2><span class="section-number">8.3.3. </span>Communication case study<a class=
 <p>If Emerson took approach #2, it is much more likely that the team leads will take away from the meeting exactly what Emerson intended for them to take away – that the way complaints and bugs are handled could be improved, and 2 solutions could be put in place to make that improvement happen. This approach would be a much more effective approach to communicating the data, and the story, that Emerson wants to communicate.</p>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">8.3.4. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">7.3.4. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>To communicate is to convey or exchange information.</p></li>
 <li><p>When communicating data, your aim shouldn’t be to simply pass along numbers to your audience. Your aim should be to communicate a story that is informed by your data.</p></li>
@@ -1875,7 +1875,7 @@ <h2><span class="section-number">8.3.4. </span>Conclusion<a class="headerlink" h
 </ul>
 </section>
 <section id="self-study">
-<h2><span class="section-number">8.3.5. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">7.3.5. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="http://articulatepersuasion.com/the-five-cs-of-storytelling/">The Five C’s of Storytelling - Articulate Persuasion</a></p></li>
 <li><p><a class="reference external" href="https://open.lib.umn.edu/businesscommunication/chapter/1-4-your-responsibilities-as-a-communicator/">1.4 Your Responsibilities as a Communicator – Business Communication for Success (umn.edu)</a></p></li>
@@ -1899,11 +1899,11 @@ <h2><span class="section-number">8.3.5. </span>Self study<a class="headerlink" h
 </ul>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">8.3.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">7.3.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p><a class="reference internal" href="../../assignments/data-science/tell-a-story.html"><span class="doc std std-doc">Tell a story</span></a></p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">8.3.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">7.3.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1945,13 +1945,13 @@ <h2><span class="section-number">8.3.7. </span>Acknowledgments<a class="headerli
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-            <p class="prev-next-title"><span class="section-number">8.2. </span>Analyzing</p>
+            <p class="prev-next-title"><span class="section-number">7.2. </span>Analyzing</p>
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-        <p class="prev-next-title"><span class="section-number">9. </span>Data Science in the cloud</p>
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-     41.45. Linear regression - California Housing
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                             <ul class="visible nav section-nav flex-column">
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 <section class="tex2jax_ignore mathjax_ignore" id="introduction-to-the-data-science-lifecycle">
-<h1><span class="section-number">8.1. </span>Introduction to the Data Science lifecycle<a class="headerlink" href="#introduction-to-the-data-science-lifecycle" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">7.1. </span>Introduction to the Data Science lifecycle<a class="headerlink" href="#introduction-to-the-data-science-lifecycle" title="Permalink to this headline">#</a></h1>
 <p>At this point, you’ve probably come to the realization that Data Science is a process. This process can be broken down into 5 stages:</p>
 <ul class="simple">
 <li><p>Capturing</p></li>
@@ -1720,11 +1720,11 @@ <h1><span class="section-number">8.1. </span>Introduction to the Data Science li
 <figure class="align-default" id="diagram-of-the-data-science-lifecycle">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/data-science-lifecycle.jpeg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/data-science-lifecycle.jpeg" />
 <figcaption>
-<p><span class="caption-number">Fig. 8.1 </span><span class="caption-text"><a class="reference external" href="https://ischoolonline.berkeley.edu/data-science/what-is-data-science-2/">Diagram of the Data Science lifecycle</a></span><a class="headerlink" href="#diagram-of-the-data-science-lifecycle" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 7.1 </span><span class="caption-text"><a class="reference external" href="https://ischoolonline.berkeley.edu/data-science/what-is-data-science-2/">Diagram of the Data Science lifecycle</a></span><a class="headerlink" href="#diagram-of-the-data-science-lifecycle" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="capturing">
-<h2><span class="section-number">8.1.1. </span>Capturing<a class="headerlink" href="#capturing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">7.1.1. </span>Capturing<a class="headerlink" href="#capturing" title="Permalink to this headline">#</a></h2>
 <p>The first stage of the lifecycle is very important as the next stages are dependent on it. It’s practically two stages combined into one: acquiring the data and defining the purpose and problems that need to be addressed. Defining the goals of the project will require deeper context into the problem or question. First, we need to identify and acquire those who need their problem solved. These may be stakeholders in a business or sponsors of the project, who can help identify who or what will benefit from this project as well as what, and why they need it. A well-defined goal should be measurable and quantifiable to define an acceptable result.</p>
 <p>Questions a data scientist may ask:</p>
 <ul class="simple">
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+<h2><span class="section-number">7.1.2. </span>Processing<a class="headerlink" href="#processing" title="Permalink to this headline">#</a></h2>
 <p>The processing stage of the lifecycle focuses on discovering patterns in the data as well as modeling. Some techniques used in the processing stage require statistical methods to uncover the patterns. Typically, this would be a tedious task for a human to do with a large data set and will rely on computers to do the heavy lifting to speed up the process. This stage is also where Data Science and machine learning will intersect. As you learned in the first section, machine learning is the process of building models to understand the data. Models are a representation of the relationship between variables in the data that help predict outcomes.</p>
 <p>Common techniques used in this stage are covered in the Machine Learning sections.  Follow the links to learn more about them:</p>
 <ul class="simple">
@@ -1757,10 +1757,10 @@ <h2><span class="section-number">8.1.2. </span>Processing<a class="headerlink" h
 </ul>
 </section>
 <section id="maintaining">
-<h2><span class="section-number">8.1.3. </span>Maintaining<a class="headerlink" href="#maintaining" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">7.1.3. </span>Maintaining<a class="headerlink" href="#maintaining" title="Permalink to this headline">#</a></h2>
 <p>In the diagram of the lifecycle, you may have noticed that maintenance sits between capturing and processing. Maintenance is an ongoing process of managing, storing, and securing the data throughout the process of a project and should be taken into consideration throughout the entirety of the project.</p>
 <section id="storing-data">
-<h3><span class="section-number">8.1.3.1. </span>Storing data<a class="headerlink" href="#storing-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">7.1.3.1. </span>Storing data<a class="headerlink" href="#storing-data" title="Permalink to this headline">#</a></h3>
 <p>Considerations of how and where the data is stored can influence the cost of its storage as well as the performance of how fast the data can be accessed. Decisions like these are not likely to be made by a data scientist alone but they may find themselves making choices on how to work with the data based on how it’s stored.</p>
 <p>Here are some aspects of modern data storage systems that can affect these choices:</p>
 <p><strong>On premise vs off premise vs public or private cloud</strong></p>
@@ -1769,11 +1769,11 @@ <h3><span class="section-number">8.1.3.1. </span>Storing data<a class="headerlin
 <p>When training your models, you may require more training data. If you’re content with your model, more data will arrive for a model to serve its purpose. In any case, the cost of storing and accessing data will increase as you accumulate more of it. Separating rarely used data, known as cold data from frequently accessed hot data can be a cheaper data storage option through hardware or software services. If cold data needs to be accessed, it may take a little longer to retrieve in comparison to hot data.</p>
 </section>
 <section id="managing-data">
-<h3><span class="section-number">8.1.3.2. </span>Managing data<a class="headerlink" href="#managing-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">7.1.3.2. </span>Managing data<a class="headerlink" href="#managing-data" title="Permalink to this headline">#</a></h3>
 <p>As you work with data you may discover that some of the data needs to be cleaned using some of the techniques covered in the section focused on <a class="reference internal" href="../working-with-data/data-preparation.html"><span class="doc std std-doc">data preparation</span></a> to build accurate models. When new data arrives, it will need some of the same applications to maintain consistency in quality. Some projects will involve the use of an automated tool for cleansing, aggregation, and compression before the data is moved to its final location. Azure Data Factory is an example of one of these tools.</p>
 </section>
 <section id="securing-the-data">
-<h3><span class="section-number">8.1.3.3. </span>Securing the data<a class="headerlink" href="#securing-the-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">7.1.3.3. </span>Securing the data<a class="headerlink" href="#securing-the-data" title="Permalink to this headline">#</a></h3>
 <p>One of the main goals of securing data is ensuring that those working on it are in control of what is collected and in what context it is being used. Keeping data secure involves limiting access to only those who need it, adhering to local laws and regulations, as well as maintaining ethical standards, as covered in the <a class="reference internal" href="../introduction/data-science-ethics.html"><span class="doc std std-doc">ethics section</span></a>.</p>
 <p>Here are some things that a team may do with security in mind:</p>
 <ul class="simple">
@@ -1785,7 +1785,7 @@ <h3><span class="section-number">8.1.3.3. </span>Securing the data<a class="head
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">8.1.4. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">7.1.4. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>There are many versions of the Data Science Lifecycle, where each step may have different names and numbers of stages but will contain the same processes mentioned within this section.</p>
 <p>Explore the <a class="reference external" href="https://docs.microsoft.com/en-us/azure/architecture/data-science-process/lifecycle">Team Data Science Process lifecycle</a> and the <a class="reference external" href="https://www.datascience-pm.com/crisp-dm-2/">Cross-industry standard process for data mining</a>. Name 3 similarities and differences between the two.</p>
 <table class="colwidths-auto table">
@@ -1806,7 +1806,7 @@ <h2><span class="section-number">8.1.4. </span>Your turn! 🚀<a class="headerli
 <p>Assignment - <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html"><span class="doc std std-doc">NYC taxi data in winter and summer</span></a></p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">8.1.5. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">7.1.5. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>Applying the Data Science Lifecycle involves multiple roles and tasks, where some may focus on particular parts of each stage. The Team Data Science Process provides a few resources that explain the types of roles and tasks that someone may have in a project.</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://docs.microsoft.com/en-us/azure/architecture/data-science-process/roles-tasks">Team Data Science Process roles and tasks</a></p></li>
@@ -1814,7 +1814,7 @@ <h2><span class="section-number">8.1.5. </span>Self study<a class="headerlink" h
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">8.1.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">7.1.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1856,13 +1856,13 @@ <h2><span class="section-number">8.1.6. </span>Acknowledgments<a class="headerli
         <i class="fas fa-angle-left"></i>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">8. </span>Data Science lifecycle</p>
+            <p class="prev-next-title"><span class="section-number">7. </span>Data Science lifecycle</p>
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-        <p class="prev-next-title"><span class="section-number">8.2. </span>Analyzing</p>
+        <p class="prev-next-title"><span class="section-number">7.2. </span>Analyzing</p>
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diff --git a/data-science/data-visualization/data-visualization.html b/data-science/data-visualization/data-visualization.html
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-    <title>7. Data visualization &#8212; Ocademy Open Machine Learning Book</title>
+    <title>6. Data visualization &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="7.1. Visualizing distributions" href="visualization-distributions.html" />
-    <link rel="prev" title="6.5. Data preparation" href="../working-with-data/data-preparation.html" />
+    <link rel="next" title="6.1. Visualizing distributions" href="visualization-distributions.html" />
+    <link rel="prev" title="5.5. Data preparation" href="../working-with-data/data-preparation.html" />
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-     41.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.36. Explore a planetary computer dataset
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-     41.39. Low code/no code Data Science project on Azure ML
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-     41.45. Linear regression - California Housing
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+            <p class="prev-next-title"><span class="section-number">5.5. </span>Data preparation</p>
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+   6.4.6. Project: Build a chart to show a network using D3.js
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+   6.4.6. Project: Build a chart to show a network using D3.js
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@@ -1693,7 +1693,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="making-meaningful-visualizations">
-<h1><span class="section-number">7.4. </span>Making meaningful visualizations<a class="headerlink" href="#making-meaningful-visualizations" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">6.4. </span>Making meaningful visualizations<a class="headerlink" href="#making-meaningful-visualizations" title="Permalink to this headline">#</a></h1>
 <p>“Data is food for AI.”   – Andrew Ng</p>
 <p>One of the basic skills of a data scientist is the ability to create a meaningful data visualization that helps answer questions you might have. Prior to visualizing your data, you need to ensure that it has been cleaned and prepared, as you did in prior sections. After that, you can start deciding how best to present the data.</p>
 <p>In this section, you will review:</p>
@@ -1706,7 +1706,7 @@ <h1><span class="section-number">7.4. </span>Making meaningful visualizations<a
 <li><p>How to build a creative visualization</p></li>
 </ol>
 <section id="choose-the-right-chart-type">
-<h2><span class="section-number">7.4.1. </span>Choose the right chart type<a class="headerlink" href="#choose-the-right-chart-type" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.4.1. </span>Choose the right chart type<a class="headerlink" href="#choose-the-right-chart-type" title="Permalink to this headline">#</a></h2>
 <p>In previous sections, you experimented with building all kinds of interesting data visualizations using Matplotlib and Seaborn for charting. In general, you can select the <a class="reference external" href="https://chartio.com/learn/charts/how-to-select-a-data-vizualization/">right kind of chart</a> for the question you are asking using this table:</p>
 <table class="colwidths-auto table">
 <thead>
@@ -1738,7 +1738,7 @@ <h2><span class="section-number">7.4.1. </span>Choose the right chart type<a cla
 <p>Depending on the makeup of your data, you might need to convert it from text to numeric to get a given chart to support it.</p>
 </section>
 <section id="avoid-deception">
-<h2><span class="section-number">7.4.2. </span>Avoid deception<a class="headerlink" href="#avoid-deception" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.4.2. </span>Avoid deception<a class="headerlink" href="#avoid-deception" title="Permalink to this headline">#</a></h2>
 <p>Even if a data scientist is careful to choose the right chart for the right data, there are plenty of ways that data can be displayed in a way to prove a point, often at the cost of undermining the data itself. There are many examples of deceptive charts and infographics!</p>
 <p><a class="reference external" href="https://www.youtube.com/watch?v=oX74Nge8Wkw"><img alt="How Charts Lie by Alberto Cairo" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/data-science/data-visualization/tornado.png" /></a></p>
 <p>Click the image above for a conference talk about deceptive charts</p>
@@ -1754,7 +1754,7 @@ <h2><span class="section-number">7.4.2. </span>Avoid deception<a class="headerli
 <p>It’s important to understand how easily the eye can be fooled by deceptive charts. Even if the data scientist’s intention is good, the choice of a bad type of chart, such as a pie chart showing too many categories, can be deceptive.</p>
 </section>
 <section id="color">
-<h2><span class="section-number">7.4.3. </span>Color<a class="headerlink" href="#color" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.4.3. </span>Color<a class="headerlink" href="#color" title="Permalink to this headline">#</a></h2>
 <p>You saw in the ‘Florida gun violence’ chart above how color can provide an additional layer of meaning to charts, especially ones not designed using libraries such as Matplotlib and Seaborn which come with various vetted color libraries and palettes. If you are making a chart by hand, do a little study of <a class="reference external" href="https://colormatters.com/color-and-design/basic-color-theory">color theory</a></p>
 <p>Be aware, when designing charts, that accessibility is an important aspect of visualization. Some of your users might be color blind - does your chart display well for users with visual impairments?</p>
 <p>Be careful when choosing colors for your chart, as color can convey meaning you might not intend. The ‘pink ladies’ in the ‘height’ chart above convey a distinctly ‘feminine’ ascribed meaning that adds to the bizarreness of the chart itself.</p>
@@ -1796,14 +1796,14 @@ <h2><span class="section-number">7.4.3. </span>Color<a class="headerlink" href="
 <p>If you are tasked with building a chart with custom colors, ensure that your charts are both accessible and the color you choose coincides with the meaning you are trying to convey.</p>
 </section>
 <section id="styling-your-charts-for-readability">
-<h2><span class="section-number">7.4.4. </span>Styling your charts for readability<a class="headerlink" href="#styling-your-charts-for-readability" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.4.4. </span>Styling your charts for readability<a class="headerlink" href="#styling-your-charts-for-readability" title="Permalink to this headline">#</a></h2>
 <p>Charts are not meaningful if they are not readable! Take a moment to consider styling the width and height of your chart to scale well with your data. If one variable (such as all 50 states) need to be displayed, show them vertically on the Y axis if possible so as to avoid a horizontally-scrolling chart.</p>
 <p>Label your axes, provide a legend if necessary, and offer tooltips for better comprehension of data.</p>
 <p>If your data is textual and verbose on the X axis, you can angle the text for better readability. <a class="reference external" href="https://matplotlib.org/stable/tutorials/toolkits/mplot3d.html">Matplotlib</a> offers 3d plotting, if you data supports it. Sophisticated data visualizations can be produced using <code class="docutils literal notranslate"><span class="pre">mpl_toolkits.mplot3d</span></code>.</p>
 <p><img alt="3d plots" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/data-science/data-visualization/3d.png" /></p>
 </section>
 <section id="animation-and-3d-chart-display">
-<h2><span class="section-number">7.4.5. </span>Animation and 3D chart display<a class="headerlink" href="#animation-and-3d-chart-display" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.4.5. </span>Animation and 3D chart display<a class="headerlink" href="#animation-and-3d-chart-display" title="Permalink to this headline">#</a></h2>
 <p>Some of the best data visualizations today are animated. Shirley Wu has amazing ones done with D3, such as ‘<a class="reference external" href="http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/">film flowers</a>’, where each flower is a visualization of a movie. Another example for the Guardian is ‘bussed out’, an interactive experience combining visualizations with Greensock and D3 plus a scrollytelling article format to show how NYC handles its homeless problem by bussing people out of the city.</p>
 <p><img alt="busing" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/data-science/data-visualization/busing.png" /></p>
 <p>Bussed out: How America moves thousands of homeless people around the country. (n.d.). The Guardian. Retrieved 5 November 2022, from <a class="reference external" href="http://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study">http://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study</a></p>
@@ -1813,7 +1813,7 @@ <h2><span class="section-number">7.4.5. </span>Animation and 3D chart display<a
 <p><img alt="liaisons" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/data-science/data-visualization/liaisons.png" /></p>
 </section>
 <section id="project-build-a-chart-to-show-a-network-using-d3-js">
-<h2><span class="section-number">7.4.6. </span>Project: Build a chart to show a network using D3.js<a class="headerlink" href="#project-build-a-chart-to-show-a-network-using-d3-js" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.4.6. </span>Project: Build a chart to show a network using D3.js<a class="headerlink" href="#project-build-a-chart-to-show-a-network-using-d3-js" title="Permalink to this headline">#</a></h2>
 <blockquote>
 <div><p>This section includes a <code class="docutils literal notranslate"><span class="pre">solution</span></code> folder where you can find the completed project, for your reference.</p>
 </div></blockquote>
@@ -1844,7 +1844,7 @@ <h2><span class="section-number">7.4.6. </span>Project: Build a chart to show a
 <p>Run your app from the terminal (npm run serve) and enjoy the visualization!</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">7.4.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.4.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>Here are some articles to read about deceptive data visualization:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://gizmodo.com/how-to-lie-with-data-visualization-1563576606">How to Lie With Data Visualization</a></p></li>
@@ -1860,7 +1860,7 @@ <h2><span class="section-number">7.4.7. </span>Self study<a class="headerlink" h
 </ul>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">7.4.8. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.4.8. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Take a tour of the internet to discover deceptive visualizations. How does the author fool the user, and is it intentional? Try correcting the visualizations to show how they should look.</p>
 <p>Assignment -</p>
 <ul class="simple">
@@ -1869,7 +1869,7 @@ <h2><span class="section-number">7.4.8. </span>Your turn! 🚀<a class="headerli
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">7.4.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.4.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1911,13 +1911,13 @@ <h2><span class="section-number">7.4.9. </span>Acknowledgments<a class="headerli
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-            <p class="prev-next-title"><span class="section-number">7.3. </span>Visualizing relationships: all about honey 🍯</p>
+            <p class="prev-next-title"><span class="section-number">6.3. </span>Visualizing relationships: all about honey 🍯</p>
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-        <p class="prev-next-title"><span class="section-number">8. </span>Data Science lifecycle</p>
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="7.2. Visualizing proportions" href="visualization-proportions.html" />
-    <link rel="prev" title="7. Data visualization" href="data-visualization.html" />
+    <link rel="next" title="6.2. Visualizing proportions" href="visualization-proportions.html" />
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+     40.14. Try it in Excel
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-     41.21. Soda profits
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   <a class="reference internal nav-link" href="#explore-the-birds-dataset">
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+   6.1.6. Your turn! 🚀
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-   7.1.7. Acknowledgments
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 </ul>
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 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="visualizing-distributions">
-<h1><span class="section-number">7.1. </span>Visualizing distributions<a class="headerlink" href="#visualizing-distributions" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">6.1. </span>Visualizing distributions<a class="headerlink" href="#visualizing-distributions" title="Permalink to this headline">#</a></h1>
 <p>In the previous section, you learned some interesting facts about a dataset about the birds of Minnesota. You found some erroneous data by visualizing outliers and looked at the differences between bird categories by their maximum length.</p>
 <section id="explore-the-birds-dataset">
-<h2><span class="section-number">7.1.1. </span>Explore the birds dataset<a class="headerlink" href="#explore-the-birds-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.1.1. </span>Explore the birds dataset<a class="headerlink" href="#explore-the-birds-dataset" title="Permalink to this headline">#</a></h2>
 <p>Another way to dig into data is by looking at its distribution, or how the data is organized along an axis. Perhaps, for example, you’d like to learn about the general distribution, for this dataset, of the maximum wingspan or maximum body mass for the birds of Minnesota.</p>
 <p>Let’s discover some facts about the distributions of data in this dataset.</p>
 <div class="cell tag_output_scroll tag_hide-input docutils container">
@@ -1847,7 +1847,7 @@ <h2><span class="section-number">7.1.1. </span>Explore the birds dataset<a class
 <p>This gives an overview of the general distribution of body length per bird Order, but it is not the optimal way to display true distributions. That task is usually handled by creating a Histogram.</p>
 </section>
 <section id="working-with-histograms">
-<h2><span class="section-number">7.1.2. </span>Working with histograms<a class="headerlink" href="#working-with-histograms" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.1.2. </span>Working with histograms<a class="headerlink" href="#working-with-histograms" title="Permalink to this headline">#</a></h2>
 <p>Matplotlib offers very good ways to visualize data distribution using Histograms. This type of chart is like a bar chart where the distribution can be seen via a rise and fall of the bars. To build a histogram, you need numeric data. To build a Histogram, you can plot a chart defining the kind as ‘hist’ for Histogram. This chart shows the distribution of MaxBodyMass for the entire dataset’s range of numeric data. By dividing the array of data it is given into smaller bins, it can display the distribution of the data’s values:</p>
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 <p>Histograms work well by default for numeric data. What if you need to see distributions according to text data?</p>
 </section>
 <section id="explore-the-dataset-for-distributions-using-text-data">
-<h2><span class="section-number">7.1.3. </span>Explore the dataset for distributions using text data<a class="headerlink" href="#explore-the-dataset-for-distributions-using-text-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.1.3. </span>Explore the dataset for distributions using text data<a class="headerlink" href="#explore-the-dataset-for-distributions-using-text-data" title="Permalink to this headline">#</a></h2>
 <p>This dataset also includes good information about the bird category and its genus, species, and family as well as its conservation status. Let’s dig into this conservation information. What is the distribution of the birds according to their conservation status?</p>
 <p>In the dataset, several acronyms are used to describe conservation status. These acronyms come from the <a class="reference external" href="https://www.iucnredlist.org/">IUCN Red List Categories</a>, an organization that catalogs species’ status.</p>
 <blockquote>
@@ -1951,7 +1951,7 @@ <h2><span class="section-number">7.1.3. </span>Explore the dataset for distribut
 <p>There doesn’t seem to be a good correlation between minimum wingspan and conservation status. Test other elements of the dataset using this method. You can try different filters as well. Do you find any correlation?</p>
 </section>
 <section id="density-plots">
-<h2><span class="section-number">7.1.4. </span>Density plots<a class="headerlink" href="#density-plots" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.1.4. </span>Density plots<a class="headerlink" href="#density-plots" title="Permalink to this headline">#</a></h2>
 <p>You may have noticed that the histograms we have looked at so far are ‘stepped’ and do not flow smoothly in an arc. To show a smoother density chart, you can try a density plot.</p>
 <p>To work with density plots, familiarize yourself with a new plotting library, <a class="reference external" href="https://seaborn.pydata.org/generated/seaborn.kdeplot.html">Seaborn</a>.</p>
 <p>Loading Seaborn, try a basic density plot:</p>
@@ -2039,16 +2039,16 @@ <h2><span class="section-number">7.1.4. </span>Density plots<a class="headerlink
 <p>Perhaps it’s worth researching whether the cluster of ‘Vulnerable’ birds according to their lengths is meaningful or not.</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">7.1.5. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.1.5. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>In this section, you used Matplotlib and started working with Seaborn to show more sophisticated charts. Do some research on <code class="docutils literal notranslate"><span class="pre">kdeplot</span></code> in Seaborn, a “continuous probability density curve in one or more dimensions”. Read through <a class="reference external" href="https://seaborn.pydata.org/generated/seaborn.kdeplot.html">the documentation</a> to understand how it works.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">7.1.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.1.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Histograms are a more sophisticated type of chart than basic scatterplots, bar charts, or line charts. Go on a search on the internet to find good examples of the use of histograms. How are they used, what do they demonstrate, and in what fields or areas of inquiry do they tend to be used?</p>
 <p>Assignment - <a class="reference external" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/assignments/data-science/apply-your-skills.md">Apply your skills</a></p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">7.1.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.1.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2090,13 +2090,13 @@ <h2><span class="section-number">7.1.7. </span>Acknowledgments<a class="headerli
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-            <p class="prev-next-title"><span class="section-number">7. </span>Data visualization</p>
+            <p class="prev-next-title"><span class="section-number">6. </span>Data visualization</p>
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-        <p class="prev-next-title"><span class="section-number">7.2. </span>Visualizing proportions</p>
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diff --git a/data-science/data-visualization/visualization-proportions.html b/data-science/data-visualization/visualization-proportions.html
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-    <title>7.2. Visualizing proportions &#8212; Ocademy Open Machine Learning Book</title>
+    <title>6.2. Visualizing proportions &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="7.3. Visualizing relationships: all about honey 🍯" href="visualization-relationships.html" />
-    <link rel="prev" title="7.1. Visualizing distributions" href="visualization-distributions.html" />
+    <link rel="next" title="6.3. Visualizing relationships: all about honey 🍯" href="visualization-relationships.html" />
+    <link rel="prev" title="6.1. Visualizing distributions" href="visualization-distributions.html" />
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 <section class="tex2jax_ignore mathjax_ignore" id="visualizing-proportions">
-<h1><span class="section-number">7.2. </span>Visualizing proportions<a class="headerlink" href="#visualizing-proportions" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">6.2. </span>Visualizing proportions<a class="headerlink" href="#visualizing-proportions" title="Permalink to this headline">#</a></h1>
 <p>In this section, you will use a different nature-focused dataset to visualize proportions, such as how many different types of fungi populate a given dataset about mushrooms. Let’s explore these fascinating fungi using a dataset sourced from Audubon listing details about 23 species of gilled mushrooms in the Agaricus and Lepiota families. You will experiment with tasty visualizations such as:</p>
 <ul class="simple">
 <li><p>Pie charts 🥧</p></li>
@@ -1693,7 +1693,7 @@ <h1><span class="section-number">7.2. </span>Visualizing proportions<a class="he
 </ul>
 <p>A very interesting project called <a class="reference external" href="https://charticulator.com">Charticulator</a> by Microsoft Research offers a free drag and drop interface for data visualizations. In one of their tutorials they also use this mushroom dataset! So you can explore the data and learn the library at the same time: <a class="reference external" href="https://charticulator.com/tutorials/tutorial4.html">Charticulator tutorial</a>.</p>
 <section id="get-to-know-your-mushrooms">
-<h2><span class="section-number">7.2.1. </span>Get to know your mushrooms 🍄<a class="headerlink" href="#get-to-know-your-mushrooms" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.2.1. </span>Get to know your mushrooms 🍄<a class="headerlink" href="#get-to-know-your-mushrooms" title="Permalink to this headline">#</a></h2>
 <p>Mushrooms are very interesting. Let’s import a dataset to study them:</p>
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@@ -2032,7 +2032,7 @@ <h2><span class="section-number">7.2.1. </span>Get to know your mushrooms 🍄<a
 <p>If you follow the order presented in this table to create your class category labels, you can build a pie chart:</p>
 </section>
 <section id="pie">
-<h2><span class="section-number">7.2.2. </span>Pie!<a class="headerlink" href="#pie" title="Permalink to this headline">#</a></h2>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Edible&#39;</span><span class="p">,</span> <span class="s1">&#39;Poisonous&#39;</span><span class="p">]</span>
@@ -2049,7 +2049,7 @@ <h2><span class="section-number">7.2.2. </span>Pie!<a class="headerlink" href="#
 <p>Voila, a pie chart showing the proportions of this data according to these two classes of mushrooms. It’s quite important to get the order of the labels correct, especially here, so be sure to verify the order with which the label array is built!</p>
 </section>
 <section id="donuts">
-<h2><span class="section-number">7.2.3. </span>Donuts!<a class="headerlink" href="#donuts" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.2.3. </span>Donuts!<a class="headerlink" href="#donuts" title="Permalink to this headline">#</a></h2>
 <p>A somewhat more visually interesting pie chart is a donut chart, which is a pie chart with a hole in the middle. Let’s look at our data using this method.</p>
 <p>Take a look at the various habitats where mushrooms grow:</p>
 <div class="cell tag_output-scoll tag_hide-input docutils container">
@@ -2327,7 +2327,7 @@ <h2><span class="section-number">7.2.3. </span>Donuts!<a class="headerlink" href
 <p>Now that you know how to group your data and then display it as a pie or donut, you can explore other types of charts. Try a waffle chart, which is just a different way of exploring quantity.</p>
 </section>
 <section id="waffles">
-<h2><span class="section-number">7.2.4. </span>Waffles!<a class="headerlink" href="#waffles" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.2.4. </span>Waffles!<a class="headerlink" href="#waffles" title="Permalink to this headline">#</a></h2>
 <p>A ‘waffle’ type chart is a different way to visualize quantities as a 2D array of squares. Try visualizing the different quantities of mushroom cap colors in this dataset. To do this, you need to install a helper library called <a class="reference external" href="https://pypi.org/project/pywaffle/">PyWaffle</a> and use Matplotlib.</p>
 <p>Select a segment of your data to group together:</p>
 <div class="cell tag_output-scoll tag_hide-input docutils container">
@@ -2657,7 +2657,7 @@ <h2><span class="section-number">7.2.4. </span>Waffles!<a class="headerlink" hre
 <p>In this section, you learned three ways to visualize proportions. First, you need to group your data into categories and then decide which is the best way to display the data - pie, donut, or waffle. All are delicious and gratify the user with an instant snapshot of a dataset.</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">7.2.5. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.2.5. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>Sometimes it’s not obvious when to use a pie, donut, or waffle chart. Here are some articles to read on this topic:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://www.beautiful.ai/blog/battle-of-the-charts-pie-chart-vs-donut-chart">Battle of the Charts: Pie Chart vs. Donut Chart| The Beautiful Blog</a></p></li>
@@ -2668,12 +2668,12 @@ <h2><span class="section-number">7.2.5. </span>Self study<a class="headerlink" h
 <p>Do some research to find more information on this sticky decision.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">7.2.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.2.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Try recreating these tasty charts in <a class="reference external" href="https://charticulator.com">Charticulator</a>.</p>
 <p>Assignment - <a class="reference external" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/assignments/data-science/try-it-in-excel.md">Try it in Excel</a></p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">7.2.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.2.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2715,13 +2715,13 @@ <h2><span class="section-number">7.2.7. </span>Acknowledgments<a class="headerli
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">7.1. </span>Visualizing distributions</p>
+            <p class="prev-next-title"><span class="section-number">6.1. </span>Visualizing distributions</p>
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-        <p class="prev-next-title"><span class="section-number">7.3. </span>Visualizing relationships: all about honey 🍯</p>
+        <p class="prev-next-title"><span class="section-number">6.3. </span>Visualizing relationships: all about honey 🍯</p>
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diff --git a/data-science/data-visualization/visualization-relationships.html b/data-science/data-visualization/visualization-relationships.html
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-    <title>7.3. Visualizing relationships: all about honey 🍯 &#8212; Ocademy Open Machine Learning Book</title>
+    <title>6.3. Visualizing relationships: all about honey 🍯 &#8212; Ocademy Open Machine Learning Book</title>
     
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 </div>
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 <section class="tex2jax_ignore mathjax_ignore" id="visualizing-relationships-all-about-honey">
-<h1><span class="section-number">7.3. </span>Visualizing relationships: all about honey 🍯<a class="headerlink" href="#visualizing-relationships-all-about-honey" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">6.3. </span>Visualizing relationships: all about honey 🍯<a class="headerlink" href="#visualizing-relationships-all-about-honey" title="Permalink to this headline">#</a></h1>
 <p>Continuing with the nature focus of our research, let’s discover interesting visualizations to show the relationships between various types of honey, according to a dataset derived from the <a class="reference external" href="https://www.nass.usda.gov/About_NASS/index.php">United States Department of Agriculture</a>.</p>
 <p>This dataset of about 600 items displays honey production in many U.S. states. So, for example, you can look at the number of colonies, yield per colony, total production, stocks, price per pound, and value of the honey produced in a given state from 1998-2012, with one row per year for each state.</p>
 <p>It will be interesting to visualize the relationship between a given state’s production per year and, for example, the price of honey in that state. Alternatively, you could visualize the relationship between states’ honey yield per colony. This year’s span covers the devastating ‘CCD’ or ‘<a class="reference external" href="http://npic.orst.edu/envir/ccd.html">Colony Collapse Disorder</a>’ first seen in 2006, so it is a poignant dataset to study. 🐝</p>
 <p>In this section, you can use Seaborn, which you have used before, as a good library to visualize relationships between variables. Particularly interesting is the use of Seaborn’s <code class="docutils literal notranslate"><span class="pre">relplot</span></code> function that allows scatter plots and line plots to quickly visualize ‘<a class="reference external" href="https://seaborn.pydata.org/tutorial/relational.html?highlight=relationships">statistical relationships</a>’, which allows the data scientist to better understand how variables relate to each other.</p>
 <section id="scatterplots">
-<h2><span class="section-number">7.3.1. </span>Scatterplots<a class="headerlink" href="#scatterplots" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.3.1. </span>Scatterplots<a class="headerlink" href="#scatterplots" title="Permalink to this headline">#</a></h2>
 <p>Use a scatterplot to show how the price of honey has evolved, year over year, per state. Seaborn, using <code class="docutils literal notranslate"><span class="pre">relplot</span></code>, conveniently groups the state data and displays data points for both categorical and numeric data.</p>
 <p>Let’s start by importing the data and Seaborn:</p>
 <div class="cell tag_output-scoll tag_hide-input docutils container">
@@ -1984,7 +1984,7 @@ <h2><span class="section-number">7.3.1. </span>Scatterplots<a class="headerlink"
 <p>To discover a correlation between some of the variables in this dataset, let’s explore some line charts.</p>
 </section>
 <section id="line-charts">
-<h2><span class="section-number">7.3.2. </span>Line charts<a class="headerlink" href="#line-charts" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.3.2. </span>Line charts<a class="headerlink" href="#line-charts" title="Permalink to this headline">#</a></h2>
 <p>Question: Is there a clear rise in price of honey per pound year over year? You can most easily discover that by creating a single line chart:</p>
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@@ -2014,7 +2014,7 @@ <h2><span class="section-number">7.3.2. </span>Line charts<a class="headerlink"
 <p>To discover this, you can explore a facet grid.</p>
 </section>
 <section id="facet-grids">
-<h2><span class="section-number">7.3.3. </span>Facet grids<a class="headerlink" href="#facet-grids" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.3.3. </span>Facet grids<a class="headerlink" href="#facet-grids" title="Permalink to this headline">#</a></h2>
 <p>Facet grids take one facet of your dataset (in our case, you can choose ‘year’ to avoid having too many facets produced). Seaborn can then make a plot for each of those facets of your chosen x and y coordinates for more easy visual comparison. Does 2003 stand out in this type of comparison?</p>
 <p>Create a facet grid by continuing to use <code class="docutils literal notranslate"><span class="pre">relplot</span></code> as recommended by <a class="reference external" href="https://seaborn.pydata.org/generated/seaborn.FacetGrid.html?highlight=facetgrid#seaborn.FacetGrid">Seaborn’s documentation</a>.</p>
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 </div>
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-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;seaborn.axisgrid.FacetGrid at 0x7f93e4585550&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;seaborn.axisgrid.FacetGrid at 0x7fe404455940&gt;
 </pre></div>
 </div>
 <img alt="../../_images/visualization-relationships_18_1.png" src="../../_images/visualization-relationships_18_1.png" />
@@ -2039,7 +2039,7 @@ <h2><span class="section-number">7.3.3. </span>Facet grids<a class="headerlink"
 <p>For this dataset, nothing particularly stands out with regards to the number of colonies and their yield, year over year and state over state. Is there a different way to look at finding a correlation between these two variables?</p>
 </section>
 <section id="dual-line-plots">
-<h2><span class="section-number">7.3.4. </span>Dual-line Plots<a class="headerlink" href="#dual-line-plots" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.3.4. </span>Dual-line Plots<a class="headerlink" href="#dual-line-plots" title="Permalink to this headline">#</a></h2>
 <p>Try a multiline plot by superimposing two lineplots on top of each other, using Seaborn’s <code class="docutils literal notranslate"><span class="pre">despine</span></code> to remove their top and right spines, and using <code class="docutils literal notranslate"><span class="pre">ax.twinx</span></code> <a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.twinx.html">derived from Matplotlib</a>. Twinx allows a chart to share the x axis and display two y axes. So, display the yield per colony and the number of colonies, superimposed:</p>
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@@ -2068,16 +2068,16 @@ <h2><span class="section-number">7.3.4. </span>Dual-line Plots<a class="headerli
 <p>🐝❤️</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">7.3.5. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.3.5. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>Line plots can be simple or quite complex. Do a bit of reading in the <a class="reference external" href="https://seaborn.pydata.org/generated/seaborn.lineplot.html">Seaborn documentation</a> on the various ways you can build them. Try to enhance the line charts you built in this section with other methods listed in the docs.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">7.3.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.3.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>In this section, you learned a bit more about other uses of scatterplots and line grids, including facet grids. Challenge yourself to create a facet grid using a different dataset, maybe one you used prior to these sections. Note how long they take to create and how you need to be careful about how many grids you need to draw using these techniques.</p>
 <p>Assignment - <a class="reference external" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/assignments/data-science/dive-into-the-beehive.md">Dive into the beehive</a></p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">7.3.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">6.3.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
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               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="data-science-ethics">
-<h1><span class="section-number">5.2. </span>Data Science ethics<a class="headerlink" href="#data-science-ethics" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">4.2. </span>Data Science ethics<a class="headerlink" href="#data-science-ethics" title="Permalink to this headline">#</a></h1>
 <p>We are all data citizens living in a datafied world.</p>
 <p>Market trends tell us that by 2022, 1-in-3 large organizations will buy and sell their data through online <a class="reference external" href="https://www.gartner.com/smarterwithgartner/gartner-top-10-trends-in-data-and-analytics-for-2020/">Marketplaces and Exchanges</a>. As <strong>app developers</strong>, we’ll find it easier and cheaper to integrate data-driven insights and algorithm-driven automation into daily user experiences. But as AI becomes pervasive, we’ll also need to understand the potential harms caused by the <a class="reference external" href="https://www.youtube.com/watch?v=TQHs8SA1qpk">weaponization</a> of such algorithms at scale.</p>
 <p>Trends also indicate that we will create and consume over <a class="reference external" href="https://www.statista.com/statistics/871513/worldwide-data-created/">180 zettabytes</a> of data by 2025. As <strong>Data Scientists</strong>, this gives us unprecedented levels of access to personal data. This means we can build behavioral profiles of users and influence decision-making in ways that create an <a class="reference external" href="https://www.verywellmind.com/what-is-the-illusion-of-choice-5224973">illusion of free choice</a> while potentially nudging users towards outcomes we prefer. It also raises broader questions on data privacy and user protections.</p>
@@ -1749,12 +1749,12 @@ <h1><span class="section-number">5.2. </span>Data Science ethics<a class="header
 <figure class="align-default" id="gartner-hype-cycle-for-ai-2020">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/hype-cycle-for-ai.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/hype-cycle-for-ai.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 5.3 </span><span class="caption-text">Gartner Hype Cycle for AI - 2020</span><a class="headerlink" href="#gartner-hype-cycle-for-ai-2020" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 4.3 </span><span class="caption-text">Gartner Hype Cycle for AI - 2020</span><a class="headerlink" href="#gartner-hype-cycle-for-ai-2020" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>In this section, we’ll explore the fascinating area of data ethics - from core concepts and challenges, to case studies and applied AI concepts like governance - that help establish an ethics culture in teams and organizations that work with data and AI.</p>
 <section id="basic-definitions">
-<h2><span class="section-number">5.2.1. </span>Basic definitions<a class="headerlink" href="#basic-definitions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.2.1. </span>Basic definitions<a class="headerlink" href="#basic-definitions" title="Permalink to this headline">#</a></h2>
 <p>Let’s start by understanding the basic terminology.</p>
 <p>The word “ethics” comes from the <a class="reference external" href="https://en.wikipedia.org/wiki/Ethics">Greek word “ethikos”</a> (and its root “ethos”) meaning <em>character or moral nature</em>.</p>
 <p><strong>Ethics</strong> is about the shared values and moral principles that govern our behavior in society. Ethics is based not on laws but on widely accepted norms of what is “right vs. wrong”. However, ethical considerations can influence corporate governance initiatives and government regulations that create more incentives for compliance.</p>
@@ -1763,16 +1763,16 @@ <h2><span class="section-number">5.2.1. </span>Basic definitions<a class="header
 <p><strong>Ethics culture</strong> is about <a class="reference external" href="https://hbr.org/2019/05/how-to-design-an-ethical-organization"><em>operationalizing</em> applied ethics</a> to make sure that our ethical principles and practices are adopted in a consistent and scalable manner across the entire organization. Successful ethics cultures define organization-wide ethical principles, provide meaningful incentives for compliance, and reinforce ethics norms by encouraging and amplifying desired behaviors at every level of the organization.</p>
 </section>
 <section id="ethics-concepts">
-<h2><span class="section-number">5.2.2. </span>Ethics concepts<a class="headerlink" href="#ethics-concepts" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.2.2. </span>Ethics concepts<a class="headerlink" href="#ethics-concepts" title="Permalink to this headline">#</a></h2>
 <p>In this section, we’ll discuss concepts like <strong>shared values</strong> (principles) and <strong>ethical challenges</strong> (problems) for data ethics - and explore <strong>case studies</strong> that help you understand these concepts in real-world contexts.</p>
 <section id="ethics-principles">
-<h3><span class="section-number">5.2.2.1. </span>Ethics principles<a class="headerlink" href="#ethics-principles" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">4.2.2.1. </span>Ethics principles<a class="headerlink" href="#ethics-principles" title="Permalink to this headline">#</a></h3>
 <p>Every data ethics strategy begins by defining <em>ethical principles</em> - the “shared values” that describe acceptable behaviors, and guide compliant actions, in our data &amp; AI projects. You can define these at an individual or team level. However, most large organizations outline these in an <em>ethical AI</em> mission statement or framework that is defined at corporate levels and enforced consistently across all teams.</p>
 <p><strong>Example:</strong> Microsoft’s <a class="reference external" href="https://www.microsoft.com/en-us/ai/responsible-ai">Responsible AI</a> mission statement reads: <em>“We are committed to the advancement of AI-driven by ethical principles that put people first”</em> - identifying 6 ethical principles in the framework below:</p>
 <figure class="align-default" id="responsible-ai-at-microsoft">
 <img alt="https://docs.microsoft.com/en-gb/azure/cognitive-services/personalizer/media/ethics-and-responsible-use/ai-values-future-computed.png" src="https://docs.microsoft.com/en-gb/azure/cognitive-services/personalizer/media/ethics-and-responsible-use/ai-values-future-computed.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 5.4 </span><span class="caption-text">Responsible AI at Microsoft</span><a class="headerlink" href="#responsible-ai-at-microsoft" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 4.4 </span><span class="caption-text">Responsible AI at Microsoft</span><a class="headerlink" href="#responsible-ai-at-microsoft" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Let’s briefly explore these principles. <em>Transparency</em> and <em>accountability</em> are foundational values that other principles built upon - so let’s begin there:</p>
@@ -1794,7 +1794,7 @@ <h3><span class="section-number">5.2.2.1. </span>Ethics principles<a class="head
 </div>
 </section>
 <section id="ethics-challenges">
-<h3><span class="section-number">5.2.2.2. </span>Ethics challenges<a class="headerlink" href="#ethics-challenges" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">4.2.2.2. </span>Ethics challenges<a class="headerlink" href="#ethics-challenges" title="Permalink to this headline">#</a></h3>
 <p>Once we have ethical principles defined, the next step is to evaluate our data and AI actions to see if they align with those shared values. Think about your actions in two categories: <em>data collection</em> and <em>algorithm design</em>.</p>
 <p>With data collection, actions will likely involve <strong>personal data</strong> or personally identifiable information (PII) for identifiable living individuals. This includes <a class="reference external" href="https://ec.europa.eu/info/law/law-topic/data-protection/reform/what-personal-data_en">diverse items of non-personal data</a> that <em>collectively</em> identify an individual. Ethical challenges can relate to <em>data privacy</em>, <em>data ownership</em>, and related topics like <em>informed consent</em> and <em>intellectual property rights</em> for users.</p>
 <p>With algorithm design, actions will involve collecting &amp; curating <strong>datasets</strong>, then using them to train &amp; deploy <strong>data models</strong> that predict outcomes or automate decisions in real-world contexts. Ethical challenges can arise from <em>dataset bias</em>, <em>data quality</em> issues, <em>unfairness</em>, and <em>misrepresentation</em> in algorithms - including some issues that are systemic in nature.</p>
@@ -1887,7 +1887,7 @@ <h3><span class="section-number">5.2.2.2. </span>Ethics challenges<a class="head
 </ul>
 </section>
 <section id="case-studies">
-<h3><span class="section-number">5.2.2.3. </span>Case studies<a class="headerlink" href="#case-studies" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">4.2.2.3. </span>Case studies<a class="headerlink" href="#case-studies" title="Permalink to this headline">#</a></h3>
 <p>To put these ethical challenges in real-world contexts, it helps to look at case studies that highlight the potential harms and consequences to individuals and society, when such ethics violations are overlooked.</p>
 <p>Here are a few examples:</p>
 <table class="colwidths-auto table">
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 </section>
 </section>
 <section id="applied-ethics">
-<h2><span class="section-number">5.2.3. </span>Applied ethics<a class="headerlink" href="#applied-ethics" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.2.3. </span>Applied ethics<a class="headerlink" href="#applied-ethics" title="Permalink to this headline">#</a></h2>
 <p>We’ve talked about ethics concepts, challenges, and case studies in real-world contexts. But how do we get started <em>applying</em> ethical principles and practices in our projects? And how do we <em>operationalize</em> these practices for better governance? Let’s explore some real-world solutions:</p>
 <section id="professional-codes">
-<h3><span class="section-number">5.2.3.1. </span>Professional codes<a class="headerlink" href="#professional-codes" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">4.2.3.1. </span>Professional codes<a class="headerlink" href="#professional-codes" title="Permalink to this headline">#</a></h3>
 <p>Professional codes offer one option for organizations to “incentivize” members to support their ethical principles and mission statement. Codes are <em>moral guidelines</em> for professional behavior, helping employees or members make decisions that align with their organization’s principles. They are only as good as voluntary compliance from members; however, many organizations offer additional rewards and penalties to motivate compliance from members.</p>
 <p>Examples include:</p>
 <ul class="simple">
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 </div>
 </section>
 <section id="ethics-checklists">
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+<h3><span class="section-number">4.2.3.2. </span>Ethics checklists<a class="headerlink" href="#ethics-checklists" title="Permalink to this headline">#</a></h3>
 <p>While professional codes define required <em>ethical behavior</em> from practitioners, they <a class="reference external" href="https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md">have known limitations</a> in enforcement, particularly in large-scale projects. Instead, many data Science experts <a class="reference external" href="https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md">advocate for checklists</a>, that can <strong>connect principles to practices</strong> in more deterministic and actionable ways.</p>
 <p>Checklists convert questions into “yes/no” tasks that can be operationalized, allowing them to be tracked as part of standard product release workflows.</p>
 <p>Examples include:</p>
@@ -1965,7 +1965,7 @@ <h3><span class="section-number">5.2.3.2. </span>Ethics checklists<a class="head
 </ul>
 </section>
 <section id="ethics-regulations">
-<h3><span class="section-number">5.2.3.3. </span>Ethics regulations<a class="headerlink" href="#ethics-regulations" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">4.2.3.3. </span>Ethics regulations<a class="headerlink" href="#ethics-regulations" title="Permalink to this headline">#</a></h3>
 <p>Ethics is about defining shared values and doing the right thing <em>voluntarily</em>. <strong>Compliance</strong> is about <em>following the law</em> if and where defined. <strong>Governance</strong> broadly covers all the ways in which organizations operate to enforce ethical principles and comply with established laws.</p>
 <p>Today, governance takes two forms within organizations. First, it’s about defining <strong>ethical AI</strong> principles and establishing practices to operationalize adoption across all AI-related projects in the organization. Second, it’s about complying with all government-mandated <strong>data protection regulations</strong> for regions it operates in.</p>
 <p>Examples of data protection and privacy regulations:</p>
@@ -1982,17 +1982,17 @@ <h3><span class="section-number">5.2.3.3. </span>Ethics regulations<a class="hea
 </div></blockquote>
 </section>
 <section id="ethics-culture">
-<h3><span class="section-number">5.2.3.4. </span>Ethics culture<a class="headerlink" href="#ethics-culture" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">4.2.3.4. </span>Ethics culture<a class="headerlink" href="#ethics-culture" title="Permalink to this headline">#</a></h3>
 <p>Note that there remains an intangible gap between <em>compliance</em> (doing enough to meet “the letter of the law”) and addressing <a class="reference external" href="https://www.coursera.org/learn/data-science-ethics/home/week/4">systemic issues</a> (like ossification, information asymmetry, and distributional unfairness) that can speed up the weaponization of AI.</p>
 <p>The latter requires <a class="reference external" href="https://towardsdatascience.com/why-ai-ethics-requires-a-culture-driven-approach-26f451afa29f">collaborative approaches to defining ethics cultures</a> that build emotional connections and consistent shared values <em>across organizations</em> in the industry. This calls for more <a class="reference external" href="https://www.codeforamerica.org/news/formalizing-an-ethical-data-culture/">formalized data ethics cultures</a> in organizations - allowing <em>anyone</em> to <a class="reference external" href="https://en.wikipedia.org/wiki/Andon_(manufacturing)">pull the Andon cord</a> (to raise ethics concerns early in the process) and making <em>ethical assessments</em> (e.g., in hiring) a core criterion for team formation in AI projects.</p>
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">5.2.4. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.2.4. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p><a class="reference external" href="https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/write-a-data-ethics-case-study.md">Write a data ethics case study</a></p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">5.2.5. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.2.5. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>Courses and books help with understanding core ethics concepts and challenges, while case studies and tools help with applied ethics practices in real-world contexts. Here are a few resources to start with.</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://github.com/microsoft/ML-For-Beginners/blob/main/1-Introduction/3-fairness/README.md">Machine Learning For Beginners</a> - section on Fairness, from Microsoft.</p></li>
@@ -2003,7 +2003,7 @@ <h2><span class="section-number">5.2.5. </span>Self study<a class="headerlink" h
 </ul>
 </section>
 <section id="acknowledgments">
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+<h2><span class="section-number">4.2.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 <hr class="docutils" />
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-     41.45. Linear regression - California Housing
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               <div>
                 
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-<h1><span class="section-number">5.1. </span>Defining data science<a class="headerlink" href="#defining-data-science" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">4.1. </span>Defining data science<a class="headerlink" href="#defining-data-science" title="Permalink to this headline">#</a></h1>
 <section id="what-is-data">
-<h2><span class="section-number">5.1.1. </span>What is data?<a class="headerlink" href="#what-is-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.1.1. </span>What is data?<a class="headerlink" href="#what-is-data" title="Permalink to this headline">#</a></h2>
 <p>In our everyday life, we are constantly surrounded by data. The text you are reading now is data. The list of phone numbers of your friends on your smartphone is data, as well as the current time displayed on your watch. As human beings, we naturally operate with data by counting the money we have or by writing letters to our friends.</p>
 <p>However, data became much more critical with the creation of computers. The primary role of computers is to perform computations, but they need data to operate on. Thus, we need to understand how computers store and process data.</p>
 <p>With the emergence of the Internet, the role of computers as data-handling devices increased. If you think about it, we now use computers more and more for data processing and communication, rather than actual computations. When we write an e-mail to a friend or search for some information on the Internet - we are essentially creating, storing, transmitting, and manipulating data.</p>
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+<h2><span class="section-number">4.1.2. </span>What is Data Science?<a class="headerlink" href="#what-is-data-science" title="Permalink to this headline">#</a></h2>
 <p>In <a class="reference external" href="https://en.wikipedia.org/wiki/Data_science">Wikipedia</a>, <strong>Data Science</strong> is defined as <em>a scientific field that uses scientific methods to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains</em>.</p>
 <p>This definition highlights the following important aspects of Data Science:</p>
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+<h2><span class="section-number">4.1.3. </span>Other related fields<a class="headerlink" href="#other-related-fields" title="Permalink to this headline">#</a></h2>
 <p>Since data is pervasive, Data Science itself is also a broad field, touching many other disciplines.</p>
 <p><strong>Databases</strong></p>
 <p>A critical consideration is <strong>how to store</strong> the data, i.e. how to structure it in a way that allows faster processing. There are different types of databases that store structured and unstructured data, which <a class="reference external" href="https://static-1300131294.cos.qp-shanghai.myqcloud.com/working-with-data/working-with-data.md">we will consider in our book</a>.</p>
@@ -1752,7 +1752,7 @@ <h2><span class="section-number">5.1.3. </span>Other related fields<a class="hea
 <p>Vast amounts of data are incomprehensible to a human being, but once we create useful visualizations using that data, we can make more sense of the data, and draw some conclusions. Thus, it is important to know many ways to visualize information - something that we will cover in the <a class="reference external" href="https://static-1300131294.cos.qp-shanghai.myqcloud.com/data-visualization/data-visualization.md">Data visualization</a> section. Related fields also include <strong>Infographics</strong> and <strong>Human-Computer Interaction</strong> in general.</p>
 </section>
 <section id="types-of-data">
-<h2><span class="section-number">5.1.4. </span>Types of data<a class="headerlink" href="#types-of-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.1.4. </span>Types of data<a class="headerlink" href="#types-of-data" title="Permalink to this headline">#</a></h2>
 <p>As we have already mentioned, data is everywhere. We just need to capture it in the right way! It is useful to distinguish between <strong>structured</strong> and <strong>unstructured</strong> data. The former is typically represented in some well-structured form, often as a table or number of tables, while the latter is just a collection of files. Sometimes we can also talk about <strong>semi-structured</strong> data, that have some sort of structure that may vary greatly.</p>
 <table class="colwidths-auto table">
 <thead>
@@ -1778,7 +1778,7 @@ <h2><span class="section-number">5.1.4. </span>Types of data<a class="headerlink
 </table>
 </section>
 <section id="where-to-get-data">
-<h2><span class="section-number">5.1.5. </span>Where to get data<a class="headerlink" href="#where-to-get-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.1.5. </span>Where to get data<a class="headerlink" href="#where-to-get-data" title="Permalink to this headline">#</a></h2>
 <p>There are many possible sources of data, and it will be impossible to list all of them! However, let’s mention some of the typical places where you can get data:</p>
 <ul class="simple">
 <li><p><strong>Structured</strong></p>
@@ -1805,7 +1805,7 @@ <h2><span class="section-number">5.1.5. </span>Where to get data<a class="header
 <p>By knowing different possible sources of data, you can try to think about different scenarios where Data Science techniques can be applied to know the situation better and to improve business processes.</p>
 </section>
 <section id="what-you-can-do-with-data">
-<h2><span class="section-number">5.1.6. </span>What you can do with data<a class="headerlink" href="#what-you-can-do-with-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.1.6. </span>What you can do with data<a class="headerlink" href="#what-you-can-do-with-data" title="Permalink to this headline">#</a></h2>
 <p>In Data Science, we focus on the following steps of the data journey:</p>
 <p><strong>Data acquisition.</strong> The first step is to collect the data. While in many cases it can be a straightforward process, like data coming to a database from a web application, sometimes we need to use special techniques. For example, data from IoT sensors can be overwhelming, and it is a good practice to use buffering endpoints such as IoT Hub to collect all the data before further processing.</p>
 <p><strong>Data storage.</strong> Storing data can be challenging, especially if we are talking about big data. When deciding how to store data, it makes sense to anticipate the way you would query the data in the future. There are several ways data can be stored:</p>
@@ -1820,7 +1820,7 @@ <h2><span class="section-number">5.1.6. </span>What you can do with data<a class
 <p>Of course, depending on the actual data, some steps might be missing (e.g., when we already have the data in the database, or when we do not need model training), or some steps might be repeated several times (such as data processing).</p>
 </section>
 <section id="digitalization-and-digital-transformation">
-<h2><span class="section-number">5.1.7. </span>Digitalization and digital transformation<a class="headerlink" href="#digitalization-and-digital-transformation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.1.7. </span>Digitalization and digital transformation<a class="headerlink" href="#digitalization-and-digital-transformation" title="Permalink to this headline">#</a></h2>
 <p>In the last decade, many businesses started to understand the importance of data when making business decisions. To apply Data Science principles to running a business, one first needs to collect some data, i.e. translate business processes into digital form. This is known as <strong>digitalization</strong>. Applying Data Science techniques to this data to guide decisions can lead to significant increases in productivity (or even business pivot), called <strong>digital transformation</strong>.</p>
 <p>Let’s consider an example. Suppose we have a Data Science course (like this one) that we deliver online to students, and we want to use Data Science to improve it. How can we do it?</p>
 <p>We can start by asking “What can be digitized?” The simplest way would be to measure the time it takes each student to complete each module and to measure the obtained knowledge by giving a multiple-choice test at the end of each module. By averaging time-to-complete across all students, we can find out which modules cause the most difficulties for students, and work on simplifying them.</p>
@@ -1832,12 +1832,12 @@ <h2><span class="section-number">5.1.7. </span>Digitalization and digital transf
 <p>If we want to get even more complicated, we can plot the time taken for each module against the age category of students. We might find out that for some age categories, it takes an inappropriately long time to complete the module, or that students drop out before completing it. This can help us provide age recommendations for the module, and minimize people’s dissatisfaction with wrong expectations.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">5.1.8. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.1.8. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>In this challenge, we will try to find concepts relevant to the field of Data Science by looking at texts. We will take a Wikipedia article on Data Science, download and process the text, and then build a word cloud like this one:</p>
 <figure class="align-default" id="world-of-data-science">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/data-science-word-cloud.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/data-science-word-cloud.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 5.2 </span><span class="caption-text">World of Data Science<span id="id1">[<a class="reference internal" href="#id50" title="Raffaele Nolli. How to identify the most requested skills on the data science job market, with data science. July 2020. URL: https://towardsdatascience.com/how-to-identify-the-most-requested-skills-on-the-data-science-job-market-with-data-science-726845ca9638 (visited on 2022-10-01).">Nol20</a>]</span></span><a class="headerlink" href="#world-of-data-science" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 4.2 </span><span class="caption-text">World of Data Science<span id="id1">[<a class="reference internal" href="#id50" title="Raffaele Nolli. How to identify the most requested skills on the data science job market, with data science. July 2020. URL: https://towardsdatascience.com/how-to-identify-the-most-requested-skills-on-the-data-science-job-market-with-data-science-726845ca9638 (visited on 2022-10-01).">Nol20</a>]</span></span><a class="headerlink" href="#world-of-data-science" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Visit <a class="reference external" href="https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/analyzing-text-about-data-science.ipynb">Analyzing text about data science</a> to read through the code. You can also run the code, and see how it performs all data transformations in real-time.</p>
@@ -1847,11 +1847,11 @@ <h2><span class="section-number">5.1.8. </span>Your turn! 🚀<a class="headerli
 </ul>
 </section>
 <section id="self-study">
-<h2><span class="section-number">5.1.9. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.1.9. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>Here is a list of free/open-source learning resources for <a class="reference external" href="https://github.com/ocademy-ai/open-learning-resources/blob/main/README.md#data-science--engineering">Data Science</a>.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">5.1.10. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.1.10. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 <hr class="docutils" />
 <div class="docutils container" id="id2">
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         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">5. </span>Introduction</p>
+            <p class="prev-next-title"><span class="section-number">4. </span>Introduction</p>
         </div>
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     <a class='right-next' id="next-link" href="data-science-ethics.html" title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">5.2. </span>Data Science ethics</p>
+        <p class="prev-next-title"><span class="section-number">4.2. </span>Data Science ethics</p>
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+    <title>4.3. Defining data &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
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+    <link rel="next" title="4.4. Introduction to statistics and probability" href="introduction-to-statistics-and-probability.html" />
+    <link rel="prev" title="4.2. Data Science ethics" href="data-science-ethics.html" />
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+<h1><span class="section-number">4.3. </span>Defining data<a class="headerlink" href="#defining-data" title="Permalink to this headline">#</a></h1>
 <p>Data is facts, information, observations, and measurements that are used to make discoveries and support informed decisions. A data point is a single unit of data within a dataset, which is a collection of data points. Datasets may come in different formats and structures, and will usually be based on their source, or where the data came from. For example, a company’s monthly earnings might be in a spreadsheet but hourly heart rate data from a smartwatch may be in <a class="reference external" href="https://stackoverflow.com/a/383699">JSON</a> format. It’s common for data scientists to work with different types of data within a dataset.</p>
 <p>This section focuses on identifying and classifying data by its characteristics and its sources.</p>
 <section id="how-data-is-described">
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+<h2><span class="section-number">4.3.1. </span>How data is described<a class="headerlink" href="#how-data-is-described" title="Permalink to this headline">#</a></h2>
 <p><strong>Raw data</strong> is data that has come from its source in its initial state and has not been analyzed or organized. In order to make sense of what is happening with a dataset, it needs to be organized into a format that can be understood by humans as well as the technology they may use to analyze it further. The structure of a dataset describes how it’s organized and can be classified as structured, unstructured, and semi-structured. These types of structures will vary, depending on the source but will ultimately fit into these three categories.</p>
 <p><strong>1. Quantitative data</strong></p>
 <p>Quantitative data is numerical observations within a dataset and can typically be analyzed, measured and used mathematically. Some examples of quantitative data are a country’s population, a person’s height, or a company’s quarterly earnings. With some additional analysis, quantitative data could be used to discover seasonal trends of the Air Quality Index (AQI) or estimate the probability of rush hour traffic on a typical work day.</p>
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 <p>Examples of semi-structured data: HTML, CSV files, JavaScript Object Notation (JSON)</p>
 </section>
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+<h2><span class="section-number">4.3.2. </span>Sources of data<a class="headerlink" href="#sources-of-data" title="Permalink to this headline">#</a></h2>
 <p>A data source is an initial location of where the data was generated, or where it “lives” and will vary based on how and when it was collected. Data generated by its user(s) are known as primary data while secondary data comes from a source that has collected data for general use. For example, a group of scientists collecting observations in a rainforest would be considered primary and if they decide to share it with other scientists it would be considered secondary to those that use it.</p>
 <p>Databases are a common source and rely on a database management system to host and maintain the data where users use commands called queries to explore the data. Files as data sources can be audio, image, and video files as well as spreadsheets like Excel. Internet sources are a common location for hosting data, where databases, as well as files, can be found. Application programming interfaces, also known as APIs allow programmers to create ways to share data with external users through the internet, while the process of web scraping extracts data from a web page. The sections in <a class="reference external" href="https://static-1300131294.cos.qp-shanghai.myqcloud.com/working-with-data/working-with-data.md">Working with data</a> focus on how to use various data sources.</p>
 </section>
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+<h2><span class="section-number">4.3.3. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
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+<h3><span class="section-number">4.3.3.1. </span>Task 1<a class="headerlink" href="#task-1" title="Permalink to this headline">#</a></h3>
 <p>Kaggle is an excellent source of open datasets. Use the <a class="reference external" href="https://www.kaggle.com/datasets">dataset search tool</a> to find some interesting datasets and classify 3-5 datasets with these criteria:</p>
 <ul class="simple">
 <li><p>Is the data quantitative or qualitative?</p></li>
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+<h3><span class="section-number">4.3.3.2. </span>Task 2<a class="headerlink" href="#task-2" title="Permalink to this headline">#</a></h3>
 <p><a class="reference external" href="https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/classifying-datasets.md">Classifying datasets</a></p>
 </section>
 </section>
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+<h2><span class="section-number">4.3.4. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>This Microsoft Learn unit, titled <a class="reference external" href="https://docs.microsoft.com/en-us/learn/modules/choose-storage-approach-in-azure/2-classify-data">Classify your data</a> has a detailed breakdown of structured, semi-structured, and unstructured data.</p>
 </section>
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+<h2><span class="section-number">4.3.5. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
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 <section class="tex2jax_ignore mathjax_ignore" id="introduction-to-statistics-and-probability">
-<h1><span class="section-number">5.4. </span>Introduction to statistics and probability<a class="headerlink" href="#introduction-to-statistics-and-probability" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">4.4. </span>Introduction to statistics and probability<a class="headerlink" href="#introduction-to-statistics-and-probability" title="Permalink to this headline">#</a></h1>
 <p>Statistics and Probability Theory are two highly related areas of Mathematics that are highly relevant to Data Science. It is possible to operate with data without deep knowledge of mathematics, but it is still better to know at least some basic concepts. Here we will present a short introduction that will help you get started.</p>
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 - ‘ml-advanced/unsupervised-learning.md’
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 <section id="probability-and-random-variables">
-<h2><span class="section-number">5.4.1. </span>Probability and random variables<a class="headerlink" href="#probability-and-random-variables" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.4.1. </span>Probability and random variables<a class="headerlink" href="#probability-and-random-variables" title="Permalink to this headline">#</a></h2>
 <p><strong>Probability</strong> is a number between 0 and 1 that expresses how probable an <strong>event</strong> is. It is defined as a number of positive outcomes (that lead to the event), divided by a total number of outcomes, given that all outcomes are equally probable. For example, when we roll a dice, the probability that we get an even number is <span class="math notranslate nohighlight">\(3/6 = 0.5\)</span>.</p>
 <p>When we talk about events, we use <strong>random variables</strong>. For example, the random variable that represents a number obtained when rolling a dice would take values from <span class="math notranslate nohighlight">\(1\)</span> to <span class="math notranslate nohighlight">\(6\)</span>. A set of numbers from <span class="math notranslate nohighlight">\(1\)</span> to <span class="math notranslate nohighlight">\(6\)</span> is called <strong>sample space</strong>. We can talk about the probability of a random variable taking a certain value, for example, <span class="math notranslate nohighlight">\(P(X=3)=1/6\)</span>.</p>
 <p>The random variable in the previous example is called <strong>discrete</strong> because it has a countable sample space, i.e. there are separate values that can be enumerated. There are cases when sample space is a range of real numbers or the whole set of real numbers. Such variables are called <strong>continuous</strong>. A good example is a time when the bus arrives.</p>
 </section>
 <section id="probability-distribution">
-<h2><span class="section-number">5.4.2. </span>Probability distribution<a class="headerlink" href="#probability-distribution" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.4.2. </span>Probability distribution<a class="headerlink" href="#probability-distribution" title="Permalink to this headline">#</a></h2>
 <p>In the case of discrete random variables, it is easy to describe the probability of each event by a function <span class="math notranslate nohighlight">\(P(X)\)</span>. For each value <span class="math notranslate nohighlight">\(s\)</span> from sample space <span class="math notranslate nohighlight">\(S\)</span> it will give a number from <span class="math notranslate nohighlight">\(0\)</span> to <span class="math notranslate nohighlight">\(1\)</span>, such that the sum of all values of <span class="math notranslate nohighlight">\(P(X=s)\)</span> for all events would be <span class="math notranslate nohighlight">\(1\)</span>.</p>
 <p>The most well-known discrete distribution is a <strong>uniform distribution</strong>, in which there is a sample space of <span class="math notranslate nohighlight">\(N\)</span> elements, with an equal probability of <span class="math notranslate nohighlight">\(1/N\)</span> for each of them.</p>
 <p>It is more difficult to describe the probability distribution of a continuous variable, with values drawn from some interval <span class="math notranslate nohighlight">\([a,b]\)</span>, or the whole set of real numbers <span class="math notranslate nohighlight">\(\mathbb{R}\)</span>. Consider the case of bus arrival time. In fact, for each exact arrival time <span class="math notranslate nohighlight">\(t\)</span>, the probability of a bus arriving at exactly that time is <span class="math notranslate nohighlight">\(0\)</span>!</p>
@@ -1821,7 +1821,7 @@ <h2><span class="section-number">5.4.2. </span>Probability distribution<a class=
 <p>Another important distribution is <strong>normal distribution</strong>, which we will talk about in more detail below.</p>
 </section>
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-<h2><span class="section-number">5.4.3. </span>Mean, variance and standard deviation<a class="headerlink" href="#mean-variance-and-standard-deviation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.4.3. </span>Mean, variance and standard deviation<a class="headerlink" href="#mean-variance-and-standard-deviation" title="Permalink to this headline">#</a></h2>
 <p>Suppose we draw a sequence of n samples of a random variable <span class="math notranslate nohighlight">\(X: x_1, x_2, ..., x_n\)</span>. We can define the <strong>mean</strong> (or <strong>arithmetic average</strong>) value of the sequence in the traditional way as <span class="math notranslate nohighlight">\((x_1+x_2+x_n)/n\)</span>. As we grow the size of the sample (i.e. take the limit with <span class="math notranslate nohighlight">\(n\to\infty\)</span>), we will obtain the mean (also called <strong>expectation</strong>) of the distribution. We will denote expectation by <span class="math notranslate nohighlight">\(E(x)\)</span>.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1830,7 +1830,7 @@ <h2><span class="section-number">5.4.3. </span>Mean, variance and standard devia
 <p>To identify how far the values are spread, we can compute the variance <span class="math notranslate nohighlight">\(\sigma^2 = \sum(x_i - \mu;)^2/n\)</span>, where <span class="math notranslate nohighlight">\(\mu\)</span> is the mean of the sequence. The value <span class="math notranslate nohighlight">\(\sigma\)</span> is called <strong>standard deviation</strong>, and <span class="math notranslate nohighlight">\(\sigma^2\)</span> is called a <strong>variance</strong>.</p>
 </section>
 <section id="mode-median-and-quartiles">
-<h2><span class="section-number">5.4.4. </span>Mode, median and quartiles<a class="headerlink" href="#mode-median-and-quartiles" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.4.4. </span>Mode, median and quartiles<a class="headerlink" href="#mode-median-and-quartiles" title="Permalink to this headline">#</a></h2>
 <p>Sometimes, the mean does not adequately represent the “typical” value for data. For example, when there are a few extreme values that are completely out of range, they can affect the mean. Another good indication is a <strong>median</strong>, a value such that half of the data points are lower than it, and another half - higher.</p>
 <p>To help us understand the distribution of data, it is helpful to talk about <strong>quartiles</strong>:</p>
 <ul class="simple">
@@ -1843,7 +1843,7 @@ <h2><span class="section-number">5.4.4. </span>Mode, median and quartiles<a clas
 <p>For the finite distribution that contains a small number of possible values, a good “typical” value is the one that appears the most frequently, which is called <strong>mode</strong>. It is often applied to categorical data, such as colors. Consider a situation where we have two groups of people - some that strongly prefer red, and others who prefer blue. If we code colors by numbers, the mean value for a favorite color would be somewhere in the orange-green spectrum, which does not indicate the actual preference for either group. However, the mode would be either one of the colors, or both colors, if the number of people voting for them is equal (in this case we call the sample <strong>multimodal</strong>).</p>
 </section>
 <section id="real-world-data">
-<h2><span class="section-number">5.4.5. </span>Real-world data<a class="headerlink" href="#real-world-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.4.5. </span>Real-world data<a class="headerlink" href="#real-world-data" title="Permalink to this headline">#</a></h2>
 <p>When we analyze data from real life, they often are not random variables as such, in the sense that we do not perform experiments with unknown results. For example, consider a team of baseball players, and their body data, such as height, weight and age. Those numbers are not exactly random, but we can still apply the same mathematical concepts. For example, a sequence of people’s weights can be considered to be a sequence of values drawn from some random variable. Below is the sequence of weights of actual baseball players from <a class="reference external" href="http://mlb.mlb.com/index.jsp">Major League Baseball</a>, taken from <a class="reference external" href="http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights">this dataset</a> (for your convenience, only the first 20 values are shown):</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1898,7 +1898,7 @@ <h2><span class="section-number">5.4.5. </span>Real-world data<a class="headerli
 </div>
 </section>
 <section id="normal-distribution">
-<h2><span class="section-number">5.4.6. </span>Normal distribution<a class="headerlink" href="#normal-distribution" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.4.6. </span>Normal distribution<a class="headerlink" href="#normal-distribution" title="Permalink to this headline">#</a></h2>
 <p>The distribution of weights that we have seen above is very typical, and many measurements from the real world follow the same type of distribution, but with different mean and variance. This distribution is called <strong>normal distribution</strong>, and it plays a very important role in statistics.</p>
 <p>Using normal distribution is the correct way to generate random weights of potential baseball players. Once we know the mean weight <code class="docutils literal notranslate"><span class="pre">mean</span></code> and standard deviation <code class="docutils literal notranslate"><span class="pre">std</span></code>, we can generate 1000 weight samples in the following way:</p>
 <div class="cell tag_output-scoll tag_hide-input docutils container">
@@ -1912,10 +1912,10 @@ <h2><span class="section-number">5.4.6. </span>Normal distribution<a class="head
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([ 8.30720424,  8.13219365,  9.20122921,  9.7524782 ,  8.55157618,
-        8.11298852, 11.96200553, 11.78009884,  6.43728013, 15.71391395,
-        6.0602059 ,  8.13028732, 14.56105406, 12.26160482, 10.51000799,
-       12.27603982, 11.49581615, 11.3551881 , 12.41682997, 10.39855498])
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([11.28811917,  9.17295329,  6.56371529,  9.84241983,  7.27205865,
+        8.01697559, 12.43056531,  9.00755935, 10.91226239,  8.97359615,
+       10.02707036, 10.79299852, 11.5942073 ,  7.83968173,  7.2937034 ,
+       12.07886138, 13.57946497, 10.92063479,  8.77085797, 10.20937492])
 </pre></div>
 </div>
 </div>
@@ -1924,12 +1924,12 @@ <h2><span class="section-number">5.4.6. </span>Normal distribution<a class="head
 <figure class="align-default" id="normal-distribution-with-mean-0-and-std-dev-1">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/normal-histogram.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/normal-histogram.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 5.5 </span><span class="caption-text">Normal distribution with <span class="math notranslate nohighlight">\(mean=0\)</span> and <span class="math notranslate nohighlight">\(std.dev=1\)</span></span><a class="headerlink" href="#normal-distribution-with-mean-0-and-std-dev-1" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 4.5 </span><span class="caption-text">Normal distribution with <span class="math notranslate nohighlight">\(mean=0\)</span> and <span class="math notranslate nohighlight">\(std.dev=1\)</span></span><a class="headerlink" href="#normal-distribution-with-mean-0-and-std-dev-1" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="confidence-intervals">
-<h2><span class="section-number">5.4.7. </span>Confidence intervals<a class="headerlink" href="#confidence-intervals" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.4.7. </span>Confidence intervals<a class="headerlink" href="#confidence-intervals" title="Permalink to this headline">#</a></h2>
 <p>When we talk about the weights of baseball players, we assume that there is a certain <strong>random variable</strong> <span class="math notranslate nohighlight">\(W\)</span> that corresponds to the ideal probability distribution of weights of all baseball players (so-called <strong>population</strong>). Our sequence of weights corresponds to a subset of all baseball players that we call <strong>sample</strong>. An interesting question is, can we know the parameters of the distribution of <span class="math notranslate nohighlight">\(W\)</span>, i.e. mean and variance of the population?</p>
 <p>The easiest answer would be to calculate the mean and variance of our sample. However, it could happen that our random sample does not accurately represent the complete population. Thus it makes sense to talk about <strong>confidence interval</strong>.</p>
 <div class="admonition note">
@@ -1969,7 +1969,7 @@ <h2><span class="section-number">5.4.7. </span>Confidence intervals<a class="hea
 <p>Notice that the higher the confidence probability, the wider the confidence interval.</p>
 </section>
 <section id="hypothesis-testing">
-<h2><span class="section-number">5.4.8. </span>Hypothesis testing<a class="headerlink" href="#hypothesis-testing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.4.8. </span>Hypothesis testing<a class="headerlink" href="#hypothesis-testing" title="Permalink to this headline">#</a></h2>
 <p>In our baseball players dataset, there are different player roles, that can be summarized below (look at the <a class="reference external" href="https://static-1300131294.cos.qp-shanghai.myqcloud.com/assignments/data-science/introduction-to-statistics-and-probability.ipynb">accompanying notebooks</a>) to see how this table can be calculated):</p>
 <table class="colwidths-auto table">
 <thead>
@@ -2114,7 +2114,7 @@ <h2><span class="section-number">5.4.8. </span>Hypothesis testing<a class="heade
 </ul>
 </section>
 <section id="law-of-large-numbers-and-central-limit-theorem">
-<h2><span class="section-number">5.4.9. </span>Law of large numbers and central limit theorem<a class="headerlink" href="#law-of-large-numbers-and-central-limit-theorem" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.4.9. </span>Law of large numbers and central limit theorem<a class="headerlink" href="#law-of-large-numbers-and-central-limit-theorem" title="Permalink to this headline">#</a></h2>
 <p>One of the reasons why normal distribution is so important is the so-called <strong>central limit theorem</strong>. Suppose we have a large sample of independent <span class="math notranslate nohighlight">\(N\)</span> values <span class="math notranslate nohighlight">\(X_1, ..., X_N\)</span>, sampled from any distribution with mean <span class="math notranslate nohighlight">\(\mu\)</span> and variance <span class="math notranslate nohighlight">\(\sigma^2\)</span>. Then, for sufficiently large <span class="math notranslate nohighlight">\(N\)</span> (in other words, when <span class="math notranslate nohighlight">\(N\to\infty\)</span>), the mean <span class="math notranslate nohighlight">\(\Sigma_iX_i\)</span> would be normally distributed, with mean <span class="math notranslate nohighlight">\(\mu\)</span> and variance <span class="math notranslate nohighlight">\(\sigma^2/N\)</span>.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -2123,7 +2123,7 @@ <h2><span class="section-number">5.4.9. </span>Law of large numbers and central
 <p>From the central limit theorem, it also follows that when <span class="math notranslate nohighlight">\(N\to\infty\)</span>, the probability of the sample mean to be equal to <span class="math notranslate nohighlight">\(\mu\)</span> becomes <span class="math notranslate nohighlight">\(1\)</span>. This is known as <strong>the law of large numbers</strong>.</p>
 </section>
 <section id="covariance-and-correlation">
-<h2><span class="section-number">5.4.10. </span>Covariance and correlation<a class="headerlink" href="#covariance-and-correlation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.4.10. </span>Covariance and correlation<a class="headerlink" href="#covariance-and-correlation" title="Permalink to this headline">#</a></h2>
 <p>One of the things Data Science does is find relations between data. We say that two sequences <strong>correlate</strong> when they exhibit similar behavior at the same time, i.e. they either rise/fall simultaneously, or one sequence rises when another one falls and vice versa. In other words, there seems to be some relation between the two sequences.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -2175,7 +2175,7 @@ <h2><span class="section-number">5.4.10. </span>Covariance and correlation<a cla
 </div>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">5.4.11. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.4.11. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>In this section, we have learned:</p>
 <ul class="simple">
 <li><p>basic statistical properties of data, such as mean, variance, mode, and quartiles,</p></li>
@@ -2187,7 +2187,7 @@ <h2><span class="section-number">5.4.11. </span>Conclusion<a class="headerlink"
 <p>While this is definitely not an exhaustive list of topics that exist within probability and statistics, it should be enough to give you a good start on this course.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">5.4.12. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.4.12. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Use the sample code in the notebook to test another hypothesis:</p>
 <ol class="simple">
 <li><p>First basemen are older than second basemen</p></li>
@@ -2200,7 +2200,7 @@ <h2><span class="section-number">5.4.12. </span>Your turn! 🚀<a class="headerl
 </ul>
 </section>
 <section id="self-study">
-<h2><span class="section-number">5.4.13. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.4.13. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>Probability and statistics are such a broad topic that it deserves their own course. If you are interested to go deeper into theory, you may want to continue reading some of the following books:</p>
 <ol class="simple">
 <li><p><a class="reference external" href="https://cims.nyu.edu/~cfgranda/">Carlos Fernandez-Granda</a> from New York University has great lecture notes <a class="reference external" href="https://cims.nyu.edu/~cfgranda/pages/stuff/probability_stats_for_DS.pdf">Probability and Statistics for Data Science</a> (available online)</p></li>
@@ -2209,7 +2209,7 @@ <h2><span class="section-number">5.4.13. </span>Self study<a class="headerlink"
 </ol>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">5.4.14. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">4.4.14. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2251,13 +2251,13 @@ <h2><span class="section-number">5.4.14. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
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-            <p class="prev-next-title"><span class="section-number">5.3. </span>Defining data</p>
+            <p class="prev-next-title"><span class="section-number">4.3. </span>Defining data</p>
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diff --git a/data-science/introduction/introduction.html b/data-science/introduction/introduction.html
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+    <title>4. Introduction &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../genindex.html" />
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-    <link rel="next" title="5.1. Defining data science" href="defining-data-science.html" />
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-     5.1. Defining data science
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-     5.2. Data Science ethics
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+<h1><span class="section-number">4. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h1>
 <figure class="align-default" id="the-data-science-venn-diagram">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/the-data-science-venn-diagram.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/the-data-science-venn-diagram.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 5.1 </span><span class="caption-text">The data science venn diagram{cite}:TheDataScienceVennDiagram:</span><a class="headerlink" href="#the-data-science-venn-diagram" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 4.1 </span><span class="caption-text">The data science venn diagram{cite}:TheDataScienceVennDiagram:</span><a class="headerlink" href="#the-data-science-venn-diagram" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>In this part, you will discover how Data Science is defined and learn about ethical considerations that must be considered by a data scientist. You will also learn how data is defined and learn a bit about statistics and probability, the core academic domains of Data Science.</p>
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-<li class="toctree-l1"><a class="reference internal" href="introduction-to-statistics-and-probability.html">5.4. Introduction to statistics and probability</a></li>
+<li class="toctree-l1"><a class="reference internal" href="defining-data-science.html">4.1. Defining data science</a></li>
+<li class="toctree-l1"><a class="reference internal" href="data-science-ethics.html">4.2. Data Science ethics</a></li>
+<li class="toctree-l1"><a class="reference internal" href="defining-data.html">4.3. Defining data</a></li>
+<li class="toctree-l1"><a class="reference internal" href="introduction-to-statistics-and-probability.html">4.4. Introduction to statistics and probability</a></li>
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-    <link rel="prev" title="6.4.3. Advanced Pandas Techniques" href="pandas/advanced-pandas-techniques.html" />
+    <link rel="next" title="6. Data visualization" href="../data-visualization/data-visualization.html" />
+    <link rel="prev" title="5.4.3. Advanced Pandas Techniques" href="pandas/advanced-pandas-techniques.html" />
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                             <ul class="visible nav section-nav flex-column">
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   </a>
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@@ -1684,10 +1684,10 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="data-preparation">
-<h1><span class="section-number">6.5. </span>Data preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">5.5. </span>Data preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">#</a></h1>
 <p>Depending on its source, raw data may contain some inconsistencies that will cause challenges in analysis and modeling. In other words, this data can be categorized as “dirty” and will need to be cleaned up. This section focuses on techniques for cleaning and transforming the data to handle the challenges of missing, inaccurate, or incomplete data. Topics covered in this section will utilize Python and the Pandas library and will be <a class="reference internal" href="../../assignments/data-science/data-preparation.html"><span class="doc std std-doc">demonstrated in the notebook</span></a> within this directory.</p>
 <section id="the-importance-of-cleaning-data">
-<h2><span class="section-number">6.5.1. </span>The importance of cleaning data<a class="headerlink" href="#the-importance-of-cleaning-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.5.1. </span>The importance of cleaning data<a class="headerlink" href="#the-importance-of-cleaning-data" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><strong>Ease of use and reuse</strong>: When data is properly organized and normalized it’s easier to search, use, and share with others.</p></li>
 <li><p><strong>Consistency</strong>: Data science often requires working with more than one dataset, where datasets from different sources need to be joined together. Making sure that each individual data set has common standardization will ensure that the data is still useful when they are all merged into one dataset.</p></li>
@@ -1695,7 +1695,7 @@ <h2><span class="section-number">6.5.1. </span>The importance of cleaning data<a
 </ul>
 </section>
 <section id="common-cleaning-goals-and-strategies">
-<h2><span class="section-number">6.5.2. </span>Common cleaning goals and strategies<a class="headerlink" href="#common-cleaning-goals-and-strategies" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.5.2. </span>Common cleaning goals and strategies<a class="headerlink" href="#common-cleaning-goals-and-strategies" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><strong>Exploring a dataset</strong>: Data exploration, which is covered in a <a class="reference internal" href="../data-science-lifecycle/analyzing.html"><span class="doc std std-doc">later section</span></a> can help you discover data that needs to be cleaned up. Visually observing values within a dataset can set expectations of what the rest of it will look like, or provide an idea of the problems that can be resolved. Exploration can involve basic querying, visualizations, and sampling.</p></li>
 <li><p><strong>Formatting</strong>: Depending on the source, data can have inconsistencies in how it’s presented. This can cause problems in searching for and representing the value, where it’s seen within the dataset but is not properly represented in visualizations or query results. Common formatting problems involve resolving whitespace, dates, and data types. Resolving formatting issues is typically up to the people who are using the data. For example, standards on how dates and numbers are presented can differ by country.</p></li>
@@ -1704,7 +1704,7 @@ <h2><span class="section-number">6.5.2. </span>Common cleaning goals and strateg
 </ul>
 </section>
 <section id="exploring-dataframe-information">
-<h2><span class="section-number">6.5.3. </span>Exploring DataFrame information<a class="headerlink" href="#exploring-dataframe-information" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.5.3. </span>Exploring DataFrame information<a class="headerlink" href="#exploring-dataframe-information" title="Permalink to this headline">#</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p><strong>Learning goal:</strong> By the end of this subsection, you should be comfortable finding general information about the data stored in pandas DataFrames.</p>
@@ -1895,7 +1895,7 @@ <h2><span class="section-number">6.5.3. </span>Exploring DataFrame information<a
 </div>
 </section>
 <section id="dealing-with-missing-data">
-<h2><span class="section-number">6.5.4. </span>Dealing with missing data<a class="headerlink" href="#dealing-with-missing-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.5.4. </span>Dealing with missing data<a class="headerlink" href="#dealing-with-missing-data" title="Permalink to this headline">#</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>Learning goal: By the end of this subsection, you should know how to replace or remove null values from DataFrames.</p>
@@ -2294,7 +2294,7 @@ <h2><span class="section-number">6.5.4. </span>Dealing with missing data<a class
 <p>Takeaway: There are multiple ways to deal with missing values in your datasets. The specific strategy you use (removing them, replacing them, or even how you replace them) should be dictated by the particulars of that data. You will develop a better sense of how to deal with missing values the more you handle and interact with datasets.</p>
 </section>
 <section id="removing-duplicate-data">
-<h2><span class="section-number">6.5.5. </span>Removing duplicate data<a class="headerlink" href="#removing-duplicate-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.5.5. </span>Removing duplicate data<a class="headerlink" href="#removing-duplicate-data" title="Permalink to this headline">#</a></h2>
 <p><strong>Learning goal:</strong> By the end of this subsection, you should be comfortable identifying and removing duplicate values from DataFrames.</p>
 <p>In addition to missing data, you will often encounter duplicated data in real-world datasets. Fortunately, Pandas provides an easy means of detecting and removing duplicate entries.</p>
 <p><strong>Identifying duplicates: <code class="docutils literal notranslate"><span class="pre">duplicated</span></code></strong>: You can easily spot duplicate values using the <code class="docutils literal notranslate"><span class="pre">duplicated</span></code> method in Pandas, which returns a Boolean mask indicating whether an entry in a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> is a duplicate of an earlier one. Let’s create another example <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> to see this in action.</p>
@@ -2474,12 +2474,12 @@ <h2><span class="section-number">6.5.5. </span>Removing duplicate data<a class="
 <p><strong>Takeaway:</strong> Removing duplicate data is an essential part of almost every data-science project. Duplicate data can change the results of your analyses and give you inaccurate results!</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">6.5.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.5.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>All of the discussed materials are provided as a <a class="reference internal" href="../../assignments/data-science/data-preparation.html"><span class="doc std std-doc">Jupyter Notebook</span></a>. Additionally, there are exercises present after each section, give them a try!</p>
 <p>Task - <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html"><span class="doc std std-doc">Evaluating data from a form</span></a></p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">6.5.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.5.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2521,13 +2521,13 @@ <h2><span class="section-number">6.5.7. </span>Acknowledgments<a class="headerli
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">6.4.3. </span>Advanced Pandas Techniques</p>
+            <p class="prev-next-title"><span class="section-number">5.4.3. </span>Advanced Pandas Techniques</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../data-visualization/data-visualization.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">7. </span>Data visualization</p>
+        <p class="prev-next-title"><span class="section-number">6. </span>Data visualization</p>
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diff --git a/data-science/working-with-data/non-relational-data.html b/data-science/working-with-data/non-relational-data.html
index f511dbc57..76f1dfec8 100644
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     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>6.2. Non-relational data &#8212; Ocademy Open Machine Learning Book</title>
+    <title>5.2. Non-relational data &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="6.3. NumPy" href="numpy.html" />
-    <link rel="prev" title="6.1. Relational databases" href="relational-databases.html" />
+    <link rel="next" title="5.3. NumPy" href="numpy.html" />
+    <link rel="prev" title="5.1. Relational databases" href="relational-databases.html" />
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     <a class="reference internal" href="../../assignments/project-plan-template.html">
-     41.5. Project Plan​ Template
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-introduction.html">
-     41.6. Python programming introduction
+     40.6. Python programming introduction
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-basics.html">
-     41.7. Python programming basics
+     40.7. Python programming basics
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     <a class="reference internal" href="../../assignments/prerequisites/python-programming-advanced.html">
-     41.8. Python programming advanced
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html">
-     41.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html">
-     41.10. Data Science scenarios
+     40.10. Data Science scenarios
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/write-a-data-ethics-case-study.html">
-     41.11. Write a data ethics case study
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     <a class="reference internal" href="../../assignments/data-science/lines-scatters-and-bars.html">
-     41.12. Lines, scatters and bars
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/apply-your-skills.html">
-     41.13. Apply your skills
+     40.13. Apply your skills
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     <a class="reference internal" href="../../assignments/data-science/try-it-in-excel.html">
-     41.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/dive-into-the-beehive.html">
-     41.15. Dive into the beehive
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/build-your-own-custom-vis.html">
-     41.16. Build your own custom vis
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/classifying-datasets.html">
-     41.17. Classifying datasets
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     <a class="reference internal" href="../../assignments/data-science/small-diabetes-study.html">
-     41.18. Small diabetes study
+     40.18. Small diabetes study
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
-     41.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
-     41.20. Displaying airport data
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/soda-profits.html">
-     41.21. Soda profits
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     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
-     41.22. Analyzing COVID-19 papers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     41.23. Estimation of COVID-19 pandemic
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
-     41.24. Data processing in Python
+     40.24. Data processing in Python
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
-     41.25. Evaluating data from a form
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-preparation.html">
-     41.26. Data preparation
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     <a class="reference internal" href="../../assignments/data-science/analyzing-data.html">
-     41.27. Analyzing data
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     <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     41.28. NYC taxi data in winter and summer
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     <a class="reference internal" href="../../assignments/data-science/matplotlib-applied.html">
-     41.29. Matplotlib applied
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     <a class="reference internal" href="../../assignments/data-science/tell-a-story.html">
-     41.35. Tell a story
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     <a class="reference internal" href="../../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     41.36. Explore a planetary computer dataset
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html">
-     41.37. Exploring for answers
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     <a class="reference internal" href="../../assignments/data-science/market-research.html">
-     41.38. Market research
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     <a class="reference internal" href="../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     41.39. Low code/no code Data Science project on Azure ML
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     41.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     41.41. Data Science in the cloud: The “Azure ML SDK” way
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
-     41.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     41.43. Machine Learning overview - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     41.44. Regression with Scikit-learn
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     41.45. Linear regression - California Housing
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     41.46. Linear Regression Metrics
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     41.47. Loss Function
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     41.48. Gradient descent
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     41.49. Linear Regression Implementation from Scratch
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     41.50. ML logistic regression - assignment 1
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     41.51. ML logistic regression - assignment 2
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
-     41.52. ML neural network - Assignment 1
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     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
-     41.53. Regression tools
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-     41.54. Managing data
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-     41.55. Exploring visualizations
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-     41.56. Try a different model
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-     41.57. Create a regression model
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
-     41.58. Linear and polynomial regression
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-     41.59. Retrying some regression
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     <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
-     41.60. Pumpkin varieties and color
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     <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
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-     41.62. Explore classification methods
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     41.63. Kernel method assignment 1
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     41.65. Support Vector Machines (SVM) - Classification
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     41.70. Dropout and Batch Normalization
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/regularized-linear-models.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
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     <a class="reference internal" href="../../assignments/machine-learning-productionization/debugging-in-classification.html">
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
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     <a class="reference internal" href="../../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
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     <a class="reference internal" href="../../assignments/deep-learning/gan/art-by-gan.html">
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+       5.2.2.1.1. Exploring data with the Cosmos DB emulator
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-       6.2.2.1.2. Querying document data with the Cosmos DB emulator
+       5.2.2.1.2. Querying document data with the Cosmos DB emulator
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+     5.2.2.1. Document data stores with the Azure Cosmos DB
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+       5.2.2.1.1. Exploring data with the Cosmos DB emulator
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-       6.2.2.1.2. Querying document data with the Cosmos DB emulator
+       5.2.2.1.2. Querying document data with the Cosmos DB emulator
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+   5.2.3. Your turn! 🚀
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 </div>
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 <section class="tex2jax_ignore mathjax_ignore" id="non-relational-data">
-<h1><span class="section-number">6.2. </span>Non-relational data<a class="headerlink" href="#non-relational-data" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">5.2. </span>Non-relational data<a class="headerlink" href="#non-relational-data" title="Permalink to this headline">#</a></h1>
 <p>Data is not limited to relational databases. This section focuses on non-relational data and will cover the basics of spreadsheets and NoSQL.</p>
 <section id="spreadsheets">
-<h2><span class="section-number">6.2.1. </span>Spreadsheets<a class="headerlink" href="#spreadsheets" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.2.1. </span>Spreadsheets<a class="headerlink" href="#spreadsheets" title="Permalink to this headline">#</a></h2>
 <p>Spreadsheets are a popular way to store and explore data because it requires less work to set up and get started. In this section, you’ll learn the basic components of a spreadsheet, as well as formulas and functions. The examples will be illustrated with Microsoft Excel, but most of the parts and topics will have similar names and steps in comparison to other spreadsheet software.</p>
 <p><img alt="An empty Microsoft Excel workbook with two worksheets" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/data-science/work-with-data/parts-of-spreadsheet.png" /></p>
 <p>A spreadsheet is a file and will be accessible in the file system of a computer, device, or cloud-based file system. The software itself may be browser-based or an application that must be installed on a computer or downloaded as an app. In Excel, these files are also defined as <strong>workbooks</strong> and this terminology will be used for the remainder of this section.</p>
 <p>A workbook contains one or more <strong>worksheets</strong>, where each worksheet is labeled by tabs. Within a worksheet are rectangles called <strong>cells</strong>, which will contain the actual data. A cell is the intersection of a row and a column, where the columns are labeled with alphabetical characters and rows are labeled numerically. Some spreadsheets will contain headers in the first few rows to describe the data in a cell.</p>
 <p>With these basic elements of an Excel workbook, we’ll use an example from <a class="reference external" href="https://templates.office.com/">Microsoft Templates</a> focused on an inventory to walk through some additional parts of a spreadsheet.</p>
 <section id="managing-an-inventory">
-<h3><span class="section-number">6.2.1.1. </span>Managing an inventory<a class="headerlink" href="#managing-an-inventory" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.2.1.1. </span>Managing an inventory<a class="headerlink" href="#managing-an-inventory" title="Permalink to this headline">#</a></h3>
 <p>The spreadsheet file named “InventoryExample” is a formatted spreadsheet of items within an inventory that contains three worksheets, where the tabs are labeled “Inventory List”, “Inventory Pick List” and “Bin Lookup”. Row 4 of the Inventory List worksheet is the header, which describes the value of each cell in the header column.</p>
 <p><img alt="A highlighted formula from an example inventory list in Microsoft Excel" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/data-science/work-with-data/function-excel.png" /></p>
 <p>There are instances where a cell is dependent on the values of other cells to generate its value. The Inventory List spreadsheet keeps track of the cost of every item in its inventory, but what if we need to know the value of everything in the inventory? <a class="reference external" href="https://support.microsoft.com/en-us/office/overview-of-formulas-34519a4e-1e8d-4f4b-84d4-d642c4f63263"><strong>Formulas</strong></a> perform actions on cell data and are used to calculate the cost of the inventory in this example. This spreadsheet used a formula in the Inventory Value column to calculate the value of each item by multiplying the quantity under the QTY header and its costs by the cells under the COST header. Double-clicking or highlighting a cell will show the formula. You’ll notice that formulas start with an equals sign, followed by the calculation or operation.</p>
@@ -1745,13 +1745,13 @@ <h3><span class="section-number">6.2.1.1. </span>Managing an inventory<a class="
 </section>
 </section>
 <section id="nosql">
-<h2><span class="section-number">6.2.2. </span>NoSQL<a class="headerlink" href="#nosql" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.2.2. </span>NoSQL<a class="headerlink" href="#nosql" title="Permalink to this headline">#</a></h2>
 <p>NoSQL is an umbrella term for the different ways to store non-relational data and can be interpreted as “non-SQL”, “non-relational” or  “not only SQL”. These types of database systems can be categorized into 4 types.</p>
 <p><a class="reference external" href="https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#keyvalue-data-stores">Key-value</a> databases pair unique keys, which are unique identifiers associated with a value. These pairs are stored using a <a class="reference external" href="https://www.hackerearth.com/practice/data-structures/hash-tables/basics-of-hash-tables/tutorial/">hash table</a> with an appropriate hashing function.</p>
 <p><a class="reference external" href="https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#graph-data-stores">Graph</a> databases describe relationships in data and are represented as a collection of nodes and edges. A node represents an entity, something that exists in the real world such as a student or bank statement. Edges represent the relationship between two entities  Each node and edge have properties that provide additional information about each node and edge.</p>
 <p><a class="reference external" href="https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#columnar-data-stores">Columnar</a> data stores organize data into columns and rows like a relational data structure but each column is divided into groups called a column family, where all the data under one column is related and can be retrieved and changed in one unit.</p>
 <section id="document-data-stores-with-the-azure-cosmos-db">
-<h3><span class="section-number">6.2.2.1. </span>Document data stores with the Azure Cosmos DB<a class="headerlink" href="#document-data-stores-with-the-azure-cosmos-db" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.2.2.1. </span>Document data stores with the Azure Cosmos DB<a class="headerlink" href="#document-data-stores-with-the-azure-cosmos-db" title="Permalink to this headline">#</a></h3>
 <p><a class="reference external" href="https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#document-data-stores">Document</a> data stores build on the concept of a key-value data store and are made up of a series of fields and objects. This section will explore document databases with the Cosmos DB emulator.</p>
 <p>A Cosmos DB database fits the definition of “Not Only SQL”, where Cosmos DB’s document database relies on SQL to query the data. The <a class="reference internal" href="relational-databases.html"><span class="doc std std-doc">previous section</span></a> on SQL covers the basics of the language, and we’ll be able to apply some of the same queries to a document database here. We’ll be using the Cosmos DB Emulator, which allows us to create and explore a document database locally on a computer. Read more about the Emulator <a class="reference external" href="https://docs.microsoft.com/en-us/azure/cosmos-db/local-emulator?tabs=ssl-netstd21">here</a>.</p>
 <p>A document is a collection of fields and object values, where the fields describe what the object value represents. Below is an example of a document.</p>
@@ -1785,7 +1785,7 @@ <h3><span class="section-number">6.2.2.1. </span>Document data stores with the A
 </div>
 <p>The fields of interest in this document are <code class="docutils literal notranslate"><span class="pre">firstname</span></code>, <code class="docutils literal notranslate"><span class="pre">id</span></code>, and <code class="docutils literal notranslate"><span class="pre">age</span></code>. The rest of the fields with the underscores were generated by Cosmos DB.</p>
 <section id="exploring-data-with-the-cosmos-db-emulator">
-<h4><span class="section-number">6.2.2.1.1. </span>Exploring data with the Cosmos DB emulator<a class="headerlink" href="#exploring-data-with-the-cosmos-db-emulator" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.2.2.1.1. </span>Exploring data with the Cosmos DB emulator<a class="headerlink" href="#exploring-data-with-the-cosmos-db-emulator" title="Permalink to this headline">#</a></h4>
 <p>You can download and install the emulator <a class="reference external" href="https://aka.ms/cosmosdb-emulator">for Windows here</a>. Refer to this <a class="reference external" href="https://docs.microsoft.com/en-us/azure/cosmos-db/local-emulator?tabs=ssl-netstd21#run-on-linux-macos">documentation</a> for options on how to run the Emulator for macOS and Linux.</p>
 <p>The Emulator launches a browser window, where the Explorer view allows you to explore documents.</p>
 <p><img alt="The Explorer view of the Cosmos DB Emulator" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/data-science/work-with-data/cosmosdb-emulator-explorer.png" /></p>
@@ -1793,14 +1793,14 @@ <h4><span class="section-number">6.2.2.1.1. </span>Exploring data with the Cosmo
 <p><img alt="Exploring sample data in the Cosmos DB Emulator" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/data-science/work-with-data/cosmosdb-emulator-persons.png" /></p>
 </section>
 <section id="querying-document-data-with-the-cosmos-db-emulator">
-<h4><span class="section-number">6.2.2.1.2. </span>Querying document data with the Cosmos DB emulator<a class="headerlink" href="#querying-document-data-with-the-cosmos-db-emulator" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.2.2.1.2. </span>Querying document data with the Cosmos DB emulator<a class="headerlink" href="#querying-document-data-with-the-cosmos-db-emulator" title="Permalink to this headline">#</a></h4>
 <p>We can also query the sample data by clicking on the new SQL Query button (second button from the left).</p>
 <p><code class="docutils literal notranslate"><span class="pre">SELECT</span> <span class="pre">*</span> <span class="pre">FROM</span> <span class="pre">c</span></code> returns all the documents in the container. Let’s add a where clause and find everyone younger than 40. <code class="docutils literal notranslate"><span class="pre">SELECT</span> <span class="pre">*</span> <span class="pre">FROM</span> <span class="pre">c</span> <span class="pre">where</span> <span class="pre">c.age</span> <span class="pre">&lt;</span> <span class="pre">40</span></code></p>
 <p><img alt="Running a SELECT query on sample data in the Cosmos DB Emulator to find documents that have an age field value that is less than 40" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/data-science/work-with-data/cosmosdb-emulator-persons-query.png" /></p>
 <p>The query returns two documents, notice the age value for each document is less than 40.</p>
 </section>
 <section id="json-and-documents">
-<h4><span class="section-number">6.2.2.1.3. </span>JSON and Documents<a class="headerlink" href="#json-and-documents" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.2.2.1.3. </span>JSON and Documents<a class="headerlink" href="#json-and-documents" title="Permalink to this headline">#</a></h4>
 <p>If you’re familiar with JavaScript Object Notation (JSON) you’ll notice that documents look similar to JSON. There is a <code class="docutils literal notranslate"><span class="pre">PersonsData.json</span></code> file in this directory with more data that you may upload to the <code class="docutils literal notranslate"><span class="pre">Persons</span></code> container in the Emulator via the <code class="docutils literal notranslate"><span class="pre">Upload</span> <span class="pre">Item</span></code> button.</p>
 <p>In most instances, APIs that return JSON data can be directly transferred and stored in document databases. Below is another document, it represents tweets from the Microsoft Twitter account that were retrieved using the Twitter API, then inserted into Cosmos DB.</p>
 <div class="cell docutils container">
@@ -1836,7 +1836,7 @@ <h4><span class="section-number">6.2.2.1.3. </span>JSON and Documents<a class="h
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">6.2.3. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.2.3. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>There is a <a href="../../assets/data/twitter-data.json"><code class="docutils literal notranslate"><span class="pre">twitter-data.json</span></code></a> file that you can upload to the SampleDB database. It’s recommended that you add it to a separate container. This can be done by:</p>
 <ol class="simple">
 <li><p>Clicking the new container button in the top right</p></li>
@@ -1849,7 +1849,7 @@ <h2><span class="section-number">6.2.3. </span>Your turn! 🚀<a class="headerli
 <p>Task - <a class="reference internal" href="../../assignments/data-science/soda-profits.html"><span class="doc std std-doc">Soda profits</span></a></p>
 </section>
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 <li><p>There are some additional formatting and features added to this spreadsheet that this section does not cover. Microsoft has a <a class="reference external" href="https://support.microsoft.com/excel">large library of documentation and videos</a> on Excel if you’re interested in learning more.</p></li>
 <li><p>This architectural documentation details the characteristics of the different types of non-relational data: <a class="reference external" href="https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data">Non-relational Data and NoSQL</a></p></li>
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 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
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+     5.3.1.1. Create a basic array
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+     5.3.1.2. Adding, removing, and sorting elements
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-     6.3.1.5. Convert a 1D array into a 2D array(add a new axis to an array)
+     5.3.1.5. Convert a 1D array into a 2D array(add a new axis to an array)
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+     5.3.1.7. Create an array from existing data
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+       5.3.2.3.1. General Broadcasting Rules
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+         5.3.3.2.1.3. Example 3
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+       5.3.3.2.2. Boolean array indexing
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+         5.3.3.2.2.2. Example 2
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+         5.3.3.2.2.3. Example 3
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+       5.3.3.2.3. Combining advanced and basic indexing
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+         5.3.3.2.3.1. Example 1
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         <a class="reference internal nav-link" href="#id5">
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+         5.3.3.2.3.2. Example 2
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+         5.3.3.2.3.3. Example 3
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+     5.3.3.3. Field access
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+     5.3.3.4. Flat Iterator indexing
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-     6.3.3.5. Assigning values to indexed arrays
+     5.3.3.5. Assigning values to indexed arrays
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-     6.3.3.6. Dealing with variable numbers of indices within programs
+     5.3.3.6. Dealing with variable numbers of indices within programs
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+   5.3.4. Structured arrays
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     <a class="reference internal nav-link" href="#introduction">
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+     5.3.4.1. Introduction
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+       5.3.4.2.1. Structured datatype creation
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+       5.3.4.2.2. Manipulating and Displaying Structured Datatypes
       </a>
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+       5.3.4.2.3. Field Titles
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+     5.3.4.3. Indexing and Assignment to Structured arrays
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+       5.3.4.3.1. Assigning data to a Structured Array
       </a>
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-         6.3.4.3.1.1. Assignment from Python Native Types (Tuples)
+         5.3.4.3.1.1. Assignment from Python Native Types (Tuples)
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#assignment-from-scalars">
-         6.3.4.3.1.2. Assignment from Scalars
+         5.3.4.3.1.2. Assignment from Scalars
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#assignment-from-other-structured-arrays">
-         6.3.4.3.1.3. Assignment from other Structured Arrays
+         5.3.4.3.1.3. Assignment from other Structured Arrays
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#assignment-involving-subarrays">
-         6.3.4.3.1.4. Assignment involving subarrays
+         5.3.4.3.1.4. Assignment involving subarrays
         </a>
        </li>
       </ul>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#indexing-structured-arrays">
-       6.3.4.3.2. Indexing Structured Arrays
+       5.3.4.3.2. Indexing Structured Arrays
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#accessing-individual-fields">
-         6.3.4.3.2.1. Accessing Individual Fields
+         5.3.4.3.2.1. Accessing Individual Fields
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#accessing-multiple-fields">
-         6.3.4.3.2.2. Accessing multiple fields
+         5.3.4.3.2.2. Accessing multiple fields
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#indexing-with-an-integer-to-get-a-structured-scalar">
-         6.3.4.3.2.3. Indexing with an integer to get a structured scalar
+         5.3.4.3.2.3. Indexing with an integer to get a structured scalar
         </a>
        </li>
       </ul>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#viewing-structured-arrays-containing-objects">
-       6.3.4.3.3. Viewing structured arrays containing objects
+       5.3.4.3.3. Viewing structured arrays containing objects
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#structure-comparison-and-promotion">
-       6.3.4.3.4. Structure Comparison and Promotion
+       5.3.4.3.4. Structure Comparison and Promotion
       </a>
      </li>
     </ul>
@@ -1921,34 +1921,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   6.3.5. Your turn! 🚀
+   5.3.5. Your turn! 🚀
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#challenge-1-analyzing-covid-spread">
-     6.3.5.1. 🚀 Challenge 1: analyzing COVID spread
+     5.3.5.1. 🚀 Challenge 1: analyzing COVID spread
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#working-with-unstructured-data">
-     6.3.5.2. Working with unstructured Data
+     5.3.5.2. Working with unstructured Data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#challenge-2-analyzing-covid-papers">
-     6.3.5.3. 🚀 Challenge 2: analyzing COVID papers
+     5.3.5.3. 🚀 Challenge 2: analyzing COVID papers
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   6.3.6. Self study
+   5.3.6. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   6.3.7. Acknowledgments
+   5.3.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1972,84 +1972,84 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#basic-introduction-to-array">
-   6.3.1. Basic introduction to array
+   5.3.1. Basic introduction to array
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#create-a-basic-array">
-     6.3.1.1. Create a basic array
+     5.3.1.1. Create a basic array
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#adding-removing-and-sorting-elements">
-     6.3.1.2. Adding, removing, and sorting elements
+     5.3.1.2. Adding, removing, and sorting elements
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#numpy-array-attributes">
-     6.3.1.3. NumPy array attributes
+     5.3.1.3. NumPy array attributes
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#reshape-an-array">
-     6.3.1.4. Reshape an array
+     5.3.1.4. Reshape an array
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#convert-a-1d-array-into-a-2d-array-add-a-new-axis-to-an-array">
-     6.3.1.5. Convert a 1D array into a 2D array(add a new axis to an array)
+     5.3.1.5. Convert a 1D array into a 2D array(add a new axis to an array)
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#indexing-and-slicing">
-     6.3.1.6. Indexing and slicing
+     5.3.1.6. Indexing and slicing
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#create-an-array-from-existing-data">
-     6.3.1.7. Create an array from existing data
+     5.3.1.7. Create an array from existing data
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#array-operations">
-   6.3.2. Array operations
+   5.3.2. Array operations
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#basic-array-operations">
-     6.3.2.1. Basic array operations
+     5.3.2.1. Basic array operations
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#universal-functions-ufunc">
-     6.3.2.2. Universal functions(ufunc)
+     5.3.2.2. Universal functions(ufunc)
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#available-ufuncs">
-       6.3.2.2.1. Available ufuncs
+       5.3.2.2.1. Available ufuncs
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#math-operations">
-         6.3.2.2.1.1. Math operations
+         5.3.2.2.1.1. Math operations
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#trigonometric-functions">
-         6.3.2.2.1.2. Trigonometric functions
+         5.3.2.2.1.2. Trigonometric functions
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#bit-twiddling-functions">
-         6.3.2.2.1.3. Bit-twiddling functions
+         5.3.2.2.1.3. Bit-twiddling functions
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#comparison-functions">
-         6.3.2.2.1.4. Comparison functions
+         5.3.2.2.1.4. Comparison functions
         </a>
        </li>
       </ul>
@@ -2058,117 +2058,117 @@ <h2> Contents </h2>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#computation-on-arrays-broadcasting">
-     6.3.2.3. Computation on arrays: broadcasting
+     5.3.2.3. Computation on arrays: broadcasting
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#general-broadcasting-rules">
-       6.3.2.3.1. General Broadcasting Rules
+       5.3.2.3.1. General Broadcasting Rules
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#aggregations-min-max-and-everything-in-between">
-     6.3.2.4. Aggregations: min, max and everything in between
+     5.3.2.4. Aggregations: min, max and everything in between
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#indexing-on-ndarrays">
-   6.3.3. Indexing on ndarrays
+   5.3.3. Indexing on ndarrays
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#basic-indexing">
-     6.3.3.1. Basic indexing
+     5.3.3.1. Basic indexing
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#single-element-indexing">
-       6.3.3.1.1. Single element indexing
+       5.3.3.1.1. Single element indexing
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#slicing-and-striding">
-       6.3.3.1.2. Slicing and striding
+       5.3.3.1.2. Slicing and striding
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#dimensional-indexing-tools">
-       6.3.3.1.3. Dimensional indexing tools
+       5.3.3.1.3. Dimensional indexing tools
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#advanced-indexing">
-     6.3.3.2. Advanced indexing
+     5.3.3.2. Advanced indexing
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#integer-array-indexing">
-       6.3.3.2.1. Integer array indexing
+       5.3.3.2.1. Integer array indexing
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#example-1">
-         6.3.3.2.1.1. Example 1
+         5.3.3.2.1.1. Example 1
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#example-2">
-         6.3.3.2.1.2. Example 2
+         5.3.3.2.1.2. Example 2
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#example-3">
-         6.3.3.2.1.3. Example 3
+         5.3.3.2.1.3. Example 3
         </a>
        </li>
       </ul>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#boolean-array-indexing">
-       6.3.3.2.2. Boolean array indexing
+       5.3.3.2.2. Boolean array indexing
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#id1">
-         6.3.3.2.2.1. Example 1
+         5.3.3.2.2.1. Example 1
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#id2">
-         6.3.3.2.2.2. Example 2
+         5.3.3.2.2.2. Example 2
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#id3">
-         6.3.3.2.2.3. Example 3
+         5.3.3.2.2.3. Example 3
         </a>
        </li>
       </ul>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#combining-advanced-and-basic-indexing">
-       6.3.3.2.3. Combining advanced and basic indexing
+       5.3.3.2.3. Combining advanced and basic indexing
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#id4">
-         6.3.3.2.3.1. Example 1
+         5.3.3.2.3.1. Example 1
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#id5">
-         6.3.3.2.3.2. Example 2
+         5.3.3.2.3.2. Example 2
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#id6">
-         6.3.3.2.3.3. Example 3
+         5.3.3.2.3.3. Example 3
         </a>
        </li>
       </ul>
@@ -2177,120 +2177,120 @@ <h2> Contents </h2>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#field-access">
-     6.3.3.3. Field access
+     5.3.3.3. Field access
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#flat-iterator-indexing">
-     6.3.3.4. Flat Iterator indexing
+     5.3.3.4. Flat Iterator indexing
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#assigning-values-to-indexed-arrays">
-     6.3.3.5. Assigning values to indexed arrays
+     5.3.3.5. Assigning values to indexed arrays
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dealing-with-variable-numbers-of-indices-within-programs">
-     6.3.3.6. Dealing with variable numbers of indices within programs
+     5.3.3.6. Dealing with variable numbers of indices within programs
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#structured-arrays">
-   6.3.4. Structured arrays
+   5.3.4. Structured arrays
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#introduction">
-     6.3.4.1. Introduction
+     5.3.4.1. Introduction
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#structured-datatypes">
-     6.3.4.2. Structured datatypes
+     5.3.4.2. Structured datatypes
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#structured-datatype-creation">
-       6.3.4.2.1. Structured datatype creation
+       5.3.4.2.1. Structured datatype creation
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#manipulating-and-displaying-structured-datatypes">
-       6.3.4.2.2. Manipulating and Displaying Structured Datatypes
+       5.3.4.2.2. Manipulating and Displaying Structured Datatypes
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#field-titles">
-       6.3.4.2.3. Field Titles
+       5.3.4.2.3. Field Titles
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#indexing-and-assignment-to-structured-arrays">
-     6.3.4.3. Indexing and Assignment to Structured arrays
+     5.3.4.3. Indexing and Assignment to Structured arrays
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#assigning-data-to-a-structured-array">
-       6.3.4.3.1. Assigning data to a Structured Array
+       5.3.4.3.1. Assigning data to a Structured Array
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#assignment-from-python-native-types-tuples">
-         6.3.4.3.1.1. Assignment from Python Native Types (Tuples)
+         5.3.4.3.1.1. Assignment from Python Native Types (Tuples)
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#assignment-from-scalars">
-         6.3.4.3.1.2. Assignment from Scalars
+         5.3.4.3.1.2. Assignment from Scalars
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#assignment-from-other-structured-arrays">
-         6.3.4.3.1.3. Assignment from other Structured Arrays
+         5.3.4.3.1.3. Assignment from other Structured Arrays
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#assignment-involving-subarrays">
-         6.3.4.3.1.4. Assignment involving subarrays
+         5.3.4.3.1.4. Assignment involving subarrays
         </a>
        </li>
       </ul>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#indexing-structured-arrays">
-       6.3.4.3.2. Indexing Structured Arrays
+       5.3.4.3.2. Indexing Structured Arrays
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#accessing-individual-fields">
-         6.3.4.3.2.1. Accessing Individual Fields
+         5.3.4.3.2.1. Accessing Individual Fields
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#accessing-multiple-fields">
-         6.3.4.3.2.2. Accessing multiple fields
+         5.3.4.3.2.2. Accessing multiple fields
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#indexing-with-an-integer-to-get-a-structured-scalar">
-         6.3.4.3.2.3. Indexing with an integer to get a structured scalar
+         5.3.4.3.2.3. Indexing with an integer to get a structured scalar
         </a>
        </li>
       </ul>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#viewing-structured-arrays-containing-objects">
-       6.3.4.3.3. Viewing structured arrays containing objects
+       5.3.4.3.3. Viewing structured arrays containing objects
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#structure-comparison-and-promotion">
-       6.3.4.3.4. Structure Comparison and Promotion
+       5.3.4.3.4. Structure Comparison and Promotion
       </a>
      </li>
     </ul>
@@ -2299,34 +2299,34 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   6.3.5. Your turn! 🚀
+   5.3.5. Your turn! 🚀
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#challenge-1-analyzing-covid-spread">
-     6.3.5.1. 🚀 Challenge 1: analyzing COVID spread
+     5.3.5.1. 🚀 Challenge 1: analyzing COVID spread
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#working-with-unstructured-data">
-     6.3.5.2. Working with unstructured Data
+     5.3.5.2. Working with unstructured Data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#challenge-2-analyzing-covid-papers">
-     6.3.5.3. 🚀 Challenge 2: analyzing COVID papers
+     5.3.5.3. 🚀 Challenge 2: analyzing COVID papers
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   6.3.6. Self study
+   5.3.6. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   6.3.7. Acknowledgments
+   5.3.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -2340,10 +2340,10 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="numpy">
-<h1><span class="section-number">6.3. </span>NumPy<a class="headerlink" href="#numpy" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">5.3. </span>NumPy<a class="headerlink" href="#numpy" title="Permalink to this headline">#</a></h1>
 <p>NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.</p>
 <section id="basic-introduction-to-array">
-<h2><span class="section-number">6.3.1. </span>Basic introduction to array<a class="headerlink" href="#basic-introduction-to-array" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.3.1. </span>Basic introduction to array<a class="headerlink" href="#basic-introduction-to-array" title="Permalink to this headline">#</a></h2>
 <p>NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of non-negative integers. In NumPy dimensions are called axes.</p>
 <p>For example, the array for the coordinates of a point in 3D space, <code class="docutils literal notranslate"><span class="pre">[1,</span> <span class="pre">2,</span> <span class="pre">1]</span></code>, has one axis. That axis has 3 elements in it, so we say it has a length of 3. In the example pictured below, the array has 2 axes. The first axis has a length of 2, the second axis has a length of 3.</p>
 <div class="cell docutils container">
@@ -2360,7 +2360,7 @@ <h2><span class="section-number">6.3.1. </span>Basic introduction to array<a cla
 </div>
 </div>
 <section id="create-a-basic-array">
-<h3><span class="section-number">6.3.1.1. </span>Create a basic array<a class="headerlink" href="#create-a-basic-array" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.1.1. </span>Create a basic array<a class="headerlink" href="#create-a-basic-array" title="Permalink to this headline">#</a></h3>
 <p>To create a NumPy array, you can use the function <code class="docutils literal notranslate"><span class="pre">np.array()</span></code>.</p>
 <p>All you need to do to create a simple array is pass a list to it. If you choose to, you can also specify the type of data in your list.</p>
 <div class="cell docutils container">
@@ -2470,7 +2470,7 @@ <h3><span class="section-number">6.3.1.1. </span>Create a basic array<a class="h
 </div>
 </section>
 <section id="adding-removing-and-sorting-elements">
-<h3><span class="section-number">6.3.1.2. </span>Adding, removing, and sorting elements<a class="headerlink" href="#adding-removing-and-sorting-elements" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.1.2. </span>Adding, removing, and sorting elements<a class="headerlink" href="#adding-removing-and-sorting-elements" title="Permalink to this headline">#</a></h3>
 <p>Sorting an element is simple with <code class="docutils literal notranslate"><span class="pre">np.sort()</span></code>. You can specify the axis, kind, and order when you call the function.</p>
 <p>If you start with this array:</p>
 <div class="cell docutils container">
@@ -2549,7 +2549,7 @@ <h3><span class="section-number">6.3.1.2. </span>Adding, removing, and sorting e
 <p>In order to remove elements from an array, it’s simple to use indexing to select the elements that you want to keep.</p>
 </section>
 <section id="numpy-array-attributes">
-<h3><span class="section-number">6.3.1.3. </span>NumPy array attributes<a class="headerlink" href="#numpy-array-attributes" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.1.3. </span>NumPy array attributes<a class="headerlink" href="#numpy-array-attributes" title="Permalink to this headline">#</a></h3>
 <p>NumPy’s array class is called <code class="docutils literal notranslate"><span class="pre">ndarray</span></code>. It is also known by the alias <code class="docutils literal notranslate"><span class="pre">array</span></code>. Note that <code class="docutils literal notranslate"><span class="pre">numpy.array</span></code> is not the same as the Standard Python Library class <code class="docutils literal notranslate"><span class="pre">array.array</span></code>, which only handles one-dimensional arrays and offers less functionality. The more important attributes of an <code class="docutils literal notranslate"><span class="pre">ndarray</span></code> object are:</p>
 <ul class="simple">
 <li><p>ndarray.ndim
@@ -2670,14 +2670,14 @@ <h3><span class="section-number">6.3.1.3. </span>NumPy array attributes<a class=
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;memory at 0x7f39491c1d40&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;memory at 0x7f806c85fd40&gt;
 </pre></div>
 </div>
 </div>
 </div>
 </section>
 <section id="reshape-an-array">
-<h3><span class="section-number">6.3.1.4. </span>Reshape an array<a class="headerlink" href="#reshape-an-array" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.1.4. </span>Reshape an array<a class="headerlink" href="#reshape-an-array" title="Permalink to this headline">#</a></h3>
 <p>Using <code class="docutils literal notranslate"><span class="pre">arr.reshape()</span></code> will give a new shape to an array without changing the data. Just remember that when you use the reshape method, the array you want to produce needs to have the same number of elements as the original array. If you start with an array with 12 elements, you’ll need to make sure that your new array also has a total of 12 elements.</p>
 <p>If you start with this array:</p>
 <div class="cell docutils container">
@@ -2727,7 +2727,7 @@ <h3><span class="section-number">6.3.1.4. </span>Reshape an array<a class="heade
 <p><code class="docutils literal notranslate"><span class="pre">order:</span></code> <code class="docutils literal notranslate"><span class="pre">C</span></code> means to read/write the elements using C-like index order, <code class="docutils literal notranslate"><span class="pre">F</span></code> means to read/write the elements using Fortran-like index order, <code class="docutils literal notranslate"><span class="pre">A</span></code> means to read/write the elements in Fortran-like index order if a is Fortran contiguous in memory, C-like order otherwise. (This is an optional parameter and doesn’t need to be specified.)</p>
 </section>
 <section id="convert-a-1d-array-into-a-2d-array-add-a-new-axis-to-an-array">
-<h3><span class="section-number">6.3.1.5. </span>Convert a 1D array into a 2D array(add a new axis to an array)<a class="headerlink" href="#convert-a-1d-array-into-a-2d-array-add-a-new-axis-to-an-array" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.1.5. </span>Convert a 1D array into a 2D array(add a new axis to an array)<a class="headerlink" href="#convert-a-1d-array-into-a-2d-array-add-a-new-axis-to-an-array" title="Permalink to this headline">#</a></h3>
 <p>You can use <code class="docutils literal notranslate"><span class="pre">np.newaxis</span></code> and <code class="docutils literal notranslate"><span class="pre">np.expand_dims</span></code> to increase the dimensions of your existing array.</p>
 <p>Using <code class="docutils literal notranslate"><span class="pre">np.newaxis</span></code> will increase the dimensions of your array by one dimension when used once. This means that a 1D array will become a 2D array, a 2D array will become a 3D array, and so on.</p>
 <p>For example, if you start with this array:</p>
@@ -2831,7 +2831,7 @@ <h3><span class="section-number">6.3.1.5. </span>Convert a 1D array into a 2D ar
 </div>
 </section>
 <section id="indexing-and-slicing">
-<h3><span class="section-number">6.3.1.6. </span>Indexing and slicing<a class="headerlink" href="#indexing-and-slicing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.1.6. </span>Indexing and slicing<a class="headerlink" href="#indexing-and-slicing" title="Permalink to this headline">#</a></h3>
 <p>You can index and slice NumPy arrays in the same ways you can slice Python lists.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3040,7 +3040,7 @@ <h3><span class="section-number">6.3.1.6. </span>Indexing and slicing<a class="h
 </div>
 </section>
 <section id="create-an-array-from-existing-data">
-<h3><span class="section-number">6.3.1.7. </span>Create an array from existing data<a class="headerlink" href="#create-an-array-from-existing-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.1.7. </span>Create an array from existing data<a class="headerlink" href="#create-an-array-from-existing-data" title="Permalink to this headline">#</a></h3>
 <p>You can easily create a new array from a section of an existing array.</p>
 <p>Let’s say you have this array:</p>
 <div class="cell docutils container">
@@ -3220,9 +3220,9 @@ <h3><span class="section-number">6.3.1.7. </span>Create an array from existing d
 </section>
 </section>
 <section id="array-operations">
-<h2><span class="section-number">6.3.2. </span>Array operations<a class="headerlink" href="#array-operations" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.3.2. </span>Array operations<a class="headerlink" href="#array-operations" title="Permalink to this headline">#</a></h2>
 <section id="basic-array-operations">
-<h3><span class="section-number">6.3.2.1. </span>Basic array operations<a class="headerlink" href="#basic-array-operations" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.2.1. </span>Basic array operations<a class="headerlink" href="#basic-array-operations" title="Permalink to this headline">#</a></h3>
 <p>Once you’ve created your arrays, you can start to work with them. Let’s say, for example, that you’ve created two arrays, one called <code class="docutils literal notranslate"><span class="pre">data</span></code> and one called <code class="docutils literal notranslate"><span class="pre">ones</span></code>.</p>
 <div class="cell docutils container">
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@@ -3313,10 +3313,10 @@ <h3><span class="section-number">6.3.2.1. </span>Basic array operations<a class=
 </div>
 </section>
 <section id="universal-functions-ufunc">
-<h3><span class="section-number">6.3.2.2. </span>Universal functions(ufunc)<a class="headerlink" href="#universal-functions-ufunc" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.2.2. </span>Universal functions(ufunc)<a class="headerlink" href="#universal-functions-ufunc" title="Permalink to this headline">#</a></h3>
 <p>A universal function (or ufunc for short) is a function that operates on ndarrays in an element-by-element fashion, supporting array broadcasting, type casting, and several other standard features. That is, a ufunc is a “vectorized” wrapper for a function that takes a fixed number of specific inputs and produces a fixed number of specific outputs.</p>
 <section id="available-ufuncs">
-<h4><span class="section-number">6.3.2.2.1. </span>Available ufuncs<a class="headerlink" href="#available-ufuncs" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.3.2.2.1. </span>Available ufuncs<a class="headerlink" href="#available-ufuncs" title="Permalink to this headline">#</a></h4>
 <p>There are currently more than 60 universal functions defined in numpy on one or more types, covering a wide variety of operations. Some of these ufuncs are called automatically on arrays when the relevant infix notation is used (e.g., <code class="docutils literal notranslate"><span class="pre">add(a,</span> <span class="pre">b)</span></code> is called internally when <code class="docutils literal notranslate"><span class="pre">a</span> <span class="pre">+</span> <span class="pre">b</span></code> is written and a or b is an ndarray). Nevertheless, you may still want to use the ufunc call in order to use the optional output argument(s) to place the output(s) in an object (or objects) of your choice.</p>
 <p>Recall that each ufunc operates element-by-element. Therefore, each scalar ufunc will be described as if acting on a set of scalar inputs to return a set of scalar outputs.</p>
 <div class="admonition note">
@@ -3324,7 +3324,7 @@ <h4><span class="section-number">6.3.2.2.1. </span>Available ufuncs<a class="hea
 <p>The ufunc still returns its output(s) even if you use the optional output argument(s).</p>
 </div>
 <section id="math-operations">
-<h5><span class="section-number">6.3.2.2.1.1. </span>Math operations<a class="headerlink" href="#math-operations" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.3.2.2.1.1. </span>Math operations<a class="headerlink" href="#math-operations" title="Permalink to this headline">#</a></h5>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="text-align:left head"><p>Syntax</p></th>
@@ -3375,7 +3375,7 @@ <h5><span class="section-number">6.3.2.2.1.1. </span>Math operations<a class="he
 </table>
 </section>
 <section id="trigonometric-functions">
-<h5><span class="section-number">6.3.2.2.1.2. </span>Trigonometric functions<a class="headerlink" href="#trigonometric-functions" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.3.2.2.1.2. </span>Trigonometric functions<a class="headerlink" href="#trigonometric-functions" title="Permalink to this headline">#</a></h5>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="text-align:left head"><p>Syntax</p></th>
@@ -3405,7 +3405,7 @@ <h5><span class="section-number">6.3.2.2.1.2. </span>Trigonometric functions<a c
 </table>
 </section>
 <section id="bit-twiddling-functions">
-<h5><span class="section-number">6.3.2.2.1.3. </span>Bit-twiddling functions<a class="headerlink" href="#bit-twiddling-functions" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.3.2.2.1.3. </span>Bit-twiddling functions<a class="headerlink" href="#bit-twiddling-functions" title="Permalink to this headline">#</a></h5>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="text-align:left head"><p>Syntax</p></th>
@@ -3429,7 +3429,7 @@ <h5><span class="section-number">6.3.2.2.1.3. </span>Bit-twiddling functions<a c
 </table>
 </section>
 <section id="comparison-functions">
-<h5><span class="section-number">6.3.2.2.1.4. </span>Comparison functions<a class="headerlink" href="#comparison-functions" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.3.2.2.1.4. </span>Comparison functions<a class="headerlink" href="#comparison-functions" title="Permalink to this headline">#</a></h5>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="text-align:left head"><p>Syntax</p></th>
@@ -3490,7 +3490,7 @@ <h5><span class="section-number">6.3.2.2.1.4. </span>Comparison functions<a clas
 </section>
 </section>
 <section id="computation-on-arrays-broadcasting">
-<h3><span class="section-number">6.3.2.3. </span>Computation on arrays: broadcasting<a class="headerlink" href="#computation-on-arrays-broadcasting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.2.3. </span>Computation on arrays: broadcasting<a class="headerlink" href="#computation-on-arrays-broadcasting" title="Permalink to this headline">#</a></h3>
 <p>The term broadcasting describes how NumPy treats arrays with different shapes during arithmetic operations. Subject to certain constraints, the smaller array is “broadcast” across the larger array so that they have compatible shapes. Broadcasting provides a means of vectorizing array operations so that looping occurs in C instead of Python. It does this without making needless copies of data and usually leads to efficient algorithm implementations. There are, however, cases where broadcasting is a bad idea because it leads to inefficient use of memory that slows computation.</p>
 <p>NumPy operations are usually done on pairs of arrays on an element-by-element basis. In the simplest case, the two arrays must have exactly the same shape, as in the following example:</p>
 <div class="cell docutils container">
@@ -3524,7 +3524,7 @@ <h3><span class="section-number">6.3.2.3. </span>Computation on arrays: broadcas
 </div>
 <p>The result is equivalent to the previous example where <code class="docutils literal notranslate"><span class="pre">b</span></code> was an array.  NumPy is smart enough to use the original scalar value without actually making copies so that broadcasting operations are as memory and computationally efficient as possible.</p>
 <section id="general-broadcasting-rules">
-<h4><span class="section-number">6.3.2.3.1. </span>General Broadcasting Rules<a class="headerlink" href="#general-broadcasting-rules" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.3.2.3.1. </span>General Broadcasting Rules<a class="headerlink" href="#general-broadcasting-rules" title="Permalink to this headline">#</a></h4>
 <p>When operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing (i.e. rightmost) dimension and works its way left. Two dimensions are compatible when</p>
 <ul class="simple">
 <li><p>they are equal, or</p></li>
@@ -3548,7 +3548,7 @@ <h4><span class="section-number">6.3.2.3.1. </span>General Broadcasting Rules<a
 </section>
 </section>
 <section id="aggregations-min-max-and-everything-in-between">
-<h3><span class="section-number">6.3.2.4. </span>Aggregations: min, max and everything in between<a class="headerlink" href="#aggregations-min-max-and-everything-in-between" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.2.4. </span>Aggregations: min, max and everything in between<a class="headerlink" href="#aggregations-min-max-and-everything-in-between" title="Permalink to this headline">#</a></h3>
 <p>NumPy also performs aggregation functions. In addition to <code class="docutils literal notranslate"><span class="pre">min</span></code>, <code class="docutils literal notranslate"><span class="pre">max</span></code>, and <code class="docutils literal notranslate"><span class="pre">sum</span></code>, you can easily run <code class="docutils literal notranslate"><span class="pre">mean</span></code> to get the average, <code class="docutils literal notranslate"><span class="pre">prod</span></code> to get the result of multiplying the elements together, <code class="docutils literal notranslate"><span class="pre">std</span></code> to get the standard deviation, and more.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3639,16 +3639,16 @@ <h3><span class="section-number">6.3.2.4. </span>Aggregations: min, max and ever
 </section>
 </section>
 <section id="indexing-on-ndarrays">
-<h2><span class="section-number">6.3.3. </span>Indexing on ndarrays<a class="headerlink" href="#indexing-on-ndarrays" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.3.3. </span>Indexing on ndarrays<a class="headerlink" href="#indexing-on-ndarrays" title="Permalink to this headline">#</a></h2>
 <p>ndarrays can be indexed using the standard Python <code class="docutils literal notranslate"><span class="pre">x[obj]</span></code> syntax, where <code class="docutils literal notranslate"><span class="pre">x</span></code> is the array and <code class="docutils literal notranslate"><span class="pre">obj</span></code> the selection. There are different kinds of indexing available depending on <code class="docutils literal notranslate"><span class="pre">obj</span></code>: basic indexing, advanced indexing and field access.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>In Python, <code class="docutils literal notranslate"><span class="pre">x[(exp1,</span> <span class="pre">exp2,</span> <span class="pre">...,</span> <span class="pre">expN)]</span></code> is equivalent to <code class="docutils literal notranslate"><span class="pre">x[exp1,</span> <span class="pre">exp2,</span> <span class="pre">...,</span> <span class="pre">expN]</span></code>; the latter is just syntactic sugar for the former.</p>
 </div>
 <section id="basic-indexing">
-<h3><span class="section-number">6.3.3.1. </span>Basic indexing<a class="headerlink" href="#basic-indexing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.3.1. </span>Basic indexing<a class="headerlink" href="#basic-indexing" title="Permalink to this headline">#</a></h3>
 <section id="single-element-indexing">
-<h4><span class="section-number">6.3.3.1.1. </span>Single element indexing<a class="headerlink" href="#single-element-indexing" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.3.3.1.1. </span>Single element indexing<a class="headerlink" href="#single-element-indexing" title="Permalink to this headline">#</a></h4>
 <p>Single element indexing works exactly like that for other standard Python sequences. It is 0-based, and accepts negative indices for indexing from the end of the array.</p>
 <div class="cell docutils container">
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@@ -3746,7 +3746,7 @@ <h4><span class="section-number">6.3.3.1.1. </span>Single element indexing<a cla
 </div>
 </section>
 <section id="slicing-and-striding">
-<h4><span class="section-number">6.3.3.1.2. </span>Slicing and striding<a class="headerlink" href="#slicing-and-striding" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.3.3.1.2. </span>Slicing and striding<a class="headerlink" href="#slicing-and-striding" title="Permalink to this headline">#</a></h4>
 <p>Basic slicing extends Python’s basic concept of slicing to N dimensions. Basic slicing occurs when obj is a <code class="docutils literal notranslate"><span class="pre">slice</span></code> object (constructed by <code class="docutils literal notranslate"><span class="pre">start:stop:step</span></code> notation inside of brackets), an integer, or a tuple of slice objects and integers. <code class="docutils literal notranslate"><span class="pre">Ellipsis</span></code> and <code class="docutils literal notranslate"><span class="pre">newaxis</span></code> objects can be interspersed with these as well.</p>
 <p>The simplest case of indexing with <em>N</em> integers returns an array scalar representing the corresponding item. As in Python, all indices are zero-based: for the i-th index , the valid range is <span class="math notranslate nohighlight">\(0 \leq n_i \leq d_i\)</span>  where <span class="math notranslate nohighlight">\(d_i\)</span> is the <em>i</em>-th element of the shape of the array. Negative indices are interpreted as counting from the end of the array (i.e., if , it <span class="math notranslate nohighlight">\(n_i &lt; 0\)</span>, it means <span class="math notranslate nohighlight">\(n_i + d_i\)</span>).</p>
 <p>All arrays generated by basic slicing are always <strong>views</strong> of the original array.</p>
@@ -3854,7 +3854,7 @@ <h4><span class="section-number">6.3.3.1.2. </span>Slicing and striding<a class=
 </ul>
 </section>
 <section id="dimensional-indexing-tools">
-<h4><span class="section-number">6.3.3.1.3. </span>Dimensional indexing tools<a class="headerlink" href="#dimensional-indexing-tools" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.3.3.1.3. </span>Dimensional indexing tools<a class="headerlink" href="#dimensional-indexing-tools" title="Permalink to this headline">#</a></h4>
 <p>There are some tools to facilitate the easy matching of array shapes with expressions and in assignments.</p>
 <p>Ellipsis expands to the number of <code class="docutils literal notranslate"><span class="pre">:</span></code> objects needed for the selection tuple to index all dimensions. In most cases, this means that the length of the expanded selection tuple is <code class="docutils literal notranslate"><span class="pre">x.ndim</span></code>. There may only be a single ellipsis present. From the above example:</p>
 <div class="cell docutils container">
@@ -3930,13 +3930,13 @@ <h4><span class="section-number">6.3.3.1.3. </span>Dimensional indexing tools<a
 </section>
 </section>
 <section id="advanced-indexing">
-<h3><span class="section-number">6.3.3.2. </span>Advanced indexing<a class="headerlink" href="#advanced-indexing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.3.2. </span>Advanced indexing<a class="headerlink" href="#advanced-indexing" title="Permalink to this headline">#</a></h3>
 <p>Advanced indexing is triggered when the selection object, obj, is a non-tuple sequence object, an <code class="docutils literal notranslate"><span class="pre">ndarray</span></code> (of data type integer or bool), or a tuple with at least one sequence object or ndarray (of data type integer or bool). There are two types of advanced indexing: integer and Boolean.</p>
 <p>Advanced indexing always returns a copy of the data (contrast with basic slicing that returns a <code class="docutils literal notranslate"><span class="pre">view</span></code>).</p>
 <p><strong>WARNING:</strong> The definition of advanced indexing means that <code class="docutils literal notranslate"><span class="pre">x[(1,</span> <span class="pre">2,</span> <span class="pre">3),]</span></code> is fundamentally different than <code class="docutils literal notranslate"><span class="pre">x[(1,</span> <span class="pre">2,</span> <span class="pre">3)]</span></code>. The latter is equivalent to <code class="docutils literal notranslate"><span class="pre">x[1,</span> <span class="pre">2,</span> <span class="pre">3]</span></code> which will trigger basic selection while the former will trigger advanced indexing. Be sure to understand why this occurs.</p>
 <p>Also recognize that <code class="docutils literal notranslate"><span class="pre">x[[1,</span> <span class="pre">2,</span> <span class="pre">3]]</span></code> will trigger advanced indexing, whereas due to the deprecated Numeric compatibility mentioned above, <code class="docutils literal notranslate"><span class="pre">x[[1,</span> <span class="pre">2,</span> <span class="pre">slice(None)]]</span></code> will trigger basic slicing.</p>
 <section id="integer-array-indexing">
-<h4><span class="section-number">6.3.3.2.1. </span>Integer array indexing<a class="headerlink" href="#integer-array-indexing" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.3.3.2.1. </span>Integer array indexing<a class="headerlink" href="#integer-array-indexing" title="Permalink to this headline">#</a></h4>
 <p>Integer array indexing allows selection of arbitrary items in the array based on their <em>N</em>-dimensional index. Each integer array represents a number of indices into that dimension.</p>
 <p>Negative values are permitted in the index arrays and work as they do with single indices or slices:</p>
 <div class="cell docutils container">
@@ -4095,7 +4095,7 @@ <h4><span class="section-number">6.3.3.2.1. </span>Integer array indexing<a clas
 <p>It results in the construction of a new array where each value of the index array selects one row from the array being indexed and the resultant array has the resulting shape (number of index elements, size of row).</p>
 <p>In general, the shape of the resultant array will be the concatenation of the shape of the index array (or the shape that all the index arrays were broadcast to) with the shape of any unused dimensions (those not indexed) in the array being indexed.</p>
 <section id="example-1">
-<h5><span class="section-number">6.3.3.2.1.1. </span>Example 1<a class="headerlink" href="#example-1" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.3.3.2.1.1. </span>Example 1<a class="headerlink" href="#example-1" title="Permalink to this headline">#</a></h5>
 <p>From each row, a specific element should be selected. The row index is just <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">1,</span> <span class="pre">2]</span></code> and the column index specifies the element to choose for the corresponding row, here <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">1,</span> <span class="pre">0]</span></code>. Using both together the task can be solved using advanced indexing:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -4113,7 +4113,7 @@ <h5><span class="section-number">6.3.3.2.1.1. </span>Example 1<a class="headerli
 <p>To achieve a behaviour similar to the basic slicing above, broadcasting can be used. The function <code class="docutils literal notranslate"><span class="pre">ix_</span></code> can help with this broadcasting. This is best understood with an example.</p>
 </section>
 <section id="example-2">
-<h5><span class="section-number">6.3.3.2.1.2. </span>Example 2<a class="headerlink" href="#example-2" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.3.3.2.1.2. </span>Example 2<a class="headerlink" href="#example-2" title="Permalink to this headline">#</a></h5>
 <p>From a <span class="math notranslate nohighlight">\(4x3\)</span> array the corner elements should be selected using advanced indexing. Thus all elements for which the column is one of <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">2]</span></code> and the row is one of <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">3]</span></code> need to be selected. To use advanced indexing one needs to select all elements explicitly. Using the method explained previously one could write:</p>
 <div class="cell docutils container">
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@@ -4201,12 +4201,12 @@ <h5><span class="section-number">6.3.3.2.1.2. </span>Example 2<a class="headerli
 <p>This difference is the most important thing to remember about indexing with multiple advanced indices.</p>
 </section>
 <section id="example-3">
-<h5><span class="section-number">6.3.3.2.1.3. </span>Example 3<a class="headerlink" href="#example-3" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.3.3.2.1.3. </span>Example 3<a class="headerlink" href="#example-3" title="Permalink to this headline">#</a></h5>
 <p>A real-life example of where advanced indexing may be useful is for a color lookup table where we want to map the values of an image into RGB triples for display. The lookup table could have a shape (nlookup, 3). Indexing such an array with an image with shape (ny, nx) with dtype=np.uint8 (or any integer type so long as values are with the bounds of the lookup table) will result in an array of shape (ny, nx, 3) where a triple of RGB values is associated with each pixel location.</p>
 </section>
 </section>
 <section id="boolean-array-indexing">
-<h4><span class="section-number">6.3.3.2.2. </span>Boolean array indexing<a class="headerlink" href="#boolean-array-indexing" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.3.3.2.2. </span>Boolean array indexing<a class="headerlink" href="#boolean-array-indexing" title="Permalink to this headline">#</a></h4>
 <p>This advanced indexing occurs when obj is an array object of Boolean type, such as may be returned from comparison operators. A single boolean index array is practically identical to <code class="docutils literal notranslate"><span class="pre">x[obj.nonzero()]</span></code> where, as described above, <code class="docutils literal notranslate"><span class="pre">obj.nonzero()</span></code> returns a tuple (of length <code class="docutils literal notranslate"><span class="pre">obj.ndim</span></code>) of integer index arrays showing the <code class="docutils literal notranslate"><span class="pre">True</span></code> elements of obj. However, it is faster when <code class="docutils literal notranslate"><span class="pre">obj.shape</span> <span class="pre">==</span> <span class="pre">x.shape</span></code>.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">obj.ndim</span> <span class="pre">==</span> <span class="pre">x.ndim</span></code>, <code class="docutils literal notranslate"><span class="pre">x[obj]</span></code> returns a 1-dimensional array filled with the elements of x corresponding to the <code class="docutils literal notranslate"><span class="pre">True</span></code> values of obj. The search order will be <strong>row-major</strong>, C-style. If obj has <code class="docutils literal notranslate"><span class="pre">True</span></code> values at entries that are outside of the bounds of x, then an index error will be raised. If obj is smaller than x it is identical to filling it with <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
 <p>A common use case for this is filtering for desired element values. For example, one may wish to select all entries from an array which are not <code class="docutils literal notranslate"><span class="pre">NaN</span></code>:</p>
@@ -4276,7 +4276,7 @@ <h4><span class="section-number">6.3.3.2.2. </span>Boolean array indexing<a clas
 </div>
 <p>Here the 4th and 5th rows are selected from the indexed array and combined to make a 2-D array.</p>
 <section id="id1">
-<h5><span class="section-number">6.3.3.2.2.1. </span>Example 1<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.3.3.2.2.1. </span>Example 1<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h5>
 <p>From an array, select all rows which sum up to less or equal two:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -4296,7 +4296,7 @@ <h5><span class="section-number">6.3.3.2.2.1. </span>Example 1<a class="headerli
 <p>Combining multiple Boolean indexing arrays or a Boolean with an integer indexing array can best be understood with the <code class="docutils literal notranslate"><span class="pre">obj.nonzero()</span></code> analogy. The function <code class="docutils literal notranslate"><span class="pre">ix_</span></code> also supports boolean arrays and will work without any surprises.</p>
 </section>
 <section id="id2">
-<h5><span class="section-number">6.3.3.2.2.2. </span>Example 2<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.3.3.2.2.2. </span>Example 2<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h5>
 <p>Use boolean indexing to select all rows adding up to an even number. At the same time columns 0 and 2 should be selected with an advanced integer index. Using the <code class="docutils literal notranslate"><span class="pre">ix_</span></code> function this can be done with:</p>
 <div class="cell docutils container">
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@@ -4359,7 +4359,7 @@ <h5><span class="section-number">6.3.3.2.2.2. </span>Example 2<a class="headerli
 </div>
 </section>
 <section id="id3">
-<h5><span class="section-number">6.3.3.2.2.3. </span>Example 3<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.3.3.2.2.3. </span>Example 3<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h5>
 <p>Use a 2-D boolean array of shape (2, 3) with four True elements to select rows from a 3-D array of shape (2, 3, 5) results in a 2-D result of shape (4, 5):</p>
 <div class="cell docutils container">
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@@ -4399,7 +4399,7 @@ <h5><span class="section-number">6.3.3.2.2.3. </span>Example 3<a class="headerli
 </section>
 </section>
 <section id="combining-advanced-and-basic-indexing">
-<h4><span class="section-number">6.3.3.2.3. </span>Combining advanced and basic indexing<a class="headerlink" href="#combining-advanced-and-basic-indexing" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.3.3.2.3. </span>Combining advanced and basic indexing<a class="headerlink" href="#combining-advanced-and-basic-indexing" title="Permalink to this headline">#</a></h4>
 <p>When there is at least one slice (<code class="docutils literal notranslate"><span class="pre">:</span></code>), ellipsis (<code class="docutils literal notranslate"><span class="pre">...</span></code>) or <code class="docutils literal notranslate"><span class="pre">newaxis</span></code> in the index (or the array has more dimensions than there are advanced indices), then the behaviour can be more complicated. It is like concatenating the indexing result for each advanced index element.</p>
 <p>In the simplest case, there is only a single advanced index combined with a slice. For example:</p>
 <div class="cell docutils container">
@@ -4474,15 +4474,15 @@ <h4><span class="section-number">6.3.3.2.3. </span>Combining advanced and basic
 </ul>
 <p>In the first case, the dimensions resulting from the advanced indexing operation come first in the result array, and the subspace dimensions after that. In the second case, the dimensions from the advanced indexing operations are inserted into the result array at the same spot as they were in the initial array (the latter logic is what makes simple advanced indexing behave just like slicing).</p>
 <section id="id4">
-<h5><span class="section-number">6.3.3.2.3.1. </span>Example 1<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.3.3.2.3.1. </span>Example 1<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h5>
 <p>Suppose <code class="docutils literal notranslate"><span class="pre">x.shape</span></code> is (10, 20, 30) and <code class="docutils literal notranslate"><span class="pre">ind</span></code> is a (2, 3, 4)-shaped indexing intp array, then <code class="docutils literal notranslate"><span class="pre">result</span> <span class="pre">=</span> <span class="pre">x[...,</span> <span class="pre">ind,</span> <span class="pre">:]</span></code> has shape (10, 2, 3, 4, 30) because the (20,)-shaped subspace has been replaced with a (2, 3, 4)-shaped broadcasted indexing subspace. If we let i, j, k loop over the (2, 3, 4)-shaped subspace then <code class="docutils literal notranslate"><span class="pre">result[...,</span> <span class="pre">i,</span> <span class="pre">j,</span> <span class="pre">k,</span> <span class="pre">:]</span> <span class="pre">=</span> <span class="pre">x[...,</span> <span class="pre">ind[i,</span> <span class="pre">j,</span> <span class="pre">k],</span> <span class="pre">:]</span></code>. This example produces the same result as <code class="docutils literal notranslate"><span class="pre">x.take(ind,</span> <span class="pre">axis=-2)</span></code>.</p>
 </section>
 <section id="id5">
-<h5><span class="section-number">6.3.3.2.3.2. </span>Example 2<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.3.3.2.3.2. </span>Example 2<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h5>
 <p>Let <code class="docutils literal notranslate"><span class="pre">x.shape</span></code> be (10, 20, 30, 40, 50) and suppose <code class="docutils literal notranslate"><span class="pre">ind_1</span></code> and <code class="docutils literal notranslate"><span class="pre">ind_2</span></code> can be broadcast to the shape (2, 3, 4). Then <code class="docutils literal notranslate"><span class="pre">x[:,</span> <span class="pre">ind_1,</span> <span class="pre">ind_2]</span></code> has shape (10, 2, 3, 4, 40, 50) because the (20, 30)-shaped subspace from X has been replaced with the (2, 3, 4) subspace from the indices. However, <code class="docutils literal notranslate"><span class="pre">x[:,</span> <span class="pre">ind_1,</span> <span class="pre">:,</span> <span class="pre">ind_2]</span></code> has shape (2, 3, 4, 10, 30, 50) because there is no unambiguous place to drop in the indexing subspace, thus it is tacked-on to the beginning. It is always possible to use <code class="docutils literal notranslate"><span class="pre">.transpose()</span></code> to move the subspace anywhere desired. Note that this example cannot be replicated using <code class="docutils literal notranslate"><span class="pre">take</span></code>.</p>
 </section>
 <section id="id6">
-<h5><span class="section-number">6.3.3.2.3.3. </span>Example 3<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.3.3.2.3.3. </span>Example 3<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h5>
 <p>Slicing can be combined with broadcasted boolean indices:</p>
 <div class="cell docutils container">
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@@ -4525,7 +4525,7 @@ <h5><span class="section-number">6.3.3.2.3.3. </span>Example 3<a class="headerli
 </section>
 </section>
 <section id="field-access">
-<h3><span class="section-number">6.3.3.3. </span>Field access<a class="headerlink" href="#field-access" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.3.3. </span>Field access<a class="headerlink" href="#field-access" title="Permalink to this headline">#</a></h3>
 <p>If the <code class="docutils literal notranslate"><span class="pre">ndarray</span></code> object is a structured array the fields of the array can be accessed by indexing the array with strings, dictionary-like.</p>
 <p>Indexing <code class="docutils literal notranslate"><span class="pre">x['field-name']</span></code> returns a new <code class="docutils literal notranslate"><span class="pre">view</span></code> to the array, which is of the same shape as x (except when the field is a sub-array) but of data type <code class="docutils literal notranslate"><span class="pre">x.dtype['field-name']</span></code> and contains only the part of the data in the specified field. Also, record array scalars can be “indexed” this way.</p>
 <p>Indexing into a structured array can also be done with a list of field names, e.g. <code class="docutils literal notranslate"><span class="pre">x[['field-name1',</span> <span class="pre">'field-name2']]</span></code>. As of NumPy 1.16, this returns a view containing only those fields. In older versions of NumPy, it returned a copy. See the user guide section on <code class="docutils literal notranslate"><span class="pre">Structured</span> <span class="pre">arrays</span></code> for more information on multifield indexing.</p>
@@ -4587,11 +4587,11 @@ <h3><span class="section-number">6.3.3.3. </span>Field access<a class="headerlin
 </div>
 </section>
 <section id="flat-iterator-indexing">
-<h3><span class="section-number">6.3.3.4. </span>Flat Iterator indexing<a class="headerlink" href="#flat-iterator-indexing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.3.4. </span>Flat Iterator indexing<a class="headerlink" href="#flat-iterator-indexing" title="Permalink to this headline">#</a></h3>
 <p><code class="docutils literal notranslate"><span class="pre">x.flat</span></code> returns an iterator that will iterate over the entire array (in C-contiguous style with the last index varying the fastest). This iterator object can also be indexed using basic slicing or advanced indexing as long as the selection object is not a tuple. This should be clear from the fact that<code class="docutils literal notranslate"><span class="pre">x.flat</span></code> is a 1-dimensional view. It can be used for integer indexing with 1-dimensional C-style-flat indices. The shape of any returned array is therefore the shape of the integer indexing object.</p>
 </section>
 <section id="assigning-values-to-indexed-arrays">
-<h3><span class="section-number">6.3.3.5. </span>Assigning values to indexed arrays<a class="headerlink" href="#assigning-values-to-indexed-arrays" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.3.5. </span>Assigning values to indexed arrays<a class="headerlink" href="#assigning-values-to-indexed-arrays" title="Permalink to this headline">#</a></h3>
 <p>As mentioned, one can select a subset of an array to assign to using a single index, slices, and index and mask arrays. The value being assigned to the indexed array must be shape consistent (the same shape or broadcastable to the shape the index produces). For example, it is permitted to assign a constant to a slice:</p>
 <div class="cell docutils container">
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@@ -4661,7 +4661,7 @@ <h3><span class="section-number">6.3.3.5. </span>Assigning values to indexed arr
 <p>Where people expect that the 1st location will be incremented by 3. In fact, it will only be incremented by 1. The reason is that a new array is extracted from the original (as a temporary) containing the values at 1, 1, 3, 1, then the value 1 is added to the temporary, and then the temporary is assigned back to the original array. Thus the value of the array at <code class="docutils literal notranslate"><span class="pre">x[1]</span> <span class="pre">+</span> <span class="pre">1</span></code> is assigned to <code class="docutils literal notranslate"><span class="pre">x[1]</span></code> three times, rather than being incremented 3 times.</p>
 </section>
 <section id="dealing-with-variable-numbers-of-indices-within-programs">
-<h3><span class="section-number">6.3.3.6. </span>Dealing with variable numbers of indices within programs<a class="headerlink" href="#dealing-with-variable-numbers-of-indices-within-programs" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.3.6. </span>Dealing with variable numbers of indices within programs<a class="headerlink" href="#dealing-with-variable-numbers-of-indices-within-programs" title="Permalink to this headline">#</a></h3>
 <p>The indexing syntax is very powerful but limiting when dealing with a variable number of indices. For example, if you want to write a function that can handle arguments with various numbers of dimensions without having to write special case code for each number of possible dimensions, how can that be done? If one supplies to the index a tuple, the tuple will be interpreted as a list of indices. For example:</p>
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@@ -4786,9 +4786,9 @@ <h3><span class="section-number">6.3.3.6. </span>Dealing with variable numbers o
 </section>
 </section>
 <section id="structured-arrays">
-<h2><span class="section-number">6.3.4. </span>Structured arrays<a class="headerlink" href="#structured-arrays" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.3.4. </span>Structured arrays<a class="headerlink" href="#structured-arrays" title="Permalink to this headline">#</a></h2>
 <section id="introduction">
-<h3><span class="section-number">6.3.4.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.4.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h3>
 <p>Structured arrays are ndarrays whose datatype is a composition of simpler datatypes organized as a sequence of named <code class="docutils literal notranslate"><span class="pre">fields</span></code>. For example,</p>
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@@ -4861,10 +4861,10 @@ <h3><span class="section-number">6.3.4.1. </span>Introduction<a class="headerlin
 <p>Users looking to manipulate tabular data, such as stored in csv files, may find other pydata projects more suitable, such as xarray, pandas, or DataArray. These provide a high-level interface for tabular data analysis and are better optimized for that use. For instance, the C-struct-like memory layout of structured arrays in numpy can lead to poor cache behavior in comparison.</p>
 </section>
 <section id="structured-datatypes">
-<h3><span class="section-number">6.3.4.2. </span>Structured datatypes<a class="headerlink" href="#structured-datatypes" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.4.2. </span>Structured datatypes<a class="headerlink" href="#structured-datatypes" title="Permalink to this headline">#</a></h3>
 <p>A structured datatype can be thought of as a sequence of bytes of a certain length (the structure’s <code class="docutils literal notranslate"><span class="pre">itemsize</span></code>) which is interpreted as a collection of fields. Each field has a name, a datatype, and a byte offset within the structure. The datatype of a field may be any numpy datatype including other structured datatypes, and it may also be a <em>subarray data type</em> which behaves like an ndarray of a specified shape. The offsets of the fields are arbitrary, and fields may even overlap. These offsets are usually determined automatically by numpy, but can also be specified.</p>
 <section id="structured-datatype-creation">
-<h4><span class="section-number">6.3.4.2.1. </span>Structured datatype creation<a class="headerlink" href="#structured-datatype-creation" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.3.4.2.1. </span>Structured datatype creation<a class="headerlink" href="#structured-datatype-creation" title="Permalink to this headline">#</a></h4>
 <p>Structured datatypes may be created using the function <code class="docutils literal notranslate"><span class="pre">numpy.dtype</span></code>. There are 4 alternative forms of specification which vary in flexibility and conciseness. In summary they are:</p>
 <ul class="simple">
 <li><p>A list of tuples, one tuple per field</p></li>
@@ -4976,7 +4976,7 @@ <h4><span class="section-number">6.3.4.2.1. </span>Structured datatype creation<
 </div>
 </section>
 <section id="manipulating-and-displaying-structured-datatypes">
-<h4><span class="section-number">6.3.4.2.2. </span>Manipulating and Displaying Structured Datatypes<a class="headerlink" href="#manipulating-and-displaying-structured-datatypes" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.3.4.2.2. </span>Manipulating and Displaying Structured Datatypes<a class="headerlink" href="#manipulating-and-displaying-structured-datatypes" title="Permalink to this headline">#</a></h4>
 <p>The list of field names of a structured datatype can be found in the <code class="docutils literal notranslate"><span class="pre">names</span></code> attribute of the dtype object:</p>
 <div class="cell docutils container">
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@@ -5009,7 +5009,7 @@ <h4><span class="section-number">6.3.4.2.2. </span>Manipulating and Displaying S
 <p>The string representation of a structured datatype is shown in the ‘‘list of tuples’’ form if possible, otherwise numpy falls back to using the more general dictionary form.</p>
 </section>
 <section id="field-titles">
-<h4><span class="section-number">6.3.4.2.3. </span>Field Titles<a class="headerlink" href="#field-titles" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.3.4.2.3. </span>Field Titles<a class="headerlink" href="#field-titles" title="Permalink to this headline">#</a></h4>
 <p>In addition to field names, fields may also have an associated title, an alternate name, which is sometimes used as an additional description or alias for the field. The title may be used to index an array, just like a field name.</p>
 <p>To add titles when using the list-of-tuples form of dtype specification, the field name may be specified as a tuple of two strings instead of a single string, which will be the field’s title and field name respectively. For example:</p>
 <div class="cell docutils container">
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-<h3><span class="section-number">6.3.4.3. </span>Indexing and Assignment to Structured arrays<a class="headerlink" href="#indexing-and-assignment-to-structured-arrays" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.4.3. </span>Indexing and Assignment to Structured arrays<a class="headerlink" href="#indexing-and-assignment-to-structured-arrays" title="Permalink to this headline">#</a></h3>
 <section id="assigning-data-to-a-structured-array">
-<h4><span class="section-number">6.3.4.3.1. </span>Assigning data to a Structured Array<a class="headerlink" href="#assigning-data-to-a-structured-array" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.3.4.3.1. </span>Assigning data to a Structured Array<a class="headerlink" href="#assigning-data-to-a-structured-array" title="Permalink to this headline">#</a></h4>
 <p>There are a number of ways to assign values to a structured array: Using python tuples, using scalar values, or using other structured arrays.</p>
 <section id="assignment-from-python-native-types-tuples">
-<h5><span class="section-number">6.3.4.3.1.1. </span>Assignment from Python Native Types (Tuples)<a class="headerlink" href="#assignment-from-python-native-types-tuples" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.3.4.3.1.1. </span>Assignment from Python Native Types (Tuples)<a class="headerlink" href="#assignment-from-python-native-types-tuples" title="Permalink to this headline">#</a></h5>
 <p>The simplest way to assign values to a structured array is using python tuples. Each assigned value should be a tuple of length equal to the number of fields in the array, and not a list or array as these will trigger numpy’s broadcasting rules. The tuple’s elements are assigned to the successive fields of the array, from left to right:</p>
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 <section id="assignment-from-scalars">
-<h5><span class="section-number">6.3.4.3.1.2. </span>Assignment from Scalars<a class="headerlink" href="#assignment-from-scalars" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.3.4.3.1.2. </span>Assignment from Scalars<a class="headerlink" href="#assignment-from-scalars" title="Permalink to this headline">#</a></h5>
 <p>A scalar assigned to a structured element will be assigned to all fields. This happens when a scalar is assigned to a structured array, or when an unstructured array is assigned to a structured array:</p>
 <div class="cell docutils container">
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 <section id="assignment-from-other-structured-arrays">
-<h5><span class="section-number">6.3.4.3.1.3. </span>Assignment from other Structured Arrays<a class="headerlink" href="#assignment-from-other-structured-arrays" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.3.4.3.1.3. </span>Assignment from other Structured Arrays<a class="headerlink" href="#assignment-from-other-structured-arrays" title="Permalink to this headline">#</a></h5>
 <p>Assignment between two structured arrays occurs as if the source elements had been converted to tuples and then assigned to the destination elements. That is, the first field of the source array is assigned to the first field of the destination array, and the second field likewise, and so on, regardless of field names. Structured arrays with a different number of fields cannot be assigned to each other. Bytes of the destination structure which are not included in any of the fields are unaffected.</p>
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-<h5><span class="section-number">6.3.4.3.1.4. </span>Assignment involving subarrays<a class="headerlink" href="#assignment-involving-subarrays" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.3.4.3.1.4. </span>Assignment involving subarrays<a class="headerlink" href="#assignment-involving-subarrays" title="Permalink to this headline">#</a></h5>
 <p>When assigning to fields which are subarrays, the assigned value will first be broadcast to the shape of the subarray.</p>
 </section>
 </section>
 <section id="indexing-structured-arrays">
-<h4><span class="section-number">6.3.4.3.2. </span>Indexing Structured Arrays<a class="headerlink" href="#indexing-structured-arrays" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.3.4.3.2. </span>Indexing Structured Arrays<a class="headerlink" href="#indexing-structured-arrays" title="Permalink to this headline">#</a></h4>
 <section id="accessing-individual-fields">
-<h5><span class="section-number">6.3.4.3.2.1. </span>Accessing Individual Fields<a class="headerlink" href="#accessing-individual-fields" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.3.4.3.2.1. </span>Accessing Individual Fields<a class="headerlink" href="#accessing-individual-fields" title="Permalink to this headline">#</a></h5>
 <p>Individual fields of a structured array may be accessed and modified by indexing the array with the field name.</p>
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-<h5><span class="section-number">6.3.4.3.2.2. </span>Accessing multiple fields<a class="headerlink" href="#accessing-multiple-fields" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.3.4.3.2.2. </span>Accessing multiple fields<a class="headerlink" href="#accessing-multiple-fields" title="Permalink to this headline">#</a></h5>
 <p>One can index and assign to a structured array with a multi-field index, where the index is a list of field names.</p>
 <p>The result of indexing with a multi-field index is a view into the original array, as follows:</p>
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@@ -5308,7 +5308,7 @@ <h5><span class="section-number">6.3.4.3.2.2. </span>Accessing multiple fields<a
 </div>
 </section>
 <section id="indexing-with-an-integer-to-get-a-structured-scalar">
-<h5><span class="section-number">6.3.4.3.2.3. </span>Indexing with an integer to get a structured scalar<a class="headerlink" href="#indexing-with-an-integer-to-get-a-structured-scalar" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.3.4.3.2.3. </span>Indexing with an integer to get a structured scalar<a class="headerlink" href="#indexing-with-an-integer-to-get-a-structured-scalar" title="Permalink to this headline">#</a></h5>
 <p>Indexing a single element of a structured array (with an integer index) returns a structured scalar:</p>
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 </section>
 </section>
 <section id="viewing-structured-arrays-containing-objects">
-<h4><span class="section-number">6.3.4.3.3. </span>Viewing structured arrays containing objects<a class="headerlink" href="#viewing-structured-arrays-containing-objects" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.3.4.3.3. </span>Viewing structured arrays containing objects<a class="headerlink" href="#viewing-structured-arrays-containing-objects" title="Permalink to this headline">#</a></h4>
 <p>In order to prevent clobbering object pointers in fields of <code class="docutils literal notranslate"><span class="pre">object</span></code> type, numpy currently does not allow views of structured arrays containing objects.</p>
 </section>
 <section id="structure-comparison-and-promotion">
-<h4><span class="section-number">6.3.4.3.4. </span>Structure Comparison and Promotion<a class="headerlink" href="#structure-comparison-and-promotion" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.3.4.3.4. </span>Structure Comparison and Promotion<a class="headerlink" href="#structure-comparison-and-promotion" title="Permalink to this headline">#</a></h4>
 <p>If the dtypes of two void structured arrays are equal, testing the equality of the arrays will result in a boolean array with the dimensions of the original arrays, with elements set to <code class="docutils literal notranslate"><span class="pre">True</span></code> where all fields of the corresponding structures are equal:</p>
 <div class="cell docutils container">
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@@ -5530,15 +5530,15 @@ <h4><span class="section-number">6.3.4.3.4. </span>Structure Comparison and Prom
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">6.3.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.3.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <section id="challenge-1-analyzing-covid-spread">
-<h3><span class="section-number">6.3.5.1. </span>🚀 Challenge 1: analyzing COVID spread<a class="headerlink" href="#challenge-1-analyzing-covid-spread" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.5.1. </span>🚀 Challenge 1: analyzing COVID spread<a class="headerlink" href="#challenge-1-analyzing-covid-spread" title="Permalink to this headline">#</a></h3>
 <p>First problem we will focus on is modelling of epidemic spread of COVID-19. In order to do that, we will use the data on the number of infected individuals in different countries, provided by the <a class="reference external" href="https://systems.jhu.edu/">Center for Systems Science and Engineering</a> (CSSE) at <a class="reference external" href="https://jhu.edu/">Johns Hopkins University</a>. Dataset is available in <a class="reference external" href="https://github.com/CSSEGISandData/COVID-19">this GitHub Repository</a>.</p>
 <p>Since we want to demonstrate how to deal with data, we invite you to open <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html"><span class="doc std std-doc">Estimation of COVID-19 pandemic</span></a> and read it from top to bottom. You can also execute cells, and do some challenges that we have left for you at the end.</p>
 <p><img alt="COVID Spread" src="../../_images/covidspread.png" /></p>
 </section>
 <section id="working-with-unstructured-data">
-<h3><span class="section-number">6.3.5.2. </span>Working with unstructured Data<a class="headerlink" href="#working-with-unstructured-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.5.2. </span>Working with unstructured Data<a class="headerlink" href="#working-with-unstructured-data" title="Permalink to this headline">#</a></h3>
 <p>While data very often comes in tabular form, in some cases we need to deal with less structured data, for example, text or images. In this case, to apply data processing techniques we have seen above, we need to somehow <strong>extract</strong> structured data. Here are a few examples:</p>
 <ul class="simple">
 <li><p>Extracting keywords from text, and seeing how often those keywords appear</p></li>
@@ -5547,7 +5547,7 @@ <h3><span class="section-number">6.3.5.2. </span>Working with unstructured Data<
 </ul>
 </section>
 <section id="challenge-2-analyzing-covid-papers">
-<h3><span class="section-number">6.3.5.3. </span>🚀 Challenge 2: analyzing COVID papers<a class="headerlink" href="#challenge-2-analyzing-covid-papers" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.3.5.3. </span>🚀 Challenge 2: analyzing COVID papers<a class="headerlink" href="#challenge-2-analyzing-covid-papers" title="Permalink to this headline">#</a></h3>
 <p>In this challenge, we will continue with the topic of COVID pandemic, and focus on processing scientific papers on the subject. There is <a class="reference external" href="https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge">CORD-19 Dataset</a> with more than 7000 (at the time of writing) papers on COVID, available with metadata and abstracts (and for about half of them there is also full text provided).</p>
 <p>A full example of analyzing this dataset using <a class="reference external" href="https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health/?WT.mc_id=academic-77958-bethanycheum">Text Analytics for Health</a> cognitive service is described <a class="reference external" href="https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/">in this blog post</a>. We will discuss simplified version of this analysis.</p>
 <div class="admonition note">
@@ -5559,7 +5559,7 @@ <h3><span class="section-number">6.3.5.3. </span>🚀 Challenge 2: analyzing COV
 </section>
 </section>
 <section id="self-study">
-<h2><span class="section-number">6.3.6. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.3.6. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p><strong>Books</strong></p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://www.amazon.com/gp/product/1491957662">Wes McKinney. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython</a></p></li>
@@ -5576,7 +5576,7 @@ <h2><span class="section-number">6.3.6. </span>Self study<a class="headerlink" h
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">6.3.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.3.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks for <a class="reference external" href="https://numpy.org/doc/stable/user/index.html#user">NumPy user guide</a>. It contributes the introduction to NumPy.</p>
 <p>Thanks to Microsoft for creating the open source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It contributes assignment section in this chapter.</p>
 </section>
@@ -5619,13 +5619,13 @@ <h2><span class="section-number">6.3.7. </span>Acknowledgments<a class="headerli
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">6.2. </span>Non-relational data</p>
+            <p class="prev-next-title"><span class="section-number">5.2. </span>Non-relational data</p>
         </div>
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     <a class='right-next' id="next-link" href="pandas/pandas.html" title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">6.4. </span>Pandas</p>
+        <p class="prev-next-title"><span class="section-number">5.4. </span>Pandas</p>
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-    <title>6.4.3. Advanced Pandas Techniques &#8212; Ocademy Open Machine Learning Book</title>
+    <title>5.4.3. Advanced Pandas Techniques &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="6.5. Data preparation" href="../data-preparation.html" />
-    <link rel="prev" title="6.4.2. Data Selection" href="data-selection.html" />
+    <link rel="next" title="5.5. Data preparation" href="../data-preparation.html" />
+    <link rel="prev" title="5.4.2. Data Selection" href="data-selection.html" />
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 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../../prerequisites/python-programming-introduction.html">
-   1. Python programming basics
+   1. Python programming introduction
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../prerequisites/python-programming-basics.html">
-   3. Python programming basics
+   2. Python programming basics
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-   4. Python programming advanced
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 <ul class="current nav bd-sidenav">
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   <a class="reference internal" href="../../introduction/introduction.html">
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+   4. Introduction
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   <ul>
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     <a class="reference internal" href="../../introduction/defining-data-science.html">
-     5.1. Defining data science
+     4.1. Defining data science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../introduction/data-science-ethics.html">
-     5.2. Data Science ethics
+     4.2. Data Science ethics
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../introduction/defining-data.html">
-     5.3. Defining data
+     4.3. Defining data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../introduction/introduction-to-statistics-and-probability.html">
-     5.4. Introduction to statistics and probability
+     4.4. Introduction to statistics and probability
     </a>
    </li>
   </ul>
  </li>
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-     6.1. Relational databases
+     5.1. Relational databases
     </a>
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     <a class="reference internal" href="../non-relational-data.html">
-     6.2. Non-relational data
+     5.2. Non-relational data
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     <a class="reference internal" href="../numpy.html">
-     6.3. NumPy
+     5.3. NumPy
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     <a class="reference internal" href="pandas.html">
-     6.4. Pandas
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     <ul class="current">
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       <a class="reference internal" href="introduction-and-data-structures.html">
-       6.4.1. Introduction and Data Structures
+       5.4.1. Introduction and Data Structures
       </a>
      </li>
      <li class="toctree-l3">
       <a class="reference internal" href="data-selection.html">
-       6.4.2. Data Selection
+       5.4.2. Data Selection
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      </li>
      <li class="toctree-l3 current active">
       <a class="current reference internal" href="#">
-       6.4.3. Advanced Pandas Techniques
+       5.4.3. Advanced Pandas Techniques
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-preparation.html">
-     6.5. Data preparation
+     5.5. Data preparation
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-     7.1. Visualizing distributions
+     6.1. Visualizing distributions
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     <a class="reference internal" href="../../data-visualization/visualization-proportions.html">
-     7.2. Visualizing proportions
+     6.2. Visualizing proportions
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     <a class="reference internal" href="../../data-visualization/visualization-relationships.html">
-     7.3. Visualizing relationships: all about honey 🍯
+     6.3. Visualizing relationships: all about honey 🍯
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     <a class="reference internal" href="../../data-visualization/meaningful-visualizations.html">
-     7.4. Making meaningful visualizations
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-     6.4.3.2.1. concat
+     5.4.3.2.1. concat
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#merge">
-     6.4.3.2.2. merge
+     5.4.3.2.2. merge
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#join">
-     6.4.3.2.3. join
+     5.4.3.2.3. join
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#aggregation-and-grouping">
-   6.4.3.3. Aggregation and grouping
+   5.4.3.3. Aggregation and grouping
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#hierarchical-indexes">
-     6.4.3.3.1. Hierarchical Indexes
+     5.4.3.3.1. Hierarchical Indexes
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#pivot-table">
-   6.4.3.4. Pivot table
+   5.4.3.4. Pivot table
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#high-performance-pandas-eval-and-query">
-   6.4.3.5. High-performance Pandas: eval() and query()
+   5.4.3.5. High-performance Pandas: eval() and query()
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#eval">
-     6.4.3.5.1. eval()
+     5.4.3.5.1. eval()
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#query">
-     6.4.3.5.2. query()
+     5.4.3.5.2. query()
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   6.4.3.6. Your turn! 🚀
+   5.4.3.6. Your turn! 🚀
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#processing-image-data">
-     6.4.3.6.1. Processing image data
+     5.4.3.6.1. Processing image data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#assignment">
-     6.4.3.6.2. Assignment
+     5.4.3.6.2. Assignment
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   6.4.3.7. Self study
+   5.4.3.7. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   6.4.3.8. Acknowledgments
+   5.4.3.8. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1682,90 +1682,90 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#overview">
-   6.4.3.1. Overview
+   5.4.3.1. Overview
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#combining-datasets-concat-merge-and-join">
-   6.4.3.2. Combining datasets: concat, merge and join
+   5.4.3.2. Combining datasets: concat, merge and join
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#concat">
-     6.4.3.2.1. concat
+     5.4.3.2.1. concat
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#merge">
-     6.4.3.2.2. merge
+     5.4.3.2.2. merge
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#join">
-     6.4.3.2.3. join
+     5.4.3.2.3. join
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#aggregation-and-grouping">
-   6.4.3.3. Aggregation and grouping
+   5.4.3.3. Aggregation and grouping
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#hierarchical-indexes">
-     6.4.3.3.1. Hierarchical Indexes
+     5.4.3.3.1. Hierarchical Indexes
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#pivot-table">
-   6.4.3.4. Pivot table
+   5.4.3.4. Pivot table
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#high-performance-pandas-eval-and-query">
-   6.4.3.5. High-performance Pandas: eval() and query()
+   5.4.3.5. High-performance Pandas: eval() and query()
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#eval">
-     6.4.3.5.1. eval()
+     5.4.3.5.1. eval()
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#query">
-     6.4.3.5.2. query()
+     5.4.3.5.2. query()
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   6.4.3.6. Your turn! 🚀
+   5.4.3.6. Your turn! 🚀
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#processing-image-data">
-     6.4.3.6.1. Processing image data
+     5.4.3.6.1. Processing image data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#assignment">
-     6.4.3.6.2. Assignment
+     5.4.3.6.2. Assignment
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   6.4.3.7. Self study
+   5.4.3.7. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   6.4.3.8. Acknowledgments
+   5.4.3.8. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1792,15 +1792,15 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="advanced-pandas-techniques">
-<h1><span class="section-number">6.4.3. </span>Advanced Pandas Techniques<a class="headerlink" href="#advanced-pandas-techniques" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">5.4.3. </span>Advanced Pandas Techniques<a class="headerlink" href="#advanced-pandas-techniques" title="Permalink to this headline">#</a></h1>
 <section id="overview">
-<h2><span class="section-number">6.4.3.1. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.4.3.1. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h2>
 <p>In this section, we’ll continue to introduce combining datasets: concat, merge and join along with data aggregation and grouping.</p>
 </section>
 <section id="combining-datasets-concat-merge-and-join">
-<h2><span class="section-number">6.4.3.2. </span>Combining datasets: concat, merge and join<a class="headerlink" href="#combining-datasets-concat-merge-and-join" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.4.3.2. </span>Combining datasets: concat, merge and join<a class="headerlink" href="#combining-datasets-concat-merge-and-join" title="Permalink to this headline">#</a></h2>
 <section id="concat">
-<h3><span class="section-number">6.4.3.2.1. </span>concat<a class="headerlink" href="#concat" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.4.3.2.1. </span>concat<a class="headerlink" href="#concat" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Concatenate Pandas objects along a particular axis.</p></li>
 <li><p>Allows optional set logic along the other axes.</p></li>
@@ -2515,7 +2515,7 @@ <h3><span class="section-number">6.4.3.2.1. </span>concat<a class="headerlink" h
 </div>
 </section>
 <section id="merge">
-<h3><span class="section-number">6.4.3.2.2. </span>merge<a class="headerlink" href="#merge" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.4.3.2.2. </span>merge<a class="headerlink" href="#merge" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Merge DataFrame or named Series objects with a database-style join.</p></li>
 <li><p>A named Series object is treated as a DataFrame with a single named column.</p></li>
@@ -3030,7 +3030,7 @@ <h3><span class="section-number">6.4.3.2.2. </span>merge<a class="headerlink" hr
 </div>
 </section>
 <section id="join">
-<h3><span class="section-number">6.4.3.2.3. </span>join<a class="headerlink" href="#join" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.4.3.2.3. </span>join<a class="headerlink" href="#join" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Join columns of another DataFrame.</p></li>
 <li><p>Join columns with other DataFrame either on index or on a key column. Efficiently join multiple DataFrame objects by index at once by passing a list.</p></li>
@@ -3504,7 +3504,7 @@ <h3><span class="section-number">6.4.3.2.3. </span>join<a class="headerlink" hre
 </section>
 </section>
 <section id="aggregation-and-grouping">
-<h2><span class="section-number">6.4.3.3. </span>Aggregation and grouping<a class="headerlink" href="#aggregation-and-grouping" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.4.3.3. </span>Aggregation and grouping<a class="headerlink" href="#aggregation-and-grouping" title="Permalink to this headline">#</a></h2>
 <p>Group <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> using a mapper or by a <code class="docutils literal notranslate"><span class="pre">Series</span></code> of columns.</p>
 <p>A <code class="docutils literal notranslate"><span class="pre">groupby</span></code> operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.</p>
 <p>For example:</p>
@@ -3601,7 +3601,7 @@ <h2><span class="section-number">6.4.3.3. </span>Aggregation and grouping<a clas
 </div></div>
 </div>
 <section id="hierarchical-indexes">
-<h3><span class="section-number">6.4.3.3.1. </span>Hierarchical Indexes<a class="headerlink" href="#hierarchical-indexes" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.4.3.3.1. </span>Hierarchical Indexes<a class="headerlink" href="#hierarchical-indexes" title="Permalink to this headline">#</a></h3>
 <p>We can <code class="docutils literal notranslate"><span class="pre">groupby</span></code> different levels of a hierarchical index using the <code class="docutils literal notranslate"><span class="pre">level</span></code> parameter:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -4271,7 +4271,7 @@ <h3><span class="section-number">6.4.3.3.1. </span>Hierarchical Indexes<a class=
 </section>
 </section>
 <section id="pivot-table">
-<h2><span class="section-number">6.4.3.4. </span>Pivot table<a class="headerlink" href="#pivot-table" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.4.3.4. </span>Pivot table<a class="headerlink" href="#pivot-table" title="Permalink to this headline">#</a></h2>
 <p>Create a spreadsheet-style pivot table as a DataFrame.</p>
 <p>The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame.</p>
 <div class="cell docutils container">
@@ -4682,9 +4682,9 @@ <h2><span class="section-number">6.4.3.4. </span>Pivot table<a class="headerlink
 </div>
 </section>
 <section id="high-performance-pandas-eval-and-query">
-<h2><span class="section-number">6.4.3.5. </span>High-performance Pandas: eval() and query()<a class="headerlink" href="#high-performance-pandas-eval-and-query" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.4.3.5. </span>High-performance Pandas: eval() and query()<a class="headerlink" href="#high-performance-pandas-eval-and-query" title="Permalink to this headline">#</a></h2>
 <section id="eval">
-<h3><span class="section-number">6.4.3.5.1. </span>eval()<a class="headerlink" href="#eval" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.4.3.5.1. </span>eval()<a class="headerlink" href="#eval" title="Permalink to this headline">#</a></h3>
 <p>Evaluate a string describing operations on DataFrame columns.</p>
 <p>Operates on columns only, not specific rows or elements. This allows <code class="docutils literal notranslate"><span class="pre">eval</span></code> to run arbitrary code, which can make you vulnerable to code injection if you pass user input to this function.</p>
 <p>For example:</p>
@@ -5036,7 +5036,7 @@ <h3><span class="section-number">6.4.3.5.1. </span>eval()<a class="headerlink" h
 </div>
 </section>
 <section id="query">
-<h3><span class="section-number">6.4.3.5.2. </span>query()<a class="headerlink" href="#query" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.4.3.5.2. </span>query()<a class="headerlink" href="#query" title="Permalink to this headline">#</a></h3>
 <p>Query the columns of a DataFrame with a boolean expression.</p>
 <p>For example:</p>
 <div class="cell docutils container">
@@ -5316,9 +5316,9 @@ <h3><span class="section-number">6.4.3.5.2. </span>query()<a class="headerlink"
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">6.4.3.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.4.3.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <section id="processing-image-data">
-<h3><span class="section-number">6.4.3.6.1. </span>Processing image data<a class="headerlink" href="#processing-image-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.4.3.6.1. </span>Processing image data<a class="headerlink" href="#processing-image-data" title="Permalink to this headline">#</a></h3>
 <p>Recently, very powerful AI models have been developed that allow us to understand images. There are many tasks that can be solved using pre-trained neural networks, or cloud services. Some examples include:</p>
 <ul class="simple">
 <li><p><strong>Image Classification</strong>, can help you categorize the image into one of the pre-defined classes. You can easily train your own image classifiers using services such as <a class="reference external" href="https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum">Custom Vision</a></p></li>
@@ -5333,12 +5333,12 @@ <h3><span class="section-number">6.4.3.6.1. </span>Processing image data<a class
 </ul>
 </section>
 <section id="assignment">
-<h3><span class="section-number">6.4.3.6.2. </span>Assignment<a class="headerlink" href="#assignment" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.4.3.6.2. </span>Assignment<a class="headerlink" href="#assignment" title="Permalink to this headline">#</a></h3>
 <p><a class="reference internal" href="../../../assignments/data-science/data-processing-in-python.html"><span class="doc std std-doc">Perform more detailed data study for the challenges above</span></a></p>
 </section>
 </section>
 <section id="self-study">
-<h2><span class="section-number">6.4.3.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.4.3.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>In this chapter, we’ve covered many of the basics of using Pandas effectively for data analysis. Still, much has been omitted from our discussion. To learn more about Pandas, we recommend the following resources:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="http://pandas.pydata.org/">Pandas online documentation</a>: This is the go-to source for complete documentation of the package. While the examples in the documentation tend to be small generated datasets, the description of the options is complete and generally very useful for understanding the use of various functions.</p></li>
@@ -5349,7 +5349,7 @@ <h2><span class="section-number">6.4.3.7. </span>Self study<a class="headerlink"
 <p>Using these resources, combined with the walk-through given in this chapter, my hope is that you’ll be poised to use Pandas to tackle any data analysis problem you come across!</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">6.4.3.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.4.3.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks for <a class="reference external" href="https://pandas.pydata.org/docs/user_guide/index.html">Pandas user guide</a>. It contributes the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -5391,13 +5391,13 @@ <h2><span class="section-number">6.4.3.8. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">6.4.2. </span>Data Selection</p>
+            <p class="prev-next-title"><span class="section-number">5.4.2. </span>Data Selection</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../data-preparation.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">6.5. </span>Data preparation</p>
+        <p class="prev-next-title"><span class="section-number">5.5. </span>Data preparation</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/data-science/working-with-data/pandas/data-selection.html b/data-science/working-with-data/pandas/data-selection.html
index ce800b2c4..bd10b561c 100644
--- a/data-science/working-with-data/pandas/data-selection.html
+++ b/data-science/working-with-data/pandas/data-selection.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>6.4.2. Data Selection &#8212; Ocademy Open Machine Learning Book</title>
+    <title>5.4.2. Data Selection &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="6.4.3. Advanced Pandas Techniques" href="advanced-pandas-techniques.html" />
-    <link rel="prev" title="6.4.1. Introduction and Data Structures" href="introduction-and-data-structures.html" />
+    <link rel="next" title="5.4.3. Advanced Pandas Techniques" href="advanced-pandas-techniques.html" />
+    <link rel="prev" title="5.4.1. Introduction and Data Structures" href="introduction-and-data-structures.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -159,17 +159,17 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../../prerequisites/python-programming-introduction.html">
-   1. Python programming basics
+   1. Python programming introduction
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../prerequisites/python-programming-basics.html">
-   3. Python programming basics
+   2. Python programming basics
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../prerequisites/python-programming-advanced.html">
-   4. Python programming advanced
+   3. Python programming advanced
   </a>
  </li>
 </ul>
@@ -181,7 +181,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../introduction/introduction.html">
-   5. Introduction
+   4. Introduction
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/>
   <label for="toctree-checkbox-1">
@@ -191,29 +191,29 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../introduction/defining-data-science.html">
-     5.1. Defining data science
+     4.1. Defining data science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../introduction/data-science-ethics.html">
-     5.2. Data Science ethics
+     4.2. Data Science ethics
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    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/numpy-and-pandas.html">
-     42.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/data-visualization.html">
-     42.7. Data visualization
+     41.7. Data visualization
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/data-science-lifecycle.html">
-     42.8. Data Science lifecycle
+     41.8. Data Science lifecycle
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/data-science-in-the-cloud.html">
-     42.9. Data Science in the cloud
+     41.9. Data Science in the cloud
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/data-science/data-science-in-real-world.html">
-     42.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/ml-overview.html">
-     42.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/linear-regression.html">
-     42.12. Linear Regression
+     41.12. Linear Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/logistic-regression.html">
-     42.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/logistic-regression-condensed.html">
-     42.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/neural-network.html">
-     42.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-fundamentals/build-an-ml-web-app.html">
-     42.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/unsupervised-learning.html">
-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/kernel-method.html">
-     42.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/model-selection.html">
-     42.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1575,39 +1575,39 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#overview">
-   6.4.2.1. Overview
+   5.4.2.1. Overview
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#selection-by-label">
-   6.4.2.2. Selection by label
+   5.4.2.2. Selection by label
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#slicing-with-labels">
-   6.4.2.3. Slicing with labels
+   5.4.2.3. Slicing with labels
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#selection-by-position">
-   6.4.2.4. Selection by position
+   5.4.2.4. Selection by position
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#selection-by-callable">
-   6.4.2.5. Selection by callable
+   5.4.2.5. Selection by callable
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#combining-positional-and-label-based-indexing">
-     6.4.2.5.1. Combining positional and label-based indexing
+     5.4.2.5.1. Combining positional and label-based indexing
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   6.4.2.6. Acknowledgments
+   5.4.2.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1631,39 +1631,39 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#overview">
-   6.4.2.1. Overview
+   5.4.2.1. Overview
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#selection-by-label">
-   6.4.2.2. Selection by label
+   5.4.2.2. Selection by label
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#slicing-with-labels">
-   6.4.2.3. Slicing with labels
+   5.4.2.3. Slicing with labels
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#selection-by-position">
-   6.4.2.4. Selection by position
+   5.4.2.4. Selection by position
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#selection-by-callable">
-   6.4.2.5. Selection by callable
+   5.4.2.5. Selection by callable
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#combining-positional-and-label-based-indexing">
-     6.4.2.5.1. Combining positional and label-based indexing
+     5.4.2.5.1. Combining positional and label-based indexing
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   6.4.2.6. Acknowledgments
+   5.4.2.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1690,13 +1690,13 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="data-selection">
-<h1><span class="section-number">6.4.2. </span>Data Selection<a class="headerlink" href="#data-selection" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">5.4.2. </span>Data Selection<a class="headerlink" href="#data-selection" title="Permalink to this headline">#</a></h1>
 <section id="overview">
-<h2><span class="section-number">6.4.2.1. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.4.2.1. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h2>
 <p>In this section, we’ll focus on how to slice, dice, and generally get and set subsets of Pandas objects.</p>
 </section>
 <section id="selection-by-label">
-<h2><span class="section-number">6.4.2.2. </span>Selection by label<a class="headerlink" href="#selection-by-label" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.4.2.2. </span>Selection by label<a class="headerlink" href="#selection-by-label" title="Permalink to this headline">#</a></h2>
 <p>Whether a copy or a reference is returned for a setting operation, may depend on the context. This is sometimes called <code class="docutils literal notranslate"><span class="pre">chained</span> <span class="pre">assignment</span></code> and should be avoided.</p>
 <p><code class="docutils literal notranslate"><span class="pre">.loc</span></code> is strict when you present slicers that are not compatible (or convertible) with the index type. For example using integers in a <code class="docutils literal notranslate"><span class="pre">DatetimeIndex</span></code>. These will raise a <code class="docutils literal notranslate"><span class="pre">TypeError</span></code>.</p>
 <div class="cell docutils container">
@@ -1834,24 +1834,24 @@ <h2><span class="section-number">6.4.2.2. </span>Selection by label<a class="hea
   <tbody>
     <tr>
       <th>2013-01-02</th>
-      <td>-0.303878</td>
-      <td>1.836445</td>
-      <td>1.986826</td>
-      <td>1.892046</td>
+      <td>-0.456435</td>
+      <td>-0.830210</td>
+      <td>-0.891633</td>
+      <td>0.485772</td>
     </tr>
     <tr>
       <th>2013-01-03</th>
-      <td>-0.508992</td>
-      <td>-1.250177</td>
-      <td>0.471012</td>
-      <td>0.730638</td>
+      <td>-1.859359</td>
+      <td>-0.539319</td>
+      <td>0.474526</td>
+      <td>-0.253166</td>
     </tr>
     <tr>
       <th>2013-01-04</th>
-      <td>0.692157</td>
-      <td>-1.350391</td>
-      <td>2.440602</td>
-      <td>-0.140785</td>
+      <td>0.699300</td>
+      <td>0.948732</td>
+      <td>0.784142</td>
+      <td>0.845687</td>
     </tr>
   </tbody>
 </table>
@@ -1919,10 +1919,10 @@ <h2><span class="section-number">6.4.2.2. </span>Selection by label<a class="hea
 </div>
 </div>
 <div class="cell_output docutils container">
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-d    0.125475
-e    0.325354
-f   -0.694217
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>c    0.038589
+d   -0.197195
+e   -0.130250
+f    0.067106
 dtype: float64
 </pre></div>
 </div>
@@ -1978,7 +1978,7 @@ <h2><span class="section-number">6.4.2.2. </span>Selection by label<a class="hea
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.36036375454581043
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.9518306057687721
 </pre></div>
 </div>
 </div>
@@ -2035,8 +2035,8 @@ <h2><span class="section-number">6.4.2.2. </span>Selection by label<a class="hea
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a   -0.989197
-b    0.360364
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    0.004982
+b   -0.951831
 c    0.000000
 d    0.000000
 e    0.000000
@@ -2123,24 +2123,24 @@ <h2><span class="section-number">6.4.2.2. </span>Selection by label<a class="hea
   <tbody>
     <tr>
       <th>a</th>
-      <td>-0.877606</td>
-      <td>-0.320232</td>
-      <td>1.629080</td>
-      <td>0.453485</td>
+      <td>1.699244</td>
+      <td>-1.280140</td>
+      <td>0.683763</td>
+      <td>-0.078884</td>
     </tr>
     <tr>
       <th>b</th>
-      <td>0.372555</td>
-      <td>0.393265</td>
-      <td>0.772296</td>
-      <td>-0.438121</td>
+      <td>0.210853</td>
+      <td>-1.048199</td>
+      <td>-0.302834</td>
+      <td>-2.402504</td>
     </tr>
     <tr>
       <th>d</th>
-      <td>1.507568</td>
-      <td>-1.570737</td>
-      <td>-1.534888</td>
-      <td>0.431771</td>
+      <td>-0.639539</td>
+      <td>0.850331</td>
+      <td>1.754627</td>
+      <td>-0.189413</td>
     </tr>
   </tbody>
 </table>
@@ -2221,21 +2221,21 @@ <h2><span class="section-number">6.4.2.2. </span>Selection by label<a class="hea
   <tbody>
     <tr>
       <th>d</th>
-      <td>1.507568</td>
-      <td>-1.570737</td>
-      <td>-1.534888</td>
+      <td>-0.639539</td>
+      <td>0.850331</td>
+      <td>1.754627</td>
     </tr>
     <tr>
       <th>e</th>
-      <td>-1.100359</td>
-      <td>-0.583692</td>
-      <td>0.752013</td>
+      <td>2.519738</td>
+      <td>1.375126</td>
+      <td>0.338291</td>
     </tr>
     <tr>
       <th>f</th>
-      <td>-1.378086</td>
-      <td>-2.321877</td>
-      <td>-0.352170</td>
+      <td>1.627156</td>
+      <td>0.865174</td>
+      <td>-0.872900</td>
     </tr>
   </tbody>
 </table>
@@ -2249,10 +2249,10 @@ <h2><span class="section-number">6.4.2.2. </span>Selection by label<a class="hea
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>A   -0.877606
-B   -0.320232
-C    1.629080
-D    0.453485
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>A    1.699244
+B   -1.280140
+C    0.683763
+D   -0.078884
 Name: a, dtype: float64
 </pre></div>
 </div>
@@ -2307,10 +2307,10 @@ <h2><span class="section-number">6.4.2.2. </span>Selection by label<a class="hea
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>A    False
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>A     True
 B    False
 C     True
-D     True
+D    False
 Name: a, dtype: bool
 </pre></div>
 </div>
@@ -2380,40 +2380,40 @@ <h2><span class="section-number">6.4.2.2. </span>Selection by label<a class="hea
   <thead>
     <tr style="text-align: right;">
       <th></th>
+      <th>A</th>
       <th>C</th>
-      <th>D</th>
     </tr>
   </thead>
   <tbody>
     <tr>
       <th>a</th>
-      <td>1.629080</td>
-      <td>0.453485</td>
+      <td>1.699244</td>
+      <td>0.683763</td>
     </tr>
     <tr>
       <th>b</th>
-      <td>0.772296</td>
-      <td>-0.438121</td>
+      <td>0.210853</td>
+      <td>-0.302834</td>
     </tr>
     <tr>
       <th>c</th>
-      <td>1.313720</td>
-      <td>-0.677374</td>
+      <td>-2.148546</td>
+      <td>-0.813316</td>
     </tr>
     <tr>
       <th>d</th>
-      <td>-1.534888</td>
-      <td>0.431771</td>
+      <td>-0.639539</td>
+      <td>1.754627</td>
     </tr>
     <tr>
       <th>e</th>
-      <td>0.752013</td>
-      <td>-1.157017</td>
+      <td>2.519738</td>
+      <td>0.338291</td>
     </tr>
     <tr>
       <th>f</th>
-      <td>-0.352170</td>
-      <td>-0.314890</td>
+      <td>1.627156</td>
+      <td>-0.872900</td>
     </tr>
   </tbody>
 </table>
@@ -2512,17 +2512,17 @@ <h2><span class="section-number">6.4.2.2. </span>Selection by label<a class="hea
   <tbody>
     <tr>
       <th>a</th>
-      <td>-0.877606</td>
-      <td>-0.320232</td>
-      <td>1.62908</td>
-      <td>0.453485</td>
+      <td>1.699244</td>
+      <td>-1.280140</td>
+      <td>0.683763</td>
+      <td>-0.078884</td>
     </tr>
     <tr>
       <th>c</th>
-      <td>2.046267</td>
-      <td>-1.393599</td>
-      <td>1.31372</td>
-      <td>-0.677374</td>
+      <td>-2.148546</td>
+      <td>-1.361452</td>
+      <td>-0.813316</td>
+      <td>0.347396</td>
     </tr>
   </tbody>
 </table>
@@ -2579,7 +2579,7 @@ <h2><span class="section-number">6.4.2.2. </span>Selection by label<a class="hea
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.8776063353503641
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>1.699243786401637
 </pre></div>
 </div>
 </div>
@@ -2631,7 +2631,7 @@ <h2><span class="section-number">6.4.2.2. </span>Selection by label<a class="hea
 </div>
 </section>
 <section id="slicing-with-labels">
-<h2><span class="section-number">6.4.2.3. </span>Slicing with labels<a class="headerlink" href="#slicing-with-labels" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.4.2.3. </span>Slicing with labels<a class="headerlink" href="#slicing-with-labels" title="Permalink to this headline">#</a></h2>
 <p>When using <code class="docutils literal notranslate"><span class="pre">.loc</span></code> with slices, if both the start and the stop labels are present in the index, then elements located between the two (including them) are returned:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2883,7 +2883,7 @@ <h2><span class="section-number">6.4.2.3. </span>Slicing with labels<a class="he
 <p>Also, if the index has duplicate labels and either the start or the stop label is duplicated, an error will be raised. For instance, in the above example, <code class="docutils literal notranslate"><span class="pre">s.loc[2:5]</span></code> would raise a <code class="docutils literal notranslate"><span class="pre">KeyError</span></code>.</p>
 </section>
 <section id="selection-by-position">
-<h2><span class="section-number">6.4.2.4. </span>Selection by position<a class="headerlink" href="#selection-by-position" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.4.2.4. </span>Selection by position<a class="headerlink" href="#selection-by-position" title="Permalink to this headline">#</a></h2>
 <p>Whether a copy or a reference is returned for a setting operation, may depend on the context. This is sometimes called <code class="docutils literal notranslate"><span class="pre">chained</span> <span class="pre">assignment</span></code> and should be avoided.</p>
 <p>Pandas provides a suite of methods in order to get purely integer-based indexing. The semantics follow closely Python and NumPy slicing. These are 0-based indexing. When slicing, the start bound is included, while the upper bound is excluded. Trying to use a non-integer, even a valid label will raise an <code class="docutils literal notranslate"><span class="pre">IndexError</span></code>.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">.iloc</span></code> attribute is the primary access method. The following are valid inputs:</p>
@@ -2903,9 +2903,9 @@ <h2><span class="section-number">6.4.2.4. </span>Selection by position<a class="
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    1.261922
-2    2.202420
-4   -0.629340
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    0.608692
+2    1.039676
+4    0.704550
 dtype: float64
 </pre></div>
 </div>
@@ -2957,7 +2957,7 @@ <h2><span class="section-number">6.4.2.4. </span>Selection by position<a class="
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.0733032626744459
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>1.6168817892052105
 </pre></div>
 </div>
 </div>
@@ -3013,8 +3013,8 @@ <h2><span class="section-number">6.4.2.4. </span>Selection by position<a class="
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    0.000000
 2    0.000000
 4    0.000000
-6   -0.073303
-8   -2.096596
+6    1.616882
+8   -0.946230
 dtype: float64
 </pre></div>
 </div>
@@ -3098,24 +3098,24 @@ <h2><span class="section-number">6.4.2.4. </span>Selection by position<a class="
   <tbody>
     <tr>
       <th>0</th>
-      <td>-0.479827</td>
-      <td>2.543896</td>
-      <td>-1.150533</td>
-      <td>1.534685</td>
+      <td>0.951573</td>
+      <td>1.408150</td>
+      <td>1.863327</td>
+      <td>-1.520441</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>-1.522906</td>
-      <td>1.657508</td>
-      <td>2.040886</td>
-      <td>1.331977</td>
+      <td>0.336735</td>
+      <td>1.495843</td>
+      <td>-0.695248</td>
+      <td>0.940683</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>2.341698</td>
-      <td>1.403538</td>
-      <td>0.667542</td>
-      <td>-0.569661</td>
+      <td>0.228899</td>
+      <td>0.356077</td>
+      <td>-0.637129</td>
+      <td>1.176355</td>
     </tr>
   </tbody>
 </table>
@@ -3190,23 +3190,23 @@ <h2><span class="section-number">6.4.2.4. </span>Selection by position<a class="
   <tbody>
     <tr>
       <th>2</th>
-      <td>2.040886</td>
-      <td>1.331977</td>
+      <td>-0.695248</td>
+      <td>0.940683</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>0.667542</td>
-      <td>-0.569661</td>
+      <td>-0.637129</td>
+      <td>1.176355</td>
     </tr>
     <tr>
       <th>6</th>
-      <td>1.094247</td>
-      <td>-2.482127</td>
+      <td>0.229872</td>
+      <td>0.658689</td>
     </tr>
     <tr>
       <th>8</th>
-      <td>-1.101257</td>
-      <td>-0.224748</td>
+      <td>-0.349288</td>
+      <td>-0.935375</td>
     </tr>
   </tbody>
 </table>
@@ -3245,18 +3245,18 @@ <h2><span class="section-number">6.4.2.4. </span>Selection by position<a class="
   <tbody>
     <tr>
       <th>2</th>
-      <td>1.657508</td>
-      <td>1.331977</td>
+      <td>1.495843</td>
+      <td>0.940683</td>
     </tr>
     <tr>
       <th>6</th>
-      <td>0.935474</td>
-      <td>-2.482127</td>
+      <td>0.027112</td>
+      <td>0.658689</td>
     </tr>
     <tr>
       <th>10</th>
-      <td>-0.296816</td>
-      <td>-0.479721</td>
+      <td>-0.322177</td>
+      <td>1.171140</td>
     </tr>
   </tbody>
 </table>
@@ -3333,17 +3333,17 @@ <h2><span class="section-number">6.4.2.4. </span>Selection by position<a class="
   <tbody>
     <tr>
       <th>2</th>
-      <td>-1.522906</td>
-      <td>1.657508</td>
-      <td>2.040886</td>
-      <td>1.331977</td>
+      <td>0.336735</td>
+      <td>1.495843</td>
+      <td>-0.695248</td>
+      <td>0.940683</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>2.341698</td>
-      <td>1.403538</td>
-      <td>0.667542</td>
-      <td>-0.569661</td>
+      <td>0.228899</td>
+      <td>0.356077</td>
+      <td>-0.637129</td>
+      <td>1.176355</td>
     </tr>
   </tbody>
 </table>
@@ -3424,33 +3424,33 @@ <h2><span class="section-number">6.4.2.4. </span>Selection by position<a class="
   <tbody>
     <tr>
       <th>0</th>
-      <td>2.543896</td>
-      <td>-1.150533</td>
+      <td>1.408150</td>
+      <td>1.863327</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>1.657508</td>
-      <td>2.040886</td>
+      <td>1.495843</td>
+      <td>-0.695248</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>1.403538</td>
-      <td>0.667542</td>
+      <td>0.356077</td>
+      <td>-0.637129</td>
     </tr>
     <tr>
       <th>6</th>
-      <td>0.935474</td>
-      <td>1.094247</td>
+      <td>0.027112</td>
+      <td>0.229872</td>
     </tr>
     <tr>
       <th>8</th>
-      <td>0.308616</td>
-      <td>-1.101257</td>
+      <td>2.044324</td>
+      <td>-0.349288</td>
     </tr>
     <tr>
       <th>10</th>
-      <td>-0.296816</td>
-      <td>0.447063</td>
+      <td>-0.322177</td>
+      <td>-0.441221</td>
     </tr>
   </tbody>
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@@ -3504,7 +3504,7 @@ <h2><span class="section-number">6.4.2.4. </span>Selection by position<a class="
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>1.6575081622737764
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 </div>
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@@ -3558,10 +3558,10 @@ <h2><span class="section-number">6.4.2.4. </span>Selection by position<a class="
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 </div>
 <div class="cell_output docutils container">
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-2    1.657508
-4    2.040886
-6    1.331977
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    0.336735
+2    1.495843
+4   -0.695248
+6    0.940683
 Name: 2, dtype: float64
 </pre></div>
 </div>
@@ -3853,23 +3853,23 @@ <h2><span class="section-number">6.4.2.4. </span>Selection by position<a class="
   <tbody>
     <tr>
       <th>0</th>
-      <td>-1.809654</td>
+      <td>-0.778377</td>
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     <tr>
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     <tr>
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@@ -3946,8 +3946,8 @@ <h2><span class="section-number">6.4.2.4. </span>Selection by position<a class="
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     <tr>
       <th>4</th>
-      <td>-1.154162</td>
-      <td>0.384492</td>
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     </tr>
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@@ -4127,7 +4127,7 @@ <h2><span class="section-number">6.4.2.4. </span>Selection by position<a class="
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 <section id="selection-by-callable">
-<h2><span class="section-number">6.4.2.5. </span>Selection by callable<a class="headerlink" href="#selection-by-callable" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.4.2.5. </span>Selection by callable<a class="headerlink" href="#selection-by-callable" title="Permalink to this headline">#</a></h2>
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 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -4165,33 +4165,40 @@ <h2><span class="section-number">6.4.2.5. </span>Selection by callable<a class="
     </tr>
   </thead>
   <tbody>
+    <tr>
+      <th>b</th>
+      <td>0.920658</td>
+      <td>0.709170</td>
+      <td>0.323474</td>
+      <td>-0.541892</td>
+    </tr>
     <tr>
       <th>c</th>
-      <td>1.953733</td>
-      <td>0.086020</td>
-      <td>-0.454969</td>
-      <td>0.350733</td>
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+      <td>-2.714789</td>
+      <td>0.798567</td>
+      <td>0.015424</td>
     </tr>
     <tr>
       <th>d</th>
-      <td>0.925243</td>
-      <td>-0.002790</td>
-      <td>-0.122201</td>
-      <td>0.095934</td>
+      <td>0.259017</td>
+      <td>-0.601825</td>
+      <td>1.002502</td>
+      <td>-0.181562</td>
     </tr>
     <tr>
       <th>e</th>
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-      <td>-0.651783</td>
-      <td>-0.576496</td>
-      <td>-0.361154</td>
+      <td>2.184927</td>
+      <td>0.048558</td>
+      <td>0.255997</td>
+      <td>-2.783334</td>
     </tr>
     <tr>
       <th>f</th>
-      <td>0.600336</td>
-      <td>-0.730947</td>
-      <td>-0.052900</td>
-      <td>0.989631</td>
+      <td>0.153795</td>
+      <td>0.786828</td>
+      <td>-0.450318</td>
+      <td>-1.326000</td>
     </tr>
   </tbody>
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@@ -4266,33 +4273,33 @@ <h2><span class="section-number">6.4.2.5. </span>Selection by callable<a class="
   <tbody>
     <tr>
       <th>a</th>
-      <td>-0.596417</td>
-      <td>0.648893</td>
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+      <td>0.161584</td>
     </tr>
     <tr>
       <th>b</th>
-      <td>-0.326902</td>
-      <td>0.698662</td>
+      <td>0.920658</td>
+      <td>0.709170</td>
     </tr>
     <tr>
       <th>c</th>
-      <td>1.953733</td>
-      <td>0.086020</td>
+      <td>0.945481</td>
+      <td>-2.714789</td>
     </tr>
     <tr>
       <th>d</th>
-      <td>0.925243</td>
-      <td>-0.002790</td>
+      <td>0.259017</td>
+      <td>-0.601825</td>
     </tr>
     <tr>
       <th>e</th>
-      <td>0.430558</td>
-      <td>-0.651783</td>
+      <td>2.184927</td>
+      <td>0.048558</td>
     </tr>
     <tr>
       <th>f</th>
-      <td>0.600336</td>
-      <td>-0.730947</td>
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+      <td>0.786828</td>
     </tr>
   </tbody>
 </table>
@@ -4373,33 +4380,33 @@ <h2><span class="section-number">6.4.2.5. </span>Selection by callable<a class="
   <tbody>
     <tr>
       <th>a</th>
-      <td>-0.596417</td>
-      <td>0.648893</td>
+      <td>-0.248610</td>
+      <td>0.161584</td>
     </tr>
     <tr>
       <th>b</th>
-      <td>-0.326902</td>
-      <td>0.698662</td>
+      <td>0.920658</td>
+      <td>0.709170</td>
     </tr>
     <tr>
       <th>c</th>
-      <td>1.953733</td>
-      <td>0.086020</td>
+      <td>0.945481</td>
+      <td>-2.714789</td>
     </tr>
     <tr>
       <th>d</th>
-      <td>0.925243</td>
-      <td>-0.002790</td>
+      <td>0.259017</td>
+      <td>-0.601825</td>
     </tr>
     <tr>
       <th>e</th>
-      <td>0.430558</td>
-      <td>-0.651783</td>
+      <td>2.184927</td>
+      <td>0.048558</td>
     </tr>
     <tr>
       <th>f</th>
-      <td>0.600336</td>
-      <td>-0.730947</td>
+      <td>0.153795</td>
+      <td>0.786828</td>
     </tr>
   </tbody>
 </table>
@@ -4453,12 +4460,12 @@ <h2><span class="section-number">6.4.2.5. </span>Selection by callable<a class="
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a   -0.596417
-b   -0.326902
-c    1.953733
-d    0.925243
-e    0.430558
-f    0.600336
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a   -0.248610
+b    0.920658
+c    0.945481
+d    0.259017
+e    2.184927
+f    0.153795
 Name: A, dtype: float64
 </pre></div>
 </div>
@@ -4511,10 +4518,11 @@ <h2><span class="section-number">6.4.2.5. </span>Selection by callable<a class="
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>c    1.953733
-d    0.925243
-e    0.430558
-f    0.600336
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>b    0.920658
+c    0.945481
+d    0.259017
+e    2.184927
+f    0.153795
 Name: A, dtype: float64
 </pre></div>
 </div>
@@ -4560,7 +4568,7 @@ <h2><span class="section-number">6.4.2.5. </span>Selection by callable<a class="
 </div></div>
 </div>
 <section id="combining-positional-and-label-based-indexing">
-<h3><span class="section-number">6.4.2.5.1. </span>Combining positional and label-based indexing<a class="headerlink" href="#combining-positional-and-label-based-indexing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.4.2.5.1. </span>Combining positional and label-based indexing<a class="headerlink" href="#combining-positional-and-label-based-indexing" title="Permalink to this headline">#</a></h3>
 <p>If you wish to get the 0th and the 2nd elements from the index in the <code class="docutils literal notranslate"><span class="pre">'A'</span></code> column, you can do:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -4766,7 +4774,7 @@ <h3><span class="section-number">6.4.2.5.1. </span>Combining positional and labe
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">6.4.2.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.4.2.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks for <a class="reference external" href="https://pandas.pydata.org/docs/user_guide/index.html">Pandas user guide</a>. It contributes the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -4808,13 +4816,13 @@ <h2><span class="section-number">6.4.2.6. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">6.4.1. </span>Introduction and Data Structures</p>
+            <p class="prev-next-title"><span class="section-number">5.4.1. </span>Introduction and Data Structures</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="advanced-pandas-techniques.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">6.4.3. </span>Advanced Pandas Techniques</p>
+        <p class="prev-next-title"><span class="section-number">5.4.3. </span>Advanced Pandas Techniques</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/data-science/working-with-data/pandas/introduction-and-data-structures.html b/data-science/working-with-data/pandas/introduction-and-data-structures.html
index 78be763b2..4e0909872 100644
--- a/data-science/working-with-data/pandas/introduction-and-data-structures.html
+++ b/data-science/working-with-data/pandas/introduction-and-data-structures.html
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-    <title>6.4.1. Introduction and Data Structures &#8212; Ocademy Open Machine Learning Book</title>
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     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="6.4.2. Data Selection" href="data-selection.html" />
-    <link rel="prev" title="6.4. Pandas" href="pandas.html" />
+    <link rel="next" title="5.4.2. Data Selection" href="data-selection.html" />
+    <link rel="prev" title="5.4. Pandas" href="pandas.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -159,17 +159,17 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../../prerequisites/python-programming-introduction.html">
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+   1. Python programming introduction
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../../prerequisites/python-programming-basics.html">
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+   2. Python programming basics
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+   3. Python programming advanced
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 <ul class="current nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../introduction/introduction.html">
-   5. Introduction
+   4. Introduction
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    <li class="toctree-l2">
     <a class="reference internal" href="../../introduction/defining-data-science.html">
-     5.1. Defining data science
+     4.1. Defining data science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../introduction/data-science-ethics.html">
-     5.2. Data Science ethics
+     4.2. Data Science ethics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../introduction/defining-data.html">
-     5.3. Defining data
+     4.3. Defining data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../introduction/introduction-to-statistics-and-probability.html">
-     5.4. Introduction to statistics and probability
+     4.4. Introduction to statistics and probability
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 current active has-children">
   <a class="reference internal" href="../working-with-data.html">
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+   5. Working with data
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     <a class="reference internal" href="../relational-databases.html">
-     6.1. Relational databases
+     5.1. Relational databases
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../non-relational-data.html">
-     6.2. Non-relational data
+     5.2. Non-relational data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../numpy.html">
-     6.3. NumPy
+     5.3. NumPy
     </a>
    </li>
    <li class="toctree-l2 current active has-children">
     <a class="reference internal" href="pandas.html">
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+     5.4. Pandas
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      <li class="toctree-l3 current active">
       <a class="current reference internal" href="#">
-       6.4.1. Introduction and Data Structures
+       5.4.1. Introduction and Data Structures
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     <a class="reference internal" href="../../../slides/ml-fundamentals/build-an-ml-web-app.html">
-     42.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/unsupervised-learning.html">
-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/kernel-method.html">
-     42.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/ml-advanced/model-selection.html">
-     42.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../../slides/deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
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@@ -1575,76 +1575,76 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introducing-pandas-objects">
-   6.4.1.1. Introducing Pandas objects
+   5.4.1.1. Introducing Pandas objects
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#overview">
-   6.4.1.2. Overview
+   5.4.1.2. Overview
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#series">
-     6.4.1.2.1. Series
+     5.4.1.2.1. Series
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#create-a-series">
-       6.4.1.2.1.1. Create a Series
+       5.4.1.2.1.1. Create a Series
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#from-ndarray">
-         6.4.1.2.1.1.1. From ndarray
+         5.4.1.2.1.1.1. From ndarray
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#from-dict">
-         6.4.1.2.1.1.2. From dict
+         5.4.1.2.1.1.2. From dict
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#from-scalar-value">
-         6.4.1.2.1.1.3. From scalar value
+         5.4.1.2.1.1.3. From scalar value
         </a>
        </li>
       </ul>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#series-is-ndarray-like">
-       6.4.1.2.1.2. Series is ndarray-like
+       5.4.1.2.1.2. Series is ndarray-like
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#series-is-dict-like">
-       6.4.1.2.1.3. Series is dict-like
+       5.4.1.2.1.3. Series is dict-like
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#vectorized-operations-and-label-alignment-with-series">
-       6.4.1.2.1.4. Vectorized operations and label alignment with Series
+       5.4.1.2.1.4. Vectorized operations and label alignment with Series
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#name-attribute">
-       6.4.1.2.1.5. Name attribute
+       5.4.1.2.1.5. Name attribute
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dataframe">
-     6.4.1.2.2. DataFrame
+     5.4.1.2.2. DataFrame
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#create-a-dataframe">
-       6.4.1.2.2.1. Create a Dataframe
+       5.4.1.2.2.1. Create a Dataframe
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#from-dict-of-series-or-dicts">
-         6.4.1.2.2.1.1. From dict of
+         5.4.1.2.2.1.1. From dict of
          <code class="docutils literal notranslate">
           <span class="pre">
            Series
@@ -1655,64 +1655,64 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
        </li>
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         <a class="reference internal nav-link" href="#from-dict-of-ndarrays-lists">
-         6.4.1.2.2.1.2. From dict of ndarrays / lists
+         5.4.1.2.2.1.2. From dict of ndarrays / lists
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#from-structured-or-record-array">
-         6.4.1.2.2.1.3. From structured or record array
+         5.4.1.2.2.1.3. From structured or record array
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#from-a-list-of-dicts">
-         6.4.1.2.2.1.4. From a list of dicts
+         5.4.1.2.2.1.4. From a list of dicts
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#from-a-dict-of-tuples">
-         6.4.1.2.2.1.5. From a dict of tuples
+         5.4.1.2.2.1.5. From a dict of tuples
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#from-a-series">
-         6.4.1.2.2.1.6. From a Series
+         5.4.1.2.2.1.6. From a Series
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#from-a-list-of-namedtuples">
-         6.4.1.2.2.1.7. From a list of namedtuples
+         5.4.1.2.2.1.7. From a list of namedtuples
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#from-a-list-of-dataclasses">
-         6.4.1.2.2.1.8. From a list of dataclasses
+         5.4.1.2.2.1.8. From a list of dataclasses
         </a>
        </li>
       </ul>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#column-selection-addition-deletion">
-       6.4.1.2.2.2. Column selection, addition, deletion
+       5.4.1.2.2.2. Column selection, addition, deletion
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#assigning-new-columns-in-method-chains">
-       6.4.1.2.2.3. Assigning new columns in method chains
+       5.4.1.2.2.3. Assigning new columns in method chains
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#indexing-selection">
-       6.4.1.2.2.4. Indexing / selection
+       5.4.1.2.2.4. Indexing / selection
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#data-alignment-and-arithmetic">
-       6.4.1.2.2.5. Data alignment and arithmetic
+       5.4.1.2.2.5. Data alignment and arithmetic
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#transposing">
-       6.4.1.2.2.6. Transposing
+       5.4.1.2.2.6. Transposing
       </a>
      </li>
     </ul>
@@ -1721,34 +1721,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
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   <a class="reference internal nav-link" href="#data-indexing-and-selection">
-   6.4.1.3. Data indexing and selection
+   5.4.1.3. Data indexing and selection
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#different-choices-for-indexing">
-     6.4.1.3.1. Different choices for indexing
+     5.4.1.3.1. Different choices for indexing
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#basics">
-     6.4.1.3.2. Basics
+     5.4.1.3.2. Basics
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#attribute-access">
-     6.4.1.3.3. Attribute access
+     5.4.1.3.3. Attribute access
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#slicing-ranges">
-     6.4.1.3.4. Slicing ranges
+     5.4.1.3.4. Slicing ranges
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   6.4.1.4. Acknowledgments
+   5.4.1.4. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1772,76 +1772,76 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introducing-pandas-objects">
-   6.4.1.1. Introducing Pandas objects
+   5.4.1.1. Introducing Pandas objects
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#overview">
-   6.4.1.2. Overview
+   5.4.1.2. Overview
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#series">
-     6.4.1.2.1. Series
+     5.4.1.2.1. Series
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#create-a-series">
-       6.4.1.2.1.1. Create a Series
+       5.4.1.2.1.1. Create a Series
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#from-ndarray">
-         6.4.1.2.1.1.1. From ndarray
+         5.4.1.2.1.1.1. From ndarray
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#from-dict">
-         6.4.1.2.1.1.2. From dict
+         5.4.1.2.1.1.2. From dict
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#from-scalar-value">
-         6.4.1.2.1.1.3. From scalar value
+         5.4.1.2.1.1.3. From scalar value
         </a>
        </li>
       </ul>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#series-is-ndarray-like">
-       6.4.1.2.1.2. Series is ndarray-like
+       5.4.1.2.1.2. Series is ndarray-like
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#series-is-dict-like">
-       6.4.1.2.1.3. Series is dict-like
+       5.4.1.2.1.3. Series is dict-like
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#vectorized-operations-and-label-alignment-with-series">
-       6.4.1.2.1.4. Vectorized operations and label alignment with Series
+       5.4.1.2.1.4. Vectorized operations and label alignment with Series
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#name-attribute">
-       6.4.1.2.1.5. Name attribute
+       5.4.1.2.1.5. Name attribute
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dataframe">
-     6.4.1.2.2. DataFrame
+     5.4.1.2.2. DataFrame
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#create-a-dataframe">
-       6.4.1.2.2.1. Create a Dataframe
+       5.4.1.2.2.1. Create a Dataframe
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#from-dict-of-series-or-dicts">
-         6.4.1.2.2.1.1. From dict of
+         5.4.1.2.2.1.1. From dict of
          <code class="docutils literal notranslate">
           <span class="pre">
            Series
@@ -1852,64 +1852,64 @@ <h2> Contents </h2>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#from-dict-of-ndarrays-lists">
-         6.4.1.2.2.1.2. From dict of ndarrays / lists
+         5.4.1.2.2.1.2. From dict of ndarrays / lists
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#from-structured-or-record-array">
-         6.4.1.2.2.1.3. From structured or record array
+         5.4.1.2.2.1.3. From structured or record array
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#from-a-list-of-dicts">
-         6.4.1.2.2.1.4. From a list of dicts
+         5.4.1.2.2.1.4. From a list of dicts
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#from-a-dict-of-tuples">
-         6.4.1.2.2.1.5. From a dict of tuples
+         5.4.1.2.2.1.5. From a dict of tuples
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#from-a-series">
-         6.4.1.2.2.1.6. From a Series
+         5.4.1.2.2.1.6. From a Series
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#from-a-list-of-namedtuples">
-         6.4.1.2.2.1.7. From a list of namedtuples
+         5.4.1.2.2.1.7. From a list of namedtuples
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#from-a-list-of-dataclasses">
-         6.4.1.2.2.1.8. From a list of dataclasses
+         5.4.1.2.2.1.8. From a list of dataclasses
         </a>
        </li>
       </ul>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#column-selection-addition-deletion">
-       6.4.1.2.2.2. Column selection, addition, deletion
+       5.4.1.2.2.2. Column selection, addition, deletion
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#assigning-new-columns-in-method-chains">
-       6.4.1.2.2.3. Assigning new columns in method chains
+       5.4.1.2.2.3. Assigning new columns in method chains
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#indexing-selection">
-       6.4.1.2.2.4. Indexing / selection
+       5.4.1.2.2.4. Indexing / selection
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#data-alignment-and-arithmetic">
-       6.4.1.2.2.5. Data alignment and arithmetic
+       5.4.1.2.2.5. Data alignment and arithmetic
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#transposing">
-       6.4.1.2.2.6. Transposing
+       5.4.1.2.2.6. Transposing
       </a>
      </li>
     </ul>
@@ -1918,34 +1918,34 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-indexing-and-selection">
-   6.4.1.3. Data indexing and selection
+   5.4.1.3. Data indexing and selection
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#different-choices-for-indexing">
-     6.4.1.3.1. Different choices for indexing
+     5.4.1.3.1. Different choices for indexing
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#basics">
-     6.4.1.3.2. Basics
+     5.4.1.3.2. Basics
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#attribute-access">
-     6.4.1.3.3. Attribute access
+     5.4.1.3.3. Attribute access
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#slicing-ranges">
-     6.4.1.3.4. Slicing ranges
+     5.4.1.3.4. Slicing ranges
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   6.4.1.4. Acknowledgments
+   5.4.1.4. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1972,17 +1972,17 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="introduction-and-data-structures">
-<h1><span class="section-number">6.4.1. </span>Introduction and Data Structures<a class="headerlink" href="#introduction-and-data-structures" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">5.4.1. </span>Introduction and Data Structures<a class="headerlink" href="#introduction-and-data-structures" title="Permalink to this headline">#</a></h1>
 <p>Pandas is a fast, powerful, flexible and easy-to-use open-source data analysis and manipulation tool, built on top of the Python programming language.</p>
 <section id="introducing-pandas-objects">
-<h2><span class="section-number">6.4.1.1. </span>Introducing Pandas objects<a class="headerlink" href="#introducing-pandas-objects" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.4.1.1. </span>Introducing Pandas objects<a class="headerlink" href="#introducing-pandas-objects" title="Permalink to this headline">#</a></h2>
 <p>In 3 sections, we’ll start with a quick, non-comprehensive overview of the fundamental data structures in Pandas to get you started. The fundamental behavior about data types, indexing, axis labeling, and alignment apply across all of the objects.</p>
 </section>
 <section id="overview">
-<h2><span class="section-number">6.4.1.2. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.4.1.2. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h2>
 <p>In this section, we’ll introduce two data structure of pandas and the basic concept of data indexing and selection.</p>
 <section id="series">
-<h3><span class="section-number">6.4.1.2.1. </span>Series<a class="headerlink" href="#series" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.4.1.2.1. </span>Series<a class="headerlink" href="#series" title="Permalink to this headline">#</a></h3>
 <p><code class="docutils literal notranslate"><span class="pre">Series</span></code> is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). The axis labels are collectively referred to as the <strong>index</strong>. The basic method to create a <code class="docutils literal notranslate"><span class="pre">Series</span></code> is to call:</p>
 <p><code class="docutils literal notranslate"><span class="pre">s</span> <span class="pre">=</span> <span class="pre">pd.Series(data,</span> <span class="pre">index=index)</span></code></p>
 <p>Here, <code class="docutils literal notranslate"><span class="pre">data</span></code> can be many different things:</p>
@@ -1993,9 +1993,9 @@ <h3><span class="section-number">6.4.1.2.1. </span>Series<a class="headerlink" h
 </ul>
 <p>The passed <strong>index</strong> is a list of axis labels. Thus, this separates into a few cases depending on what the <strong>data is</strong>:</p>
 <section id="create-a-series">
-<h4><span class="section-number">6.4.1.2.1.1. </span>Create a Series<a class="headerlink" href="#create-a-series" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.4.1.2.1.1. </span>Create a Series<a class="headerlink" href="#create-a-series" title="Permalink to this headline">#</a></h4>
 <section id="from-ndarray">
-<h5><span class="section-number">6.4.1.2.1.1.1. </span>From ndarray<a class="headerlink" href="#from-ndarray" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.4.1.2.1.1.1. </span>From ndarray<a class="headerlink" href="#from-ndarray" title="Permalink to this headline">#</a></h5>
 <p>If <code class="docutils literal notranslate"><span class="pre">data</span></code> is an ndarray, <strong>index</strong> must be the same length as the <strong>data</strong>. If no index is passed, one will be created having values <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">...,</span> <span class="pre">len(data)</span> <span class="pre">-</span> <span class="pre">1]</span></code>.</p>
 <div class="cell docutils container">
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@@ -2011,11 +2011,11 @@ <h5><span class="section-number">6.4.1.2.1.1.1. </span>From ndarray<a class="hea
 </div>
 </div>
 <div class="cell_output docutils container">
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-b   -1.138321
-c    1.966055
-d    0.538852
-e   -0.124284
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a   -0.471148
+b   -0.093864
+c   -0.240233
+d    1.084377
+e    0.740195
 dtype: float64
 </pre></div>
 </div>
@@ -2040,11 +2040,11 @@ <h5><span class="section-number">6.4.1.2.1.1.1. </span>From ndarray<a class="hea
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0   -1.015670
-1   -0.599388
-2    0.734619
-3    0.024725
-4    0.817301
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    0.718215
+1    0.451950
+2   -2.147534
+3    0.749949
+4    0.606131
 dtype: float64
 </pre></div>
 </div>
@@ -2056,7 +2056,7 @@ <h5><span class="section-number">6.4.1.2.1.1.1. </span>From ndarray<a class="hea
 </div>
 </section>
 <section id="from-dict">
-<h5><span class="section-number">6.4.1.2.1.1.2. </span>From dict<a class="headerlink" href="#from-dict" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.4.1.2.1.1.2. </span>From dict<a class="headerlink" href="#from-dict" title="Permalink to this headline">#</a></h5>
 <p><code class="docutils literal notranslate"><span class="pre">Series</span></code> can be instantiated from dicts:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2125,7 +2125,7 @@ <h5><span class="section-number">6.4.1.2.1.1.2. </span>From dict<a class="header
 </div>
 </section>
 <section id="from-scalar-value">
-<h5><span class="section-number">6.4.1.2.1.1.3. </span>From scalar value<a class="headerlink" href="#from-scalar-value" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.4.1.2.1.1.3. </span>From scalar value<a class="headerlink" href="#from-scalar-value" title="Permalink to this headline">#</a></h5>
 <p>If <code class="docutils literal notranslate"><span class="pre">data</span></code> is a scalar value, an index must be provided. The value will be repeated to match the length of <strong>index</strong>.</p>
 <div class="cell docutils container">
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@@ -2147,7 +2147,7 @@ <h5><span class="section-number">6.4.1.2.1.1.3. </span>From scalar value<a class
 </section>
 </section>
 <section id="series-is-ndarray-like">
-<h4><span class="section-number">6.4.1.2.1.2. </span>Series is ndarray-like<a class="headerlink" href="#series-is-ndarray-like" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.4.1.2.1.2. </span>Series is ndarray-like<a class="headerlink" href="#series-is-ndarray-like" title="Permalink to this headline">#</a></h4>
 <p><code class="docutils literal notranslate"><span class="pre">Series</span></code> acts very similarly to a <code class="docutils literal notranslate"><span class="pre">ndarray</span></code> and is a valid argument to most NumPy functions. However, operations such as slicing will also slice the index.</p>
 <div class="cell docutils container">
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@@ -2156,7 +2156,7 @@ <h4><span class="section-number">6.4.1.2.1.2. </span>Series is ndarray-like<a cl
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.11616360234416723
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.47114789842073596
 </pre></div>
 </div>
 </div>
@@ -2168,9 +2168,9 @@ <h4><span class="section-number">6.4.1.2.1.2. </span>Series is ndarray-like<a cl
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a   -0.116164
-b   -1.138321
-c    1.966055
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a   -0.471148
+b   -0.093864
+c   -0.240233
 dtype: float64
 </pre></div>
 </div>
@@ -2183,8 +2183,8 @@ <h4><span class="section-number">6.4.1.2.1.2. </span>Series is ndarray-like<a cl
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>c    1.966055
-d    0.538852
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>d    1.084377
+e    0.740195
 dtype: float64
 </pre></div>
 </div>
@@ -2197,9 +2197,9 @@ <h4><span class="section-number">6.4.1.2.1.2. </span>Series is ndarray-like<a cl
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>e   -0.124284
-d    0.538852
-b   -1.138321
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>e    0.740195
+d    1.084377
+b   -0.093864
 dtype: float64
 </pre></div>
 </div>
@@ -2212,11 +2212,11 @@ <h4><span class="section-number">6.4.1.2.1.2. </span>Series is ndarray-like<a cl
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    0.890330
-b    0.320356
-c    7.142441
-d    1.714039
-e    0.883129
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    0.624285
+b    0.910406
+c    0.786445
+d    2.957596
+e    2.096344
 dtype: float64
 </pre></div>
 </div>
@@ -2244,8 +2244,8 @@ <h4><span class="section-number">6.4.1.2.1.2. </span>Series is ndarray-like<a cl
 </div>
 <div class="cell_output docutils container">
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;PandasArray&gt;
-[-0.11616360234416723,  -1.1383208411571835,   1.9660545462400327,
-   0.5388524270028725, -0.12428413775704976]
+[-0.47114789842073596, -0.09386425367761429, -0.24023274525915267,
+   1.0843766433680067,   0.7401950570540115]
 Length: 5, dtype: float64
 </pre></div>
 </div>
@@ -2259,7 +2259,7 @@ <h4><span class="section-number">6.4.1.2.1.2. </span>Series is ndarray-like<a cl
 </div>
 </div>
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-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([-0.1161636 , -1.13832084,  1.96605455,  0.53885243, -0.12428414])
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([-0.4711479 , -0.09386425, -0.24023275,  1.08437664,  0.74019506])
 </pre></div>
 </div>
 </div>
@@ -2267,7 +2267,7 @@ <h4><span class="section-number">6.4.1.2.1.2. </span>Series is ndarray-like<a cl
 <p>Even if the <code class="docutils literal notranslate"><span class="pre">Series</span></code> is backed by an <code class="docutils literal notranslate"><span class="pre">ExtensionArray</span></code>, <code class="docutils literal notranslate"><span class="pre">Series.to_numpy()</span></code> will return a NumPy ndarray.</p>
 </section>
 <section id="series-is-dict-like">
-<h4><span class="section-number">6.4.1.2.1.3. </span>Series is dict-like<a class="headerlink" href="#series-is-dict-like" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.4.1.2.1.3. </span>Series is dict-like<a class="headerlink" href="#series-is-dict-like" title="Permalink to this headline">#</a></h4>
 <p>A <code class="docutils literal notranslate"><span class="pre">Series</span></code> is also like a fixed-size dict in that you can get and set values by index label:</p>
 <div class="cell docutils container">
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@@ -2276,7 +2276,7 @@ <h4><span class="section-number">6.4.1.2.1.3. </span>Series is dict-like<a class
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.11616360234416723
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.47114789842073596
 </pre></div>
 </div>
 </div>
@@ -2295,10 +2295,10 @@ <h4><span class="section-number">6.4.1.2.1.3. </span>Series is dict-like<a class
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    -0.116164
-b    -1.138321
-c     1.966055
-d     0.538852
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    -0.471148
+b    -0.093864
+c    -0.240233
+d     1.084377
 e    12.000000
 dtype: float64
 </pre></div>
@@ -2413,7 +2413,7 @@ <h4><span class="section-number">6.4.1.2.1.3. </span>Series is dict-like<a class
 <p>These labels can also be accessed by <code class="docutils literal notranslate"><span class="pre">attribute</span></code>.</p>
 </section>
 <section id="vectorized-operations-and-label-alignment-with-series">
-<h4><span class="section-number">6.4.1.2.1.4. </span>Vectorized operations and label alignment with Series<a class="headerlink" href="#vectorized-operations-and-label-alignment-with-series" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.4.1.2.1.4. </span>Vectorized operations and label alignment with Series<a class="headerlink" href="#vectorized-operations-and-label-alignment-with-series" title="Permalink to this headline">#</a></h4>
 <p>When working with raw NumPy arrays, looping through value-by-value is usually not necessary. The same is true when working with <code class="docutils literal notranslate"><span class="pre">Series</span></code> in Pandas. <code class="docutils literal notranslate"><span class="pre">Series</span></code> can also be passed into most NumPy methods expecting an ndarray.</p>
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@@ -2422,10 +2422,10 @@ <h4><span class="section-number">6.4.1.2.1.4. </span>Vectorized operations and l
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    -0.232327
-b    -2.276642
-c     3.932109
-d     1.077705
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    -0.942296
+b    -0.187729
+c    -0.480465
+d     2.168753
 e    24.000000
 dtype: float64
 </pre></div>
@@ -2439,10 +2439,10 @@ <h4><span class="section-number">6.4.1.2.1.4. </span>Vectorized operations and l
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    -0.232327
-b    -2.276642
-c     3.932109
-d     1.077705
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a    -0.942296
+b    -0.187729
+c    -0.480465
+d     2.168753
 e    24.000000
 dtype: float64
 </pre></div>
@@ -2456,10 +2456,10 @@ <h4><span class="section-number">6.4.1.2.1.4. </span>Vectorized operations and l
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a         0.890330
-b         0.320356
-c         7.142441
-d         1.714039
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a         0.624285
+b         0.910406
+c         0.786445
+d         2.957596
 e    162754.791419
 dtype: float64
 </pre></div>
@@ -2475,9 +2475,9 @@ <h4><span class="section-number">6.4.1.2.1.4. </span>Vectorized operations and l
 </div>
 <div class="cell_output docutils container">
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>a         NaN
-b   -2.276642
-c    3.932109
-d    1.077705
+b   -0.187729
+c   -0.480465
+d    2.168753
 e         NaN
 dtype: float64
 </pre></div>
@@ -2491,7 +2491,7 @@ <h4><span class="section-number">6.4.1.2.1.4. </span>Vectorized operations and l
 </div>
 </section>
 <section id="name-attribute">
-<h4><span class="section-number">6.4.1.2.1.5. </span>Name attribute<a class="headerlink" href="#name-attribute" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.4.1.2.1.5. </span>Name attribute<a class="headerlink" href="#name-attribute" title="Permalink to this headline">#</a></h4>
 <p><code class="docutils literal notranslate"><span class="pre">Series</span></code> also has a <code class="docutils literal notranslate"><span class="pre">name</span></code> attribute:</p>
 <div class="cell docutils container">
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@@ -2507,11 +2507,11 @@ <h4><span class="section-number">6.4.1.2.1.5. </span>Name attribute<a class="hea
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    0.391413
-1   -0.488687
-2    0.799206
-3    1.300596
-4   -1.115459
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0    0.857799
+1    0.804311
+2    0.105946
+3    1.011575
+4    0.002346
 Name: something, dtype: float64
 </pre></div>
 </div>
@@ -2554,7 +2554,7 @@ <h4><span class="section-number">6.4.1.2.1.5. </span>Name attribute<a class="hea
 </section>
 </section>
 <section id="dataframe">
-<h3><span class="section-number">6.4.1.2.2. </span>DataFrame<a class="headerlink" href="#dataframe" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.4.1.2.2. </span>DataFrame<a class="headerlink" href="#dataframe" title="Permalink to this headline">#</a></h3>
 <p><code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table, or a <code class="docutils literal notranslate"><span class="pre">dict</span></code> of <code class="docutils literal notranslate"><span class="pre">Series</span></code> objects. It is generally the most commonly used Pandas object. Like <code class="docutils literal notranslate"><span class="pre">Series</span></code>, <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> accepts many different kinds of input:</p>
 <ul class="simple">
 <li><p>Dict of 1D ndarrays, lists, dicts, or <code class="docutils literal notranslate"><span class="pre">Series</span></code></p></li>
@@ -2566,9 +2566,9 @@ <h3><span class="section-number">6.4.1.2.2. </span>DataFrame<a class="headerlink
 <p>Along with the data, you can optionally pass <strong>index</strong> (row labels) and <strong>columns</strong> (column labels) arguments. If you pass an index and / or columns, you are guaranteeing the index and / or columns of the resulting <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>. Thus, a <code class="docutils literal notranslate"><span class="pre">dict</span></code> of Series plus a specific index will discard all data not matching up to the passed index.</p>
 <p>If axis labels are not passed, they will be constructed from the input data based on common sense rules.</p>
 <section id="create-a-dataframe">
-<h4><span class="section-number">6.4.1.2.2.1. </span>Create a Dataframe<a class="headerlink" href="#create-a-dataframe" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.4.1.2.2.1. </span>Create a Dataframe<a class="headerlink" href="#create-a-dataframe" title="Permalink to this headline">#</a></h4>
 <section id="from-dict-of-series-or-dicts">
-<h5><span class="section-number">6.4.1.2.2.1.1. </span>From dict of <code class="docutils literal notranslate"><span class="pre">Series</span></code> or dicts<a class="headerlink" href="#from-dict-of-series-or-dicts" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.4.1.2.2.1.1. </span>From dict of <code class="docutils literal notranslate"><span class="pre">Series</span></code> or dicts<a class="headerlink" href="#from-dict-of-series-or-dicts" title="Permalink to this headline">#</a></h5>
 <p>The resulting <strong>index</strong> will be the <strong>union</strong> of the indexes of the various Series. If there are any nested dicts, these will first be converted to Series. If no columns are passed, the columns will be the ordered list of <code class="docutils literal notranslate"><span class="pre">dict</span></code> keys.</p>
 <div class="cell docutils container">
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@@ -2770,7 +2770,7 @@ <h5><span class="section-number">6.4.1.2.2.1.1. </span>From dict of <code class=
 </div>
 </section>
 <section id="from-dict-of-ndarrays-lists">
-<h5><span class="section-number">6.4.1.2.2.1.2. </span>From dict of ndarrays / lists<a class="headerlink" href="#from-dict-of-ndarrays-lists" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.4.1.2.2.1.2. </span>From dict of ndarrays / lists<a class="headerlink" href="#from-dict-of-ndarrays-lists" title="Permalink to this headline">#</a></h5>
 <p>The ndarrays must all be the same length. If an index is passed, it must also be the same length as the arrays. If no index is passed, the result will be <code class="docutils literal notranslate"><span class="pre">range(n)</span></code>, where <code class="docutils literal notranslate"><span class="pre">n</span></code> is the array length.</p>
 <div class="cell docutils container">
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@@ -2889,7 +2889,7 @@ <h5><span class="section-number">6.4.1.2.2.1.2. </span>From dict of ndarrays / l
 </div>
 </section>
 <section id="from-structured-or-record-array">
-<h5><span class="section-number">6.4.1.2.2.1.3. </span>From structured or record array<a class="headerlink" href="#from-structured-or-record-array" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.4.1.2.2.1.3. </span>From structured or record array<a class="headerlink" href="#from-structured-or-record-array" title="Permalink to this headline">#</a></h5>
 <p>This case is handled identically to a dict of arrays.</p>
 <div class="cell docutils container">
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@@ -3052,7 +3052,7 @@ <h5><span class="section-number">6.4.1.2.2.1.3. </span>From structured or record
 </div>
 </section>
 <section id="from-a-list-of-dicts">
-<h5><span class="section-number">6.4.1.2.2.1.4. </span>From a list of dicts<a class="headerlink" href="#from-a-list-of-dicts" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.4.1.2.2.1.4. </span>From a list of dicts<a class="headerlink" href="#from-a-list-of-dicts" title="Permalink to this headline">#</a></h5>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data2</span> <span class="o">=</span> <span class="p">[{</span><span class="s2">&quot;a&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">},</span> <span class="p">{</span><span class="s2">&quot;a&quot;</span><span class="p">:</span> <span class="mi">5</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">:</span> <span class="mi">20</span><span class="p">}]</span>
@@ -3200,7 +3200,7 @@ <h5><span class="section-number">6.4.1.2.2.1.4. </span>From a list of dicts<a cl
 </div>
 </section>
 <section id="from-a-dict-of-tuples">
-<h5><span class="section-number">6.4.1.2.2.1.5. </span>From a dict of tuples<a class="headerlink" href="#from-a-dict-of-tuples" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.4.1.2.2.1.5. </span>From a dict of tuples<a class="headerlink" href="#from-a-dict-of-tuples" title="Permalink to this headline">#</a></h5>
 <p>You can automatically create a MultiIndexed frame by passing a tuples dictionary.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3281,7 +3281,7 @@ <h5><span class="section-number">6.4.1.2.2.1.5. </span>From a dict of tuples<a c
 </div>
 </section>
 <section id="from-a-series">
-<h5><span class="section-number">6.4.1.2.2.1.6. </span>From a Series<a class="headerlink" href="#from-a-series" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.4.1.2.2.1.6. </span>From a Series<a class="headerlink" href="#from-a-series" title="Permalink to this headline">#</a></h5>
 <p>The result will be a DataFrame with the same index as the input Series, and with one column whose name is the original name of the Series (only if no other column name provided).</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3337,7 +3337,7 @@ <h5><span class="section-number">6.4.1.2.2.1.6. </span>From a Series<a class="he
 </div>
 </section>
 <section id="from-a-list-of-namedtuples">
-<h5><span class="section-number">6.4.1.2.2.1.7. </span>From a list of namedtuples<a class="headerlink" href="#from-a-list-of-namedtuples" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.4.1.2.2.1.7. </span>From a list of namedtuples<a class="headerlink" href="#from-a-list-of-namedtuples" title="Permalink to this headline">#</a></h5>
 <p>The field names of the first <code class="docutils literal notranslate"><span class="pre">namedtuple</span></code> in the list determine the columns of the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>. The remaining namedtuples (or tuples) are simply unpacked and their values are fed into the rows of the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>. If any of those tuples is shorter than the first <code class="docutils literal notranslate"><span class="pre">namedtuple</span></code> then the later columns in the corresponding row are marked as missing values. If any are longer than the first <code class="docutils literal notranslate"><span class="pre">namedtuple</span></code> , a <code class="docutils literal notranslate"><span class="pre">ValueError</span></code> is raised.</p>
 <div class="cell docutils container">
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@@ -3464,7 +3464,7 @@ <h5><span class="section-number">6.4.1.2.2.1.7. </span>From a list of namedtuple
 </div>
 </section>
 <section id="from-a-list-of-dataclasses">
-<h5><span class="section-number">6.4.1.2.2.1.8. </span>From a list of dataclasses<a class="headerlink" href="#from-a-list-of-dataclasses" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">5.4.1.2.2.1.8. </span>From a list of dataclasses<a class="headerlink" href="#from-a-list-of-dataclasses" title="Permalink to this headline">#</a></h5>
 <p>Data Classes as introduced in PEP557, can be passed into the DataFrame constructor. Passing a list of dataclasses is equivalent to passing a list of dictionaries.</p>
 <p>Please be aware, that all values in the list should be dataclasses, mixing types in the list would result in a <code class="docutils literal notranslate"><span class="pre">TypeError</span></code>.</p>
 <div class="cell docutils container">
@@ -3533,7 +3533,7 @@ <h5><span class="section-number">6.4.1.2.2.1.8. </span>From a list of dataclasse
 </section>
 </section>
 <section id="column-selection-addition-deletion">
-<h4><span class="section-number">6.4.1.2.2.2. </span>Column selection, addition, deletion<a class="headerlink" href="#column-selection-addition-deletion" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.4.1.2.2.2. </span>Column selection, addition, deletion<a class="headerlink" href="#column-selection-addition-deletion" title="Permalink to this headline">#</a></h4>
 <p>You can treat a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> semantically like a dict of like-indexed <code class="docutils literal notranslate"><span class="pre">Series</span></code> objects. Getting, setting, and deleting columns works with the same syntax as the analogous dict operations:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -4016,7 +4016,7 @@ <h4><span class="section-number">6.4.1.2.2.2. </span>Column selection, addition,
 </div>
 </section>
 <section id="assigning-new-columns-in-method-chains">
-<h4><span class="section-number">6.4.1.2.2.3. </span>Assigning new columns in method chains<a class="headerlink" href="#assigning-new-columns-in-method-chains" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.4.1.2.2.3. </span>Assigning new columns in method chains<a class="headerlink" href="#assigning-new-columns-in-method-chains" title="Permalink to this headline">#</a></h4>
 <p>DataFrame has an <code class="docutils literal notranslate"><span class="pre">assign()</span></code> method that allows you to easily create new columns that are potentially derived from existing columns.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -4361,7 +4361,7 @@ <h4><span class="section-number">6.4.1.2.2.3. </span>Assigning new columns in me
 <p>In the second expression, <code class="docutils literal notranslate"><span class="pre">x['C']</span></code> will refer to the newly created column, that’s equal to <code class="docutils literal notranslate"><span class="pre">dfa['A']</span> <span class="pre">+</span> <span class="pre">dfa['B']</span></code>.</p>
 </section>
 <section id="indexing-selection">
-<h4><span class="section-number">6.4.1.2.2.4. </span>Indexing / selection<a class="headerlink" href="#indexing-selection" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.4.1.2.2.4. </span>Indexing / selection<a class="headerlink" href="#indexing-selection" title="Permalink to this headline">#</a></h4>
 <p>The basics of indexing are as follows:</p>
 <table class="colwidths-auto table">
 <thead>
@@ -4473,7 +4473,7 @@ <h4><span class="section-number">6.4.1.2.2.4. </span>Indexing / selection<a clas
 </div>
 </section>
 <section id="data-alignment-and-arithmetic">
-<h4><span class="section-number">6.4.1.2.2.5. </span>Data alignment and arithmetic<a class="headerlink" href="#data-alignment-and-arithmetic" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.4.1.2.2.5. </span>Data alignment and arithmetic<a class="headerlink" href="#data-alignment-and-arithmetic" title="Permalink to this headline">#</a></h4>
 <p>Data alignment between <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> objects automatically aligns on <strong>both</strong> the columns and the index (row labels)**. Again, the resulting object will have the union of the column and row labels.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -4523,51 +4523,51 @@ <h4><span class="section-number">6.4.1.2.2.5. </span>Data alignment and arithmet
   <tbody>
     <tr>
       <th>0</th>
-      <td>-0.372196</td>
-      <td>0.000528</td>
-      <td>1.687640</td>
+      <td>-2.976357</td>
+      <td>-1.316627</td>
+      <td>0.672159</td>
       <td>NaN</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>-1.829334</td>
-      <td>1.243372</td>
-      <td>-3.280719</td>
+      <td>0.477964</td>
+      <td>0.339977</td>
+      <td>1.442367</td>
       <td>NaN</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>1.382189</td>
-      <td>-1.479555</td>
-      <td>-0.909017</td>
+      <td>0.554947</td>
+      <td>-0.146307</td>
+      <td>1.304767</td>
       <td>NaN</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>1.006382</td>
-      <td>-0.888496</td>
-      <td>0.842447</td>
+      <td>0.947704</td>
+      <td>0.868173</td>
+      <td>2.125977</td>
       <td>NaN</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>1.186080</td>
-      <td>-1.552519</td>
-      <td>-1.203525</td>
+      <td>1.597975</td>
+      <td>-0.998993</td>
+      <td>-1.187223</td>
       <td>NaN</td>
     </tr>
     <tr>
       <th>5</th>
-      <td>0.051602</td>
-      <td>1.150964</td>
-      <td>-1.357796</td>
+      <td>-1.066379</td>
+      <td>1.095593</td>
+      <td>-0.988821</td>
       <td>NaN</td>
     </tr>
     <tr>
       <th>6</th>
-      <td>-1.497613</td>
-      <td>0.649596</td>
-      <td>-1.304838</td>
+      <td>0.067644</td>
+      <td>-0.587502</td>
+      <td>1.405716</td>
       <td>NaN</td>
     </tr>
     <tr>
@@ -4637,66 +4637,66 @@ <h4><span class="section-number">6.4.1.2.2.5. </span>Data alignment and arithmet
     </tr>
     <tr>
       <th>1</th>
-      <td>0.010711</td>
-      <td>-1.256066</td>
-      <td>-2.101760</td>
-      <td>-1.559879</td>
+      <td>1.884459</td>
+      <td>-0.153521</td>
+      <td>0.043441</td>
+      <td>-0.381469</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>1.715931</td>
-      <td>-1.447889</td>
-      <td>-1.039832</td>
-      <td>0.024055</td>
+      <td>3.101853</td>
+      <td>0.780966</td>
+      <td>1.009441</td>
+      <td>-3.495847</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>1.920727</td>
-      <td>-1.584547</td>
-      <td>0.067213</td>
-      <td>0.607714</td>
+      <td>1.830482</td>
+      <td>0.956365</td>
+      <td>-0.295293</td>
+      <td>-0.735077</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>2.437215</td>
-      <td>-0.666180</td>
-      <td>-1.775297</td>
-      <td>-0.537169</td>
+      <td>2.716588</td>
+      <td>1.019025</td>
+      <td>-1.171295</td>
+      <td>0.352544</td>
     </tr>
     <tr>
       <th>5</th>
-      <td>1.442794</td>
-      <td>-0.546065</td>
-      <td>-0.611120</td>
-      <td>-1.383651</td>
+      <td>1.565483</td>
+      <td>2.371978</td>
+      <td>-1.698923</td>
+      <td>-1.818532</td>
     </tr>
     <tr>
       <th>6</th>
-      <td>1.820119</td>
-      <td>0.818159</td>
-      <td>-1.423893</td>
-      <td>0.039268</td>
+      <td>0.744948</td>
+      <td>-0.013414</td>
+      <td>-0.221765</td>
+      <td>-1.769200</td>
     </tr>
     <tr>
       <th>7</th>
-      <td>0.164854</td>
-      <td>-1.135353</td>
-      <td>-0.183192</td>
-      <td>0.520606</td>
+      <td>0.312613</td>
+      <td>-0.304193</td>
+      <td>0.816620</td>
+      <td>-1.095031</td>
     </tr>
     <tr>
       <th>8</th>
-      <td>1.107806</td>
-      <td>-0.249061</td>
-      <td>-0.472744</td>
-      <td>-1.586064</td>
+      <td>1.756536</td>
+      <td>0.536441</td>
+      <td>1.088142</td>
+      <td>-0.657246</td>
     </tr>
     <tr>
       <th>9</th>
-      <td>2.804475</td>
-      <td>1.032218</td>
-      <td>-1.406820</td>
-      <td>-0.891837</td>
+      <td>2.103343</td>
+      <td>-0.251893</td>
+      <td>-2.375203</td>
+      <td>-1.608417</td>
     </tr>
   </tbody>
 </table>
@@ -4737,73 +4737,73 @@ <h4><span class="section-number">6.4.1.2.2.5. </span>Data alignment and arithmet
   <tbody>
     <tr>
       <th>0</th>
-      <td>-4.735936</td>
-      <td>6.999095</td>
-      <td>4.758488</td>
-      <td>3.256836</td>
+      <td>-6.492551</td>
+      <td>-0.114228</td>
+      <td>5.049975</td>
+      <td>4.893690</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>-4.682383</td>
-      <td>0.718763</td>
-      <td>-5.750311</td>
-      <td>-4.542560</td>
+      <td>2.929745</td>
+      <td>-0.881832</td>
+      <td>5.267180</td>
+      <td>2.986346</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>3.843718</td>
-      <td>-0.240347</td>
-      <td>-0.440670</td>
-      <td>3.377111</td>
+      <td>9.016714</td>
+      <td>3.790604</td>
+      <td>10.097182</td>
+      <td>-12.585544</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>4.867699</td>
-      <td>-0.923638</td>
-      <td>5.094554</td>
-      <td>6.295406</td>
+      <td>2.659859</td>
+      <td>4.667597</td>
+      <td>3.573509</td>
+      <td>1.218305</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>7.450138</td>
-      <td>3.668195</td>
-      <td>-4.117994</td>
-      <td>0.570989</td>
+      <td>7.090390</td>
+      <td>4.980895</td>
+      <td>-0.806500</td>
+      <td>6.656411</td>
     </tr>
     <tr>
       <th>5</th>
-      <td>2.478033</td>
-      <td>4.268770</td>
-      <td>1.702890</td>
-      <td>-3.661422</td>
+      <td>1.334864</td>
+      <td>11.745660</td>
+      <td>-3.444640</td>
+      <td>-4.198972</td>
     </tr>
     <tr>
       <th>6</th>
-      <td>4.364661</td>
-      <td>11.089893</td>
-      <td>-2.360975</td>
-      <td>3.453176</td>
+      <td>-2.767812</td>
+      <td>-0.181299</td>
+      <td>3.941152</td>
+      <td>-3.952308</td>
     </tr>
     <tr>
       <th>7</th>
-      <td>-3.911666</td>
-      <td>1.322331</td>
-      <td>3.842529</td>
-      <td>5.859866</td>
+      <td>-4.929484</td>
+      <td>-1.635194</td>
+      <td>9.133073</td>
+      <td>-0.581465</td>
     </tr>
     <tr>
       <th>8</th>
-      <td>0.803095</td>
-      <td>5.753791</td>
-      <td>2.394768</td>
-      <td>-4.673486</td>
+      <td>2.290130</td>
+      <td>2.567976</td>
+      <td>10.490684</td>
+      <td>1.607458</td>
     </tr>
     <tr>
       <th>9</th>
-      <td>9.286441</td>
-      <td>12.160183</td>
-      <td>-2.275612</td>
-      <td>-1.202349</td>
+      <td>4.024164</td>
+      <td>-1.373693</td>
+      <td>-6.826041</td>
+      <td>-3.148394</td>
     </tr>
   </tbody>
 </table>
@@ -4843,73 +4843,73 @@ <h4><span class="section-number">6.4.1.2.2.5. </span>Data alignment and arithmet
   <tbody>
     <tr>
       <th>0</th>
-      <td>-0.742287</td>
-      <td>1.000181</td>
-      <td>1.812587</td>
-      <td>3.978245</td>
+      <td>-0.588751</td>
+      <td>-2.364930</td>
+      <td>1.639358</td>
+      <td>1.727897</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>-0.748236</td>
-      <td>-3.902480</td>
-      <td>-0.645135</td>
-      <td>-0.764227</td>
+      <td>5.377818</td>
+      <td>-1.735008</td>
+      <td>1.530372</td>
+      <td>5.069215</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>2.711912</td>
-      <td>-2.231797</td>
-      <td>-2.048618</td>
-      <td>3.630790</td>
+      <td>0.712584</td>
+      <td>2.792353</td>
+      <td>0.617499</td>
+      <td>-0.342805</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>1.743558</td>
-      <td>-1.710198</td>
-      <td>1.615742</td>
-      <td>1.164034</td>
+      <td>7.577377</td>
+      <td>1.874346</td>
+      <td>3.177611</td>
+      <td>-6.396355</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>0.917408</td>
-      <td>2.997251</td>
-      <td>-0.817261</td>
-      <td>-3.498922</td>
+      <td>0.982243</td>
+      <td>1.677348</td>
+      <td>-1.781578</td>
+      <td>1.073788</td>
     </tr>
     <tr>
       <th>5</th>
-      <td>10.459523</td>
-      <td>2.203837</td>
-      <td>-16.828788</td>
-      <td>-0.883170</td>
+      <td>-7.517254</td>
+      <td>0.513049</td>
+      <td>-0.918334</td>
+      <td>-0.806585</td>
     </tr>
     <tr>
       <th>6</th>
-      <td>2.114468</td>
-      <td>0.550062</td>
-      <td>-1.146532</td>
-      <td>3.440739</td>
+      <td>-1.048699</td>
+      <td>-2.292212</td>
+      <td>2.575790</td>
+      <td>-0.840010</td>
     </tr>
     <tr>
       <th>7</th>
-      <td>-0.845785</td>
-      <td>-7.378228</td>
-      <td>2.713661</td>
-      <td>1.295382</td>
+      <td>-0.721554</td>
+      <td>-1.375442</td>
+      <td>0.700960</td>
+      <td>-1.936885</td>
     </tr>
     <tr>
       <th>8</th>
-      <td>-4.177442</td>
-      <td>1.331987</td>
-      <td>12.665677</td>
-      <td>-0.749234</td>
+      <td>17.233678</td>
+      <td>8.803186</td>
+      <td>0.588881</td>
+      <td>-12.737492</td>
     </tr>
     <tr>
       <th>9</th>
-      <td>0.686206</td>
-      <td>0.492117</td>
-      <td>-1.169423</td>
-      <td>-1.561354</td>
+      <td>2.470156</td>
+      <td>-1.482055</td>
+      <td>-0.566505</td>
+      <td>-0.971177</td>
     </tr>
   </tbody>
 </table>
@@ -4949,73 +4949,73 @@ <h4><span class="section-number">6.4.1.2.2.5. </span>Data alignment and arithmet
   <tbody>
     <tr>
       <th>0</th>
-      <td>3.293911</td>
-      <td>0.999276</td>
-      <td>0.092641</td>
-      <td>0.003992</td>
+      <td>8.322860</td>
+      <td>0.031969</td>
+      <td>0.138454</td>
+      <td>0.112183</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>3.190403</td>
-      <td>0.004312</td>
-      <td>5.772933</td>
-      <td>2.931642</td>
+      <td>0.001196</td>
+      <td>0.110356</td>
+      <td>0.182311</td>
+      <td>0.001514</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>0.018488</td>
-      <td>0.040307</td>
-      <td>0.056775</td>
-      <td>0.005754</td>
+      <td>3.878422</td>
+      <td>0.016448</td>
+      <td>6.877897</td>
+      <td>72.411995</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>0.108207</td>
-      <td>0.116900</td>
-      <td>0.146728</td>
-      <td>0.544674</td>
+      <td>0.000303</td>
+      <td>0.081022</td>
+      <td>0.009808</td>
+      <td>0.000597</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>1.411725</td>
-      <td>0.012391</td>
-      <td>2.241590</td>
-      <td>0.006672</td>
+      <td>1.074297</td>
+      <td>0.126330</td>
+      <td>0.099261</td>
+      <td>0.752186</td>
     </tr>
     <tr>
       <th>5</th>
-      <td>0.000084</td>
-      <td>0.042392</td>
-      <td>0.000012</td>
-      <td>1.643698</td>
+      <td>0.000313</td>
+      <td>14.433281</td>
+      <td>1.406037</td>
+      <td>2.362645</td>
     </tr>
     <tr>
       <th>6</th>
-      <td>0.050026</td>
-      <td>10.923329</td>
-      <td>0.578701</td>
-      <td>0.007135</td>
+      <td>0.826793</td>
+      <td>0.036223</td>
+      <td>0.022717</td>
+      <td>2.008453</td>
     </tr>
     <tr>
       <th>7</th>
-      <td>1.954157</td>
-      <td>0.000337</td>
-      <td>0.018441</td>
-      <td>0.355148</td>
+      <td>3.689128</td>
+      <td>0.279403</td>
+      <td>4.142158</td>
+      <td>0.071053</td>
     </tr>
     <tr>
       <th>8</th>
-      <td>0.003284</td>
-      <td>0.317687</td>
-      <td>0.000039</td>
-      <td>3.173446</td>
+      <td>0.000011</td>
+      <td>0.000167</td>
+      <td>8.315544</td>
+      <td>0.000038</td>
     </tr>
     <tr>
       <th>9</th>
-      <td>4.510054</td>
-      <td>17.050068</td>
-      <td>0.534704</td>
-      <td>0.168265</td>
+      <td>0.026860</td>
+      <td>0.207273</td>
+      <td>9.709205</td>
+      <td>1.124105</td>
     </tr>
   </tbody>
 </table>
@@ -5234,7 +5234,7 @@ <h4><span class="section-number">6.4.1.2.2.5. </span>Data alignment and arithmet
 </div>
 </section>
 <section id="transposing">
-<h4><span class="section-number">6.4.1.2.2.6. </span>Transposing<a class="headerlink" href="#transposing" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">5.4.1.2.2.6. </span>Transposing<a class="headerlink" href="#transposing" title="Permalink to this headline">#</a></h4>
 <p>To transpose, access the <code class="docutils literal notranslate"><span class="pre">T</span></code> attribute or <code class="docutils literal notranslate"><span class="pre">DataFrame.transpose()</span></code>, similar to an ndarray:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -5271,35 +5271,35 @@ <h4><span class="section-number">6.4.1.2.2.6. </span>Transposing<a class="header
   <tbody>
     <tr>
       <th>A</th>
-      <td>-1.347187</td>
-      <td>-1.336477</td>
-      <td>0.368744</td>
-      <td>0.573540</td>
-      <td>1.090028</td>
+      <td>-1.698510</td>
+      <td>0.185949</td>
+      <td>1.403343</td>
+      <td>0.131972</td>
+      <td>1.018078</td>
     </tr>
     <tr>
       <th>B</th>
-      <td>0.999819</td>
-      <td>-0.256247</td>
-      <td>-0.448069</td>
-      <td>-0.584728</td>
-      <td>0.333639</td>
+      <td>-0.422846</td>
+      <td>-0.576366</td>
+      <td>0.358121</td>
+      <td>0.533519</td>
+      <td>0.596179</td>
     </tr>
     <tr>
       <th>C</th>
-      <td>0.551698</td>
-      <td>-1.550062</td>
-      <td>-0.488134</td>
-      <td>0.618911</td>
-      <td>-1.223599</td>
+      <td>0.609995</td>
+      <td>0.653436</td>
+      <td>1.619436</td>
+      <td>0.314702</td>
+      <td>-0.561300</td>
     </tr>
     <tr>
       <th>D</th>
-      <td>0.251367</td>
-      <td>-1.308512</td>
-      <td>0.275422</td>
-      <td>0.859081</td>
-      <td>-0.285802</td>
+      <td>0.578738</td>
+      <td>0.197269</td>
+      <td>-2.917109</td>
+      <td>-0.156339</td>
+      <td>0.931282</td>
     </tr>
   </tbody>
 </table>
@@ -5309,7 +5309,7 @@ <h4><span class="section-number">6.4.1.2.2.6. </span>Transposing<a class="header
 </section>
 </section>
 <section id="data-indexing-and-selection">
-<h2><span class="section-number">6.4.1.3. </span>Data indexing and selection<a class="headerlink" href="#data-indexing-and-selection" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.4.1.3. </span>Data indexing and selection<a class="headerlink" href="#data-indexing-and-selection" title="Permalink to this headline">#</a></h2>
 <p>The axis labeling information in Pandas objects serves many purposes:</p>
 <ul class="simple">
 <li><p>Identifies data (i.e. provides metadata) using known indicators, important for analysis, visualization, and interactive console display.</p></li>
@@ -5323,7 +5323,7 @@ <h2><span class="section-number">6.4.1.3. </span>Data indexing and selection<a c
 </div>
 <p>Whether a copy or a reference is returned for a setting operation, may depend on the context. This is sometimes called <code class="docutils literal notranslate"><span class="pre">chained</span> <span class="pre">assignment</span></code> and should be avoided.</p>
 <section id="different-choices-for-indexing">
-<h3><span class="section-number">6.4.1.3.1. </span>Different choices for indexing<a class="headerlink" href="#different-choices-for-indexing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.4.1.3.1. </span>Different choices for indexing<a class="headerlink" href="#different-choices-for-indexing" title="Permalink to this headline">#</a></h3>
 <p>Object selection has had a number of user-requested additions in order to support more explicit location-based indexing. Pandas now supports three types of multi-axis indexing.</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">.loc</span></code> is primarily label based, but may also be used with a boolean array. <code class="docutils literal notranslate"><span class="pre">.loc</span></code> will raise <code class="docutils literal notranslate"><span class="pre">KeyError</span></code> when the items are not found. Allowed inputs are:</p>
@@ -5364,7 +5364,7 @@ <h3><span class="section-number">6.4.1.3.1. </span>Different choices for indexin
 </table>
 </section>
 <section id="basics">
-<h3><span class="section-number">6.4.1.3.2. </span>Basics<a class="headerlink" href="#basics" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.4.1.3.2. </span>Basics<a class="headerlink" href="#basics" title="Permalink to this headline">#</a></h3>
 <p>As mentioned when introducing the data structures in the last section, the primary function of indexing with <code class="docutils literal notranslate"><span class="pre">[]</span></code> (a.k.a.<code class="docutils literal notranslate"> <span class="pre">__getitem__</span></code> for those familiar with implementing class behavior in Python) is selecting out lower-dimensional slices. The following table shows return type values when indexing Pandas objects with <code class="docutils literal notranslate"><span class="pre">[]</span></code>:</p>
 <table class="colwidths-auto table">
 <thead>
@@ -5422,59 +5422,59 @@ <h3><span class="section-number">6.4.1.3.2. </span>Basics<a class="headerlink" h
   <tbody>
     <tr>
       <th>2000-01-01</th>
-      <td>-0.018744</td>
-      <td>-1.149986</td>
-      <td>-1.272402</td>
-      <td>0.923053</td>
+      <td>0.801076</td>
+      <td>1.465342</td>
+      <td>-0.166177</td>
+      <td>-0.788715</td>
     </tr>
     <tr>
       <th>2000-01-02</th>
-      <td>0.376106</td>
-      <td>0.498873</td>
-      <td>1.189349</td>
-      <td>-0.527774</td>
+      <td>0.348102</td>
+      <td>-1.058147</td>
+      <td>-1.531875</td>
+      <td>-1.316167</td>
     </tr>
     <tr>
       <th>2000-01-03</th>
-      <td>-0.543060</td>
-      <td>-2.634839</td>
-      <td>1.032112</td>
-      <td>1.094636</td>
+      <td>1.736169</td>
+      <td>-0.839949</td>
+      <td>1.481805</td>
+      <td>-0.264777</td>
     </tr>
     <tr>
       <th>2000-01-04</th>
-      <td>-0.907917</td>
-      <td>0.179096</td>
-      <td>1.598011</td>
-      <td>-1.264800</td>
+      <td>-0.435348</td>
+      <td>0.694244</td>
+      <td>1.922322</td>
+      <td>-0.349232</td>
     </tr>
     <tr>
       <th>2000-01-05</th>
-      <td>1.095305</td>
-      <td>0.866036</td>
-      <td>1.128055</td>
-      <td>0.955879</td>
+      <td>-0.745863</td>
+      <td>-0.692943</td>
+      <td>-0.828683</td>
+      <td>2.427089</td>
     </tr>
     <tr>
       <th>2000-01-06</th>
-      <td>0.395284</td>
-      <td>0.705907</td>
-      <td>-1.039017</td>
-      <td>-1.469390</td>
+      <td>0.738183</td>
+      <td>0.224374</td>
+      <td>-0.953017</td>
+      <td>-0.067219</td>
     </tr>
     <tr>
       <th>2000-01-07</th>
-      <td>0.001039</td>
-      <td>-1.095615</td>
-      <td>1.198270</td>
-      <td>0.875681</td>
+      <td>0.387631</td>
+      <td>-0.026705</td>
+      <td>-0.647102</td>
+      <td>-0.489077</td>
     </tr>
     <tr>
       <th>2000-01-08</th>
-      <td>1.530022</td>
-      <td>-0.654648</td>
-      <td>-0.899138</td>
-      <td>0.503391</td>
+      <td>-1.247095</td>
+      <td>-0.649027</td>
+      <td>-0.993315</td>
+      <td>0.407961</td>
     </tr>
   </tbody>
 </table>
@@ -5494,7 +5494,7 @@ <h3><span class="section-number">6.4.1.3.2. </span>Basics<a class="headerlink" h
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.3952843326071609
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.7381831630668674
 </pre></div>
 </div>
 </div>
@@ -5534,59 +5534,59 @@ <h3><span class="section-number">6.4.1.3.2. </span>Basics<a class="headerlink" h
   <tbody>
     <tr>
       <th>2000-01-01</th>
-      <td>-0.018744</td>
-      <td>-1.149986</td>
-      <td>-1.272402</td>
-      <td>0.923053</td>
+      <td>0.801076</td>
+      <td>1.465342</td>
+      <td>-0.166177</td>
+      <td>-0.788715</td>
     </tr>
     <tr>
       <th>2000-01-02</th>
-      <td>0.376106</td>
-      <td>0.498873</td>
-      <td>1.189349</td>
-      <td>-0.527774</td>
+      <td>0.348102</td>
+      <td>-1.058147</td>
+      <td>-1.531875</td>
+      <td>-1.316167</td>
     </tr>
     <tr>
       <th>2000-01-03</th>
-      <td>-0.543060</td>
-      <td>-2.634839</td>
-      <td>1.032112</td>
-      <td>1.094636</td>
+      <td>1.736169</td>
+      <td>-0.839949</td>
+      <td>1.481805</td>
+      <td>-0.264777</td>
     </tr>
     <tr>
       <th>2000-01-04</th>
-      <td>-0.907917</td>
-      <td>0.179096</td>
-      <td>1.598011</td>
-      <td>-1.264800</td>
+      <td>-0.435348</td>
+      <td>0.694244</td>
+      <td>1.922322</td>
+      <td>-0.349232</td>
     </tr>
     <tr>
       <th>2000-01-05</th>
-      <td>1.095305</td>
-      <td>0.866036</td>
-      <td>1.128055</td>
-      <td>0.955879</td>
+      <td>-0.745863</td>
+      <td>-0.692943</td>
+      <td>-0.828683</td>
+      <td>2.427089</td>
     </tr>
     <tr>
       <th>2000-01-06</th>
-      <td>0.395284</td>
-      <td>0.705907</td>
-      <td>-1.039017</td>
-      <td>-1.469390</td>
+      <td>0.738183</td>
+      <td>0.224374</td>
+      <td>-0.953017</td>
+      <td>-0.067219</td>
     </tr>
     <tr>
       <th>2000-01-07</th>
-      <td>0.001039</td>
-      <td>-1.095615</td>
-      <td>1.198270</td>
-      <td>0.875681</td>
+      <td>0.387631</td>
+      <td>-0.026705</td>
+      <td>-0.647102</td>
+      <td>-0.489077</td>
     </tr>
     <tr>
       <th>2000-01-08</th>
-      <td>1.530022</td>
-      <td>-0.654648</td>
-      <td>-0.899138</td>
-      <td>0.503391</td>
+      <td>-1.247095</td>
+      <td>-0.649027</td>
+      <td>-0.993315</td>
+      <td>0.407961</td>
     </tr>
   </tbody>
 </table>
@@ -5627,59 +5627,59 @@ <h3><span class="section-number">6.4.1.3.2. </span>Basics<a class="headerlink" h
   <tbody>
     <tr>
       <th>2000-01-01</th>
-      <td>-1.149986</td>
-      <td>-0.018744</td>
-      <td>-1.272402</td>
-      <td>0.923053</td>
+      <td>1.465342</td>
+      <td>0.801076</td>
+      <td>-0.166177</td>
+      <td>-0.788715</td>
     </tr>
     <tr>
       <th>2000-01-02</th>
-      <td>0.498873</td>
-      <td>0.376106</td>
-      <td>1.189349</td>
-      <td>-0.527774</td>
+      <td>-1.058147</td>
+      <td>0.348102</td>
+      <td>-1.531875</td>
+      <td>-1.316167</td>
     </tr>
     <tr>
       <th>2000-01-03</th>
-      <td>-2.634839</td>
-      <td>-0.543060</td>
-      <td>1.032112</td>
-      <td>1.094636</td>
+      <td>-0.839949</td>
+      <td>1.736169</td>
+      <td>1.481805</td>
+      <td>-0.264777</td>
     </tr>
     <tr>
       <th>2000-01-04</th>
-      <td>0.179096</td>
-      <td>-0.907917</td>
-      <td>1.598011</td>
-      <td>-1.264800</td>
+      <td>0.694244</td>
+      <td>-0.435348</td>
+      <td>1.922322</td>
+      <td>-0.349232</td>
     </tr>
     <tr>
       <th>2000-01-05</th>
-      <td>0.866036</td>
-      <td>1.095305</td>
-      <td>1.128055</td>
-      <td>0.955879</td>
+      <td>-0.692943</td>
+      <td>-0.745863</td>
+      <td>-0.828683</td>
+      <td>2.427089</td>
     </tr>
     <tr>
       <th>2000-01-06</th>
-      <td>0.705907</td>
-      <td>0.395284</td>
-      <td>-1.039017</td>
-      <td>-1.469390</td>
+      <td>0.224374</td>
+      <td>0.738183</td>
+      <td>-0.953017</td>
+      <td>-0.067219</td>
     </tr>
     <tr>
       <th>2000-01-07</th>
-      <td>-1.095615</td>
-      <td>0.001039</td>
-      <td>1.198270</td>
-      <td>0.875681</td>
+      <td>-0.026705</td>
+      <td>0.387631</td>
+      <td>-0.647102</td>
+      <td>-0.489077</td>
     </tr>
     <tr>
       <th>2000-01-08</th>
-      <td>-0.654648</td>
-      <td>1.530022</td>
-      <td>-0.899138</td>
-      <td>0.503391</td>
+      <td>-0.649027</td>
+      <td>-1.247095</td>
+      <td>-0.993315</td>
+      <td>0.407961</td>
     </tr>
   </tbody>
 </table>
@@ -5720,43 +5720,43 @@ <h3><span class="section-number">6.4.1.3.2. </span>Basics<a class="headerlink" h
   <tbody>
     <tr>
       <th>2000-01-01</th>
-      <td>-1.149986</td>
-      <td>-0.018744</td>
+      <td>1.465342</td>
+      <td>0.801076</td>
     </tr>
     <tr>
       <th>2000-01-02</th>
-      <td>0.498873</td>
-      <td>0.376106</td>
+      <td>-1.058147</td>
+      <td>0.348102</td>
     </tr>
     <tr>
       <th>2000-01-03</th>
-      <td>-2.634839</td>
-      <td>-0.543060</td>
+      <td>-0.839949</td>
+      <td>1.736169</td>
     </tr>
     <tr>
       <th>2000-01-04</th>
-      <td>0.179096</td>
-      <td>-0.907917</td>
+      <td>0.694244</td>
+      <td>-0.435348</td>
     </tr>
     <tr>
       <th>2000-01-05</th>
-      <td>0.866036</td>
-      <td>1.095305</td>
+      <td>-0.692943</td>
+      <td>-0.745863</td>
     </tr>
     <tr>
       <th>2000-01-06</th>
-      <td>0.705907</td>
-      <td>0.395284</td>
+      <td>0.224374</td>
+      <td>0.738183</td>
     </tr>
     <tr>
       <th>2000-01-07</th>
-      <td>-1.095615</td>
-      <td>0.001039</td>
+      <td>-0.026705</td>
+      <td>0.387631</td>
     </tr>
     <tr>
       <th>2000-01-08</th>
-      <td>-0.654648</td>
-      <td>1.530022</td>
+      <td>-0.649027</td>
+      <td>-1.247095</td>
     </tr>
   </tbody>
 </table>
@@ -5795,43 +5795,43 @@ <h3><span class="section-number">6.4.1.3.2. </span>Basics<a class="headerlink" h
   <tbody>
     <tr>
       <th>2000-01-01</th>
-      <td>-1.149986</td>
-      <td>-0.018744</td>
+      <td>1.465342</td>
+      <td>0.801076</td>
     </tr>
     <tr>
       <th>2000-01-02</th>
-      <td>0.498873</td>
-      <td>0.376106</td>
+      <td>-1.058147</td>
+      <td>0.348102</td>
     </tr>
     <tr>
       <th>2000-01-03</th>
-      <td>-2.634839</td>
-      <td>-0.543060</td>
+      <td>-0.839949</td>
+      <td>1.736169</td>
     </tr>
     <tr>
       <th>2000-01-04</th>
-      <td>0.179096</td>
-      <td>-0.907917</td>
+      <td>0.694244</td>
+      <td>-0.435348</td>
     </tr>
     <tr>
       <th>2000-01-05</th>
-      <td>0.866036</td>
-      <td>1.095305</td>
+      <td>-0.692943</td>
+      <td>-0.745863</td>
     </tr>
     <tr>
       <th>2000-01-06</th>
-      <td>0.705907</td>
-      <td>0.395284</td>
+      <td>0.224374</td>
+      <td>0.738183</td>
     </tr>
     <tr>
       <th>2000-01-07</th>
-      <td>-1.095615</td>
-      <td>0.001039</td>
+      <td>-0.026705</td>
+      <td>0.387631</td>
     </tr>
     <tr>
       <th>2000-01-08</th>
-      <td>-0.654648</td>
-      <td>1.530022</td>
+      <td>-0.649027</td>
+      <td>-1.247095</td>
     </tr>
   </tbody>
 </table>
@@ -5871,43 +5871,43 @@ <h3><span class="section-number">6.4.1.3.2. </span>Basics<a class="headerlink" h
   <tbody>
     <tr>
       <th>2000-01-01</th>
-      <td>-0.018744</td>
-      <td>-1.149986</td>
+      <td>0.801076</td>
+      <td>1.465342</td>
     </tr>
     <tr>
       <th>2000-01-02</th>
-      <td>0.376106</td>
-      <td>0.498873</td>
+      <td>0.348102</td>
+      <td>-1.058147</td>
     </tr>
     <tr>
       <th>2000-01-03</th>
-      <td>-0.543060</td>
-      <td>-2.634839</td>
+      <td>1.736169</td>
+      <td>-0.839949</td>
     </tr>
     <tr>
       <th>2000-01-04</th>
-      <td>-0.907917</td>
-      <td>0.179096</td>
+      <td>-0.435348</td>
+      <td>0.694244</td>
     </tr>
     <tr>
       <th>2000-01-05</th>
-      <td>1.095305</td>
-      <td>0.866036</td>
+      <td>-0.745863</td>
+      <td>-0.692943</td>
     </tr>
     <tr>
       <th>2000-01-06</th>
-      <td>0.395284</td>
-      <td>0.705907</td>
+      <td>0.738183</td>
+      <td>0.224374</td>
     </tr>
     <tr>
       <th>2000-01-07</th>
-      <td>0.001039</td>
-      <td>-1.095615</td>
+      <td>0.387631</td>
+      <td>-0.026705</td>
     </tr>
     <tr>
       <th>2000-01-08</th>
-      <td>1.530022</td>
-      <td>-0.654648</td>
+      <td>-1.247095</td>
+      <td>-0.649027</td>
     </tr>
   </tbody>
 </table>
@@ -5915,7 +5915,7 @@ <h3><span class="section-number">6.4.1.3.2. </span>Basics<a class="headerlink" h
 </div>
 </section>
 <section id="attribute-access">
-<h3><span class="section-number">6.4.1.3.3. </span>Attribute access<a class="headerlink" href="#attribute-access" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.4.1.3.3. </span>Attribute access<a class="headerlink" href="#attribute-access" title="Permalink to this headline">#</a></h3>
 <p>You may access an index on a <code class="docutils literal notranslate"><span class="pre">Series</span></code> or column on a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> directly as an attribute:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -5944,14 +5944,14 @@ <h3><span class="section-number">6.4.1.3.3. </span>Attribute access<a class="hea
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 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2000-01-01   -0.018744
-2000-01-02    0.376106
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-2000-01-04   -0.907917
-2000-01-05    1.095305
-2000-01-06    0.395284
-2000-01-07    0.001039
-2000-01-08    1.530022
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2000-01-01    0.801076
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+2000-01-05   -0.745863
+2000-01-06    0.738183
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+2000-01-08   -1.247095
 Freq: D, Name: A, dtype: float64
 </pre></div>
 </div>
@@ -6009,58 +6009,58 @@ <h3><span class="section-number">6.4.1.3.3. </span>Attribute access<a class="hea
     <tr>
       <th>2000-01-01</th>
       <td>0</td>
-      <td>-1.149986</td>
-      <td>-1.272402</td>
-      <td>0.923053</td>
+      <td>1.465342</td>
+      <td>-0.166177</td>
+      <td>-0.788715</td>
     </tr>
     <tr>
       <th>2000-01-02</th>
       <td>1</td>
-      <td>0.498873</td>
-      <td>1.189349</td>
-      <td>-0.527774</td>
+      <td>-1.058147</td>
+      <td>-1.531875</td>
+      <td>-1.316167</td>
     </tr>
     <tr>
       <th>2000-01-03</th>
       <td>2</td>
-      <td>-2.634839</td>
-      <td>1.032112</td>
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     </tr>
     <tr>
       <th>2000-01-04</th>
       <td>3</td>
-      <td>0.179096</td>
-      <td>1.598011</td>
-      <td>-1.264800</td>
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     </tr>
     <tr>
       <th>2000-01-05</th>
       <td>4</td>
-      <td>0.866036</td>
-      <td>1.128055</td>
-      <td>0.955879</td>
+      <td>-0.692943</td>
+      <td>-0.828683</td>
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     </tr>
     <tr>
       <th>2000-01-06</th>
       <td>5</td>
-      <td>0.705907</td>
-      <td>-1.039017</td>
-      <td>-1.469390</td>
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+      <td>-0.953017</td>
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     </tr>
     <tr>
       <th>2000-01-07</th>
       <td>6</td>
-      <td>-1.095615</td>
-      <td>1.198270</td>
-      <td>0.875681</td>
+      <td>-0.026705</td>
+      <td>-0.647102</td>
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     </tr>
     <tr>
       <th>2000-01-08</th>
       <td>7</td>
-      <td>-0.654648</td>
-      <td>-0.899138</td>
-      <td>0.503391</td>
+      <td>-0.649027</td>
+      <td>-0.993315</td>
+      <td>0.407961</td>
     </tr>
   </tbody>
 </table>
@@ -6102,58 +6102,58 @@ <h3><span class="section-number">6.4.1.3.3. </span>Attribute access<a class="hea
     <tr>
       <th>2000-01-01</th>
       <td>0</td>
-      <td>-1.149986</td>
-      <td>-1.272402</td>
-      <td>0.923053</td>
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+      <td>-0.788715</td>
     </tr>
     <tr>
       <th>2000-01-02</th>
       <td>1</td>
-      <td>0.498873</td>
-      <td>1.189349</td>
-      <td>-0.527774</td>
+      <td>-1.058147</td>
+      <td>-1.531875</td>
+      <td>-1.316167</td>
     </tr>
     <tr>
       <th>2000-01-03</th>
       <td>2</td>
-      <td>-2.634839</td>
-      <td>1.032112</td>
-      <td>1.094636</td>
+      <td>-0.839949</td>
+      <td>1.481805</td>
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     </tr>
     <tr>
       <th>2000-01-04</th>
       <td>3</td>
-      <td>0.179096</td>
-      <td>1.598011</td>
-      <td>-1.264800</td>
+      <td>0.694244</td>
+      <td>1.922322</td>
+      <td>-0.349232</td>
     </tr>
     <tr>
       <th>2000-01-05</th>
       <td>4</td>
-      <td>0.866036</td>
-      <td>1.128055</td>
-      <td>0.955879</td>
+      <td>-0.692943</td>
+      <td>-0.828683</td>
+      <td>2.427089</td>
     </tr>
     <tr>
       <th>2000-01-06</th>
       <td>5</td>
-      <td>0.705907</td>
-      <td>-1.039017</td>
-      <td>-1.469390</td>
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+      <td>-0.067219</td>
     </tr>
     <tr>
       <th>2000-01-07</th>
       <td>6</td>
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-      <td>1.198270</td>
-      <td>0.875681</td>
+      <td>-0.026705</td>
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     <tr>
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       <td>7</td>
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-      <td>-0.899138</td>
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+      <td>0.407961</td>
     </tr>
   </tbody>
 </table>
@@ -6227,7 +6227,7 @@ <h3><span class="section-number">6.4.1.3.3. </span>Attribute access<a class="hea
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_2998/269534380.py:2: UserWarning: Pandas doesn&#39;t allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_3082/269534380.py:2: UserWarning: Pandas doesn&#39;t allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
   df.two = [4, 5, 6]
 </pre></div>
 </div>
@@ -6280,7 +6280,7 @@ <h3><span class="section-number">6.4.1.3.3. </span>Attribute access<a class="hea
 </div>
 </section>
 <section id="slicing-ranges">
-<h3><span class="section-number">6.4.1.3.4. </span>Slicing ranges<a class="headerlink" href="#slicing-ranges" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.4.1.3.4. </span>Slicing ranges<a class="headerlink" href="#slicing-ranges" title="Permalink to this headline">#</a></h3>
 <p>For now, we explain the semantics of slicing using the [] operator.</p>
 <p>With Series, the syntax works exactly as with an ndarray, returning a slice of the values and the corresponding labels:</p>
 <div class="cell docutils container">
@@ -6290,14 +6290,14 @@ <h3><span class="section-number">6.4.1.3.4. </span>Slicing ranges<a class="heade
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2000-01-01   -0.018744
-2000-01-02    0.376106
-2000-01-03   -0.543060
-2000-01-04   -0.907917
-2000-01-05    1.095305
-2000-01-06    0.395284
-2000-01-07    0.001039
-2000-01-08    1.530022
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2000-01-01    0.801076
+2000-01-02    0.348102
+2000-01-03    1.736169
+2000-01-04   -0.435348
+2000-01-05   -0.745863
+2000-01-06    0.738183
+2000-01-07    0.387631
+2000-01-08   -1.247095
 Freq: D, Name: A, dtype: float64
 </pre></div>
 </div>
@@ -6310,11 +6310,11 @@ <h3><span class="section-number">6.4.1.3.4. </span>Slicing ranges<a class="heade
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2000-01-01   -0.018744
-2000-01-02    0.376106
-2000-01-03   -0.543060
-2000-01-04   -0.907917
-2000-01-05    1.095305
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2000-01-01    0.801076
+2000-01-02    0.348102
+2000-01-03    1.736169
+2000-01-04   -0.435348
+2000-01-05   -0.745863
 Freq: D, Name: A, dtype: float64
 </pre></div>
 </div>
@@ -6327,10 +6327,10 @@ <h3><span class="section-number">6.4.1.3.4. </span>Slicing ranges<a class="heade
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2000-01-01   -0.018744
-2000-01-03   -0.543060
-2000-01-05    1.095305
-2000-01-07    0.001039
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2000-01-01    0.801076
+2000-01-03    1.736169
+2000-01-05   -0.745863
+2000-01-07    0.387631
 Freq: 2D, Name: A, dtype: float64
 </pre></div>
 </div>
@@ -6343,14 +6343,14 @@ <h3><span class="section-number">6.4.1.3.4. </span>Slicing ranges<a class="heade
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2000-01-08    1.530022
-2000-01-07    0.001039
-2000-01-06    0.395284
-2000-01-05    1.095305
-2000-01-04   -0.907917
-2000-01-03   -0.543060
-2000-01-02    0.376106
-2000-01-01   -0.018744
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2000-01-08   -1.247095
+2000-01-07    0.387631
+2000-01-06    0.738183
+2000-01-05   -0.745863
+2000-01-04   -0.435348
+2000-01-03    1.736169
+2000-01-02    0.348102
+2000-01-01    0.801076
 Freq: -1D, Name: A, dtype: float64
 </pre></div>
 </div>
@@ -6371,9 +6371,9 @@ <h3><span class="section-number">6.4.1.3.4. </span>Slicing ranges<a class="heade
 2000-01-03    0.000000
 2000-01-04    0.000000
 2000-01-05    0.000000
-2000-01-06    0.395284
-2000-01-07    0.001039
-2000-01-08    1.530022
+2000-01-06    0.738183
+2000-01-07    0.387631
+2000-01-08   -1.247095
 Freq: D, Name: A, dtype: float64
 </pre></div>
 </div>
@@ -6473,7 +6473,7 @@ <h3><span class="section-number">6.4.1.3.4. </span>Slicing ranges<a class="heade
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">6.4.1.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.4.1.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks for <a class="reference external" href="https://pandas.pydata.org/docs/user_guide/index.html">Pandas user guide</a>. It contributes the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -6515,13 +6515,13 @@ <h2><span class="section-number">6.4.1.4. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">6.4. </span>Pandas</p>
+            <p class="prev-next-title"><span class="section-number">5.4. </span>Pandas</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="data-selection.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">6.4.2. </span>Data Selection</p>
+        <p class="prev-next-title"><span class="section-number">5.4.2. </span>Data Selection</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/data-science/working-with-data/pandas/pandas.html b/data-science/working-with-data/pandas/pandas.html
index 0118651fd..f23ae8c58 100644
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+++ b/data-science/working-with-data/pandas/pandas.html
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-    <title>6.4. Pandas &#8212; Ocademy Open Machine Learning Book</title>
+    <title>5.4. Pandas &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" />
-    <link rel="next" title="6.4.1. Introduction and Data Structures" href="introduction-and-data-structures.html" />
-    <link rel="prev" title="6.3. NumPy" href="../numpy.html" />
+    <link rel="next" title="5.4.1. Introduction and Data Structures" href="introduction-and-data-structures.html" />
+    <link rel="prev" title="5.3. NumPy" href="../numpy.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -159,17 +159,17 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../../prerequisites/python-programming-introduction.html">
-   1. Python programming basics
+   1. Python programming introduction
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  <li class="toctree-l1">
   <a class="reference internal" href="../../../prerequisites/python-programming-basics.html">
-   3. Python programming basics
+   2. Python programming basics
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  <li class="toctree-l1">
   <a class="reference internal" href="../../../prerequisites/python-programming-advanced.html">
-   4. Python programming advanced
+   3. Python programming advanced
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  </li>
 </ul>
@@ -181,7 +181,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="current nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../introduction/introduction.html">
-   5. Introduction
+   4. Introduction
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   <input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/>
   <label for="toctree-checkbox-1">
@@ -191,29 +191,29 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../introduction/defining-data-science.html">
-     5.1. Defining data science
+     4.1. Defining data science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../introduction/data-science-ethics.html">
-     5.2. Data Science ethics
+     4.2. Data Science ethics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../introduction/defining-data.html">
-     5.3. Defining data
+     4.3. Defining data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../introduction/introduction-to-statistics-and-probability.html">
-     5.4. Introduction to statistics and probability
+     4.4. Introduction to statistics and probability
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 current active has-children">
   <a class="reference internal" href="../working-with-data.html">
-   6. Working with data
+   5. Working with data
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   <input checked="" class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/>
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   <ul class="current">
    <li class="toctree-l2">
     <a class="reference internal" href="../relational-databases.html">
-     6.1. Relational databases
+     5.1. Relational databases
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../non-relational-data.html">
-     6.2. Non-relational data
+     5.2. Non-relational data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../numpy.html">
-     6.3. NumPy
+     5.3. NumPy
     </a>
    </li>
    <li class="toctree-l2 current active has-children">
     <a class="current reference internal" href="#">
-     6.4. Pandas
+     5.4. Pandas
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     <input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
     <label for="toctree-checkbox-3">
@@ -248,31 +248,31 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
     <ul>
      <li class="toctree-l3">
       <a class="reference internal" href="introduction-and-data-structures.html">
-       6.4.1. Introduction and Data Structures
+       5.4.1. Introduction and Data Structures
       </a>
      </li>
      <li class="toctree-l3">
       <a class="reference internal" href="data-selection.html">
-       6.4.2. Data Selection
+       5.4.2. Data Selection
       </a>
      </li>
      <li class="toctree-l3">
       <a class="reference internal" href="advanced-pandas-techniques.html">
-       6.4.3. Advanced Pandas Techniques
+       5.4.3. Advanced Pandas Techniques
       </a>
      </li>
     </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-preparation.html">
-     6.5. Data preparation
+     5.5. Data preparation
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../data-visualization/data-visualization.html">
-   7. Data visualization
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 </div>
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 <section class="tex2jax_ignore mathjax_ignore" id="relational-databases">
-<h1><span class="section-number">6.1. </span>Relational databases<a class="headerlink" href="#relational-databases" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">5.1. </span>Relational databases<a class="headerlink" href="#relational-databases" title="Permalink to this headline">#</a></h1>
 <p>Chances are you have used a spreadsheet in the past to store information. You had a set of rows and columns, where the rows contained the information (or data), and the columns described the information (sometimes called metadata). A relational database is built upon this core principle of columns and rows in tables, allowing you to have information spread across multiple tables. This allows you to work with more complex data, avoid duplication, and have flexibility in the way you explore the data. Let’s explore the concepts of a relational database.</p>
 <section id="it-all-starts-with-tables">
-<h2><span class="section-number">6.1.1. </span>It all starts with tables<a class="headerlink" href="#it-all-starts-with-tables" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.1.1. </span>It all starts with tables<a class="headerlink" href="#it-all-starts-with-tables" title="Permalink to this headline">#</a></h2>
 <p>A relational database has at its core tables. Just as with the spreadsheet, a table is a collection of columns and rows. The row contains the data or information we wish to work with, such as the name of a city or the amount of rainfall. The columns describe the data they store.</p>
 <p>Let’s begin our exploration by starting a table to store information about cities. We might start with their name and country. You could store this in a table as follows:</p>
 <table class="colwidths-auto table">
@@ -1755,7 +1755,7 @@ <h2><span class="section-number">6.1.1. </span>It all starts with tables<a class
 <p>Notice the column names of <code class="docutils literal notranslate"><span class="pre">city</span></code>, <code class="docutils literal notranslate"><span class="pre">country</span></code> and <code class="docutils literal notranslate"><span class="pre">population</span></code> describe the data being stored, and each row has information about one city.</p>
 </section>
 <section id="the-shortcomings-of-a-single-table-approach">
-<h2><span class="section-number">6.1.2. </span>The shortcomings of a single table approach<a class="headerlink" href="#the-shortcomings-of-a-single-table-approach" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.1.2. </span>The shortcomings of a single table approach<a class="headerlink" href="#the-shortcomings-of-a-single-table-approach" title="Permalink to this headline">#</a></h2>
 <p>Chances are, the table above seems relatively familiar to you. Let’s start to add some additional data to our burgeoning database - annual rainfall (in millimeters). We’ll focus on the years 2018, 2019 and 2020. If we were to add it for Tokyo, it might look something like this:</p>
 <table class="colwidths-auto table">
 <thead>
@@ -1819,7 +1819,7 @@ <h2><span class="section-number">6.1.2. </span>The shortcomings of a single tabl
 <p>This is why we need multiple tables and relationships. By breaking apart our data we can avoid duplication and have more flexibility in how we work with our data.</p>
 </section>
 <section id="the-concepts-of-relationships">
-<h2><span class="section-number">6.1.3. </span>The concepts of relationships<a class="headerlink" href="#the-concepts-of-relationships" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.1.3. </span>The concepts of relationships<a class="headerlink" href="#the-concepts-of-relationships" title="Permalink to this headline">#</a></h2>
 <p>Let’s return to our data and determine how we want to split things up. We know we want to store the name and country of our cities, so this will probably work best in one table.</p>
 <table class="colwidths-auto table">
 <thead>
@@ -1842,7 +1842,7 @@ <h2><span class="section-number">6.1.3. </span>The concepts of relationships<a c
 <p>But before we create the next table, we need to figure out how to reference each city. We need some form of an identifier, ID or (in technical database terms) a primary key. A primary key is a value used to identify one specific row in a table. While this could be based on a value itself (we could use the name of the city, for example), it should almost always be a number or other identifier. We don’t want the id to ever change as it would break the relationship. You will find in most cases the primary key or id will be an auto-generated number.</p>
 <p>✅ Primary key is frequently abbreviated as PK</p>
 <section id="cities">
-<h3><span class="section-number">6.1.3.1. </span>cities<a class="headerlink" href="#cities" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.1.3.1. </span>cities<a class="headerlink" href="#cities" title="Permalink to this headline">#</a></h3>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>city_id</p></th>
@@ -1869,7 +1869,7 @@ <h3><span class="section-number">6.1.3.1. </span>cities<a class="headerlink" hre
 <p>With our cities table created, let’s store the rainfall. Rather than duplicating the full information about the city, we can use the id. We should also ensure the newly created table has an <em>id</em> column as well, as all tables should have an id or primary key.</p>
 </section>
 <section id="rainfall">
-<h3><span class="section-number">6.1.3.2. </span>rainfall<a class="headerlink" href="#rainfall" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">5.1.3.2. </span>rainfall<a class="headerlink" href="#rainfall" title="Permalink to this headline">#</a></h3>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>rainfall_id</p></th>
@@ -1931,7 +1931,7 @@ <h3><span class="section-number">6.1.3.2. </span>rainfall<a class="headerlink" h
 </section>
 </section>
 <section id="retrieving-the-data">
-<h2><span class="section-number">6.1.4. </span>Retrieving the data<a class="headerlink" href="#retrieving-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.1.4. </span>Retrieving the data<a class="headerlink" href="#retrieving-the-data" title="Permalink to this headline">#</a></h2>
 <p>With our data separated into two tables, you may be wondering how we retrieve it. If we are using a relational database such as MySQL, SQL Server or Oracle, we can use a language called Structured Query Language or SQL. SQL (sometimes pronounced sequel) is a standard language used to retrieve and modify data in a relational database.</p>
 <p>To retrieve data you use the command <code class="docutils literal notranslate"><span class="pre">SELECT</span></code>. At its core, you <strong>select</strong> the columns you want to see <strong>from</strong> the table they’re contained in. If you wanted to display just the names of the cities, you could use the following:</p>
 <p><code class="docutils literal notranslate"><span class="pre">SELECT</span></code> is where you list the columns, and <code class="docutils literal notranslate"><span class="pre">FROM</span></code> is where you list the tables.</p>
@@ -1939,7 +1939,7 @@ <h2><span class="section-number">6.1.4. </span>Retrieving the data<a class="head
 <p>The query above will display all cities. Let’s imagine we only wanted to display cities in New Zealand. We need some form of a filter. The SQL keyword for this is <code class="docutils literal notranslate"><span class="pre">WHERE</span></code>, or “where something is true”.</p>
 </section>
 <section id="joining-data">
-<h2><span class="section-number">6.1.5. </span>Joining data<a class="headerlink" href="#joining-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.1.5. </span>Joining data<a class="headerlink" href="#joining-data" title="Permalink to this headline">#</a></h2>
 <p>Until now we’ve retrieved data from a single table. Now we want to bring the data together from both <code class="docutils literal notranslate"><span class="pre">cities</span></code> and <code class="docutils literal notranslate"><span class="pre">rainfall</span></code>. This is done by <em>joining</em> them together. You will effectively create a seam between the two tables, and match up the values from a column from each table.</p>
 <p>In our example, we will match the <code class="docutils literal notranslate"><span class="pre">city_id</span></code> column in <code class="docutils literal notranslate"><span class="pre">rainfall</span></code> with the city_id column in <code class="docutils literal notranslate"><span class="pre">cities</span></code>. This will match the rainfall value with its respective city. The type of join we will perform is what’s called an <em>inner</em> join, meaning if any rows don’t match with anything from the other table they won’t be displayed. In our case every city has rainfall, so everything will be displayed.</p>
 <p>Let’s retrieve the rainfall for 2019 for all our cities.</p>
@@ -1947,16 +1947,16 @@ <h2><span class="section-number">6.1.5. </span>Joining data<a class="headerlink"
 <p>We have highlighted the two columns we want, and the fact we want to join the tables together by the <code class="docutils literal notranslate"><span class="pre">city_id</span></code>. Now we can add the <code class="docutils literal notranslate"><span class="pre">WHERE</span></code> statement to filter out the only year 2019.</p>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">6.1.6. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.1.6. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>Relational databases are centered around dividing information between multiple tables which is then brought back together for display and analysis. This provides a high degree of flexibility to perform calculations and otherwise manipulate data. You have seen the core concepts of a relational database, and how to perform a join between two tables.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">6.1.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.1.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>There are numerous relational databases available on the internet. You can explore the data by using the skills you’ve learned above.</p>
 <p>Assignment - <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html"><span class="doc std std-doc">Displaying airport data</span></a></p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">6.1.8. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.1.8. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>There are several resources available on <a class="reference external" href="https://docs.microsoft.com/learn?WT.mc_id=academic-77958-bethanycheum">Microsoft Learn</a> for you to continue your exploration of SQL and relational database concepts</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://docs.microsoft.com//learn/modules/describe-concepts-of-relational-data?WT.mc_id=academic-77958-bethanycheum">Describe concepts of relational data</a></p></li>
@@ -1965,7 +1965,7 @@ <h2><span class="section-number">6.1.8. </span>Self study<a class="headerlink" h
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">6.1.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">5.1.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/Data-Science-For-Beginners">Data Science for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2007,13 +2007,13 @@ <h2><span class="section-number">6.1.9. </span>Acknowledgments<a class="headerli
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">6. </span>Working with data</p>
+            <p class="prev-next-title"><span class="section-number">5. </span>Working with data</p>
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diff --git a/data-science/working-with-data/working-with-data.html b/data-science/working-with-data/working-with-data.html
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+    <title>5. Working with data &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="6.1. Relational databases" href="relational-databases.html" />
-    <link rel="prev" title="5.4. Introduction to statistics and probability" href="../introduction/introduction-to-statistics-and-probability.html" />
+    <link rel="next" title="5.1. Relational databases" href="relational-databases.html" />
+    <link rel="prev" title="4.4. Introduction to statistics and probability" href="../introduction/introduction-to-statistics-and-probability.html" />
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-     7.3. Visualizing relationships: all about honey 🍯
+     6.3. Visualizing relationships: all about honey 🍯
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+     8.2. The “low code/no code” way
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+     8.3. Data Science in the cloud: The “Azure ML SDK” way
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+     14.3. Yet other classifiers
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-     15.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
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+     15.1. Loss function
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+     15.2. Gradient descent
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+     16.1. Introduction to clustering
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+     17.1. Bagging
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+     18.2. Gradient boosting example
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-     19.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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+   21. Unsupervised learning: PCA and clustering
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-     25.3.1.1. Stylenet / Neural-Style
+     24.3.1.1. Stylenet / Neural-Style
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-     25.3.1.2. Deepdream in TensorFlow
+     24.3.1.2. Deepdream in TensorFlow
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+   26. Recurrent Neural Networks
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+   33. Diffusion Model
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+   34. Object detection
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+     40.3. First assignment
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+     40.9. Analyzing text about Data Science
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+     40.10. Data Science scenarios
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+     40.11. Write a data ethics case study
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+     40.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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+     40.17. Classifying datasets
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+     40.18. Small diabetes study
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+     40.19. Introduction to probability and statistics
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-     41.21. Soda profits
+     40.21. Soda profits
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+     40.24. Data processing in Python
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     <a class="reference internal" href="../slides/deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1577,49 +1577,49 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#overview">
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+   27.1. Overview
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#unsupervised-learning">
-   28.2. Unsupervised Learning
+   27.2. Unsupervised Learning
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#fully-connected-autoencoder">
-   28.3. Fully-connected Autoencoder
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   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#potential-autoencoder-applications">
-     28.3.1. Potential Autoencoder Applications
+     27.3.1. Potential Autoencoder Applications
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#convolutional-autoencoder">
-   28.4. Convolutional Autoencoder
+   27.4. Convolutional Autoencoder
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#code">
-   28.5. Code
+   27.5. Code
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   28.6. Your turn! 🚀
+   27.6. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
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-   28.7. Self study
+   27.7. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   28.8. Acknowledgments
+   27.8. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1643,49 +1643,49 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
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   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
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   <a class="reference internal nav-link" href="#fully-connected-autoencoder">
-   28.3. Fully-connected Autoencoder
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   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#potential-autoencoder-applications">
-     28.3.1. Potential Autoencoder Applications
+     27.3.1. Potential Autoencoder Applications
     </a>
    </li>
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  </li>
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   </a>
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+   27.5. Code
   </a>
  </li>
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-   28.6. Your turn! 🚀
+   27.6. Your turn! 🚀
   </a>
  </li>
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+   27.7. Self study
   </a>
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-   28.8. Acknowledgments
+   27.8. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1710,13 +1710,13 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="autoencoder">
-<h1><span class="section-number">28. </span>Autoencoder<a class="headerlink" href="#autoencoder" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">27. </span>Autoencoder<a class="headerlink" href="#autoencoder" title="Permalink to this headline">#</a></h1>
 <section id="overview">
-<h2><span class="section-number">28.1. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">27.1. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h2>
 <p>An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation. The autoencoder learns an efficient representation (encoding) for a set of data, typically for dimensionality reduction.</p>
 </section>
 <section id="unsupervised-learning">
-<h2><span class="section-number">28.2. </span>Unsupervised Learning<a class="headerlink" href="#unsupervised-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">27.2. </span>Unsupervised Learning<a class="headerlink" href="#unsupervised-learning" title="Permalink to this headline">#</a></h2>
 <p>Autoencoder is a kind of unsupervised learning, which means working with datasets without considering a target variable. There are some Applications and Goals for it:</p>
 <ul class="simple">
 <li><p>Finding hidden structures in data.</p></li>
@@ -1733,7 +1733,7 @@ <h2><span class="section-number">28.2. </span>Unsupervised Learning<a class="hea
 <figure class="align-default" id="id1">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/01_PCA1.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/01_PCA1.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 28.1 </span><span class="caption-text">Illustration of PCA</span><a class="headerlink" href="#id1" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 27.1 </span><span class="caption-text">Illustration of PCA</span><a class="headerlink" href="#id1" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul class="simple">
@@ -1742,7 +1742,7 @@ <h2><span class="section-number">28.2. </span>Unsupervised Learning<a class="hea
 <figure class="align-default" id="id2">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/02_PCA2.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/02_PCA2.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 28.2 </span><span class="caption-text">Illustration of PCA</span><a class="headerlink" href="#id2" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 27.2 </span><span class="caption-text">Illustration of PCA</span><a class="headerlink" href="#id2" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul class="simple">
@@ -1751,17 +1751,17 @@ <h2><span class="section-number">28.2. </span>Unsupervised Learning<a class="hea
 <figure class="align-default" id="id3">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/03_PCA3.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/03_PCA3.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 28.3 </span><span class="caption-text">Illustration of PCA</span><a class="headerlink" href="#id3" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 27.3 </span><span class="caption-text">Illustration of PCA</span><a class="headerlink" href="#id3" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="fully-connected-autoencoder">
-<h2><span class="section-number">28.3. </span>Fully-connected Autoencoder<a class="headerlink" href="#fully-connected-autoencoder" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">27.3. </span>Fully-connected Autoencoder<a class="headerlink" href="#fully-connected-autoencoder" title="Permalink to this headline">#</a></h2>
 <p>Here is an example of a basic fully-connected autoencoder</p>
 <figure class="align-default" id="id4">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/04_simple.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/04_simple.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 28.4 </span><span class="caption-text">Illustration of Fully Connected autoencoder</span><a class="headerlink" href="#id4" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 27.4 </span><span class="caption-text">Illustration of Fully Connected autoencoder</span><a class="headerlink" href="#id4" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="admonition note">
@@ -1771,7 +1771,7 @@ <h2><span class="section-number">28.3. </span>Fully-connected Autoencoder<a clas
 <p>The loss function of this simple model is
 $<span class="math notranslate nohighlight">\(L(x, x') = \left\lVert x - x' \right\rVert^2_2 = \sum_i (x_i - x_i')^2\)</span>$</p>
 <section id="potential-autoencoder-applications">
-<h3><span class="section-number">28.3.1. </span>Potential Autoencoder Applications<a class="headerlink" href="#potential-autoencoder-applications" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">27.3.1. </span>Potential Autoencoder Applications<a class="headerlink" href="#potential-autoencoder-applications" title="Permalink to this headline">#</a></h3>
 <p>And there are some potential autoencoder applications, for example:</p>
 <ul class="simple">
 <li><p>After training, disregard the output part, we can use embedding as input to classic machine learning methods (SVM, KNN, Random Forest, …).</p></li>
@@ -1781,13 +1781,13 @@ <h3><span class="section-number">28.3.1. </span>Potential Autoencoder Applicatio
 </section>
 </section>
 <section id="convolutional-autoencoder">
-<h2><span class="section-number">28.4. </span>Convolutional Autoencoder<a class="headerlink" href="#convolutional-autoencoder" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">27.4. </span>Convolutional Autoencoder<a class="headerlink" href="#convolutional-autoencoder" title="Permalink to this headline">#</a></h2>
 <p>For convolutional autoencoder, we mainly use transposed convolution construct the output, and transposed convolution (sometimes called “deconvolution”) allows us to increase the size of the output feature map compared to the input feature map.</p>
 <p>The difference between regular convolution and transposed convolution can be seen from the following image.</p>
 <figure class="align-default" id="id5">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/05_diff_conv.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/05_diff_conv.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 28.5 </span><span class="caption-text">Difference between regular and transposed convolution</span><a class="headerlink" href="#id5" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 27.5 </span><span class="caption-text">Difference between regular and transposed convolution</span><a class="headerlink" href="#id5" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>In transposed convolutions, we stride over the output; hence, larger strides will result in larger outputs (opposite to regular convolutions); and we pad the output; hence, larger padding will result in smaller output maps.</p>
@@ -1795,7 +1795,7 @@ <h2><span class="section-number">28.4. </span>Convolutional Autoencoder<a class=
 <figure class="align-default" id="id6">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/06_convmodel.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/autoencoder/06_convmodel.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 28.6 </span><span class="caption-text">Structure of convoluted autoencoder</span><a class="headerlink" href="#id6" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 27.6 </span><span class="caption-text">Structure of convoluted autoencoder</span><a class="headerlink" href="#id6" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="admonition note">
@@ -1809,7 +1809,7 @@ <h2><span class="section-number">28.4. </span>Convolutional Autoencoder<a class=
 </div>
 </section>
 <section id="code">
-<h2><span class="section-number">28.5. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">27.5. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h2>
 <p>Let’s build a 2-layers auto-encoder with TensorFlow to compress images to a lower latent space and then reconstruct them. And this project will be done on MNIST dataste.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2064,18 +2064,18 @@ <h2><span class="section-number">28.5. </span>Code<a class="headerlink" href="#c
 </div>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">28.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">27.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">28.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">27.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>You can refer to this book chapter for further study:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://www.deeplearningbook.org/contents/autoencoders.html">deeplearningbook</a></p></li>
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">28.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">27.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/rasbt">Sebastian Raschka</a> for creating the open-source project <a class="reference external" href="https://github.com/rasbt/stat453-deep-learning-ss20">stat453-deep-learning-ss20</a> and <a class="reference external" href="https://github.com/aymericdamien">Aymeric Damien</a> for creating the open-source project <a class="reference external" href="https://github.com/aymericdamien/TensorFlow-Examples/">TensorFlow-Examples</a>. They inspire the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2117,13 +2117,13 @@ <h2><span class="section-number">28.8. </span>Acknowledgments<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">27. </span>Recurrent Neural Networks</p>
+            <p class="prev-next-title"><span class="section-number">26. </span>Recurrent Neural Networks</p>
         </div>
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     <a class='right-next' id="next-link" href="lstm.html" title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">29. </span>Long-short term memory</p>
+        <p class="prev-next-title"><span class="section-number">28. </span>Long-short term memory</p>
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diff --git a/deep-learning/cnn/cnn-deepdream.html b/deep-learning/cnn/cnn-deepdream.html
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>25.3.1.2. Deepdream in TensorFlow &#8212; Ocademy Open Machine Learning Book</title>
+    <title>24.3.1.2. Deepdream in TensorFlow &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="26. Generative adversarial networks" href="../gan.html" />
-    <link rel="prev" title="25.3.1.1. Stylenet / Neural-Style" href="cnn-vgg.html" />
+    <link rel="next" title="25. Generative adversarial networks" href="../gan.html" />
+    <link rel="prev" title="24.3.1.1. Stylenet / Neural-Style" href="cnn-vgg.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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  <li class="toctree-l1">
   <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
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+   1. Python programming introduction
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-     5.2. Data Science ethics
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-     5.3. Defining data
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+     40.14. Try it in Excel
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 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="deepdream-in-tensorflow">
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+<h1><span class="section-number">24.3.1.2. </span>Deepdream in TensorFlow<a class="headerlink" href="#deepdream-in-tensorflow" title="Permalink to this headline">#</a></h1>
 <p>Note: There is no new code in this script. It originates from the TensorFlow tutorial located here. However, this code is modified slightly to run on Python 3. The code is also commented very heavily to explain, line-by-line, what occurs in the deepdream demo.</p>
 <p>Here are some potential outputs.</p>
 <figure class="align-default" id="id1">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/06_deepdream_ex.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/06_deepdream_ex.png" />
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+<p><span class="caption-number">Fig. 24.4 </span><span class="caption-text">name: Deepdream outputs</span><a class="headerlink" href="#id1" title="Permalink to this image">#</a></p>
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 <p>Using TensorFlow for Deep Dream</p>
 <p>From: Alexander Mordvintsev
 <a class="reference external" href="https://www.tensorflow.org/tutorials/generative/deepdream">https://www.tensorflow.org/tutorials/generative/deepdream</a></p>
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+<h2><span class="section-number">24.3.1.2.2. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">25.3.1.2.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">24.3.1.2.3. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.tensorflow.org/">TensorFlow</a> for creating the open source project <a class="reference external" href="https://www.tensorflow.org/tutorials/generative/deepdream">DeepDream</a>. It inspires the majority of the content in this chapter.</p>
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         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">25.3.1.1. </span>Stylenet / Neural-Style</p>
+            <p class="prev-next-title"><span class="section-number">24.3.1.1. </span>Stylenet / Neural-Style</p>
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+    <link rel="next" title="24.3.1.2. Deepdream in TensorFlow" href="cnn-deepdream.html" />
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 <section class="tex2jax_ignore mathjax_ignore" id="stylenet-neural-style">
-<h1><span class="section-number">25.3.1.1. </span>Stylenet / Neural-Style<a class="headerlink" href="#stylenet-neural-style" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">24.3.1.1. </span>Stylenet / Neural-Style<a class="headerlink" href="#stylenet-neural-style" title="Permalink to this headline">#</a></h1>
 <p>The purpose of this script is to illustrate how to do stylenet in TensorFlow. We reference the following <a class="reference external" href="https://arxiv.org/abs/1508.06576">paper</a> for this algorithm.</p>
 <p>But there is some prerequisites,</p>
 <ul class="simple">
@@ -1710,30 +1710,30 @@ <h1><span class="section-number">25.3.1.1. </span>Stylenet / Neural-Style<a clas
 <figure class="align-default" id="id1">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/starry_night.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/starry_night.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 25.1 </span><span class="caption-text">name: Style image starry night</span><a class="headerlink" href="#id1" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 24.1 </span><span class="caption-text">name: Style image starry night</span><a class="headerlink" href="#id1" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The context image is below.</p>
 <figure class="align-default" id="id2">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/book_cover.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/book_cover.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 25.2 </span><span class="caption-text">name: Content image book cover</span><a class="headerlink" href="#id2" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 24.2 </span><span class="caption-text">name: Content image book cover</span><a class="headerlink" href="#id2" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The final result looks like</p>
 <figure class="align-default" id="id3">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/05_stylenet_ex.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/05_stylenet_ex.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 25.3 </span><span class="caption-text">name: stylenet final result</span><a class="headerlink" href="#id3" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 24.3 </span><span class="caption-text">name: stylenet final result</span><a class="headerlink" href="#id3" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>We use two images, an original image and a style image and try to make the original image in the style of the style image.</p>
 <p>Reference paper:<a class="reference external" href="https://arxiv.org/abs/1508.06576">https://arxiv.org/abs/1508.06576</a></p>
 <p>Need to download the model ‘imagenet-vgg-verydee-19.mat’ from: <a class="reference external" href="http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat">http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat</a></p>
 <section id="code">
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+<h2><span class="section-number">24.3.1.1.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h2>
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 <div class="cell docutils container">
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-<h3><span class="section-number">25.3.1.1.1.2. </span>Download data<a class="headerlink" href="#download-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">24.3.1.1.1.2. </span>Download data<a class="headerlink" href="#download-data" title="Permalink to this headline">#</a></h3>
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+<h3><span class="section-number">24.3.1.1.1.3. </span>Load data and set default arguments<a class="headerlink" href="#load-data-and-set-default-arguments" title="Permalink to this headline">#</a></h3>
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+<h3><span class="section-number">24.3.1.1.1.4. </span>Style Transfer Implementation using VGG-19 Network<a class="headerlink" href="#style-transfer-implementation-using-vgg-19-network" title="Permalink to this headline">#</a></h3>
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-<h2><span class="section-number">25.3.1.1.2. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">24.3.1.1.2. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">25.3.1.1.3. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">24.3.1.1.3. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>You can refer to those YouTube videos for further study:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://www.youtube.com/watch?v=YRhxdVk_sIs">Convolutional Neural Networks (CNNs) explained, by deeplizard</a></p></li>
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-<h2><span class="section-number">25.3.1.1.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">24.3.1.1.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/nfmcclure">Nick</a> for creating the open-source course <a class="reference external" href="https://github.com/nfmcclure/tensorflow_cookbook">tensorflow_cookbook</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
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+            <p class="prev-next-title"><span class="section-number">24. </span>Convolutional Neural Networks</p>
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+    <link rel="next" title="24.3.1.1. Stylenet / Neural-Style" href="cnn-vgg.html" />
+    <link rel="prev" title="23. Intro to Deep Learning" href="../dl-overview.html" />
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 <section class="tex2jax_ignore mathjax_ignore" id="convolutional-neural-networks">
-<h1><span class="section-number">25. </span>Convolutional Neural Networks<a class="headerlink" href="#convolutional-neural-networks" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">24. </span>Convolutional Neural Networks<a class="headerlink" href="#convolutional-neural-networks" title="Permalink to this headline">#</a></h1>
 <p>Convolutional Neural Networks (CNNs) are responsible for the latest major breakthroughs in image recognition in the past few years.</p>
 <p>In mathematics, a convolution is a function that is applied over the output of another function. In our case, we will consider applying a matrix multiplication (filter) across an image. See the below diagram for an example of how this may work.</p>
 <img src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/01_intro_cnn.png" width="90%" class="bg-white mb-1"><div class="cell docutils container">
@@ -1762,7 +1762,7 @@ <h1><span class="section-number">25. </span>Convolutional Neural Networks<a clas
 <img src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/01_intro_cnn2.png" width="90%" class="bg-white mb-1">
 <p>After the max pooling, there is generally an activation layer. One of the more common activation layers is the ReLU (Rectified Linear Unit).</p>
 <section id="mnist-handwritten-digits">
-<h2><span class="section-number">25.1. </span>MNIST handwritten digits<a class="headerlink" href="#mnist-handwritten-digits" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">24.1. </span>MNIST handwritten digits<a class="headerlink" href="#mnist-handwritten-digits" title="Permalink to this headline">#</a></h2>
 <p>Here we illustrate how to use a simple CNN with three convolutional units to predict the MNIST handwritten digits.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1822,7 +1822,7 @@ <h2><span class="section-number">25.1. </span>MNIST handwritten digits<a class="
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+<h3><span class="section-number">24.1.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
@@ -2111,13 +2111,13 @@ <h3><span class="section-number">25.1.1. </span>Code<a class="headerlink" href="
 </section>
 </section>
 <section id="cifar-10">
-<h2><span class="section-number">25.2. </span>CIFAR-10<a class="headerlink" href="#cifar-10" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">24.2. </span>CIFAR-10<a class="headerlink" href="#cifar-10" title="Permalink to this headline">#</a></h2>
 <p>Here we will build a convolutional neural network to predict the <code class="docutils literal notranslate"><span class="pre">CIFAR-10</span></code> data.</p>
 <p>The script provided will download and unzip the <code class="docutils literal notranslate"><span class="pre">CIFAR-10</span></code> data. Then it will start training a CNN from scratch. You should see similar output at the end of the following two graphs.</p>
 <img src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/03_cnn2_loss_acc.png" width="90%" class="bg-white mb-1">
 <p>Here we see the training loss (left) and the test batch accuracy (right).</p>
 <section id="id1">
-<h3><span class="section-number">25.2.1. </span>Code<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
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 <div class="cell docutils container">
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 </section>
 </section>
 <section id="how-to-fine-tune-current-cnn-architectures">
-<h2><span class="section-number">25.3. </span>How to fine-tune current CNN architectures?<a class="headerlink" href="#how-to-fine-tune-current-cnn-architectures" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">24.3. </span>How to fine-tune current CNN architectures?<a class="headerlink" href="#how-to-fine-tune-current-cnn-architectures" title="Permalink to this headline">#</a></h2>
 <p>The purpose of the script provided in this section is to download the CIFAR-10 data and sort it out in the proper folder structure for running it through the TensorFlow fine-tuning tutorial. The script should create the following folder structure.</p>
 <p>-train_dir
 |–airplane
@@ -2261,7 +2261,7 @@ <h2><span class="section-number">25.3. </span>How to fine-tune current CNN archi
 |–ship
 |–truck</p>
 <section id="id2">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># In this script, we download the CIFAR-10 images and</span>
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 <ul>
-<li class="toctree-l1"><a class="reference internal" href="cnn-vgg.html">25.3.1.1. Stylenet / Neural-Style</a></li>
-<li class="toctree-l1"><a class="reference internal" href="cnn-deepdream.html">25.3.1.2. Deepdream in TensorFlow</a></li>
+<li class="toctree-l1"><a class="reference internal" href="cnn-vgg.html">24.3.1.1. Stylenet / Neural-Style</a></li>
+<li class="toctree-l1"><a class="reference internal" href="cnn-deepdream.html">24.3.1.2. Deepdream in TensorFlow</a></li>
 </ul>
 </div>
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">25.4. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">24.4. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">25.5. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">24.5. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>You can refer to those YouTube videos for further study:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://www.youtube.com/watch?v=YRhxdVk_sIs">Convolutional Neural Networks (CNNs) explained, by deeplizard</a></p></li>
 <li><p><a class="reference external" href="https://www.youtube.com/watch?v=pj9-rr1wDhM">Convolutional Neural Networks Explained (CNN Visualized), by Futurology</a></p></li>
 </ul>
 <section id="research-trend">
-<h3><span class="section-number">25.5.1. </span>Research trend<a class="headerlink" href="#research-trend" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">24.5.1. </span>Research trend<a class="headerlink" href="#research-trend" title="Permalink to this headline">#</a></h3>
 <p>State of the Art Convolutional Neural Networks (CNNs) Explained | Deep Learning in 2020:</p>
 <div class="yt-container">
    <iframe src="https://www.youtube.com/embed/YUyec4eCEiY" allowfullscreen></iframe>
 </div></section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">25.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">24.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/nfmcclure">Nick</a> for creating the open-source course <a class="reference external" href="https://github.com/nfmcclure/tensorflow_cookbook">tensorflow_cookbook</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2444,13 +2444,13 @@ <h2><span class="section-number">25.6. </span>Acknowledgments<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">24. </span>Intro to Deep Learning</p>
+            <p class="prev-next-title"><span class="section-number">23. </span>Intro to Deep Learning</p>
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+        <p class="prev-next-title"><span class="section-number">24.3.1.1. </span>Stylenet / Neural-Style</p>
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+       33.1.3.1. Mean (Expectation)
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      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#cosine-schedule">
-       34.1.7.2. Cosine schedule
+       33.1.7.2. Cosine schedule
       </a>
      </li>
     </ul>
@@ -1881,49 +1881,49 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#code">
-   34.2. Code
+   33.2. Code
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#import-libraries">
-     34.2.1. Import Libraries
+     33.2.1. Import Libraries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-loading">
-     34.2.2. Data Loading
+     33.2.2. Data Loading
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-preprocessing">
-     34.2.3. Data Preprocessing
+     33.2.3. Data Preprocessing
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-augmentation">
-     34.2.4. Data Augmentation
+     33.2.4. Data Augmentation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data">
-     34.2.5. Data
+     33.2.5. Data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#linear-schedule-beta">
-     34.2.6. Linear schedule-beta
+     33.2.6. Linear schedule-beta
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   34.3. Your turn! 🚀
+   33.3. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   34.4. Acknowledgments
+   33.4. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1948,20 +1948,20 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="diffusion-model">
-<h1><span class="section-number">34. </span>Diffusion Model<a class="headerlink" href="#diffusion-model" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">33. </span>Diffusion Model<a class="headerlink" href="#diffusion-model" title="Permalink to this headline">#</a></h1>
 <section id="background">
-<h2><span class="section-number">34.1. </span>Background<a class="headerlink" href="#background" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">33.1. </span>Background<a class="headerlink" href="#background" title="Permalink to this headline">#</a></h2>
 <p>Before we learn the diffusion model, we have to know some background knowledge in statistics. Perhaps you already have a good mastery of them, let’s review them together.</p>
 <section id="expectation">
-<h3><span class="section-number">34.1.1. </span>Expectation<a class="headerlink" href="#expectation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.1.1. </span>Expectation<a class="headerlink" href="#expectation" title="Permalink to this headline">#</a></h3>
 <section id="definition">
-<h4><span class="section-number">34.1.1.1. </span>Definition<a class="headerlink" href="#definition" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.1.1. </span>Definition<a class="headerlink" href="#definition" title="Permalink to this headline">#</a></h4>
 <p>In probability theory, expectation (also called expected value, mean, average) is a generalization of the weighted average.</p>
 <p><span class="math notranslate nohighlight">\(E[X] = x_1 p_1 + x_2 p2 + ... +x_n p_n = \sum_{i=1}^n x_i p_1\)</span></p>
 <p>where <span class="math notranslate nohighlight">\(x_i\)</span> and <span class="math notranslate nohighlight">\(p_i\)</span> are i-th a possible outcome and its probability, respectively.</p>
 </section>
 <section id="properties">
-<h4><span class="section-number">34.1.1.2. </span>Properties<a class="headerlink" href="#properties" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.1.2. </span>Properties<a class="headerlink" href="#properties" title="Permalink to this headline">#</a></h4>
 <ul class="simple">
 <li><p><span class="math notranslate nohighlight">\(E[aX]=aE[X]\)</span> where a is a constant value.</p></li>
 <li><p><span class="math notranslate nohighlight">\(E[X+b]=E[X]+b\)</span> where b is a constant value.</p></li>
@@ -1970,14 +1970,14 @@ <h4><span class="section-number">34.1.1.2. </span>Properties<a class="headerlink
 </section>
 </section>
 <section id="variance">
-<h3><span class="section-number">34.1.2. </span>Variance<a class="headerlink" href="#variance" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.1.2. </span>Variance<a class="headerlink" href="#variance" title="Permalink to this headline">#</a></h3>
 <p>Variance is a measure of dispersion, meaning it is a measure of how far a set of numbers is spread out from their average value.</p>
 <section id="id1">
-<h4><span class="section-number">34.1.2.1. </span>Definition<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.2.1. </span>Definition<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
 <p>The variance of a random variable <span class="math notranslate nohighlight">\(X\)</span> is the expected value of the squared deviation from the expectation of <span class="math notranslate nohighlight">\(X\)</span>.</p>
 </section>
 <section id="id2">
-<h4><span class="section-number">34.1.2.2. </span>Properties<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.2.2. </span>Properties<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
 <ul class="simple">
 <li><p><span class="math notranslate nohighlight">\(Var[X]=E[X^2]−E[X]^2\)</span>.</p></li>
 <li><p><span class="math notranslate nohighlight">\(Var[aX]=a^2 Var[X]\)</span> where a is a constant value.</p></li>
@@ -1987,28 +1987,28 @@ <h4><span class="section-number">34.1.2.2. </span>Properties<a class="headerlink
 </section>
 </section>
 <section id="re-parameterization-trick">
-<h3><span class="section-number">34.1.3. </span>Re-parameterization trick<a class="headerlink" href="#re-parameterization-trick" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.1.3. </span>Re-parameterization trick<a class="headerlink" href="#re-parameterization-trick" title="Permalink to this headline">#</a></h3>
 <p>When we sample data from a probability distribution, backpropagation the gradient is not possible because it is a stochastic process. To make it trainable, the re-parameterization trick is useful.</p>
 <p>Let us assume that <span class="math notranslate nohighlight">\(z\)</span> is sampled data from a gaussian distribution which the mean is <span class="math notranslate nohighlight">\(\mu\)</span> and the variance is <span class="math notranslate nohighlight">\(\sigma^2\)</span>. Then, the mean and variance of <span class="math notranslate nohighlight">\(z\)</span> would be <span class="math notranslate nohighlight">\(\mu\)</span> and <span class="math notranslate nohighlight">\(\sigma^2\)</span>. Therefore, <span class="math notranslate nohighlight">\(z\)</span> can be written as follows.</p>
 <p><span class="math notranslate nohighlight">\(z = N(\mu, \sigma^2 I) = \mu + \sigma \odot \epsilon\)</span>, where <span class="math notranslate nohighlight">\(\epsilon \thicksim N(0, I)\)</span></p>
 <p>where <span class="math notranslate nohighlight">\(\odot\)</span> refers to element-wise product.</p>
 <p>The mean and variance of <span class="math notranslate nohighlight">\(z\)</span> correspond to <span class="math notranslate nohighlight">\(\mu\)</span> and <span class="math notranslate nohighlight">\(\sigma^2\)</span>, respectively.</p>
 <section id="mean-expectation">
-<h4><span class="section-number">34.1.3.1. </span>Mean (Expectation)<a class="headerlink" href="#mean-expectation" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.3.1. </span>Mean (Expectation)<a class="headerlink" href="#mean-expectation" title="Permalink to this headline">#</a></h4>
 <p><span class="math notranslate nohighlight">\(E[z] = E[\mu+\sigma \odot \epsilon] = E[\mu] + \sigma E[\epsilon] = \mu\)</span></p>
 <p>The expectation of <span class="math notranslate nohighlight">\(\epsilon\)</span> is 0 by definition.</p>
 </section>
 <section id="id3">
-<h4><span class="section-number">34.1.3.2. </span>Variance<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.3.2. </span>Variance<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h4>
 <p><span class="math notranslate nohighlight">\(Var[z] = Var[\mu + \sigma \odot \epsilon] = Var[\sigma \odot \epsilon] = \sigma^2 Var[\epsilon] = \sigma^2\)</span></p>
 <p>The variance of \epsilon is 1 by definition.</p>
 </section>
 </section>
 <section id="kl-divergence">
-<h3><span class="section-number">34.1.4. </span>KL Divergence<a class="headerlink" href="#kl-divergence" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.1.4. </span>KL Divergence<a class="headerlink" href="#kl-divergence" title="Permalink to this headline">#</a></h3>
 <p>In mathematical statistics, the Kullback–Leibler divergence (relative entropy), is a type of statistical distance.</p>
 <section id="id4">
-<h4><span class="section-number">34.1.4.1. </span>Definition<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.4.1. </span>Definition<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h4>
 <ol class="simple">
 <li><p>Discrete probability distribution</p></li>
 </ol>
@@ -2019,7 +2019,7 @@ <h4><span class="section-number">34.1.4.1. </span>Definition<a class="headerlink
 <p><span class="math notranslate nohighlight">\(D_{KL} (P||Q) = \int _{− \infty}^{\infty} p(x) log \frac{p(x)}{q(x)}dx\)</span>, where <span class="math notranslate nohighlight">\(p\)</span> and <span class="math notranslate nohighlight">\(q\)</span> denote the probability densities of <span class="math notranslate nohighlight">\(P\)</span> and <span class="math notranslate nohighlight">\(Q\)</span>.</p>
 </section>
 <section id="jensens-inequality">
-<h4><span class="section-number">34.1.4.2. </span>Jensen’s inequality<a class="headerlink" href="#jensens-inequality" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.4.2. </span>Jensen’s inequality<a class="headerlink" href="#jensens-inequality" title="Permalink to this headline">#</a></h4>
 <p>In mathematics, Jensen’s inequality relates the value of a convex (or concave) function of an integral to the integral of the function.</p>
 <ul class="simple">
 <li><p>Convex function: <span class="math notranslate nohighlight">\(f(E[X]) \leqq E[f(X)]\)</span>;</p></li>
@@ -2027,7 +2027,7 @@ <h4><span class="section-number">34.1.4.2. </span>Jensen’s inequality<a class=
 </ul>
 </section>
 <section id="properties-of-kl-divergence">
-<h4><span class="section-number">34.1.4.3. </span>Properties of KL Divergence<a class="headerlink" href="#properties-of-kl-divergence" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.4.3. </span>Properties of KL Divergence<a class="headerlink" href="#properties-of-kl-divergence" title="Permalink to this headline">#</a></h4>
 <ul class="simple">
 <li><p>KL Divergence is always non-negative;</p></li>
 <li><p>The cross-entropy is always larger than the entropy;</p></li>
@@ -2036,17 +2036,17 @@ <h4><span class="section-number">34.1.4.3. </span>Properties of KL Divergence<a
 </section>
 </section>
 <section id="evidence-lower-bound-elbo">
-<h3><span class="section-number">34.1.5. </span>Evidence lower bound (ELBO)<a class="headerlink" href="#evidence-lower-bound-elbo" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.1.5. </span>Evidence lower bound (ELBO)<a class="headerlink" href="#evidence-lower-bound-elbo" title="Permalink to this headline">#</a></h3>
 <p>In variational Bayesian methods, the evidence lower bound (often abbreviated ELBO) is a useful lower bound on the log-likelihood of some observed data.</p>
 <section id="id5">
-<h4><span class="section-number">34.1.5.1. </span>Definition<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.5.1. </span>Definition<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h4>
 <p><span class="math notranslate nohighlight">\(ELBO := E_{z∼q_{\phi}}[log \frac{pθ(x,z)}{q_{\phi}(z)}]\)</span></p>
 <p>where <span class="math notranslate nohighlight">\(p_{\theta}(x, z)\)</span> is joint distribution of <span class="math notranslate nohighlight">\(x\)</span> and <span class="math notranslate nohighlight">\(z\)</span>. <span class="math notranslate nohighlight">\(\theta\)</span> and <span class="math notranslate nohighlight">\(\phi\)</span> are parameters.</p>
 <p>ELBO is used to obtain the lower bound of the evidence (or log evidence). The evidence is the likelihood function evaluated at a fixed <span class="math notranslate nohighlight">\(\theta\)</span>.</p>
 <p><span class="math notranslate nohighlight">\(evidence := log p_{\theta}(x)\)</span></p>
 </section>
 <section id="id6">
-<h4><span class="section-number">34.1.5.2. </span>Properties<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.5.2. </span>Properties<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h4>
 <ul class="simple">
 <li><p>The evidence is always larger than ELBO;</p></li>
 <li><p>KL Divergence between <span class="math notranslate nohighlight">\(p_{\theta}(z|x)\)</span> and <span class="math notranslate nohighlight">\(q_{\phi}(z)\)</span> equals <span class="math notranslate nohighlight">\(evidence−ELBO\)</span>.</p></li>
@@ -2054,23 +2054,23 @@ <h4><span class="section-number">34.1.5.2. </span>Properties<a class="headerlink
 </section>
 </section>
 <section id="forward-and-reverse-process">
-<h3><span class="section-number">34.1.6. </span>Forward and Reverse process<a class="headerlink" href="#forward-and-reverse-process" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.1.6. </span>Forward and Reverse process<a class="headerlink" href="#forward-and-reverse-process" title="Permalink to this headline">#</a></h3>
 <p>The diffusion models make data into a gaussian noise (latent vector) and restore it again. The former is called the forward process, and the latter is called the reverse process.</p>
 <p>In the forward process, we add a gaussian noise to the data step by step (usually hundreds of steps). The transform of an individual step is defined as follows.</p>
 <p><span class="math notranslate nohighlight">\(x_t = q(x_t|x_{t−1}) = N(x_t, \sqrt{1−\beta_t}x_{t−1}, \beta_t I)\)</span></p>
 <p>In the reverse process, we restore image from a gaussian noise (a latent vector). If <span class="math notranslate nohighlight">\(\beta_t\)</span> is small enough, the reverse <span class="math notranslate nohighlight">\(q(x_{t-1}|x_t)\)</span> will also be gaussian. It is noteworthy that the reverse process is tractable when conditioned on <span class="math notranslate nohighlight">\(x_0\)</span>.</p>
 <img src="../../images/deep-learning/diffusion-model/01_structure.png" width="90%" class="bg-white mb-1"></section>
 <section id="noise-schedule">
-<h3><span class="section-number">34.1.7. </span>Noise schedule<a class="headerlink" href="#noise-schedule" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.1.7. </span>Noise schedule<a class="headerlink" href="#noise-schedule" title="Permalink to this headline">#</a></h3>
 <p>In diffusion models, the noise schedule define the methodology for iteratively adding noise to an image or for updating a sample based on model outputs. I’ll introduce two type of schedules which are linear schedule and cosine schedule. The linear and cosine schedule were introduced by <a class="reference external" href="https://arxiv.org/abs/2006.11239">Denoising Diffusion Probabilistic Models</a> and <a class="reference external" href="https://arxiv.org/abs/2006.11239">Improved Denoising Diffusion Probabilistic Models</a>, respectively.</p>
 <section id="linear-schedule">
-<h4><span class="section-number">34.1.7.1. </span>Linear schedule<a class="headerlink" href="#linear-schedule" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.7.1. </span>Linear schedule<a class="headerlink" href="#linear-schedule" title="Permalink to this headline">#</a></h4>
 <p>In the linear schedule, <span class="math notranslate nohighlight">\(\beta_t\)</span> changes linearly.</p>
 <img src="../../images/deep-learning/diffusion-model/02_linear.png" width="90%" class="bg-white mb-1">
 <p>Illustration of linear schedule</p>
 </section>
 <section id="cosine-schedule">
-<h4><span class="section-number">34.1.7.2. </span>Cosine schedule<a class="headerlink" href="#cosine-schedule" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">33.1.7.2. </span>Cosine schedule<a class="headerlink" href="#cosine-schedule" title="Permalink to this headline">#</a></h4>
 <p>Alex Nichol and Prafulla Dhariwal proposed the cosine schedule to prevent an image from turning into noise too quickly. They construct a different noise schedule in terms of <span class="math notranslate nohighlight">\(\overline{\alpha_t}\)</span>.</p>
 <p><span class="math notranslate nohighlight">\(\overline{\alpha_t} = \frac{f(t)}{f(0)}\)</span>, <span class="math notranslate nohighlight">\(f(t) = cos(\frac{\frac{t}{T} + s}{1+s} \frac{\pi}{2})^2\)</span>. By definition, the <span class="math notranslate nohighlight">\(\beta_t\)</span> equals <span class="math notranslate nohighlight">\(1 - \frac{\overline{\alpha_t}}{\overline{\alpha_{t-1}}}\)</span>.</p>
 <img src="../../images/deep-learning/diffusion-model/03_cosine.png" width="90%" class="bg-white mb-1">
@@ -2079,9 +2079,9 @@ <h4><span class="section-number">34.1.7.2. </span>Cosine schedule<a class="heade
 </section>
 </section>
 <section id="code">
-<h2><span class="section-number">34.2. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">33.2. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h2>
 <section id="import-libraries">
-<h3><span class="section-number">34.2.1. </span>Import Libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.2.1. </span>Import Libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
@@ -2118,7 +2118,7 @@ <h3><span class="section-number">34.2.1. </span>Import Libraries<a class="header
 </div>
 </section>
 <section id="data-loading">
-<h3><span class="section-number">34.2.2. </span>Data Loading<a class="headerlink" href="#data-loading" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.2.2. </span>Data Loading<a class="headerlink" href="#data-loading" title="Permalink to this headline">#</a></h3>
 <p>The dataset can be downloaded from <a class="reference external" href="https://www.kaggle.com/datasets/jessicali9530/celeba-dataset">here</a>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2130,7 +2130,7 @@ <h3><span class="section-number">34.2.2. </span>Data Loading<a class="headerlink
 </div>
 </section>
 <section id="data-preprocessing">
-<h3><span class="section-number">34.2.3. </span>Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.2.3. </span>Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">preprocess</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
@@ -2141,7 +2141,7 @@ <h3><span class="section-number">34.2.3. </span>Data Preprocessing<a class="head
 </div>
 </section>
 <section id="data-augmentation">
-<h3><span class="section-number">34.2.4. </span>Data Augmentation<a class="headerlink" href="#data-augmentation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.2.4. </span>Data Augmentation<a class="headerlink" href="#data-augmentation" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">augmentation</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
@@ -2152,7 +2152,7 @@ <h3><span class="section-number">34.2.4. </span>Data Augmentation<a class="heade
 </div>
 </section>
 <section id="data">
-<h3><span class="section-number">34.2.5. </span>Data<a class="headerlink" href="#data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.2.5. </span>Data<a class="headerlink" href="#data" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_dataset</span> <span class="o">=</span> <span class="p">(</span>
@@ -2169,7 +2169,7 @@ <h3><span class="section-number">34.2.5. </span>Data<a class="headerlink" href="
 </div>
 </section>
 <section id="linear-schedule-beta">
-<h3><span class="section-number">34.2.6. </span>Linear schedule-beta<a class="headerlink" href="#linear-schedule-beta" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">33.2.6. </span>Linear schedule-beta<a class="headerlink" href="#linear-schedule-beta" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">linear_beta_schedule</span><span class="p">(</span><span class="n">timesteps</span><span class="p">):</span>
@@ -2438,11 +2438,11 @@ <h3><span class="section-number">34.2.6. </span>Linear schedule-beta<a class="he
 </section>
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 <section id="your-turn">
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+<h2><span class="section-number">33.3. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">34.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">33.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/phykn">Kwangnam Yu</a> for creating the open-source project <a class="reference external" href="https://github.com/phykn/diffusion_models_tutorial">diffusion_models_tutorial</a> and <a class="reference external" href="https://www.kaggle.com/">kaggle</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/folefac/denoising-diffusion-models-with-tensorflow#3.-The-Celeb-A-Dataset-%F0%9F%92%BE">Denoising Diffusion Models with TensorFlow</a>. They inspire the majority of the content in this chapter.</p>
 </section>
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-     24.4.3. Recurrent Neural Networks (RNNs)
+     23.4.3. Recurrent Neural Networks (RNNs)
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@@ -1708,12 +1708,12 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="intro-to-deep-learning">
-<h1><span class="section-number">24. </span>Intro to Deep Learning<a class="headerlink" href="#intro-to-deep-learning" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">23. </span>Intro to Deep Learning<a class="headerlink" href="#intro-to-deep-learning" title="Permalink to this headline">#</a></h1>
 <p>Deep Learning is the study of Artificial Neural Networks(ANNs). It is a subset of Machine Learning (ML), ML being a subset of Artificial Intelligence.</p>
 <p>Inspired by the human brain’s biological neuron, ANNs has become the building block of modern intelligent systems, from our smartphones that can accurately identify our faces and voices, streaming services (like YouTube and Netflix) that know exactly what we want to watch, cars that can see the visual world and drive themselves, and not to mention AlphaGo that won world chess competition.</p>
 <p>This notebook will introduce a high level overview of Deep Learning.</p>
 <section id="why-deep-learning">
-<h2><span class="section-number">24.1. </span>Why Deep Learning<a class="headerlink" href="#why-deep-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">23.1. </span>Why Deep Learning<a class="headerlink" href="#why-deep-learning" title="Permalink to this headline">#</a></h2>
 <p>Deep Learning is used to extract the patterns in (a huge amount of) data. Its potential has been seen in complex problems such as vision oriented and natural language.</p>
 <p>Let’s take an example to develop more intuitive understanding of why deep learning has became so popular. Let’s say that as an Engineer, you are asked by your city state to build a program that can recognize car and truck moving in a given road. The program can later be deployed in a less loads-road to warn trucks that pass there that the road was not designed for these types of vehicles.</p>
 <p>With normal programming, you would have to provide and to write all instructions (codes) of that particular program. The chance (close to reality) is that it would be hard, mostly because there are different types of cars and trucks and each can be unique in its ways, which can make it hard to write the rules forming such program.</p>
@@ -1721,7 +1721,7 @@ <h2><span class="section-number">24.1. </span>Why Deep Learning<a class="headerl
 <p>With the advent of the computation power, both paid and free (Google Colab &amp; Kaggle), open source datasets, and intelligent frameworks such as TensorFlow and PyTorch, it has become very much possible to do deep learning, even at personal level. This was not possible a decade ago.</p>
 </section>
 <section id="a-single-hidden-layer-neural-network">
-<h2><span class="section-number">24.2. </span>A Single Hidden Layer Neural Network<a class="headerlink" href="#a-single-hidden-layer-neural-network" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">23.2. </span>A Single Hidden Layer Neural Network<a class="headerlink" href="#a-single-hidden-layer-neural-network" title="Permalink to this headline">#</a></h2>
 <p><img src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/dl-overview/Neuralnetwork.png" alt="Neuralnetwork.png"><br> Image: A Single hidden layer neural network, Wikimedia</p>
 <p>The image above represent a single hidden layer neural network. Usually, any typical neural network has 3 main layers:</p>
 <ul class="simple">
@@ -1733,7 +1733,7 @@ <h2><span class="section-number">24.2. </span>A Single Hidden Layer Neural Netwo
 <p>With the exception of the input layer, you will find something called activation function being applied to hidden and output layers. What is an activation function?</p>
 </section>
 <section id="common-activation-functions">
-<h2><span class="section-number">24.3. </span>Common Activation Functions<a class="headerlink" href="#common-activation-functions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">23.3. </span>Common Activation Functions<a class="headerlink" href="#common-activation-functions" title="Permalink to this headline">#</a></h2>
 <p>Activation functions are used to introduce nonlinearities in the network. I like to say that they dictate the output format of a given layer.</p>
 <p>Or simply put, these type of mathematical functions are used to make a decision of the layer’s output. In later notebooks, this will certainly make sense.</p>
 <p>Here are the 3 most used activation functions:</p>
@@ -1744,10 +1744,10 @@ <h2><span class="section-number">24.3. </span>Common Activation Functions<a clas
 </ul>
 </section>
 <section id="types-of-deep-learning-architectures">
-<h2><span class="section-number">24.4. </span>Types of Deep Learning Architectures<a class="headerlink" href="#types-of-deep-learning-architectures" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">23.4. </span>Types of Deep Learning Architectures<a class="headerlink" href="#types-of-deep-learning-architectures" title="Permalink to this headline">#</a></h2>
 <p>There are 4 main types of deep learning architectures. This is going to be a high level since they will be covered in the next notebooks.</p>
 <section id="densely-connected-networks">
-<h3><span class="section-number">24.4.1. </span>Densely Connected Networks<a class="headerlink" href="#densely-connected-networks" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">23.4.1. </span>Densely Connected Networks<a class="headerlink" href="#densely-connected-networks" title="Permalink to this headline">#</a></h3>
 <p>Densely connected networks are made of stacks of layers from the input to the output.</p>
 <p>The units (or neurons) of any layer in that type of network are connected to all other units of the next layer. This is why they are also called fully connected layers.</p>
 <p>Densely connected layers are generally used for tabular data. Tabular data are these kinds of data that are in a tabular fashion. An example of tabular data is customer records: you have a column of names, roles, data joined, etc…</p>
@@ -1757,7 +1757,7 @@ <h3><span class="section-number">24.4.1. </span>Densely Connected Networks<a cla
 <p>Dense networks are mostly used in simple tasks. When it comes to complex things like image recognition or text processing, they fail because they can’t hold spatial features (in image) or can’t learn a sequence in text for instance. But above all, the more deep they become, they tend to have many parameters which can results in having a big network/complex computations.</p>
 </section>
 <section id="convolutional-neural-networks">
-<h3><span class="section-number">24.4.2. </span>Convolutional Neural Networks<a class="headerlink" href="#convolutional-neural-networks" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">23.4.2. </span>Convolutional Neural Networks<a class="headerlink" href="#convolutional-neural-networks" title="Permalink to this headline">#</a></h3>
 <p>CNN’s, a.k.a. Convents are widely known as the go-to neural network architectures for computer vision tasks like image recognition and object detection, but they can also be used in other tasks such as texts and time series processing.</p>
 <p>Convents are typically made of convolution, pooling layers, and fully connected layers at the end. Convolutional layers are used to extract the spatial features in images, and pooling layers are used to compress the resulted feature maps from convolution layers. And fully connected layers for classification purposes.</p>
 <p><img alt="on-IOL-iG.png-2.webp" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/dl-overview/KF4gj3zPD.webp" /></p>
@@ -1765,7 +1765,7 @@ <h3><span class="section-number">24.4.2. </span>Convolutional Neural Networks<a
 <p>Conv3D is used in videos and volumetric images such as CT scans.</p>
 </section>
 <section id="recurrent-neural-networks-rnns">
-<h3><span class="section-number">24.4.3. </span>Recurrent Neural Networks (RNNs)<a class="headerlink" href="#recurrent-neural-networks-rnns" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">23.4.3. </span>Recurrent Neural Networks (RNNs)<a class="headerlink" href="#recurrent-neural-networks-rnns" title="Permalink to this headline">#</a></h3>
 <p>The standard feedforward network (or call them densely connected network) maps the input to output. RNNs go beyond that. They can maintain the recurrence of data at each time step.</p>
 <p><img alt="TF RNN.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/dl-overview/7kzTaiwxp.png" /></p>
 <p>Due to their ability to preserve the recurrence of information, RNNs are commonly used in sequential data such as texts and time series.</p>
@@ -1783,7 +1783,7 @@ <h3><span class="section-number">24.4.3. </span>Recurrent Neural Networks (RNNs)
 <p>The ability of LSTMs to handle long-term sequences makes it a suitable neural network architecture for various sequential tasks such as text classification, sentiment analysis, speech recognition, image caption generation, machine translation.</p>
 </section>
 <section id="transformers">
-<h3><span class="section-number">24.4.4. </span>Transformers<a class="headerlink" href="#transformers" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">23.4.4. </span>Transformers<a class="headerlink" href="#transformers" title="Permalink to this headline">#</a></h3>
 <p>Although recurrent neural networks are still used for sequential modeling, they have short-term memory problems when used for long sequences, and they are computationally expensive. The RNN’s inability to handle long sequences and expensiveness are the two most motivations of transformers.</p>
 <p>Transformers are one of the latest groundbreaking researches in the natural language community. They are sorely based on the attention mechanisms that learn the relationships between words of the sentence and pays attention to the relevant words.</p>
 <p>One of the most notable things about transformers is that they don’t use any recurrent or convolutional layers. It’s just only attention mechanisms and other standard layers like embedding layer, dense layer, and normalization layers.</p>
@@ -1796,7 +1796,7 @@ <h3><span class="section-number">24.4.4. </span>Transformers<a class="headerlink
 </section>
 </section>
 <section id="challenges-in-training-neural-networks">
-<h2><span class="section-number">24.5. </span>Challenges in Training Neural Networks<a class="headerlink" href="#challenges-in-training-neural-networks" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">23.5. </span>Challenges in Training Neural Networks<a class="headerlink" href="#challenges-in-training-neural-networks" title="Permalink to this headline">#</a></h2>
 <p>Training neural networks is a big challenge and this still an area of research. For example, how do you choose a right architecture? Although the type of data can tell the most architecture that should come into picture at first, it is not a trivial question. Take an example, we know CNN as image first algorithm, bit it has been used in some natural language processing and time series tasks, and it can perform well.</p>
 <p>Also, deep learning models have many hyperparameters. Hyperparameters include things like number of layers, number of neurons, learning rate, activation functions, and optimizers (optimizers are used to reduce loss during training).</p>
 <p>All these choices to be make it hard to train neural networks. Although there has been some techniques that can reduce number of chocs to be made, such as transfer learning which motivates reusing the pretrained models, training this type of models is not an easy thing.</p>
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+     30.2.1. Reward
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-     31.2.2. Task-dependent return definitions
+     30.2.2. Task-dependent return definitions
     </a>
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      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#episodic-tasks">
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+       30.2.2.1. Episodic tasks
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+       30.2.2.2. Continuing tasks
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+     30.3.1. RL-solution by policy
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     </a>
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+   30.5. Your turn! 🚀
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   </a>
  </li>
 </ul>
@@ -1876,15 +1876,15 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="deep-q-learning">
-<h1><span class="section-number">31. </span>Deep Q-learning<a class="headerlink" href="#deep-q-learning" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">30. </span>Deep Q-learning<a class="headerlink" href="#deep-q-learning" title="Permalink to this headline">#</a></h1>
 <section id="overview">
-<h2><span class="section-number">31.1. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">30.1. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h2>
 <p>Usually, we regard Deep Q Network as DQN, and it can also be called Reinforcement Learning (RL) whose aim is to achieve the desired behavior of an agent that learns from its mistakes and improves its performance.</p>
 <p>RL is a type of machine learning that allows us to create AI agents that learn from the environment by interacting with it to maximize its cumulative reward. Here is an image showing the basic RL operation principle:</p>
 <figure class="align-default" id="pipline-of-dqn">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/02_pipline.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/02_pipline.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/02_pipline.png" style="width: 100%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 31.1 </span><span class="caption-text">Pipline of DQN</span><a class="headerlink" href="#pipline-of-dqn" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 30.1 </span><span class="caption-text">Pipline of DQN</span><a class="headerlink" href="#pipline-of-dqn" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>From this image, we can see that at each step <span class="math notranslate nohighlight">\(k\)</span>, the agent picks an action <span class="math notranslate nohighlight">\(u_k\)</span>, receives an observation <span class="math notranslate nohighlight">\(y_k\)</span> and receives a reward <span class="math notranslate nohighlight">\(r_k\)</span>, and the environment receives an action <span class="math notranslate nohighlight">\(u_k\)</span>, emits an observation <span class="math notranslate nohighlight">\(y_{k+1}\)</span> and emits a reward <span class="math notranslate nohighlight">\(r_{k+1}\)</span>. Later, the time increments k ← k + 1. A one step time delay is assumed between executing the action and receiving the observation as well as reward. We assume that the resulting time interval <span class="math notranslate nohighlight">\(∆t = t_k − t_{k+1}\)</span> is constant.</p>
@@ -1901,9 +1901,9 @@ <h2><span class="section-number">31.1. </span>Overview<a class="headerlink" href
 </div>
 </section>
 <section id="basic-terminology">
-<h2><span class="section-number">31.2. </span>Basic terminology<a class="headerlink" href="#basic-terminology" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">30.2. </span>Basic terminology<a class="headerlink" href="#basic-terminology" title="Permalink to this headline">#</a></h2>
 <section id="reward">
-<h3><span class="section-number">31.2.1. </span>Reward<a class="headerlink" href="#reward" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.2.1. </span>Reward<a class="headerlink" href="#reward" title="Permalink to this headline">#</a></h3>
 <p>A reward is a scalar random variable <span class="math notranslate nohighlight">\(R_k\)</span> with realizations <span class="math notranslate nohighlight">\(r_k\)</span>:</p>
 <ul class="simple">
 <li><p>Often it is considered a real-number <span class="math notranslate nohighlight">\(r_k \in \mathbb{R}\)</span> or an integer <span class="math notranslate nohighlight">\(r_k \in \mathbb{Z}\)</span>.</p></li>
@@ -1932,13 +1932,13 @@ <h3><span class="section-number">31.2.1. </span>Reward<a class="headerlink" href
 <p>Besides, the RL agent’s learning process is heavily linked with the reward distribution over time. Designing expedient rewards functions is therefore crucially important for successfully applying RL. And often there is no predefined way on how to design the “best reward function”.</p>
 </section>
 <section id="task-dependent-return-definitions">
-<h3><span class="section-number">31.2.2. </span>Task-dependent return definitions<a class="headerlink" href="#task-dependent-return-definitions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.2.2. </span>Task-dependent return definitions<a class="headerlink" href="#task-dependent-return-definitions" title="Permalink to this headline">#</a></h3>
 <section id="episodic-tasks">
-<h4><span class="section-number">31.2.2.1. </span>Episodic tasks<a class="headerlink" href="#episodic-tasks" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">30.2.2.1. </span>Episodic tasks<a class="headerlink" href="#episodic-tasks" title="Permalink to this headline">#</a></h4>
 <p>Episodic tasks can naturally break into subsequences (finite horizon), for examples: most games, maze,… And the return becomes a finite sum: <span class="math notranslate nohighlight">\(g_k = r_{k+1} + r_{k+2} + ... + r_{N}\)</span>. Episodes end at their terminal step <span class="math notranslate nohighlight">\(k = N\)</span>.</p>
 </section>
 <section id="continuing-tasks">
-<h4><span class="section-number">31.2.2.2. </span>Continuing tasks<a class="headerlink" href="#continuing-tasks" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">30.2.2.2. </span>Continuing tasks<a class="headerlink" href="#continuing-tasks" title="Permalink to this headline">#</a></h4>
 <p>Continuing tasks lack a natural end (infinite horizon), for example: process control task, and the return should be discounted to prevent infinite numbers: <span class="math notranslate nohighlight">\(g_k = r_{k+1} + \gamma r_{k+2} + \gamma^2 r_{k+3} + ... = \sum_{i=1}^{\infty} \gamma^{i} r_{k+i+1}\)</span>. Here, <span class="math notranslate nohighlight">\(\gamma ∈ {\mathbb{R}|0 ≤ \gamma ≤ 1}\)</span> is the discount rate.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1952,9 +1952,9 @@ <h4><span class="section-number">31.2.2.2. </span>Continuing tasks<a class="head
 </section>
 </section>
 <section id="state">
-<h3><span class="section-number">31.2.3. </span>State<a class="headerlink" href="#state" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.2.3. </span>State<a class="headerlink" href="#state" title="Permalink to this headline">#</a></h3>
 <section id="environment-state">
-<h4><span class="section-number">31.2.3.1. </span>Environment state<a class="headerlink" href="#environment-state" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">30.2.3.1. </span>Environment state<a class="headerlink" href="#environment-state" title="Permalink to this headline">#</a></h4>
 <p>Random variable <span class="math notranslate nohighlight">\(X_k^{e}\)</span> with realizations <span class="math notranslate nohighlight">\(x_k^{e}\)</span>:</p>
 <ul class="simple">
 <li><p>Internal status representation of the environment, e.g.physical states (car velocity or motor current), game states (current chess board situation). financial states (stock market status).</p></li>
@@ -1963,7 +1963,7 @@ <h4><span class="section-number">31.2.3.1. </span>Environment state<a class="hea
 </ul>
 </section>
 <section id="agent-state">
-<h4><span class="section-number">31.2.3.2. </span>Agent state<a class="headerlink" href="#agent-state" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">30.2.3.2. </span>Agent state<a class="headerlink" href="#agent-state" title="Permalink to this headline">#</a></h4>
 <p>Random variable <span class="math notranslate nohighlight">\(X_k^{a}\)</span> with realizations <span class="math notranslate nohighlight">\(x_k^{a}\)</span>:</p>
 <ul class="simple">
 <li><p>Internal status representation of the agent.</p></li>
@@ -1975,7 +1975,7 @@ <h4><span class="section-number">31.2.3.2. </span>Agent state<a class="headerlin
 </section>
 </section>
 <section id="action">
-<h3><span class="section-number">31.2.4. </span>Action<a class="headerlink" href="#action" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.2.4. </span>Action<a class="headerlink" href="#action" title="Permalink to this headline">#</a></h3>
 <p>An action is the agent’s degree of freedom in order to maximize its reward. The major distinctions are:</p>
 <ul class="simple">
 <li><p>Finite action set (FAS): <span class="math notranslate nohighlight">\(u_k ∈ {u_{k,1},u_{k,2}, ...} ∈ \mathbb{R}_m\)</span>.</p></li>
@@ -1989,7 +1989,7 @@ <h3><span class="section-number">31.2.4. </span>Action<a class="headerlink" href
 </div>
 </section>
 <section id="policy">
-<h3><span class="section-number">31.2.5. </span>Policy<a class="headerlink" href="#policy" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.2.5. </span>Policy<a class="headerlink" href="#policy" title="Permalink to this headline">#</a></h3>
 <p>A policy <span class="math notranslate nohighlight">\(\pi\)</span> is the agent’s internal strategy on picking actions.</p>
 <ul class="simple">
 <li><p>Deterministic policies: maps state and action directly: <span class="math notranslate nohighlight">\(u_k = \pi (x_k)\)</span>.</p></li>
@@ -1997,7 +1997,7 @@ <h3><span class="section-number">31.2.5. </span>Policy<a class="headerlink" href
 <li><p>RL is all about changing <span class="math notranslate nohighlight">\(\pi\)</span> over time in order to maximize the expected return.</p></li>
 </ul>
 <section id="example">
-<h4><span class="section-number">31.2.5.1. </span>Example<a class="headerlink" href="#example" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">30.2.5.1. </span>Example<a class="headerlink" href="#example" title="Permalink to this headline">#</a></h4>
 <p>Here is a deterministic policy example: find optimal gains <span class="math notranslate nohighlight">\({K_p, K_i, K_d}\)</span> given the reward <span class="math notranslate nohighlight">\(r_k = −e^2_k\)</span></p>
 <ul class="simple">
 <li><p>Agent’s behavior is explicitly determined by <span class="math notranslate nohighlight">\({K_p, K_i, K_d}\)</span>.</p></li>
@@ -2007,13 +2007,13 @@ <h4><span class="section-number">31.2.5.1. </span>Example<a class="headerlink" h
 <figure class="align-default" id="classical-pid-control-loop-with-scalar-quantities">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/03_policy_example.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/03_policy_example.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/03_policy_example.png" style="width: 100%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 31.2 </span><span class="caption-text">Classical PID control loop with scalar quantities</span><a class="headerlink" href="#classical-pid-control-loop-with-scalar-quantities" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 30.2 </span><span class="caption-text">Classical PID control loop with scalar quantities</span><a class="headerlink" href="#classical-pid-control-loop-with-scalar-quantities" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 </section>
 <section id="value-functions">
-<h3><span class="section-number">31.2.6. </span>Value functions<a class="headerlink" href="#value-functions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.2.6. </span>Value functions<a class="headerlink" href="#value-functions" title="Permalink to this headline">#</a></h3>
 <p>The state-value function is the expected return being in state <span class="math notranslate nohighlight">\(x_k\)</span> following a policy <span class="math notranslate nohighlight">\(\pi:v_{\pi}(x_k)\)</span>.</p>
 <p>Assuming an MDP problem structure the state-value function is <span class="math notranslate nohighlight">\(v_{\pi}(x_k) = \mathbb{E}_{\pi} [G_k | X_k = x_k] = \mathbb{E}_{\pi}[\sum_{i=0}^{\infty} \gamma^i R_{k+i+1} | x_k]\)</span>.</p>
 <p>The action-value function is the expected return being in state <span class="math notranslate nohighlight">\(x_k\)</span> taken an action <span class="math notranslate nohighlight">\(u_k\)</span> and, thereafter, following a policy <span class="math notranslate nohighlight">\(\pi: q_{\pi}(x_k,u_k)\)</span>.</p>
@@ -2021,12 +2021,12 @@ <h3><span class="section-number">31.2.6. </span>Value functions<a class="headerl
 <p>A key task in RL is to estimate <span class="math notranslate nohighlight">\(v_{\pi}(x_k)\)</span> and <span class="math notranslate nohighlight">\(q_{\pi}(x_k,u_k)\)</span> based on sampled data.</p>
 </section>
 <section id="model">
-<h3><span class="section-number">31.2.7. </span>Model<a class="headerlink" href="#model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.2.7. </span>Model<a class="headerlink" href="#model" title="Permalink to this headline">#</a></h3>
 <p>A model predicts what will happen inside an environment.</p>
 <p>That could be a state model <span class="math notranslate nohighlight">\(\mathcal{P}\)</span>: <span class="math notranslate nohighlight">\(\mathcal{P} = \mathbb{P}[X_{k+1}=x_{k+1}|X_k=x_k, U_k=u_k]\)</span>. Or a reward model <span class="math notranslate nohighlight">\(\mathcal{R}\)</span>: <span class="math notranslate nohighlight">\(\mathcal{R} = \mathbb{P}[R_{k+1}=r_{k+1}|X_k=x_k, U_k=u_k]\)</span>. In general, those models could be stochastic (as denoted above) but in some problems relax to a deterministic form. Using data in order to fit a model is a learning problem of its own and often called system identification.</p>
 </section>
 <section id="exploration-and-exploitation">
-<h3><span class="section-number">31.2.8. </span>Exploration and exploitation<a class="headerlink" href="#exploration-and-exploitation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.2.8. </span>Exploration and exploitation<a class="headerlink" href="#exploration-and-exploitation" title="Permalink to this headline">#</a></h3>
 <p>In RL the environment is initially unknown. How to act optimal?</p>
 <ul class="simple">
 <li><p>Exploration: find out more about the environment.</p></li>
@@ -2039,7 +2039,7 @@ <h3><span class="section-number">31.2.8. </span>Exploration and exploitation<a c
 </section>
 </section>
 <section id="main-algorithms">
-<h2><span class="section-number">31.3. </span>Main algorithms<a class="headerlink" href="#main-algorithms" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">30.3. </span>Main algorithms<a class="headerlink" href="#main-algorithms" title="Permalink to this headline">#</a></h2>
 <p>In this section, we will take maze as an example. The problem statement is:</p>
 <ul class="simple">
 <li><p>Reward: <span class="math notranslate nohighlight">\(r_k = −1\)</span>.</p></li>
@@ -2051,15 +2051,15 @@ <h2><span class="section-number">31.3. </span>Main algorithms<a class="headerlin
 <figure class="align-default" id="maze-setup-statement">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/04_maze.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/04_maze.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/04_maze.png" style="width: 100%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 31.3 </span><span class="caption-text">Maze setup statement</span><a class="headerlink" href="#maze-setup-statement" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 30.3 </span><span class="caption-text">Maze setup statement</span><a class="headerlink" href="#maze-setup-statement" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="rl-solution-by-policy">
-<h3><span class="section-number">31.3.1. </span>RL-solution by policy<a class="headerlink" href="#rl-solution-by-policy" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.3.1. </span>RL-solution by policy<a class="headerlink" href="#rl-solution-by-policy" title="Permalink to this headline">#</a></h3>
 <figure class="align-default" id="maze-solved-by-policy">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/05_maze_policy.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/05_maze_policy.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/05_maze_policy.png" style="width: 100%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 31.4 </span><span class="caption-text">Maze solved by policy</span><a class="headerlink" href="#maze-solved-by-policy" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 30.4 </span><span class="caption-text">Maze solved by policy</span><a class="headerlink" href="#maze-solved-by-policy" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Key characteristics:</p>
@@ -2069,11 +2069,11 @@ <h3><span class="section-number">31.3.1. </span>RL-solution by policy<a class="h
 </ul>
 </section>
 <section id="rl-solution-by-value-function">
-<h3><span class="section-number">31.3.2. </span>RL-solution by value function<a class="headerlink" href="#rl-solution-by-value-function" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.3.2. </span>RL-solution by value function<a class="headerlink" href="#rl-solution-by-value-function" title="Permalink to this headline">#</a></h3>
 <figure class="align-default" id="maze-solved-by-value-functiony">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/06_maze_valuefunc.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/06_maze_valuefunc.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/06_maze_valuefunc.png" style="width: 100%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 31.5 </span><span class="caption-text">Maze solved by value function</span><a class="headerlink" href="#maze-solved-by-value-functiony" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 30.5 </span><span class="caption-text">Maze solved by value function</span><a class="headerlink" href="#maze-solved-by-value-functiony" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Key characteristics:</p>
@@ -2083,11 +2083,11 @@ <h3><span class="section-number">31.3.2. </span>RL-solution by value function<a
 </ul>
 </section>
 <section id="rl-solution-by-model-evaluation">
-<h3><span class="section-number">31.3.3. </span>RL-solution by model evaluation<a class="headerlink" href="#rl-solution-by-model-evaluation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.3.3. </span>RL-solution by model evaluation<a class="headerlink" href="#rl-solution-by-model-evaluation" title="Permalink to this headline">#</a></h3>
 <figure class="align-default" id="maze-solved-by-model-evaluation">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/07_maze_modeleval.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/07_maze_modeleval.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/07_maze_modeleval.png" style="width: 100%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 31.6 </span><span class="caption-text">Maze solved by model evaluation</span><a class="headerlink" href="#maze-solved-by-model-evaluation" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 30.6 </span><span class="caption-text">Maze solved by model evaluation</span><a class="headerlink" href="#maze-solved-by-model-evaluation" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Key characteristics:</p>
@@ -2098,17 +2098,17 @@ <h3><span class="section-number">31.3.3. </span>RL-solution by model evaluation<
 </ul>
 </section>
 <section id="rl-agent-taxonomy">
-<h3><span class="section-number">31.3.4. </span>RL agent taxonomy<a class="headerlink" href="#rl-agent-taxonomy" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">30.3.4. </span>RL agent taxonomy<a class="headerlink" href="#rl-agent-taxonomy" title="Permalink to this headline">#</a></h3>
 <figure class="align-default" id="main-categories-of-reinforcement-learning-algorithms">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/07_taxonomy.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/07_taxonomy.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/DQN/07_taxonomy.png" style="width: 100%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 31.7 </span><span class="caption-text">Main categories of reinforcement learning algorithms</span><a class="headerlink" href="#main-categories-of-reinforcement-learning-algorithms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 30.7 </span><span class="caption-text">Main categories of reinforcement learning algorithms</span><a class="headerlink" href="#main-categories-of-reinforcement-learning-algorithms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 </section>
 <section id="code">
-<h2><span class="section-number">31.4. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">30.4. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h2>
 <p>This is an example of DQN discrete model.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2580,18 +2580,18 @@ <h2><span class="section-number">31.4. </span>Code<a class="headerlink" href="#c
 </div>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">31.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">30.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">31.6. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">30.6. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>You can refer to this website for further study:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://pylessons.com/CartPole-reinforcement-learning">Introduction to Reinforcement Learning</a></p></li>
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">31.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">30.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/upb-lea">Paderborn University - LEA</a> for creating the open-source course <a class="reference external" href="https://github.com/upb-lea/reinforcement_learning_course_materials">reinforcement_learning_course_materials</a> and <a class="reference external" href="https://github.com/pacsuta">Gergely Pacsuta</a> for creating the open-source project <a class="reference external" href="https://github.com/pacsuta/tf-nn-maze-solver">tf-nn-maze-solver</a>. They inspire the majority of the content in this chapter.</p>
 <hr class="docutils" />
 <span class="target" id="id1"></span></section>
@@ -2634,13 +2634,13 @@ <h2><span class="section-number">31.7. </span>Acknowledgments<a class="headerlin
         <i class="fas fa-angle-left"></i>
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-            <p class="prev-next-title"><span class="section-number">30. </span>Time series</p>
+            <p class="prev-next-title"><span class="section-number">29. </span>Time series</p>
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-    <title>26. Generative adversarial networks &#8212; Ocademy Open Machine Learning Book</title>
+    <title>25. Generative adversarial networks &#8212; Ocademy Open Machine Learning Book</title>
     
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+    <link rel="next" title="26. Recurrent Neural Networks" href="rnn.html" />
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+   13. Build a web app to use a Machine Learning model
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+<h1><span class="section-number">25. </span>Generative adversarial networks<a class="headerlink" href="#generative-adversarial-networks" title="Permalink to this headline">#</a></h1>
 <p>The original purpose of Generative adversarial networks(GAN) is to generate new data. It classically generates new images, but is applicable to wide range of domains. It learns the training set distribution and can generate new images that have never been seen before. In contrast to e.g., autoregressive models or RNNs (generating one word at a time), GANs generate the whole output all at once.</p>
 <p>GAN is proposed in 2005, the paper is Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio. “Generative Adversarial Networks”, arxiv:1406.2661.</p>
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+<h2><span class="section-number">25.1. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">26.2. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">25.2. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/rasbt">Sebastian Raschka</a> for creating the open-source project <a class="reference external" href="https://github.com/rasbt/stat453-deep-learning-ss20">stat453-deep-learning-ss20</a> and <a class="reference external" href="https://github.com/diegoalejogm">Diego Gomez</a> for creating the open-source project <a class="reference external" href="https://github.com/diegoalejogm/gans">gans</a>. They inspire the majority of the content in this chapter.</p>
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-     41.45. Linear regression - California Housing
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@@ -1720,80 +1720,80 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#what-is-image-classification">
-   32.1. What is image classification?
+   31.1. What is image classification?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#challenges">
-   32.2. Challenges
+   31.2. Challenges
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#pipeline-of-image-classification">
-   32.3. Pipeline of image classification
+   31.3. Pipeline of image classification
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#history-classic-models">
-   32.4. History &amp; classic models
+   31.4. History &amp; classic models
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#vggnet">
-     32.4.1. VGGNet
+     31.4.1. VGGNet
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#code">
-       32.4.1.1. Code
+       31.4.1.1. Code
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#resnet">
-     32.4.2. Resnet
+     31.4.2. Resnet
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       32.4.2.1. Code
+       31.4.2.1. Code
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#densenet">
-     32.4.3. DenseNet
+     31.4.3. DenseNet
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id2">
-       32.4.3.1. Code
+       31.4.3.1. Code
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#mobilenet">
-     32.4.4. MobileNet
+     31.4.4. MobileNet
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id3">
-       32.4.4.1. Code
+       31.4.4.1. Code
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#vit">
-     32.4.5. ViT
+     31.4.5. ViT
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id4">
-       32.4.5.1. Code
+       31.4.5.1. Code
       </a>
      </li>
     </ul>
@@ -1802,46 +1802,46 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#classic-datasets">
-   32.5. Classic datasets
+   31.5. Classic datasets
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#cifar-10-100">
-     32.5.1. CIFAR-10/100
+     31.5.1. CIFAR-10/100
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#imagenet-1000">
-     32.5.2. ImageNet-1000
+     31.5.2. ImageNet-1000
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#standards">
-   32.6. Standards
+   31.6. Standards
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#top-1-accuracy">
-     32.6.1. Top-1 accuracy
+     31.6.1. Top-1 accuracy
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#top-5-accuracy">
-     32.6.2. Top-5 accuracy
+     31.6.2. Top-5 accuracy
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   32.7. Your turn! 🚀
+   31.7. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   32.8. Acknowledgments
+   31.8. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1866,9 +1866,9 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="image-classification">
-<h1><span class="section-number">32. </span>Image classification<a class="headerlink" href="#image-classification" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">31. </span>Image classification<a class="headerlink" href="#image-classification" title="Permalink to this headline">#</a></h1>
 <section id="what-is-image-classification">
-<h2><span class="section-number">32.1. </span>What is image classification?<a class="headerlink" href="#what-is-image-classification" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">31.1. </span>What is image classification?<a class="headerlink" href="#what-is-image-classification" title="Permalink to this headline">#</a></h2>
 <p>In this chapter we will introduce the image classification problem, which is the task of assigning an input image one label from a fixed set of categories. This is one of the core problems in Computer Vision that, despite its simplicity, has a large variety of practical applications.</p>
 <div class="cell tag_hide-input docutils container">
 <div class="cell_input docutils container">
@@ -1894,13 +1894,13 @@ <h2><span class="section-number">32.1. </span>What is image classification?<a cl
 <figure class="align-default" id="example-of-the-image-classification-task">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/01_classify_eg.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/01_classify_eg.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/01_classify_eg.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 32.1 </span><span class="caption-text">Example of the image classification task</span><a class="headerlink" href="#example-of-the-image-classification-task" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 31.1 </span><span class="caption-text">Example of the image classification task</span><a class="headerlink" href="#example-of-the-image-classification-task" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The task in image classification is to predict a single label (or a distribution over labels as shown here to indicate our confidence) for a given image. Images are 3-dimensional arrays of integers from 0 to 255, of size Width x Height x 3. The 3 represents the three color channels Red, Green, Blue.</p>
 </section>
 <section id="challenges">
-<h2><span class="section-number">32.2. </span>Challenges<a class="headerlink" href="#challenges" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">31.2. </span>Challenges<a class="headerlink" href="#challenges" title="Permalink to this headline">#</a></h2>
 <p>Since this task of recognizing a visual concept (e.g. cat) is relatively trivial for a human to perform, it is worth considering the challenges involved from the perspective of a Computer Vision algorithm. As we present (an inexhaustive) list of challenges below, keep in mind the raw representation of images as a 3-D array of brightness values:</p>
 <ul class="simple">
 <li><p>Viewpoint variation. A single instance of an object can be oriented in many ways with respect to the camera.</p></li>
@@ -1914,7 +1914,7 @@ <h2><span class="section-number">32.2. </span>Challenges<a class="headerlink" hr
 <p>A good image classification model must be invariant to the cross product of all these variations, while simultaneously retaining sensitivity to the inter-class variations.</p>
 </section>
 <section id="pipeline-of-image-classification">
-<h2><span class="section-number">32.3. </span>Pipeline of image classification<a class="headerlink" href="#pipeline-of-image-classification" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">31.3. </span>Pipeline of image classification<a class="headerlink" href="#pipeline-of-image-classification" title="Permalink to this headline">#</a></h2>
 <p>We’ve seen that the task in image classification is to take an array of pixels that represents a single image and assign a label to it. Our complete pipeline can be formalized as follows:</p>
 <ul class="simple">
 <li><p>Input: Our input consists of a set of N images, each labeled with one of K different classes. We refer to this data as the training set,</p></li>
@@ -1923,10 +1923,10 @@ <h2><span class="section-number">32.3. </span>Pipeline of image classification<a
 </ul>
 </section>
 <section id="history-classic-models">
-<h2><span class="section-number">32.4. </span>History &amp; classic models<a class="headerlink" href="#history-classic-models" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">31.4. </span>History &amp; classic models<a class="headerlink" href="#history-classic-models" title="Permalink to this headline">#</a></h2>
 <p>Since image classification is a classic task for computer vision, there are several models that are well-performed in the past. We can list them as follows: LeNet, AlexNet, VGGNet, GoogleNet, ResNet, DenseNet, SENet, MobileNet, ShuffleNet and ViT. In this part, we will introduce some of them.</p>
 <section id="vggnet">
-<h3><span class="section-number">32.4.1. </span>VGGNet<a class="headerlink" href="#vggnet" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">31.4.1. </span>VGGNet<a class="headerlink" href="#vggnet" title="Permalink to this headline">#</a></h3>
 <p>The VGG (Visual Geometry Group) multilayer network model has 19 more layers than AlexNet, verifying that increasing the depth in the network structure can directly affect the model performance. The design idea of VGG is to increase the depth of the network and use a small size convolutional kernel instead. As shown in the figure below, three 3×3 convolutional kernels are used to replace the 7×7 convolutional kernels in AlexNet, and two 3×3 convolutional kernels are used to replace the 5×5 convolutional kernels, which can increase the depth of the network and improve the model effect while ensuring the same perceptual field. The number of model parameters and operations can be reduced by using smaller 3×3 Filters, and the image feature information can be better retained. The specific advantages of the improvement are summarized as follows.</p>
 <ul class="simple">
 <li><p>Using small 3×3 filters to replace large convolutional kernels.</p></li>
@@ -1937,11 +1937,11 @@ <h3><span class="section-number">32.4.1. </span>VGGNet<a class="headerlink" href
 <figure class="align-default" id="vgg-structure">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/02_VGG.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/02_VGG.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/02_VGG.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 32.2 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1409.1556.pdf"> Structure of VGGNet </a></span><a class="headerlink" href="#vgg-structure" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 31.2 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1409.1556.pdf"> Structure of VGGNet </a></span><a class="headerlink" href="#vgg-structure" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="code">
-<h4><span class="section-number">32.4.1.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">31.4.1.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -2227,23 +2227,23 @@ <h4><span class="section-number">32.4.1.1. </span>Code<a class="headerlink" href
 </section>
 </section>
 <section id="resnet">
-<h3><span class="section-number">32.4.2. </span>Resnet<a class="headerlink" href="#resnet" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">31.4.2. </span>Resnet<a class="headerlink" href="#resnet" title="Permalink to this headline">#</a></h3>
 <p>ResNet (Residual Network) was proposed by Kaiming He and won the 2015 ILSVRC Grand Prix with an error rate of 3.57%. In the previous network, when the model is not deep enough, its network recognition is not strong, but when the network stack (Plain Network) is very deep, the network gradient disappearance and gradient dispersion are obvious, resulting in the model’s computational effectiveness but not up but down. Therefore, in view of the degradation problem of this deep network, ResNet is designed as an ultra-deep network without the gradient vanishing problem.ResNet has various types depending on the number of layers, from 18 to 1202 layers. As an example, Res Net50 consists of 49 convolutional layers and 1 fully connected layer, as shown in the figure below. This simple addition does not add additional parameters and computation to the network, but can greatly increase the training speed and improve the training effect, and this simple structure can well solve the degradation problem when the model deepens the number of layers. In this way, the network will always be in the optimal state and the performance of the network will not decrease with increasing depth.</p>
 <p>The most important part of ResNet should be the residual block, and here is the structure.</p>
 <figure class="align-default" id="residual-block">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/03_resblock.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/03_resblock.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/03_resblock.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 32.3 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1512.03385.pdf"> Structure of residual block </a></span><a class="headerlink" href="#residual-block" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 31.3 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1512.03385.pdf"> Structure of residual block </a></span><a class="headerlink" href="#residual-block" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="resnet-structure">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/04_ResNet.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/04_ResNet.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/04_ResNet.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 32.4 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1512.03385.pdf"> Structure of residual network </a></span><a class="headerlink" href="#resnet-structure" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 31.4 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1512.03385.pdf"> Structure of residual network </a></span><a class="headerlink" href="#resnet-structure" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="id1">
-<h4><span class="section-number">32.4.2.1. </span>Code<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">31.4.2.1. </span>Code<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
@@ -2290,22 +2290,22 @@ <h4><span class="section-number">32.4.2.1. </span>Code<a class="headerlink" href
 </section>
 </section>
 <section id="densenet">
-<h3><span class="section-number">32.4.3. </span>DenseNet<a class="headerlink" href="#densenet" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">31.4.3. </span>DenseNet<a class="headerlink" href="#densenet" title="Permalink to this headline">#</a></h3>
 <p>DenseNet proposes a more radical dense connection mechanism than ResNet: i.e., connecting all layers to each other, specifically each layer accepts all the layers before it as its additional input. resNet short-circuits each layer with some previous layer (usually 2-3 layers), and the connection is made by element-level summation. In DenseNet, each layer is connected (concat) with all the preceding layers in the channel dimension and used as input to the next layer, which is a dense connection. Moreover, DenseNet is directly concat feature maps from different layers, which enables feature reuse and improves efficiency, and this feature is the most important difference between DenseNet and ResNet.</p>
 <figure class="align-default" id="dense-block">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/05_denseblock.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/05_denseblock.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/05_denseblock.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 32.5 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf"> Structure of dense block </a></span><a class="headerlink" href="#dense-block" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 31.5 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf"> Structure of dense block </a></span><a class="headerlink" href="#dense-block" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="densenet-structure">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/06_DenseNet.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/06_DenseNet.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/06_DenseNet.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 32.6 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf"> Structure of dense network </a></span><a class="headerlink" href="#densenet-structure" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 31.6 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf"> Structure of dense network </a></span><a class="headerlink" href="#densenet-structure" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="id2">
-<h4><span class="section-number">32.4.3.1. </span>Code<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">31.4.3.1. </span>Code<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
@@ -2362,23 +2362,23 @@ <h4><span class="section-number">32.4.3.1. </span>Code<a class="headerlink" href
 </section>
 </section>
 <section id="mobilenet">
-<h3><span class="section-number">32.4.4. </span>MobileNet<a class="headerlink" href="#mobilenet" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">31.4.4. </span>MobileNet<a class="headerlink" href="#mobilenet" title="Permalink to this headline">#</a></h3>
 <p>MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem.</p>
 <p>Besides, the standard convolutional filters for normal CNNs are replaced by two layers: depthwise convolution and pointwise convolution to build a depthwise separable filter.</p>
 <figure class="align-default" id="mobilenet-convolution-structure">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/07_mobileconv.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/07_mobileconv.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/07_mobileconv.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 32.7 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1704.04861.pdf%E2%80%8Barxiv.org"> Replacement of standard convolution filter </a></span><a class="headerlink" href="#mobilenet-convolution-structure" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 31.7 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1704.04861.pdf%E2%80%8Barxiv.org"> Replacement of standard convolution filter </a></span><a class="headerlink" href="#mobilenet-convolution-structure" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="mobilenet-body-structure">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/08_MobileNet.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/08_MobileNet.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgcls/08_MobileNet.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 32.8 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1704.04861.pdf%E2%80%8Barxiv.org"> Body structure of MobileNet </a></span><a class="headerlink" href="#mobilenet-body-structure" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 31.8 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1704.04861.pdf%E2%80%8Barxiv.org"> Body structure of MobileNet </a></span><a class="headerlink" href="#mobilenet-body-structure" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="id3">
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+<h3><span class="section-number">31.4.5. </span>ViT<a class="headerlink" href="#vit" title="Permalink to this headline">#</a></h3>
 <p>Different from the previous models, ViT (Vision Transformer) uses the concept of Transformer. Inspired by the Transformer scaling successes in NLP, they experiment with applying a standard Transformer directly to images, with the fewest possible modifications. To do so, they split an image into patches and provide the sequence of linear embeddings of these patches as an input to a Transformer. Image patches are treated the same way as tokens (words) in an NLP application. They train the model on image classification in supervised fashion.</p>
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+<h2><span class="section-number">31.5. </span>Classic datasets<a class="headerlink" href="#classic-datasets" title="Permalink to this headline">#</a></h2>
 <p>As we said before, image classification task is mainly trained by datasets, so the importance of dataset is obvious. Here, we will introduce some widely-used datasets.</p>
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+<h3><span class="section-number">31.5.1. </span>CIFAR-10/100<a class="headerlink" href="#cifar-10-100" title="Permalink to this headline">#</a></h3>
 <p>The <a class="reference external" href="http://www.cs.toronto.edu/~kriz/cifar.html">CIFAR-10 dataset</a> consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.</p>
 <p>The CIFAR-100 dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a “fine” label (the class to which it belongs) and a “coarse” label (the superclass to which it belongs).</p>
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+<h3><span class="section-number">31.5.2. </span>ImageNet-1000<a class="headerlink" href="#imagenet-1000" title="Permalink to this headline">#</a></h3>
 <p><a class="reference external" href="https://image-net.org/">ImageNet</a> is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a “synonym set” or “synset”. There are more than 100,000 synsets in WordNet; the majority of them are nouns (80,000+). In ImageNet, we aim to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. In its completion, we hope ImageNet will offer tens of millions of cleanly labeled and sorted images for most of the concepts in the WordNet hierarchy.</p>
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+<h2><span class="section-number">31.6. </span>Standards<a class="headerlink" href="#standards" title="Permalink to this headline">#</a></h2>
 <section id="top-1-accuracy">
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+<h3><span class="section-number">31.6.1. </span>Top-1 accuracy<a class="headerlink" href="#top-1-accuracy" title="Permalink to this headline">#</a></h3>
 <p>If your predicted label takes the largest one inside the final probability vector as the prediction result, and if the one with the highest probability in your prediction result is correctly classified, then the prediction is correct. Otherwise, the prediction is wrong.</p>
 </section>
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+<h3><span class="section-number">31.6.2. </span>Top-5 accuracy<a class="headerlink" href="#top-5-accuracy" title="Permalink to this headline">#</a></h3>
 <p>Among the 50 classification probabilities of the test image, take the first 5 maximum classification probabilities, whether the correct label (classification) is in it or not, that is, whether it is one of these first 5, if it is, it is a successful classification.</p>
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">32.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">31.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">32.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">31.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.stanford.edu">Stanford</a> for creating the open-source course <a class="reference external" href="https://cs231n.github.io/classification/">CS231n: Deep Learning for Computer Vision</a>, <a class="reference external" href="https://github.com/hoangthang1607">Duc Thang HOANG</a> for creating the open-source project <a class="reference external" href="https://github.com/hoangthang1607/RepVGG-Tensorflow-2">RepVGG-Tensorflow-2</a>, <a class="reference external" href="https://github.com/taki0112">Junho Kim</a> for creating the open-source project <a class="reference external" href="https://github.com/taki0112/ResNet-Tensorflow">ResNet-Tensorflow</a>, <a class="reference external" href="https://github.com/taki0112/Densenet-Tensorflow">Densenet-Tensorflow</a> and <a class="reference external" href="https://github.com/taki0112/vit-tensorflow">vit-tensorflow</a> and <a class="reference external" href="https://github.com/ohadlights">ohadlights</a> for creating the open-source project <a class="reference external" href="https://github.com/ohadlights/mobilenetv2">mobilenetv2</a>. They inspire the majority of the content in this chapter.</p>
 </section>
 </section>
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+     32.2.1. Prepare the Oxford-IIIT Pets dataset
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#deeplab-v3">
-     33.3.4. DeepLab V3
+     32.3.4. DeepLab V3
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id2">
-       33.3.4.1. Code
+       32.3.4.1. Code
       </a>
      </li>
     </ul>
@@ -1764,12 +1764,12 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   33.4. Your turn! 🚀
+   32.4. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   33.5. Acknowledgments
+   32.5. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1795,9 +1795,9 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="image-segmentation">
-<h1><span class="section-number">33. </span>Image segmentation<a class="headerlink" href="#image-segmentation" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">32. </span>Image segmentation<a class="headerlink" href="#image-segmentation" title="Permalink to this headline">#</a></h1>
 <section id="what-is-image-segmentation">
-<h2><span class="section-number">33.1. </span>What is Image segmentation?<a class="headerlink" href="#what-is-image-segmentation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">32.1. </span>What is Image segmentation?<a class="headerlink" href="#what-is-image-segmentation" title="Permalink to this headline">#</a></h2>
 <p>In an image segmentation task (specifically semantic segmentation), the network assigns a label (or class) to each input image. However, suppose you want to know the shape of that object, which pixel belongs to which object, etc. In this case, you need to assign a class to each pixel of the image—this task is known as segmentation. A segmentation model returns much more detailed information about the image. Image segmentation has many applications in medical imaging, self-driving cars and satellite imaging, just to name a few.</p>
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 <section id="how-to-train-a-model-for-image-segmentation">
-<h2><span class="section-number">33.2. </span>How to train a model for image segmentation?<a class="headerlink" href="#how-to-train-a-model-for-image-segmentation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">32.2. </span>How to train a model for image segmentation?<a class="headerlink" href="#how-to-train-a-model-for-image-segmentation" title="Permalink to this headline">#</a></h2>
 <p>This tutorial uses the Oxford-IIIT Pet Dataset. The dataset consists of images of 37 pet breeds, with 200 images per breed (~100 each in the training and test splits). Each image includes the corresponding labels, and pixel-wise masks. The masks are class-labels for each pixel. Each pixel is given one of three categories:</p>
 <ul class="simple">
 <li><p>Class 1: Pixel belonging to the pet.</p></li>
@@ -1864,7 +1864,7 @@ <h2><span class="section-number">33.2. </span>How to train a model for image seg
 </div>
 </div>
 <section id="prepare-the-oxford-iiit-pets-dataset">
-<h3><span class="section-number">33.2.1. </span>Prepare the Oxford-IIIT Pets dataset<a class="headerlink" href="#prepare-the-oxford-iiit-pets-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">32.2.1. </span>Prepare the Oxford-IIIT Pets dataset<a class="headerlink" href="#prepare-the-oxford-iiit-pets-dataset" title="Permalink to this headline">#</a></h3>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span><span class="p">,</span> <span class="n">info</span> <span class="o">=</span> <span class="n">tfds</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;oxford_iiit_pet:3.*.*&#39;</span><span class="p">,</span> <span class="n">with_info</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
@@ -1973,12 +1973,12 @@ <h3><span class="section-number">33.2.1. </span>Prepare the Oxford-IIIT Pets dat
 <figure class="align-default" id="dataset-example-mask">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/01_display_dataset.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/01_display_dataset.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/01_display_dataset.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 33.1 </span><span class="caption-text">An example from the dataset</span><a class="headerlink" href="#dataset-example-mask" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 32.1 </span><span class="caption-text">An example from the dataset</span><a class="headerlink" href="#dataset-example-mask" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="define-the-model">
-<h3><span class="section-number">33.2.2. </span>Define the model<a class="headerlink" href="#define-the-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">32.2.2. </span>Define the model<a class="headerlink" href="#define-the-model" title="Permalink to this headline">#</a></h3>
 <p>The model being used here is a modified U-Net. A U-Net consists of an encoder (downsampler) and decoder (upsampler). To learn robust features and reduce the number of trainable parameters, use a pretrained model—MobileNetV2—as the encoder. For the decoder, you will use the upsample block, which is already implemented in the pix2pix example in the TensorFlow Examples repo.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -2065,7 +2065,7 @@ <h3><span class="section-number">33.2.2. </span>Define the model<a class="header
 <p>Note that the number of filters on the last layer is set to the number of it. This will be one output channel per class.output_channels.</p>
 </section>
 <section id="train-the-model">
-<h3><span class="section-number">33.2.3. </span>Train the model<a class="headerlink" href="#train-the-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">32.2.3. </span>Train the model<a class="headerlink" href="#train-the-model" title="Permalink to this headline">#</a></h3>
 <p>Now, all that is left to do is to compile and train the model. Since this is a multiclass classification problem, use the ‘tf.keras.losses.CategoricalCrossentropy’ loss function with the argument set to , since the labels are scalar integers instead of vectors of scores for each pixel of every class.from_logitsTrue. When running inference, the label assigned to the pixel is the channel with the highest value. This is what the function is doing.create_mask.</p>
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@@ -2471,22 +2471,22 @@ <h3><span class="section-number">33.2.3. </span>Train the model<a class="headerl
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 </section>
 <section id="make-predictions">
-<h3><span class="section-number">33.2.4. </span>Make predictions<a class="headerlink" href="#make-predictions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">32.2.4. </span>Make predictions<a class="headerlink" href="#make-predictions" title="Permalink to this headline">#</a></h3>
 <p>Now, let’s make some predictions. In the interest of saving time, the number of epochs was kept small, but you may set this higher to achieve more accurate results.</p>
 <p>show_predictions(test_batches, 3)</p>
 <figure class="align-default" id="display-prediction">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/02_show_prediction.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/02_show_prediction.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/02_show_prediction.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 33.2 </span><span class="caption-text">An example of the output prediction</span><a class="headerlink" href="#display-prediction" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 32.2 </span><span class="caption-text">An example of the output prediction</span><a class="headerlink" href="#display-prediction" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 </section>
 <section id="history-classic-models">
-<h2><span class="section-number">33.3. </span>History &amp; classic models<a class="headerlink" href="#history-classic-models" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">32.3. </span>History &amp; classic models<a class="headerlink" href="#history-classic-models" title="Permalink to this headline">#</a></h2>
 <p>In the previous part, we learn the U-Net model. Besides, there are still many excellent models, we will introduce them is this part.</p>
 <section id="code-for-assisting">
-<h3><span class="section-number">33.3.1. </span>Code for assisting<a class="headerlink" href="#code-for-assisting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">32.3.1. </span>Code for assisting<a class="headerlink" href="#code-for-assisting" title="Permalink to this headline">#</a></h3>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
@@ -2710,7 +2710,7 @@ <h3><span class="section-number">33.3.1. </span>Code for assisting<a class="head
 </div>
 </section>
 <section id="fcn">
-<h3><span class="section-number">33.3.2. </span>FCN<a class="headerlink" href="#fcn" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">32.3.2. </span>FCN<a class="headerlink" href="#fcn" title="Permalink to this headline">#</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>The link for paper is <a class="reference external" href="https://arxiv.org/pdf/1411.4038v2.pdf">https://arxiv.org/pdf/1411.4038v2.pdf</a>.</p>
@@ -2719,11 +2719,11 @@ <h3><span class="section-number">33.3.2. </span>FCN<a class="headerlink" href="#
 <figure class="align-default" id="display-structure-of-fcn">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/03_structure_FCN.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/03_structure_FCN.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/03_structure_FCN.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 33.3 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1411.4038v2.pdf"> The structure of FCN </a></span><a class="headerlink" href="#display-structure-of-fcn" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 32.3 </span><span class="caption-text"><a class="reference external" href="https://arxiv.org/pdf/1411.4038v2.pdf"> The structure of FCN </a></span><a class="headerlink" href="#display-structure-of-fcn" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="code">
-<h4><span class="section-number">33.3.2.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">32.3.2.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
@@ -2860,7 +2860,7 @@ <h4><span class="section-number">33.3.2.1. </span>Code<a class="headerlink" href
 </section>
 </section>
 <section id="segnet">
-<h3><span class="section-number">33.3.3. </span>SegNet<a class="headerlink" href="#segnet" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">32.3.3. </span>SegNet<a class="headerlink" href="#segnet" title="Permalink to this headline">#</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>The link for paper is <a class="reference external" href="https://arxiv.org/pdf/1511.00561v3.pdf">https://arxiv.org/pdf/1511.00561v3.pdf</a>.</p>
@@ -2869,12 +2869,12 @@ <h3><span class="section-number">33.3.3. </span>SegNet<a class="headerlink" href
 <figure class="align-default" id="display-structure-of-segnet">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/04_structure_SegNet.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/04_structure_SegNet.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/04_structure_SegNet.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 33.4 </span><span class="caption-text">[ The structure of SegNet ]<a class="reference external" href="https://arxiv.org/pdf/1511.00561v3.pdf">https://arxiv.org/pdf/1511.00561v3.pdf</a> )</span><a class="headerlink" href="#display-structure-of-segnet" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 32.4 </span><span class="caption-text">[ The structure of SegNet ]<a class="reference external" href="https://arxiv.org/pdf/1511.00561v3.pdf">https://arxiv.org/pdf/1511.00561v3.pdf</a> )</span><a class="headerlink" href="#display-structure-of-segnet" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>As we have learnt U-Net, SegNet is similar to it, the main difference is that U-Net does not reuse pooling indices but instead transfers the entire feature map (at the cost of more memory) to the corresponding decoders and concatenates them to upsampled (via deconvolution) decoder feature maps.</p>
 <section id="id1">
-<h4><span class="section-number">33.3.3.1. </span>Code<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">32.3.3.1. </span>Code<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
@@ -2968,7 +2968,7 @@ <h4><span class="section-number">33.3.3.1. </span>Code<a class="headerlink" href
 </section>
 </section>
 <section id="deeplab-v3">
-<h3><span class="section-number">33.3.4. </span>DeepLab V3<a class="headerlink" href="#deeplab-v3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">32.3.4. </span>DeepLab V3<a class="headerlink" href="#deeplab-v3" title="Permalink to this headline">#</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>The link for paper is <a class="reference external" href="https://arxiv.org/pdf/1706.05587v3.pdf">https://arxiv.org/pdf/1706.05587v3.pdf</a>.</p>
@@ -2977,17 +2977,17 @@ <h3><span class="section-number">33.3.4. </span>DeepLab V3<a class="headerlink"
 <figure class="align-default" id="cascade-dilated-conv-structure">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/05_cascade_DeepLab_structure.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/05_cascade_DeepLab_structure.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/05_cascade_DeepLab_structure.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 33.5 </span><span class="caption-text">[ Cascade dilated convolution for DeepLab ] (<a class="reference external" href="https://arxiv.org/pdf/1706.05587v3.pdf">https://arxiv.org/pdf/1706.05587v3.pdf</a>)</span><a class="headerlink" href="#cascade-dilated-conv-structure" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 32.5 </span><span class="caption-text">[ Cascade dilated convolution for DeepLab ] (<a class="reference external" href="https://arxiv.org/pdf/1706.05587v3.pdf">https://arxiv.org/pdf/1706.05587v3.pdf</a>)</span><a class="headerlink" href="#cascade-dilated-conv-structure" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="parallel-deeplab-structure">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/06_parallel_DeepLab_structure.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/06_parallel_DeepLab_structure.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/imgseg/06_parallel_DeepLab_structure.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 33.6 </span><span class="caption-text">[ Parallel dilated convolution for DeepLab ] (<a class="reference external" href="https://arxiv.org/pdf/1706.05587v3.pdf">https://arxiv.org/pdf/1706.05587v3.pdf</a>)</span><a class="headerlink" href="#parallel-deeplab-structure" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 32.6 </span><span class="caption-text">[ Parallel dilated convolution for DeepLab ] (<a class="reference external" href="https://arxiv.org/pdf/1706.05587v3.pdf">https://arxiv.org/pdf/1706.05587v3.pdf</a>)</span><a class="headerlink" href="#parallel-deeplab-structure" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="id2">
-<h4><span class="section-number">33.3.4.1. </span>Code<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">32.3.4.1. </span>Code<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">tensorflow.keras.layers</span> <span class="kn">import</span> <span class="n">Input</span><span class="p">,</span><span class="n">Dropout</span><span class="p">,</span><span class="n">BatchNormalization</span><span class="p">,</span><span class="n">Activation</span><span class="p">,</span><span class="n">Add</span>
@@ -3188,11 +3188,11 @@ <h4><span class="section-number">33.3.4.1. </span>Code<a class="headerlink" href
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">33.4. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">32.4. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">33.5. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">32.5. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/luyanger1799">Yang Lu</a> for creating the open-source project <a class="reference external" href="https://github.com/luyanger1799/Amazing-Semantic-Segmentation">Amazing-Semantic-Segmentation</a>, <a class="reference external" href="https://github.com/tensorflow">tensorflow</a> for creating the open-source course <a class="reference external" href="https://github.com/tensorflow/examples">examples</a>. They inspire the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -3234,13 +3234,13 @@ <h2><span class="section-number">33.5. </span>Acknowledgments<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">32. </span>Image classification</p>
+            <p class="prev-next-title"><span class="section-number">31. </span>Image classification</p>
         </div>
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">34. </span>Diffusion Model</p>
+        <p class="prev-next-title"><span class="section-number">33. </span>Diffusion Model</p>
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-    <title>29. Long-short term memory &#8212; Ocademy Open Machine Learning Book</title>
+    <title>28. Long-short term memory &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="shortcut icon" href="../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="30. Time series" href="time-series.html" />
-    <link rel="prev" title="28. Autoencoder" href="autoencoder.html" />
+    <link rel="next" title="29. Time series" href="time-series.html" />
+    <link rel="prev" title="27. Autoencoder" href="autoencoder.html" />
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   <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-   1. Python programming basics
+   1. Python programming introduction
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+     40.4. Second assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/project-plan-template.html">
-     41.5. Project Plan​ Template
+     40.5. Project Plan​ Template
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-introduction.html">
-     41.6. Python programming introduction
+     40.6. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-basics.html">
-     41.7. Python programming basics
+     40.7. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/prerequisites/python-programming-advanced.html">
-     41.8. Python programming advanced
+     40.8. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-text-about-data-science.html">
-     41.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-scenarios.html">
-     41.10. Data Science scenarios
+     40.10. Data Science scenarios
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/write-a-data-ethics-case-study.html">
-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/lines-scatters-and-bars.html">
-     41.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/apply-your-skills.html">
-     41.13. Apply your skills
+     40.13. Apply your skills
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/try-it-in-excel.html">
-     41.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/dive-into-the-beehive.html">
-     41.15. Dive into the beehive
+     40.15. Dive into the beehive
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/build-your-own-custom-vis.html">
-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/classifying-datasets.html">
-     41.17. Classifying datasets
+     40.17. Classifying datasets
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/small-diabetes-study.html">
-     41.18. Small diabetes study
+     40.18. Small diabetes study
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/introduction-to-statistics-and-probability.html">
-     41.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/displaying-airport-data.html">
-     41.20. Displaying airport data
+     40.20. Displaying airport data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/soda-profits.html">
-     41.21. Soda profits
+     40.21. Soda profits
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-COVID-19-papers.html">
-     41.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     41.23. Estimation of COVID-19 pandemic
+     40.23. Estimation of COVID-19 pandemic
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-processing-in-python.html">
-     41.24. Data processing in Python
+     40.24. Data processing in Python
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/evaluating-data-from-a-form.html">
-     41.25. Evaluating data from a form
+     40.25. Evaluating data from a form
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-preparation.html">
-     41.26. Data preparation
+     40.26. Data preparation
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/analyzing-data.html">
-     41.27. Analyzing data
+     40.27. Analyzing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     41.28. NYC taxi data in winter and summer
+     40.28. NYC taxi data in winter and summer
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/matplotlib-applied.html">
-     41.29. Matplotlib applied
+     40.29. Matplotlib applied
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/tell-a-story.html">
-     41.35. Tell a story
+     40.35. Tell a story
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     41.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/exploring-for-anwser.html">
-     41.37. Exploring for answers
+     40.37. Exploring for answers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/market-research.html">
-     41.38. Market research
+     40.38. Market research
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     41.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     41.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     41.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-iris.html">
-     41.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     41.43. Machine Learning overview - assignment 2
+     40.43. Machine Learning overview - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     41.44. Regression with Scikit-learn
+     40.44. Regression with Scikit-learn
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     41.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     41.46. Linear Regression Metrics
+     40.46. Linear Regression Metrics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     41.47. Loss Function
+     40.47. Loss Function
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     41.48. Gradient descent
+     40.48. Gradient descent
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     41.49. Linear Regression Implementation from Scratch
+     40.49. Linear Regression Implementation from Scratch
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     41.50. ML logistic regression - assignment 1
+     40.50. ML logistic regression - assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     41.51. ML logistic regression - assignment 2
+     40.51. ML logistic regression - assignment 2
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/ml-neural-network-1.html">
-     41.52. ML neural network - Assignment 1
+     40.52. ML neural network - Assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/regression-tools.html">
-     41.53. Regression tools
+     40.53. Regression tools
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/managing-data.html">
-     41.54. Managing data
+     40.54. Managing data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/exploring-visualizations.html">
-     41.55. Exploring visualizations
+     40.55. Exploring visualizations
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/try-a-different-model.html">
-     41.56. Try a different model
+     40.56. Try a different model
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/create-a-regression-model.html">
-     41.57. Create a regression model
+     40.57. Create a regression model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
-     41.58. Linear and polynomial regression
+     40.58. Linear and polynomial regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/retrying-some-regression.html">
-     41.59. Retrying some regression
+     40.59. Retrying some regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
-     41.60. Pumpkin varieties and color
+     40.60. Pumpkin varieties and color
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
-     41.61. Delicious asian and indian cuisines
+     40.61. Delicious asian and indian cuisines
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/explore-classification-methods.html">
-     41.62. Explore classification methods
+     40.62. Explore classification methods
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     41.63. Kernel method assignment 1
+     40.63. Kernel method assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     41.64. Support Vector Machines (SVM) - Intro and SVM for Regression
+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     41.65. Support Vector Machines (SVM) - Classification
+     40.65. Support Vector Machines (SVM) - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     41.66. Decision Trees - Intro and Regression
+     40.66. Decision Trees - Intro and Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/kernel-method/decision_trees_for_classification.html">
-     41.67. Decision Trees - Classification
+     40.67. Decision Trees - Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/model-selection-assignment-1.html">
-     41.68. Model selection assignment 1
+     40.68. Model selection assignment 1
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     41.69. Learning Curve To Identify Overfit &amp; Underfit
+     40.69. Learning Curve To Identify Overfit &amp; Underfit
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     41.70. Dropout and Batch Normalization
+     40.70. Dropout and Batch Normalization
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
-     41.71. Lasso and Ridge Regression
+     40.71. Lasso and Ridge Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/model-selection/regularized-linear-models.html">
-     41.72. Regularized Linear Models
+     40.72. Regularized Linear Models
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
-     41.73. Random forests intro and regression
+     40.73. Random forests intro and regression
     </a>
    </li>
    <li class="toctree-l2">
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-     41.74. Random forests for classification
+     40.74. Random forests for classification
     </a>
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    <li class="toctree-l2">
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-     41.75. Beyond random forests: more ensemble models
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/decision-trees.html">
-     41.76. Decision trees
+     40.76. Decision trees
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/gradient-boosting/hyperparameter-tuning-gradient-boosting.html">
-     41.77. Hyperparameter tuning gradient boosting
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    <li class="toctree-l2">
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-     41.78. Gradient boosting
+     40.78. Gradient boosting
     </a>
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    <li class="toctree-l2">
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-     41.79. Boosting with tuning
+     40.79. Boosting with tuning
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    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-advanced/ensemble-learning/random-forest-classifier-feature-importance.html">
-     41.80. Random Forest Classifier with Feature Importance
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/machine-learning-productionization/data-engineering.html">
-     41.82. Data engineering
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    <li class="toctree-l2">
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-     41.83. Counterintuitive Challenges in ML Debugging
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-classification.html">
-     41.84. Case Study: Debugging in Classification
+     40.84. Case Study: Debugging in Classification
     </a>
    </li>
    <li class="toctree-l2">
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-     41.85. Case Study: Debugging in Regression
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     </a>
    </li>
    <li class="toctree-l2">
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-     41.86. Study the solvers
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     </a>
    </li>
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-     41.87. Build classification models
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    <li class="toctree-l2">
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-     41.88. Build Classification Model
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     </a>
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    <li class="toctree-l2">
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-     41.89. Parameter play
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-     41.90. How to choose cnn architecture mnist
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    <li class="toctree-l2">
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-     41.92. Sign Language Digits Classification with CNN
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    </li>
    <li class="toctree-l2">
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-     41.94. Object Recognition in Images using CNN
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     </a>
    </li>
    <li class="toctree-l2">
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-     41.95. Intro to TensorFlow for Deep Learning
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    </li>
    <li class="toctree-l2">
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-     41.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
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     </a>
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-     41.99. Google Stock Price Prediction RNN
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    <li class="toctree-l2">
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-     41.101. Intro to Autoencoders
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-     41.102. Base/Denoising Autoencoder &amp; Dimension Reduction
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    <li class="toctree-l2">
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-     41.103. Fun with Variational Autoencoders
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/time-series-forecasting-assignment.html">
-     41.104. Time Series Forecasting Assignment
+     40.104. Time Series Forecasting Assignment
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/nn-for-classification-assignment.html">
-     41.106. Neural Networks for Classification with TensorFlow
+     40.106. Neural Networks for Classification with TensorFlow
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    </li>
    <li class="toctree-l2">
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-     41.107. NN Classify 15 Fruits Assignment
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     </a>
    </li>
    <li class="toctree-l2">
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-     41.112. DQN On Foreign Exchange Market
+     40.112. DQN On Foreign Exchange Market
     </a>
    </li>
    <li class="toctree-l2">
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-     41.113. Art by gan
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    <li class="toctree-l2">
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-     41.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
    <li class="toctree-l2">
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-     41.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
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 <section class="tex2jax_ignore mathjax_ignore" id="long-short-term-memory">
-<h1><span class="section-number">29. </span>Long-short term memory<a class="headerlink" href="#long-short-term-memory" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">28. </span>Long-short term memory<a class="headerlink" href="#long-short-term-memory" title="Permalink to this headline">#</a></h1>
 <p>As we have learned the normal RNNs on the previous lessons, today we are going to use long-short term memory (LSTM) to fix the problems that are induced from the RNN architecture.</p>
 <p>The vanilla LSTM is proposed in 2005, the paper is <a class="reference external" href="https://link.springer.com/chapter/10.1007/11550907_126">Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition</a>, and after that, a lot of paper based on it appeared.</p>
 <section id="overview">
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 <p>The essential problems of RNN are vanishing/exploding gradient problems. The gradient vanishing/exploding problem is a common issue in training deep neural networks. This problem occurs when the gradient of the loss function with respect to the weights of the network becomes very small or very large.</p>
 <p>There are several possibilities will bring on this kind of problem, for example:</p>
 <ul class="simple">
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 The <span class="math notranslate nohighlight">\(h^{t}\)</span> is still the activation.</p>
 <img src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/LSTM/cell_state.png" width="90%" class="bg-white mb-1"></section>
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+<h2><span class="section-number">28.2. </span>Special Gates<a class="headerlink" href="#special-gates" title="Permalink to this headline">#</a></h2>
 <p>Then, we take a look at the inside of cell. <span class="math notranslate nohighlight">\(\bigodot\)</span> means element-wise multiplication operator, <span class="math notranslate nohighlight">\(\bigoplus\)</span> means element-wise addition operator and <span class="math notranslate nohighlight">\(\sigma\)</span> means logistic sigmoid activation functions.</p>
 <p>Now, we should pay attention to ‘Gates’.</p>
 <p>The first is ‘Forget Gate’ <span class="math notranslate nohighlight">\(f\)</span>, and it controls which information is remembered, and which is forgotten; can reset the cell state. This function can be written as <span class="math notranslate nohighlight">\(f_t = \sigma (W_{fx}x^{&lt;t&gt;} + W_{fh}h^{&lt;t-1&gt;} + b_f)\)</span></p>
@@ -1698,7 +1698,7 @@ <h2><span class="section-number">29.2. </span>Special Gates<a class="headerlink"
 <p>To brief summarize the previous gates in an expression: <span class="math notranslate nohighlight">\(C^{&lt;t&gt;} = (C^{&lt;t-1&gt;} \bigodot f_t) \bigoplus (i_t \bigodot g_t)\)</span>. Since <span class="math notranslate nohighlight">\(i_t\)</span> is ‘Input Gate’ and <span class="math notranslate nohighlight">\(g_t\)</span> is ‘Input Gate’, <span class="math notranslate nohighlight">\((i_t \bigodot g_t)\)</span> is for updatting the cell state.</p>
 <p>Finally, ‘Output Gate’ is for updating the values of hidden units: <span class="math notranslate nohighlight">\(o_t = \sigma(W_{ox}x^{&lt;t&gt;} + W_{oh}x^{&lt;t-1&gt;} + b_o)\)</span>. So the activateion of the current time is <span class="math notranslate nohighlight">\(h^{&lt;t&gt;} = o_t \bigodot tanh(C^{&lt;t&gt;})\)</span>.</p>
 <section id="code">
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+<h3><span class="section-number">28.2.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h3>
 <p>Here we implement an LSTM model on all a data set of Shakespeare works.</p>
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@@ -6222,11 +6222,11 @@ <h3><span class="section-number">29.2.1. </span>Code<a class="headerlink" href="
 </section>
 </section>
 <section id="your-turn">
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+<h2><span class="section-number">28.3. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Practice the Long-Short Term Memory Networks by following this TBD.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">29.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">28.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/nfmcclure">Nick</a> for creating the open-source project <a class="reference external" href="https://github.com/nfmcclure/tensorflow_cookbook">tensorflow_cookbook</a> and <a class="reference external" href="https://github.com/rasbt">Sebastian Raschka</a> for creating the open-source project <a class="reference external" href="https://github.com/rasbt/stat453-deep-learning-ss20">stat453-deep-learning-ss20</a>. They inspire the majority of the content in this chapter.</p>
 </section>
 </section>
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         <i class="fas fa-angle-left"></i>
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+     8.2. The “low code/no code” way
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+     8.3. Data Science in the cloud: The “Azure ML SDK” way
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+     14.3. Yet other classifiers
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-     15.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
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+     15.1. Loss function
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+     15.2. Gradient descent
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+     17.1. Bagging
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-     19.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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+   21. Unsupervised learning: PCA and clustering
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-     25.3.1.1. Stylenet / Neural-Style
+     24.3.1.1. Stylenet / Neural-Style
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-     25.3.1.2. Deepdream in TensorFlow
+     24.3.1.2. Deepdream in TensorFlow
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+   26. Recurrent Neural Networks
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+     40.3. First assignment
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+     40.5. Project Plan​ Template
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+     40.6. Python programming introduction
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+     40.7. Python programming basics
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+     40.9. Analyzing text about Data Science
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+     40.10. Data Science scenarios
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-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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+     40.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.16. Build your own custom vis
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+     40.17. Classifying datasets
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+     40.19. Introduction to probability and statistics
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-     41.21. Soda profits
+     40.21. Soda profits
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-     41.36. Explore a planetary computer dataset
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+     40.39. Low code/no code Data Science project on Azure ML
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+     40.40. Data Science project using Azure ML SDK
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 <section class="tex2jax_ignore mathjax_ignore" id="object-detection">
-<h1><span class="section-number">35. </span>Object detection<a class="headerlink" href="#object-detection" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">34. </span>Object detection<a class="headerlink" href="#object-detection" title="Permalink to this headline">#</a></h1>
 <section id="what-is-object-detection">
-<h2><span class="section-number">35.1. </span>What is object detection?<a class="headerlink" href="#what-is-object-detection" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">34.1. </span>What is object detection?<a class="headerlink" href="#what-is-object-detection" title="Permalink to this headline">#</a></h2>
 <p>In this chapter we will introduce the object detection problem which can be described in this way: given an image or a video stream, an object detection model can identify which of a known set of objects might be present and provide information about their positions within the image.</p>
 <p>For example, this screenshot of the example application shows how three objects have been recognized and their positions annotated:</p>
 <figure class="align-default" id="example-of-the-object-detection-task">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/01_example.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/01_example.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/01_example.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 35.1 </span><span class="caption-text">Example of the object detection task</span><a class="headerlink" href="#example-of-the-object-detection-task" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 34.1 </span><span class="caption-text">Example of the object detection task</span><a class="headerlink" href="#example-of-the-object-detection-task" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Object detection has now been widely used in many real-world applications, such as autonomous driving, robot vision, video surveillance, etc. The following image shows the growing number of publications that are associated with “object detection” over the past two decades.</p>
 <figure class="align-default" id="number-of-pub">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/02_number_of_pub.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/02_number_of_pub.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/02_number_of_pub.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 35.2 </span><span class="caption-text">The increasing number of publications in object detection from 1998 to 2021</span><a class="headerlink" href="#number-of-pub" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 34.2 </span><span class="caption-text">The increasing number of publications in object detection from 1998 to 2021</span><a class="headerlink" href="#number-of-pub" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="challenges">
-<h2><span class="section-number">35.2. </span>Challenges<a class="headerlink" href="#challenges" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">34.2. </span>Challenges<a class="headerlink" href="#challenges" title="Permalink to this headline">#</a></h2>
 <p>In addition to some common challenges in other computer vision tasks such as objects under different viewpoints, illuminations,and intra-class variations, the challenges in object detection include but are not limited to the following aspects:</p>
 <ul class="simple">
 <li><p>object rotation and scale changes (e.g., small objects);</p></li>
@@ -1796,15 +1796,15 @@ <h2><span class="section-number">35.2. </span>Challenges<a class="headerlink" hr
 </ul>
 </section>
 <section id="history-classic-models">
-<h2><span class="section-number">35.3. </span>History &amp; classic models<a class="headerlink" href="#history-classic-models" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">34.3. </span>History &amp; classic models<a class="headerlink" href="#history-classic-models" title="Permalink to this headline">#</a></h2>
 <p>Since image classification is a classic task for computer vision, there are several models that are well-performed in the past. We can list them as follows: Faster R-CNN, Mask R-CNN, YOLO, FCOS, DETR. In this part, we will introduce them in order.</p>
 <section id="faster-r-cnn">
-<h3><span class="section-number">35.3.1. </span>Faster R-CNN<a class="headerlink" href="#faster-r-cnn" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">34.3.1. </span>Faster R-CNN<a class="headerlink" href="#faster-r-cnn" title="Permalink to this headline">#</a></h3>
 <p>Faster R-CNN is a single, unified network for object detection that utilizes a region proposal network (RPN) with the CNN model2. The RPN shares full-image convolutional features with the detection network, enabling nearly cost-free region proposals2. It is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position.</p>
 <figure class="align-default" id="the-structure-of-faster-rcnn">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/03_faster_rcnn.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/03_faster_rcnn.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/03_faster_rcnn.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 35.3 </span><span class="caption-text">The structure of Faster RCNN <span id="id1">[<a class="reference internal" href="#id85" title="Ross Girshick Shaoqing Ren, Kaiming He and Jian Sun. Faster r-cnn. URL: https://arxiv.org/abs/1506.01497 (visited on 2023).">SRS</a>]</span></span><a class="headerlink" href="#the-structure-of-faster-rcnn" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 34.3 </span><span class="caption-text">The structure of Faster RCNN <span id="id1">[<a class="reference internal" href="#id85" title="Ross Girshick Shaoqing Ren, Kaiming He and Jian Sun. Faster r-cnn. URL: https://arxiv.org/abs/1506.01497 (visited on 2023).">SRS</a>]</span></span><a class="headerlink" href="#the-structure-of-faster-rcnn" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="admonition note">
@@ -1812,7 +1812,7 @@ <h3><span class="section-number">35.3.1. </span>Faster R-CNN<a class="headerlink
 <p>We can see the paper <a class="reference external" href="https://arxiv.org/pdf/1506.01497.pdf">here</a>.</p>
 </div>
 <section id="code">
-<h4><span class="section-number">35.3.1.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">34.3.1.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h4>
 <p>Due to the complexity, here we just introduce the key parts of the model, Region Proposal Network(RPN).</p>
 <div class="cell docutils container">
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@@ -1852,12 +1852,12 @@ <h4><span class="section-number">35.3.1.1. </span>Code<a class="headerlink" href
 </section>
 </section>
 <section id="mask-r-cnn">
-<h3><span class="section-number">35.3.2. </span>Mask R-CNN<a class="headerlink" href="#mask-r-cnn" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">34.3.2. </span>Mask R-CNN<a class="headerlink" href="#mask-r-cnn" title="Permalink to this headline">#</a></h3>
 <p>Mask R-CNN is a deep neural network aimed to solve both object detection and instance segmentation problems. In other words, it can separate different objects in an image. You give it an image, it gives you the object bounding boxes, classes and masks. Mask R-CNN was developed on top of Faster R-CNN, a Region-Based Convolutional Neural Network. It is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps.</p>
 <figure class="align-default" id="the-structure-of-mask-rcnn">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/04_mask_rcnn.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/04_mask_rcnn.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/04_mask_rcnn.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 35.4 </span><span class="caption-text">The structure of Mask RCNN <span id="id2">[<a class="reference internal" href="#id86" title="Piotr Dollar Kaiming He, Georgia Gkioxari and Ross Girshick. Mask r-cnn. URL: https://arxiv.org/abs/1703.06870 (visited on 2023).">KHG</a>]</span></span><a class="headerlink" href="#the-structure-of-mask-rcnn" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 34.4 </span><span class="caption-text">The structure of Mask RCNN <span id="id2">[<a class="reference internal" href="#id86" title="Piotr Dollar Kaiming He, Georgia Gkioxari and Ross Girshick. Mask r-cnn. URL: https://arxiv.org/abs/1703.06870 (visited on 2023).">KHG</a>]</span></span><a class="headerlink" href="#the-structure-of-mask-rcnn" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="admonition note">
@@ -1865,7 +1865,7 @@ <h3><span class="section-number">35.3.2. </span>Mask R-CNN<a class="headerlink"
 <p>We can see the paper <a class="reference external" href="https://arxiv.org/pdf/1703.06870v3.pdf">here</a>.</p>
 </div>
 <section id="id3">
-<h4><span class="section-number">35.3.2.1. </span>Code<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">34.3.2.1. </span>Code<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h4>
 <p>The key parts in Mask RCNN are:</p>
 <ul class="simple">
 <li><p>RoIAlign</p></li>
@@ -2037,7 +2037,7 @@ <h4><span class="section-number">35.3.2.1. </span>Code<a class="headerlink" href
 </section>
 </section>
 <section id="fcos">
-<h3><span class="section-number">35.3.3. </span>FCOS<a class="headerlink" href="#fcos" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">34.3.3. </span>FCOS<a class="headerlink" href="#fcos" title="Permalink to this headline">#</a></h3>
 <p>FCOS (Fully Convolutional One-Stage Object Detection) is a one-stage object detection algorithm that uses a fully convolutional architecture to detect objects. It is a simple and effective method for object detection that does not require region proposal networks (RPNs) or anchor boxes.</p>
 <!---
 :::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/05_FCOS.png
@@ -2054,12 +2054,12 @@ <h3><span class="section-number">35.3.3. </span>FCOS<a class="headerlink" href="
 <p>We can see the paper <a class="reference external" href="https://arxiv.org/pdf/1904.01355v5.pdf">here</a>.</p>
 </div>
 <section id="id4">
-<h4><span class="section-number">35.3.3.1. </span>Code<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">34.3.3.1. </span>Code<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h4>
 <p>The key point in FCOS is Center-ness, which is a novel way to calculate target: <span class="math notranslate nohighlight">\(centerness = \sqrt{\frac{min(l,r)}{max(l,r)} \frac{min(t,b)}{max(t,b)}}\)</span></p>
 <figure class="align-default" id="illustration-of-centerness">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/06_centerness.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/06_centerness.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/06_centerness.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 35.5 </span><span class="caption-text">Illustration of centerness <span id="id5">[<a class="reference internal" href="#id87" title="Hao Chen Zhi Tian, Chunhua Shen and Tong He. Fcos: fully convolutional one-stage object detection. URL: https://arxiv.org/abs/1904.01355 (visited on 2023).">ZTH</a>]</span></span><a class="headerlink" href="#illustration-of-centerness" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 34.5 </span><span class="caption-text">Illustration of centerness <span id="id5">[<a class="reference internal" href="#id87" title="Hao Chen Zhi Tian, Chunhua Shen and Tong He. Fcos: fully convolutional one-stage object detection. URL: https://arxiv.org/abs/1904.01355 (visited on 2023).">ZTH</a>]</span></span><a class="headerlink" href="#illustration-of-centerness" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="cell docutils container">
@@ -2112,7 +2112,7 @@ <h4><span class="section-number">35.3.3.1. </span>Code<a class="headerlink" href
 </section>
 </section>
 <section id="detr">
-<h3><span class="section-number">35.3.4. </span>DETR<a class="headerlink" href="#detr" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">34.3.4. </span>DETR<a class="headerlink" href="#detr" title="Permalink to this headline">#</a></h3>
 <p>Similar to image classification, Transformer is also used in object detection task, and the classical one is DEtection TRansformer(DETR).</p>
 <!---
 :::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/objdet/07_DETR.png
@@ -2128,7 +2128,7 @@ <h3><span class="section-number">35.3.4. </span>DETR<a class="headerlink" href="
 <p>We can see the paper <a class="reference external" href="https://arxiv.org/pdf/2005.12872v3.pdf">here</a>.</p>
 </div>
 <section id="id6">
-<h4><span class="section-number">35.3.4.1. </span>Code<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">34.3.4.1. </span>Code<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
@@ -2194,11 +2194,11 @@ <h4><span class="section-number">35.3.4.1. </span>Code<a class="headerlink" href
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">35.4. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">34.4. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">35.5. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">34.5. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/tensorpack">Tensorpack</a> for creating the open-source project <a class="reference external" href="https://github.com/tensorpack/tensorpack">tensorpack</a>, <a class="reference external" href="https://github.com/Burf">Hyungjin Kim</a> for creating the open-source project <a class="reference external" href="https://github.com/Burf/TFDetection">TFDetection</a> and <a class="reference external" href="https://github.com/PaperCodeReview">PaperCodeReview</a> for creating the open-source project <a class="reference external" href="https://github.com/PaperCodeReview/DETR-TF">DETR-TF</a>. They inspire the majority of the content in this chapter.</p>
 <p style="text-align: center;">
 <iframe src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/object-detection/demo.html" width="105%" height="400px;"
@@ -2259,13 +2259,13 @@ <h2><span class="section-number">35.5. </span>Acknowledgments<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">34. </span>Diffusion Model</p>
+            <p class="prev-next-title"><span class="section-number">33. </span>Diffusion Model</p>
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     <a class='right-next' id="next-link" href="../machine-learning-productionization/overview.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">36. </span>Overview</p>
+        <p class="prev-next-title"><span class="section-number">35. </span>Overview</p>
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-    <title>27. Recurrent Neural Networks &#8212; Ocademy Open Machine Learning Book</title>
+    <title>26. Recurrent Neural Networks &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="search" title="Search" href="../search.html" />
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+    <link rel="next" title="27. Autoencoder" href="autoencoder.html" />
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+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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-     41.95. Intro to TensorFlow for Deep Learning
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+     40.106. Neural Networks for Classification with TensorFlow
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-     41.112. DQN On Foreign Exchange Market
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-     42.4. Data Science introduction
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-     42.16. Build an machine learning web application
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-     42.19. Model Selection
+     41.19. Model Selection
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-     42.20. Convolutional Neural Network
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-     42.21. Generative Adversarial Network
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@@ -1577,22 +1577,22 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#code">
-   27.1. Code
+   26.1. Code
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   27.2. Your turn! 🚀
+   26.2. Your turn! 🚀
   </a>
  </li>
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+   26.3. Self study
   </a>
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-   27.4. Acknowledgments
+   26.4. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1616,22 +1616,22 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#code">
-   27.1. Code
+   26.1. Code
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   27.2. Your turn! 🚀
+   26.2. Your turn! 🚀
   </a>
  </li>
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   <a class="reference internal nav-link" href="#self-study">
-   27.3. Self study
+   26.3. Self study
   </a>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   27.4. Acknowledgments
+   26.4. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1656,7 +1656,7 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="recurrent-neural-networks">
-<h1><span class="section-number">27. </span>Recurrent Neural Networks<a class="headerlink" href="#recurrent-neural-networks" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">26. </span>Recurrent Neural Networks<a class="headerlink" href="#recurrent-neural-networks" title="Permalink to this headline">#</a></h1>
 <blockquote>
 <div><p>The nervous system contains many circular paths, whose activity so regenerates the excitation of any participant neuron that reference to time past becomes indefinite, although it still implies that afferent activity has realized one of a certain class of configurations over time. Precise specification of these implications by means of recursive functions, and determination of those that can be embodied in the activity of nervous nets, completes the theory.</p>
 <p>– Warren McCulloch and Walter Pitts, 1943</p>
@@ -1666,28 +1666,28 @@ <h1><span class="section-number">27. </span>Recurrent Neural Networks<a class="h
 <figure class="align-default" id="sequential-data">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/sequential_data.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/sequential_data.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/sequential_data.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 27.1 </span><span class="caption-text">sequential data</span><a class="headerlink" href="#sequential-data" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 26.1 </span><span class="caption-text">sequential data</span><a class="headerlink" href="#sequential-data" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>And the RNNs use recurrent edge to update.</p>
 <figure class="align-default" id="rnn1">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn1.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn1.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn1.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 27.2 </span><span class="caption-text">RNN1</span><a class="headerlink" href="#rnn1" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 26.2 </span><span class="caption-text">RNN1</span><a class="headerlink" href="#rnn1" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>If unroll over a sequence <span class="math notranslate nohighlight">\((x_0,x_1,x_2)\)</span>.</p>
 <figure class="align-default" id="rnn2">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn2.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn2.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn2.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 27.3 </span><span class="caption-text">RNN2</span><a class="headerlink" href="#rnn2" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 26.3 </span><span class="caption-text">RNN2</span><a class="headerlink" href="#rnn2" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Then, the input (w0,w1,…,wt) sequence of words ( 1-hot encoded ) and the output (w1,w2,…,wt+1) shifted sequence of words ( 1-hot encoded ) have the following relation.</p>
 <figure class="align-default" id="rnn3">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn3.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn3.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn3.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 27.4 </span><span class="caption-text">RNN3</span><a class="headerlink" href="#rnn3" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 26.4 </span><span class="caption-text">RNN3</span><a class="headerlink" href="#rnn3" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The input projection is <span class="math notranslate nohighlight">\(x_t = Emb(\omega_t) = E\omega_t\)</span>, the recurrent connection is <span class="math notranslate nohighlight">\(h_t = g(W^h h_t + x_t + b^h)\)</span>, and the output projection should be <span class="math notranslate nohighlight">\(y = softmax(W^o h_t + b^o)\)</span>.
@@ -1695,7 +1695,7 @@ <h1><span class="section-number">27. </span>Recurrent Neural Networks<a class="h
 <figure class="align-default" id="rnn4">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn4.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn4.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/rnn4.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 27.5 </span><span class="caption-text">RNN4</span><a class="headerlink" href="#rnn4" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 26.5 </span><span class="caption-text">RNN4</span><a class="headerlink" href="#rnn4" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Let’s make the backpropagation process more clearly.</p>
@@ -1703,7 +1703,7 @@ <h1><span class="section-number">27. </span>Recurrent Neural Networks<a class="h
 <figure class="align-default" id="backpropagation-for-rnn">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/bp_rnn1.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/bp_rnn1.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/RNN/bp_rnn1.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 27.6 </span><span class="caption-text">backpropagation for RNN</span><a class="headerlink" href="#backpropagation-for-rnn" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 26.6 </span><span class="caption-text">backpropagation for RNN</span><a class="headerlink" href="#backpropagation-for-rnn" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Through the image, we can get the output:</p>
@@ -1724,7 +1724,7 @@ <h1><span class="section-number">27. </span>Recurrent Neural Networks<a class="h
 </ul>
 </div>
 <section id="code">
-<h2><span class="section-number">27.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">26.1. </span>Code<a class="headerlink" href="#code" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># A text classifier implemented in TensorFlow to classify SMS spam messages.</span>
@@ -1970,15 +1970,15 @@ <h2><span class="section-number">27.1. </span>Code<a class="headerlink" href="#c
 </div>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">27.2. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">26.2. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Practice the Recurrent Neural Networks by following this TBD.</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">27.3. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">26.3. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>TBD</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">27.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">26.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/nfmcclure">Nick</a> for creating the open-source course <a class="reference external" href="https://github.com/nfmcclure/tensorflow_cookbook">tensorflow_cookbook</a> and <a class="reference external" href="https://github.com/rasbt">Sebastian Raschka</a> for creating the open-sourse <a class="reference external" href="https://github.com/rasbt/stat453-deep-learning-ss20">stat453-deep-learning-ss20</a>. It inspires the majority of the content in this chapter.</p>
 <hr class="docutils" />
 <span class="target" id="id1"></span></section>
@@ -2021,13 +2021,13 @@ <h2><span class="section-number">27.4. </span>Acknowledgments<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">26. </span>Generative adversarial networks</p>
+            <p class="prev-next-title"><span class="section-number">25. </span>Generative adversarial networks</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="autoencoder.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">28. </span>Autoencoder</p>
+        <p class="prev-next-title"><span class="section-number">27. </span>Autoencoder</p>
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-    <title>30. Time series &#8212; Ocademy Open Machine Learning Book</title>
+    <title>29. Time series &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="31. Deep Q-learning" href="dqn.html" />
-    <link rel="prev" title="29. Long-short term memory" href="lstm.html" />
+    <link rel="next" title="30. Deep Q-learning" href="dqn.html" />
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-     5.1. Defining data science
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-     5.2. Data Science ethics
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-     5.3. Defining data
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-     5.4. Introduction to statistics and probability
+     4.4. Introduction to statistics and probability
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+       5.4.2. Data Selection
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+       5.4.3. Advanced Pandas Techniques
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-     7.3. Visualizing relationships: all about honey 🍯
+     6.3. Visualizing relationships: all about honey 🍯
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+     8.2. The “low code/no code” way
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-     9.3. Data Science in the cloud: The “Azure ML SDK” way
+     8.3. Data Science in the cloud: The “Azure ML SDK” way
     </a>
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+   10. Machine Learning overview
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+   13. Build a web app to use a Machine Learning model
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+     14.3. Yet other classifiers
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-     15.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
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+     15.1. Loss function
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+     15.2. Gradient descent
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+     16.1. Introduction to clustering
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-     17.2. K-Means clustering
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+     17.1. Bagging
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+     17.2. Random forest
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+     17.3. Feature importance
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-     19.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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+   21. Unsupervised learning: PCA and clustering
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-     25.3.1.1. Stylenet / Neural-Style
+     24.3.1.1. Stylenet / Neural-Style
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-     25.3.1.2. Deepdream in TensorFlow
+     24.3.1.2. Deepdream in TensorFlow
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+     40.3. First assignment
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+     40.10. Data Science scenarios
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-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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+     40.17. Classifying datasets
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+     40.18. Small diabetes study
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   </a>
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-   30.2. What is a Time Series?
+   29.2. What is a Time Series?
   </a>
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+     29.5.1. Defining the Forecasting Task
     </a>
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     </a>
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-       30.5.3.3. Recursive strategy
+       29.5.3.3. Recursive strategy
       </a>
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-       30.5.3.4. DirRec strategy
+       29.5.3.4. DirRec strategy
       </a>
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+     29.5.4. Example - Flu Trends
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+       29.5.4.2. Direct strategy
       </a>
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  </li>
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   <a class="reference internal nav-link" href="#your-turn">
-   30.6. Your turn! 🚀
+   29.6. Your turn! 🚀
   </a>
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   </a>
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@@ -1711,103 +1711,103 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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   </a>
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-   30.2. What is a Time Series?
+   29.2. What is a Time Series?
   </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#linear-regression-with-time-series">
-   30.3. Linear Regression with Time Series
+   29.3. Linear Regression with Time Series
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#time-step-features">
-     30.3.1. Time-step features
+     29.3.1. Time-step features
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
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-     30.3.2. Lag features
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     </a>
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   <a class="reference internal nav-link" href="#example-tunnel-traffic">
-   30.4. Example - Tunnel Traffic
+   29.4. Example - Tunnel Traffic
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#time-step-feature">
-     30.4.1. Time-step feature
+     29.4.1. Time-step feature
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#lag-feature">
-     30.4.2. Lag feature
+     29.4.2. Lag feature
     </a>
    </li>
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   <a class="reference internal nav-link" href="#forecasting-with-deep-learning">
-   30.5. Forecasting With Deep Learning
+   29.5. Forecasting With Deep Learning
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#defining-the-forecasting-task">
-     30.5.1. Defining the Forecasting Task
+     29.5.1. Defining the Forecasting Task
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#preparing-data-for-forecasting">
-     30.5.2. Preparing Data for Forecasting
+     29.5.2. Preparing Data for Forecasting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#multistep-forecasting-strategies">
-     30.5.3. Multistep Forecasting Strategies
+     29.5.3. Multistep Forecasting Strategies
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#multioutput-model">
-       30.5.3.1. Multioutput model
+       29.5.3.1. Multioutput model
       </a>
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-       30.5.3.2. Direct strategy
+       29.5.3.2. Direct strategy
       </a>
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       <a class="reference internal nav-link" href="#recursive-strategy">
-       30.5.3.3. Recursive strategy
+       29.5.3.3. Recursive strategy
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#dirrec-strategy">
-       30.5.3.4. DirRec strategy
+       29.5.3.4. DirRec strategy
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#example-flu-trends">
-     30.5.4. Example - Flu Trends
+     29.5.4. Example - Flu Trends
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
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+       29.5.4.1. Multioutput model
       </a>
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       <a class="reference internal nav-link" href="#id2">
-       30.5.4.2. Direct strategy
+       29.5.4.2. Direct strategy
       </a>
      </li>
     </ul>
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  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   30.6. Your turn! 🚀
+   29.6. Your turn! 🚀
   </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
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+   29.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1852,9 +1852,9 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="time-series">
-<h1><span class="section-number">30. </span>Time series<a class="headerlink" href="#time-series" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">29. </span>Time series<a class="headerlink" href="#time-series" title="Permalink to this headline">#</a></h1>
 <section id="overiew">
-<h2><span class="section-number">30.1. </span>Overiew<a class="headerlink" href="#overiew" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">29.1. </span>Overiew<a class="headerlink" href="#overiew" title="Permalink to this headline">#</a></h2>
 <p>Time series forecasting is a broad field with a long history and forcasting is perhaps the most common application in the real world. Businesses forecast product demand, governments forecast economic and population growth, meteorologists forecast the weather. The understanding of things to come is a pressing need across science, government, and industry (not to mention our personal lives!), and practitioners in these fields are increasingly applying machine learning to address this need.</p>
 <p>After finishing this course, you’ll know how to:</p>
 <ul class="simple">
@@ -1865,7 +1865,7 @@ <h2><span class="section-number">30.1. </span>Overiew<a class="headerlink" href=
 </ul>
 </section>
 <section id="what-is-a-time-series">
-<h2><span class="section-number">30.2. </span>What is a Time Series?<a class="headerlink" href="#what-is-a-time-series" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">29.2. </span>What is a Time Series?<a class="headerlink" href="#what-is-a-time-series" title="Permalink to this headline">#</a></h2>
 <p>The basic object of forecasting is the time series, which is a set of observations recorded over time. In forecasting applications, the observations are typically recorded with a regular frequency, like daily or monthly.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -1938,13 +1938,13 @@ <h2><span class="section-number">30.2. </span>What is a Time Series?<a class="he
 <p>This series records the number of hardcover book sales at a retail store over 30 days. Notice that we have a single column of observations <span class="math notranslate nohighlight">\(Hardcover\)</span> with a time index <span class="math notranslate nohighlight">\(Date\)</span>.</p>
 </section>
 <section id="linear-regression-with-time-series">
-<h2><span class="section-number">30.3. </span>Linear Regression with Time Series<a class="headerlink" href="#linear-regression-with-time-series" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">29.3. </span>Linear Regression with Time Series<a class="headerlink" href="#linear-regression-with-time-series" title="Permalink to this headline">#</a></h2>
 <p>For the first part of this course, we’ll use the linear regression algorithm to construct forecasting models. Linear regression is widely used in practice and adapts naturally to even complex forecasting tasks.</p>
 <p>The linear regression algorithm learns how to make a weighted sum from its input features. For two features, we would have:</p>
 <p><span class="math notranslate nohighlight">\(target = weight_1 * feature_1 + weight_2 * feature_2 + bias\)</span></p>
 <p>During training, the regression algorithm learns values for the parameters <span class="math notranslate nohighlight">\(weight_1\)</span>, <span class="math notranslate nohighlight">\(weight_2\)</span>, and <span class="math notranslate nohighlight">\(bias\)</span> that best fit the <span class="math notranslate nohighlight">\(target\)</span>. (This algorithm is often called ordinary least squares since it chooses values that minimize the squared error between the target and the predictions.) The weights are also called regression coefficients and the bias is also called the intercept because it tells you where the graph of this function crosses the y-axis.</p>
 <section id="time-step-features">
-<h3><span class="section-number">30.3.1. </span>Time-step features<a class="headerlink" href="#time-step-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">29.3.1. </span>Time-step features<a class="headerlink" href="#time-step-features" title="Permalink to this headline">#</a></h3>
 <p>There are two kinds of features unique to time series: time-step features and lag features.</p>
 <p>Time-step features are features we can derive directly from the time index. The most basic time-step feature is the time dummy, which counts off time steps in the series from beginning to end.</p>
 <div class="cell docutils container">
@@ -2052,7 +2052,7 @@ <h3><span class="section-number">30.3.1. </span>Time-step features<a class="head
 <p>Time-step features let you model time dependence. A series is time dependent if its values can be predicted from the time they occured. In the Hardcover Sales series, we can predict that sales later in the month are generally higher than sales earlier in the month.</p>
 </section>
 <section id="lag-features">
-<h3><span class="section-number">30.3.2. </span>Lag features<a class="headerlink" href="#lag-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">29.3.2. </span>Lag features<a class="headerlink" href="#lag-features" title="Permalink to this headline">#</a></h3>
 <p>To make a lag feature we shift the observations of the target series so that they appear to have occured later in time. Here we’ve created a 1-step lag feature, though shifting by multiple steps is possible too.</p>
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 <section id="example-tunnel-traffic">
-<h2><span class="section-number">30.4. </span>Example - Tunnel Traffic<a class="headerlink" href="#example-tunnel-traffic" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">29.4. </span>Example - Tunnel Traffic<a class="headerlink" href="#example-tunnel-traffic" title="Permalink to this headline">#</a></h2>
 <p>Tunnel Traffic is a time series describing the number of vehicles traveling through the Baregg Tunnel in Switzerland each day from November 2003 to November 2005. In this example, we’ll get some practice applying linear regression to time-step features and lag features.</p>
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 </div></div></div>
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-<h3><span class="section-number">30.4.1. </span>Time-step feature<a class="headerlink" href="#time-step-feature" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">29.4.1. </span>Time-step feature<a class="headerlink" href="#time-step-feature" title="Permalink to this headline">#</a></h3>
 <p>Provided the time series doesn’t have any missing dates, we can create a time dummy by counting out the length of the series.</p>
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 </div>
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 <section id="lag-feature">
-<h3><span class="section-number">30.4.2. </span>Lag feature<a class="headerlink" href="#lag-feature" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">29.4.2. </span>Lag feature<a class="headerlink" href="#lag-feature" title="Permalink to this headline">#</a></h3>
 <p>Pandas provides us a simple method to lag a series, the <span class="math notranslate nohighlight">\(shift\)</span> method.</p>
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 <section id="forecasting-with-deep-learning">
-<h2><span class="section-number">30.5. </span>Forecasting With Deep Learning<a class="headerlink" href="#forecasting-with-deep-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">29.5. </span>Forecasting With Deep Learning<a class="headerlink" href="#forecasting-with-deep-learning" title="Permalink to this headline">#</a></h2>
 <section id="defining-the-forecasting-task">
-<h3><span class="section-number">30.5.1. </span>Defining the Forecasting Task<a class="headerlink" href="#defining-the-forecasting-task" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">29.5.1. </span>Defining the Forecasting Task<a class="headerlink" href="#defining-the-forecasting-task" title="Permalink to this headline">#</a></h3>
 <p>There are two things to establish before designing a forecasting model:</p>
 <ul class="simple">
 <li><p>what information is available at the time a forecast is made (features),</p></li>
@@ -2495,13 +2495,13 @@ <h3><span class="section-number">30.5.1. </span>Defining the Forecasting Task<a
 <figure class="align-default" id="def">
 <a class="bg-white mb-1 reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/01_def.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/01_def.png" class="bg-white mb-1" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/01_def.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 30.1 </span><span class="caption-text">A three-step forecast horizon with a two-step lead time, using four lag features. The figure represents what would be a single row of training data – data for a single prediction, in other words.</span><a class="headerlink" href="#def" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 29.1 </span><span class="caption-text">A three-step forecast horizon with a two-step lead time, using four lag features. The figure represents what would be a single row of training data – data for a single prediction, in other words.</span><a class="headerlink" href="#def" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The time between the origin and the horizon is the lead time (or sometimes latency) of the forecast. A forecast’s lead time is described by the number of steps from origin to horizon: a “1-step ahead” or “3-step ahead” forecast, say. In practice, it may be necessary for a forecast to begin multiple steps ahead of the origin because of delays in data acquisition or processing.</p>
 </section>
 <section id="preparing-data-for-forecasting">
-<h3><span class="section-number">30.5.2. </span>Preparing Data for Forecasting<a class="headerlink" href="#preparing-data-for-forecasting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">29.5.2. </span>Preparing Data for Forecasting<a class="headerlink" href="#preparing-data-for-forecasting" title="Permalink to this headline">#</a></h3>
 <p>In order to forecast time series with ML algorithms, we need to transform the series into a dataframe we can use with those algorithms. (Unless, of course, you are only using deterministic features like trend and seasonality.)</p>
 <p>We saw the first half of this process in Lesson 4 when we created a feature set out of lags. The second half is preparing the target. How we do this depends on the forecasting task.</p>
 <p>Each row in a dataframe represents a single forecast. The time index of the row is the first time in the forecast horizon, but we arrange values for the entire horizon in the same row. For multistep forecasts, this means we are requiring a model to produce multiple outputs, one for each step.</p>
@@ -2697,51 +2697,51 @@ <h3><span class="section-number">30.5.2. </span>Preparing Data for Forecasting<a
 <p>The above illustrates how a dataset would be prepared similar to the Defining a Forecast figure: a three-step forecasting task with a two-step lead time using five lag features. The original time series is y_step_1. The missing values we could either fill-in or drop.</p>
 </section>
 <section id="multistep-forecasting-strategies">
-<h3><span class="section-number">30.5.3. </span>Multistep Forecasting Strategies<a class="headerlink" href="#multistep-forecasting-strategies" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">29.5.3. </span>Multistep Forecasting Strategies<a class="headerlink" href="#multistep-forecasting-strategies" title="Permalink to this headline">#</a></h3>
 <p>There are a number of strategies for producing the multiple target steps required for a forecast. We’ll outline four common strategies, each with strengths and weaknesses.</p>
 <section id="multioutput-model">
-<h4><span class="section-number">30.5.3.1. </span>Multioutput model<a class="headerlink" href="#multioutput-model" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">29.5.3.1. </span>Multioutput model<a class="headerlink" href="#multioutput-model" title="Permalink to this headline">#</a></h4>
 <p>Use a model that produces multiple outputs naturally. Linear regression and neural networks can both produce multiple outputs. This strategy is simple and efficient, but not possible for every algorithm you might want to use. XGBoost can’t do this, for instance.</p>
 <figure class="align-default" id="multioutput">
 <a class="bg-white mb-1 reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/02_multioutput.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/02_multioutput.png" class="bg-white mb-1" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/02_multioutput.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 30.2 </span><span class="caption-text">Multioutput Model</span><a class="headerlink" href="#multioutput" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 29.2 </span><span class="caption-text">Multioutput Model</span><a class="headerlink" href="#multioutput" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="direct-strategy">
-<h4><span class="section-number">30.5.3.2. </span>Direct strategy<a class="headerlink" href="#direct-strategy" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">29.5.3.2. </span>Direct strategy<a class="headerlink" href="#direct-strategy" title="Permalink to this headline">#</a></h4>
 <p>Train a separate model for each step in the horizon: one model forecasts 1-step ahead, another 2-steps ahead, and so on. Forecasting 1-step ahead is a different problem than 2-steps ahead (and so on), so it can help to have a different model make forecasts for each step. The downside is that training lots of models can be computationally expensive.</p>
 <figure class="align-default" id="direct">
 <a class="bg-white mb-1 reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/03_direct.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/03_direct.png" class="bg-white mb-1" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/03_direct.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 30.3 </span><span class="caption-text">Direct Strategy</span><a class="headerlink" href="#direct" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 29.3 </span><span class="caption-text">Direct Strategy</span><a class="headerlink" href="#direct" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="recursive-strategy">
-<h4><span class="section-number">30.5.3.3. </span>Recursive strategy<a class="headerlink" href="#recursive-strategy" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">29.5.3.3. </span>Recursive strategy<a class="headerlink" href="#recursive-strategy" title="Permalink to this headline">#</a></h4>
 <p>Train a single one-step model and use its forecasts to update the lag features for the next step. With the recursive method, we feed a model’s 1-step forecast back in to that same model to use as a lag feature for the next forecasting step. We only need to train one model, but since errors will propagate from step to step, forecasts can be inaccurate for long horizons.</p>
 <figure class="align-default" id="recursive">
 <a class="bg-white mb-1 reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/04_recursive.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/04_recursive.png" class="bg-white mb-1" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/04_recursive.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 30.4 </span><span class="caption-text">Recursive Strategy</span><a class="headerlink" href="#recursive" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 29.4 </span><span class="caption-text">Recursive Strategy</span><a class="headerlink" href="#recursive" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="dirrec-strategy">
-<h4><span class="section-number">30.5.3.4. </span>DirRec strategy<a class="headerlink" href="#dirrec-strategy" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">29.5.3.4. </span>DirRec strategy<a class="headerlink" href="#dirrec-strategy" title="Permalink to this headline">#</a></h4>
 <p>A combination of the direct and recursive strategies: train a model for each step and use forecasts from previous steps as new lag features. Step by step, each model gets an additional lag input. Since each model always has an up-to-date set of lag features, the DirRec strategy can capture serial dependence better than Direct, but it can also suffer from error propagation like Recursive.</p>
 <figure class="align-default" id="dirrec">
 <a class="bg-white mb-1 reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/05_dirrec.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/05_dirrec.png" class="bg-white mb-1" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/time-series/05_dirrec.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 30.5 </span><span class="caption-text">DirRec Strategy</span><a class="headerlink" href="#dirrec" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 29.5 </span><span class="caption-text">DirRec Strategy</span><a class="headerlink" href="#dirrec" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 </section>
 <section id="example-flu-trends">
-<h3><span class="section-number">30.5.4. </span>Example - Flu Trends<a class="headerlink" href="#example-flu-trends" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">29.5.4. </span>Example - Flu Trends<a class="headerlink" href="#example-flu-trends" title="Permalink to this headline">#</a></h3>
 <p>In this example we’ll apply the MultiOutput and Direct strategies to the Flu Trends data from Lesson 4, this time making true forecasts for multiple weeks beyond the training period.</p>
 <p>We’ll define our forecasting task to have an 8-week horizon with a 1-week lead time. In other words, we’ll be forecasting eight weeks of flu cases starting with the following week.</p>
 <p>The hidden cell sets up the example and defines a helper function <span class="math notranslate nohighlight">\(plot_multistep\)</span>.</p>
@@ -2840,7 +2840,7 @@ <h3><span class="section-number">30.5.4. </span>Example - Flu Trends<a class="he
 </div>
 </div>
 <section id="id1">
-<h4><span class="section-number">30.5.4.1. </span>Multioutput model<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">29.5.4.1. </span>Multioutput model<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
 <p>We’ll use linear regression as a MultiOutput strategy. Once we have our data prepared for multiple outputs, training and prediction is the same as always.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2884,7 +2884,7 @@ <h4><span class="section-number">30.5.4.1. </span>Multioutput model<a class="hea
 </div>
 </section>
 <section id="id2">
-<h4><span class="section-number">30.5.4.2. </span>Direct strategy<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">29.5.4.2. </span>Direct strategy<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
 <p>XGBoost can’t produce multiple outputs for regression tasks. But by applying the Direct reduction strategy, we can still use it to produce multi-step forecasts. This is as easy as wrapping it with scikit-learn’s <span class="math notranslate nohighlight">\(MultiOutputRegressor\)</span>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2930,11 +2930,11 @@ <h4><span class="section-number">30.5.4.2. </span>Direct strategy<a class="heade
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">30.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">29.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">30.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">29.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/">kaggle</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/learn/time-series">Time Series</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2976,13 +2976,13 @@ <h2><span class="section-number">30.7. </span>Acknowledgments<a class="headerlin
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-            <p class="prev-next-title"><span class="section-number">29. </span>Long-short term memory</p>
+            <p class="prev-next-title"><span class="section-number">28. </span>Long-short term memory</p>
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-        <p class="prev-next-title"><span class="section-number">31. </span>Deep Q-learning</p>
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+     40.14. Try it in Excel
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+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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+     6.3. Visualizing relationships: all about honey 🍯
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+     8.2. The “low code/no code” way
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+     8.3. Data Science in the cloud: The “Azure ML SDK” way
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+     14.4. Applied Machine Learning : build a web app
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+     40.12. Lines, scatters and bars
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+     40.13. Apply your skills
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+     40.14. Try it in Excel
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+     40.24. Data processing in Python
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-     41.95. Intro to TensorFlow for Deep Learning
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-     42.20. Convolutional Neural Network
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@@ -1575,47 +1575,47 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
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+   37.1. Data quality
   </a>
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-   38.4. Cataloging and search layer
+   37.4. Cataloging and search layer
   </a>
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-   38.5. Processing layer
+   37.5. Processing layer
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-   38.6. Consumption layer
+   37.6. Consumption layer
   </a>
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-   38.7. Security and governance layer
+   37.7. Security and governance layer
   </a>
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-   38.8. Showcase
+   37.8. Showcase
   </a>
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   <a class="reference internal nav-link" href="#your-turn">
-   38.9. Your turn! 🚀
+   37.9. Your turn! 🚀
   </a>
  </li>
 </ul>
@@ -1639,47 +1639,47 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-quality">
-   38.1. Data quality
+   37.1. Data quality
   </a>
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-   38.2. Ingestion layer
+   37.2. Ingestion layer
   </a>
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-   38.3. Storage layer
+   37.3. Storage layer
   </a>
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-   38.4. Cataloging and search layer
+   37.4. Cataloging and search layer
   </a>
  </li>
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-   38.5. Processing layer
+   37.5. Processing layer
   </a>
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-   38.6. Consumption layer
+   37.6. Consumption layer
   </a>
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-   38.7. Security and governance layer
+   37.7. Security and governance layer
   </a>
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-   38.8. Showcase
+   37.8. Showcase
   </a>
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   <a class="reference internal nav-link" href="#your-turn">
-   38.9. Your turn! 🚀
+   37.9. Your turn! 🚀
   </a>
  </li>
 </ul>
@@ -1704,7 +1704,7 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="data-engineering">
-<h1><span class="section-number">38. </span>Data engineering<a class="headerlink" href="#data-engineering" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">37. </span>Data engineering<a class="headerlink" href="#data-engineering" title="Permalink to this headline">#</a></h1>
 <p><strong><a class="reference external" href="https://en.wikipedia.org/wiki/Information_engineering">Data engineering</a></strong>, also known as <strong>Information engineering</strong>, is a software engineering approach to designing and developing information systems. A data engineer is someone who creates big data ETL pipelines and makes it possible to take huge amounts of data and translate it into insights.</p>
 <blockquote>
 <div><p>“Data is food for AI.”
@@ -1717,26 +1717,26 @@ <h1><span class="section-number">38. </span>Data engineering<a class="headerlink
 <figure class="align-default" id="big-data-pipeline-on-aws-microsoft-azure-and-google-cloud">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/big-data-pipelines-on-aws-azure-gcp.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/big-data-pipelines-on-aws-azure-gcp.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 38.1 </span><span class="caption-text"><a class="reference external" href="https://www.reddit.com/r/bigdata/comments/mkfsfi/big_data_pipeline_on_aws_microsoft_azure_and/">Big Data Pipeline on AWS, Microsoft Azure, and Google Cloud</a></span><a class="headerlink" href="#big-data-pipeline-on-aws-microsoft-azure-and-google-cloud" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 37.1 </span><span class="caption-text"><a class="reference external" href="https://www.reddit.com/r/bigdata/comments/mkfsfi/big_data_pipeline_on_aws_microsoft_azure_and/">Big Data Pipeline on AWS, Microsoft Azure, and Google Cloud</a></span><a class="headerlink" href="#big-data-pipeline-on-aws-microsoft-azure-and-google-cloud" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p><strong>Architecture: Capture, process, store, analyze, use</strong></p>
 <figure class="align-default" id="how-to-build-a-data-analytics-pipeline-on-google-cloud">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/how-to-build-a-scalable-data-analytics-pipeline.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/how-to-build-a-scalable-data-analytics-pipeline.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 38.2 </span><span class="caption-text"><a class="reference external" href="https://www.freecodecamp.org/news/scalable-data-analytics-pipeline/">How to build a Data Analytics Pipeline on Google Cloud?</a></span><a class="headerlink" href="#how-to-build-a-data-analytics-pipeline-on-google-cloud" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 37.2 </span><span class="caption-text"><a class="reference external" href="https://www.freecodecamp.org/news/scalable-data-analytics-pipeline/">How to build a Data Analytics Pipeline on Google Cloud?</a></span><a class="headerlink" href="#how-to-build-a-data-analytics-pipeline-on-google-cloud" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p><strong>Ingestion, storage, cataloging &amp; search, processing, consumption, security &amp; governance</strong></p>
 <figure class="align-default" id="aws-serverless-data-analytics-pipeline">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/aws-serverless-data-analytics-pipeline.jpeg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/aws-serverless-data-analytics-pipeline.jpeg" />
 <figcaption>
-<p><span class="caption-number">Fig. 38.3 </span><span class="caption-text"><a class="reference external" href="https://aws.amazon.com/blogs/big-data/aws-serverless-data-analytics-pipeline-reference-architecture/">AWS serverless data analytics pipeline</a></span><a class="headerlink" href="#aws-serverless-data-analytics-pipeline" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 37.3 </span><span class="caption-text"><a class="reference external" href="https://aws.amazon.com/blogs/big-data/aws-serverless-data-analytics-pipeline-reference-architecture/">AWS serverless data analytics pipeline</a></span><a class="headerlink" href="#aws-serverless-data-analytics-pipeline" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>In this section, the <strong>AWS serverless data analytics pipeline</strong> architecture above is used as a reference to breaking down the data pipeline into 6 layers. Before moving into each of them, we will first have a glance at the concept of data quality. And at the last, we will go through an AWS cloud-based data pipeline architecture showcase.</p>
 <section id="data-quality">
-<h2><span class="section-number">38.1. </span>Data quality<a class="headerlink" href="#data-quality" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.1. </span>Data quality<a class="headerlink" href="#data-quality" title="Permalink to this headline">#</a></h2>
 <p><a class="reference external" href="https://en.wikipedia.org/wiki/Data_quality">Data quality</a> refers to the state of qualitative or quantitative pieces of information. Generally speaking, high-quality data could “fit for [its] intended uses in operations, decision making and planning”. Data quality is a widely used concept and is split into multiple dimensions traditionally. This section will discuss the most common ones to Machine Learning data engineering, including completeness, duplicates, outlier, and consistency.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -1757,7 +1757,7 @@ <h2><span class="section-number">38.1. </span>Data quality<a class="headerlink"
 <p><strong>Data consistency</strong>. A dataset could be constructed from different data sources. For example, a retailer usually doesn’t have a centralized way to manage its supply chain. Instead, it achieves this by combining different ERP systems across demand, supply, shipment, inventory, and so on. Those ERP systems are vend from different software companies which have their solutions to structure and organize the data, e.g. to present timestamp as Unix epoch vs. the ISO format. Consistency data is necessary to build Machine Learning models. It is also important to help data scientists and business owners to have meaningful business analytics.</p>
 </section>
 <section id="ingestion-layer">
-<h2><span class="section-number">38.2. </span>Ingestion layer<a class="headerlink" href="#ingestion-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.2. </span>Ingestion layer<a class="headerlink" href="#ingestion-layer" title="Permalink to this headline">#</a></h2>
 <p>Data ingestion is used to load data records from one or more sources, e.g. IoT devices, data lakes, databases, SaaS applications, etc., into a target data warehouse. It is the layer between the data source and data processing. Once ingested, the data becomes available in the data pipeline. There are three major ways to ingest data.</p>
 <ul class="simple">
 <li><p><strong>Batch ingestion</strong>, where the ingestion layer collects data from sources incrementally and sends it to the data storage in batch. Data can be grouped based on a schedule or certain rules. This approach is generally used for use cases that don’t require real-time data. And it is typically cost-effective and less expensive.</p></li>
@@ -1766,7 +1766,7 @@ <h2><span class="section-number">38.2. </span>Ingestion layer<a class="headerlin
 </ul>
 </section>
 <section id="storage-layer">
-<h2><span class="section-number">38.3. </span>Storage layer<a class="headerlink" href="#storage-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.3. </span>Storage layer<a class="headerlink" href="#storage-layer" title="Permalink to this headline">#</a></h2>
 <p>The storage layer is responsible to provide durability, scalability, security, and cost-effectivity for big data storage. It supports storing both unstructured data and structured datasets in different formats. The data is usually loaded as-is without any processing to conform to a target format so that the ingestion layer could quickly land the source data into the storage. During processing, the data will be transformed from the original format to the one defined by the catalog layer, which is ready to be consumed downstream. To achieve the above, the storage layer could be organized into 3 different zones.</p>
 <ul class="simple">
 <li><p><strong>Raw data zone</strong>, which works as a transient area to store the ingested data from sources as-is.</p></li>
@@ -1775,11 +1775,11 @@ <h2><span class="section-number">38.3. </span>Storage layer<a class="headerlink"
 </ul>
 </section>
 <section id="cataloging-and-search-layer">
-<h2><span class="section-number">38.4. </span>Cataloging and search layer<a class="headerlink" href="#cataloging-and-search-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.4. </span>Cataloging and search layer<a class="headerlink" href="#cataloging-and-search-layer" title="Permalink to this headline">#</a></h2>
 <p>Once collected into the storage layer, the data needs to be cataloged and accessible to be actually usable in the remaining workflow, especially if the data volume is big. The cataloging and search layer is responsible to store metadata about datasets hosted in the storage layer, which is applied to business in the consumption layer and technical in the processing layer. It provides the ability to manage schema and makes datasets discoverable by providing search capabilities.</p>
 </section>
 <section id="processing-layer">
-<h2><span class="section-number">38.5. </span>Processing layer<a class="headerlink" href="#processing-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.5. </span>Processing layer<a class="headerlink" href="#processing-layer" title="Permalink to this headline">#</a></h2>
 <p>The processing layer is responsible for transforming data to be consumable across the data storage zones, which includes multiple technologies such as data validation, cleanup, normalization, transformation, and enrichment. The processing layer needs to handle large data volumes with diverse data formats. The processing layer also provides the ability to build the processing workflow across the data storage zones, so that more complex processing logic could be better handled. Therefore, the processing layer could be composed of three types of components:</p>
 <ul class="simple">
 <li><p><strong>initializing component</strong>, which is used to create a multi-step data processing workflow on schedule or in response to event triggers,</p></li>
@@ -1788,7 +1788,7 @@ <h2><span class="section-number">38.5. </span>Processing layer<a class="headerli
 </ul>
 </section>
 <section id="consumption-layer">
-<h2><span class="section-number">38.6. </span>Consumption layer<a class="headerlink" href="#consumption-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.6. </span>Consumption layer<a class="headerlink" href="#consumption-layer" title="Permalink to this headline">#</a></h2>
 <p>The consumption layer is responsible to provide scalable and performant tools to gain insights from the vast amount of data in the data storage. To achieve the best flexibility, this layer needs to support different types of data structures and formats, and use data partitioning to optimize cost and performance. The consumption layer is composed using analytics services, tools, and methods that enable:</p>
 <ul class="simple">
 <li><p><strong>interactive SQL</strong> to allow sending a SQL query to search and getting documents streamed back in response,</p></li>
@@ -1798,7 +1798,7 @@ <h2><span class="section-number">38.6. </span>Consumption layer<a class="headerl
 </ul>
 </section>
 <section id="security-and-governance-layer">
-<h2><span class="section-number">38.7. </span>Security and governance layer<a class="headerlink" href="#security-and-governance-layer" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.7. </span>Security and governance layer<a class="headerlink" href="#security-and-governance-layer" title="Permalink to this headline">#</a></h2>
 <p>The security and governance layer is responsible for protecting the data, identities, and processing resources across all other layers. It provides capabilities to manage the below aspects, including but not limited to below:</p>
 <ul class="simple">
 <li><p><strong>authentication and authorization</strong> to configure fine-grained access control for resources in all layers of the architecture,</p></li>
@@ -1808,12 +1808,12 @@ <h2><span class="section-number">38.7. </span>Security and governance layer<a cl
 </ul>
 </section>
 <section id="showcase">
-<h2><span class="section-number">38.8. </span>Showcase<a class="headerlink" href="#showcase" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.8. </span>Showcase<a class="headerlink" href="#showcase" title="Permalink to this headline">#</a></h2>
 <p>Today, most of the main cloud providers offer their own serverless data pipeline solutions. They all target to enable agile and self-service data onboarding and analytics for the whole dataset, although are built on top of different cloud service stacks. By leveraging the data pipeline, engineers and scientists could focus more time on rapidly building data and analytics pipelines to drive insights for the business.</p>
 <figure class="align-default" id="aws-serverless-data-analytics-pipeline-reference-architecture">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/aws-serverless-data-lake-centric-analytics-architecture.jpeg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/aws-serverless-data-lake-centric-analytics-architecture.jpeg" />
 <figcaption>
-<p><span class="caption-number">Fig. 38.4 </span><span class="caption-text"><a class="reference external" href="https://aws.amazon.com/blogs/big-data/aws-serverless-data-analytics-pipeline-reference-architecture/">Serverless data lake centric analytics architecture</a></span><a class="headerlink" href="#aws-serverless-data-analytics-pipeline-reference-architecture" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 37.4 </span><span class="caption-text"><a class="reference external" href="https://aws.amazon.com/blogs/big-data/aws-serverless-data-analytics-pipeline-reference-architecture/">Serverless data lake centric analytics architecture</a></span><a class="headerlink" href="#aws-serverless-data-analytics-pipeline-reference-architecture" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>This diagram describes a reference architecture that uses AWS-managed services which compose the 6 layers mentioned in the above logical architecture. More specifically to this showcase:</p>
@@ -1828,7 +1828,7 @@ <h2><span class="section-number">38.8. </span>Showcase<a class="headerlink" href
 <p>If the pipeline is built on other cloud computing platforms, equivalent services could be easily found across all the layers. The architecture could also be simplified or customized according to the business needs.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">38.9. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">37.9. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Data cleaning is a key part of data engineering to improve the <a class="reference external" href="#data-quality">data quality</a>, but it can be deeply frustrating as the situation could be highly varied in different datasets. Sometimes you will see the text fields garbled. Sometimes your dates are formatted incorrectly. In this <a class="reference internal" href="../assignments/machine-learning-productionization/data-engineering.html"><span class="doc std std-doc">assignment</span></a>, you’ll work through three hands-on exercises to deal with messy data.</p>
 </section>
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-            <p class="prev-next-title"><span class="section-number">37. </span>Problem framing</p>
+            <p class="prev-next-title"><span class="section-number">36. </span>Problem framing</p>
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-        <p class="prev-next-title"><span class="section-number">39. </span>Model training &amp; evaluation</p>
+        <p class="prev-next-title"><span class="section-number">38. </span>Model training &amp; evaluation</p>
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diff --git a/machine-learning-productionization/model-deployment.html b/machine-learning-productionization/model-deployment.html
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+    <title>39. Model deployment &#8212; Ocademy Open Machine Learning Book</title>
     
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+    <link rel="next" title="40. Self-paced assignments" href="../assignments/README.html" />
+    <link rel="prev" title="38. Model training &amp; evaluation" href="model-training-and-evaluation.html" />
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+     40.3. First assignment
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+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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@@ -1575,27 +1575,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
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+   39.5. Your turn! 🚀
   </a>
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 </ul>
@@ -1619,27 +1619,27 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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@@ -1664,7 +1664,7 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="model-deployment">
-<h1><span class="section-number">40. </span>Model deployment<a class="headerlink" href="#model-deployment" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">39. </span>Model deployment<a class="headerlink" href="#model-deployment" title="Permalink to this headline">#</a></h1>
 <p>Jez Humble and David Farley define continuous delivery as the capacity to safely and efficiently introduce a wide range of changes, such as new features, configuration adjustments, bug fixes, and experiments, into production or the hands of users. <a class="reference external" href="https://en.wikipedia.org/wiki/Continuous_delivery">Continuous delivery</a> is a critical component for the automated production of high-quality software throughout the development pipeline. By emphasizing sustainable practices, Humble and Farley highlight the importance of ensuring that changes are made in a consistent and reliable manner.</p>
 <p>Once the Machine Learning model is trained, it needs to be deployed as part of a business application such as a mobile or desktop application. The Machine Learning model requires various data points to produce predictions. The final stage of the Machine Learning workflow is the delivery of the previously engineered Machine Learning model into existing software.</p>
 <p>The model deployment is a very challenging stage, as it needs to handle both traditional software challenges as well as Machine Learning specific challenges.</p>
@@ -1673,39 +1673,39 @@ <h1><span class="section-number">40. </span>Model deployment<a class="headerlink
 <figure class="align-default" id="ml-axis-of-change">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-axis-of-change.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-axis-of-change.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 40.1 </span><span class="caption-text"><a class="reference external" href="https://martinfowler.com/articles/cd4ml.html">The 3 axis of change in a Machine Learning application — data, model, and code — and a few reasons for them to change</a></span><a class="headerlink" href="#ml-axis-of-change" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 39.1 </span><span class="caption-text"><a class="reference external" href="https://martinfowler.com/articles/cd4ml.html">The 3 axis of change in a Machine Learning application — data, model, and code — and a few reasons for them to change</a></span><a class="headerlink" href="#ml-axis-of-change" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>At last, Machine Learning development needs the coordination of scientists, software engineers, data engineers, and business professionals. This introduces extra challenges to ensure the model works reliably and delivers the desired result.</p>
 <p>In this section, we will describe the important technical components for implementing Machine Learning model deployment, explain the concepts and demonstrate how different patterns can be used to implement the full end-to-end process.</p>
 <section id="deployment-strategies">
-<h2><span class="section-number">40.1. </span>Deployment strategies<a class="headerlink" href="#deployment-strategies" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">39.1. </span>Deployment strategies<a class="headerlink" href="#deployment-strategies" title="Permalink to this headline">#</a></h2>
 <p>Deployment strategies are practices used to change or upgrade a running instance of an application.</p>
 <p><strong>Basic deployment</strong> implies that all nodes within a target environment (v1.0) are updated with a new version (v2.0) at the same time. This is the simplest but riskiest strategy because all the nodes will go down if the deployed artifacts are broken. This strategy also results in slow deployment rollback.</p>
 <figure class="align-default" id="basic-deployment-strategy">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/basic-deployment-strategy.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/basic-deployment-strategy.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 40.2 </span><span class="caption-text"><a class="reference external" href="https://harness.io/blog/blue-green-canary-deployment-strategies">Basic deployment strategy</a></span><a class="headerlink" href="#basic-deployment-strategy" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 39.2 </span><span class="caption-text"><a class="reference external" href="https://harness.io/blog/blue-green-canary-deployment-strategies">Basic deployment strategy</a></span><a class="headerlink" href="#basic-deployment-strategy" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>In practice, the real-world system is usually composed of many inter-depended modules or services. In this case, the <strong>multi-service deployment</strong> could be considered an option. To this strategy, all nodes within a target environment are updated with multiple new services simultaneously. This reduces the risk of system level failure but introduces the difficulty of managing the service dependencies in both deployment and rollback.</p>
 <figure class="align-default" id="multi-service-deployment-strategy">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/multi-service-deployment-strategy.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/multi-service-deployment-strategy.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 40.3 </span><span class="caption-text"><a class="reference external" href="https://harness.io/blog/blue-green-canary-deployment-strategies">Multi-service deployment strategy</a></span><a class="headerlink" href="#multi-service-deployment-strategy" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 39.3 </span><span class="caption-text"><a class="reference external" href="https://harness.io/blog/blue-green-canary-deployment-strategies">Multi-service deployment strategy</a></span><a class="headerlink" href="#multi-service-deployment-strategy" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>There are several options to help avoid the above issues. <strong>Rolling deployment</strong> incrementally updates all nodes in a target environment with the service or artifact version in batches. <strong>Canary development</strong> is similar but targets to roll out the new version to a certain subset of users instead of services. Both strategies could reduce the risk of failure by introducing finer granularity control to the deployment process. But they require services to support both new and old versions of an artifact, which make it more complex to manage the rollout and rollback.</p>
 <figure class="align-default" id="rolling-deployment-strategy">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/rolling-deployment-strategy.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/rolling-deployment-strategy.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 40.4 </span><span class="caption-text"><a class="reference external" href="https://harness.io/blog/blue-green-canary-deployment-strategies">Rolling deployment strategy</a></span><a class="headerlink" href="#rolling-deployment-strategy" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 39.4 </span><span class="caption-text"><a class="reference external" href="https://harness.io/blog/blue-green-canary-deployment-strategies">Rolling deployment strategy</a></span><a class="headerlink" href="#rolling-deployment-strategy" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="canary-deployment-strategy">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/canary-deployment-strategy.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/canary-deployment-strategy.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 40.5 </span><span class="caption-text"><a class="reference external" href="https://harness.io/blog/blue-green-canary-deployment-strategies">Canary deployment strategy</a></span><a class="headerlink" href="#canary-deployment-strategy" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 39.5 </span><span class="caption-text"><a class="reference external" href="https://harness.io/blog/blue-green-canary-deployment-strategies">Canary deployment strategy</a></span><a class="headerlink" href="#canary-deployment-strategy" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="admonition seealso">
@@ -1720,12 +1720,12 @@ <h2><span class="section-number">40.1. </span>Deployment strategies<a class="hea
 <figure class="align-default" id="blue-green-deployment-strategy">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/blue-green-deployment-strategy.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/blue-green-deployment-strategy.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 40.6 </span><span class="caption-text"><a class="reference external" href="https://harness.io/blog/blue-green-canary-deployment-strategies">Blue/Green deployment strategy</a></span><a class="headerlink" href="#blue-green-deployment-strategy" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 39.6 </span><span class="caption-text"><a class="reference external" href="https://harness.io/blog/blue-green-canary-deployment-strategies">Blue/Green deployment strategy</a></span><a class="headerlink" href="#blue-green-deployment-strategy" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="deployment-evolution">
-<h2><span class="section-number">40.2. </span>Deployment evolution<a class="headerlink" href="#deployment-evolution" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">39.2. </span>Deployment evolution<a class="headerlink" href="#deployment-evolution" title="Permalink to this headline">#</a></h2>
 <p>When computers were extremely large, expensive, and bulky, the software was often bundled together with the hardware by manufacturers. In the 1980s, new forms of software distribution, such as floppy disks and optical media, came together with the popularity of microcomputers, which meant the software deployment must be faster and more user-friendly. And since the internet age, agile software development has become possible. The advent of cloud computing and software as a service make software able to be deployed to a large number of customers in minutes over the internet.</p>
 <p>As software development evolved, the determining factors of software deployment are changing as well. This requires the art-of-the-state deployment strategies and modes to provide better flexibility for modern computer applications.</p>
 <p><strong>Deployment 1.0</strong></p>
@@ -1747,7 +1747,7 @@ <h2><span class="section-number">40.2. </span>Deployment evolution<a class="head
 </div>
 </section>
 <section id="serving">
-<h2><span class="section-number">40.3. </span>Serving<a class="headerlink" href="#serving" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">39.3. </span>Serving<a class="headerlink" href="#serving" title="Permalink to this headline">#</a></h2>
 <p>It is crucial to clarify the intended experiments before deploying the trained models to users. This ensures that the models adhere to the constraints specified in the problem setup. What kind of feedback should be obtained from users? Should the model be updated online with every new data point? How can the model be personalized for individual users? How often should the Machine Learning algorithm be updated? Is there a possibility of biases and misuses in the model?</p>
 <p>Regardless of which serving pattern to use, there is always an implicit contract between the model and users. The model will usually expect input data in a certain shape, and the changed model will require the contract to be updated for new input or add new features, which may cause integration issues and break the applications using it. Which leads the necessity of continuously monitoring and testing he model. However, to serve and use the model in production, there are a few patterns to achieve.</p>
 <p><strong>Model as module</strong></p>
@@ -1760,7 +1760,7 @@ <h2><span class="section-number">40.3. </span>Serving<a class="headerlink" href=
 <p>This is another approach to have the Machine Learning model treated and published independently, but the application will ingest it as data at runtime instead. This is more commonly used in streaming or real-time scenarios where the application can subscribe to events that are published whenever a new model version is released. The model data will be ingested into memory for predicting, which avoid introducing the extra latency. Deployment strategies such as Blue/Green deployment or Canary deployment can be applied in this scenario. Some of the above serialization options are also applicable for implementing the “model as data” pattern.</p>
 </section>
 <section id="infrastructure-as-code">
-<h2><span class="section-number">40.4. </span>Infrastructure as code<a class="headerlink" href="#infrastructure-as-code" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">39.4. </span>Infrastructure as code<a class="headerlink" href="#infrastructure-as-code" title="Permalink to this headline">#</a></h2>
 <p><a class="reference external" href="https://en.wikipedia.org/wiki/Infrastructure_as_code">Infrastructure as code</a> (IaC) is the process of managing and provisioning computer data centers through machine-readable definition files, rather than physical hardware configuration or interactive configuration tools. The IT infrastructure managed by this process comprises both physical equipment, such as bare-metal servers, as well as virtual machines, and associated configuration resources.</p>
 <p><a class="reference external" href="https://docs.github.com/en/actions">GitHub Actions</a> is one of the most well-known IaC implementations, although people may not treat it as IoC. Github Actions makes it easy to automate all the Github software workflows with CI/CD support. By leveraging Github Actions, the developer could manage any infrastructure tasks, including build, test, deploy as well as code reviews, branch management, and issue triaging. Below is some sample infrastructure code from <a class="reference external" href="https://ghdocs-prod.azurewebsites.net/en/actions/quickstart">Github Actions quickstart</a>.</p>
 <div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">GitHub Actions Demo</span>
@@ -1786,20 +1786,20 @@ <h2><span class="section-number">40.4. </span>Infrastructure as code<a class="he
 <figure class="align-default" id="actions-quickstart-logs">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/actions-quickstart-logs.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/actions-quickstart-logs.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 40.7 </span><span class="caption-text"><a class="reference external" href="https://ghdocs-prod.azurewebsites.net/en/actions/quickstart">Github Actions Quickstart Logs</a></span><a class="headerlink" href="#actions-quickstart-logs" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 39.7 </span><span class="caption-text"><a class="reference external" href="https://ghdocs-prod.azurewebsites.net/en/actions/quickstart">Github Actions Quickstart Logs</a></span><a class="headerlink" href="#actions-quickstart-logs" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The log shows how each of the steps is processed. And, here is the list of files in the repository.</p>
 <figure class="align-default" id="actions-quickstart-log-detail">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/actions-quickstart-log-detail.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/actions-quickstart-log-detail.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 40.8 </span><span class="caption-text"><a class="reference external" href="https://ghdocs-prod.azurewebsites.net/en/actions/quickstart">Github Actions Quickstart Log Detail</a></span><a class="headerlink" href="#actions-quickstart-log-detail" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 39.8 </span><span class="caption-text"><a class="reference external" href="https://ghdocs-prod.azurewebsites.net/en/actions/quickstart">Github Actions Quickstart Log Detail</a></span><a class="headerlink" href="#actions-quickstart-log-detail" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The RedHat-sponsored <a class="reference external" href="https://github.com/ansible/ansible">Ansible</a> is an open-source IaC framework handling configuration management, application deployment, cloud provisioning, ad-hoc task execution, network automation, and multi-node orchestration. As a public cloud hosted solution, <a class="reference external" href="https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/Welcome.html">AWS CloudFormation</a> is an IoC service that helps the development teams model and set up AWS resources so that they can spend less time managing those resources and more time focusing on the applications that run in AWS. Both the open-source and hosted frameworks altogeher provide the flexibility for development teams to choose the right solution to adopt based on their requirements.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">40.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">39.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Practice the model serving by following this <a class="reference external" href="https://madewithml.com/courses/mlops/api/">turorial</a>.</p>
 </section>
 </section>
@@ -1841,13 +1841,13 @@ <h2><span class="section-number">40.5. </span>Your turn! 🚀<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">39. </span>Model training &amp; evaluation</p>
+            <p class="prev-next-title"><span class="section-number">38. </span>Model training &amp; evaluation</p>
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     <a class='right-next' id="next-link" href="../assignments/README.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">41. </span>Self-paced assignments</p>
+        <p class="prev-next-title"><span class="section-number">40. </span>Self-paced assignments</p>
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diff --git a/machine-learning-productionization/model-training-and-evaluation.html b/machine-learning-productionization/model-training-and-evaluation.html
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>39. Model training &amp; evaluation &#8212; Ocademy Open Machine Learning Book</title>
+    <title>38. Model training &amp; evaluation &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="40. Model deployment" href="model-deployment.html" />
-    <link rel="prev" title="38. Data engineering" href="data-engineering.html" />
+    <link rel="next" title="39. Model deployment" href="model-deployment.html" />
+    <link rel="prev" title="37. Data engineering" href="data-engineering.html" />
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+   1. Python programming introduction
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-     5.2. Data Science ethics
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+       5.4.3. Advanced Pandas Techniques
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+     40.3. First assignment
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+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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+     40.65. Support Vector Machines (SVM) - Classification
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+     38.1.2. Transfer learning
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+     38.2.2. Check metrics for important data slices
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+   38.5. Your turn! 🚀
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+     38.1.1. Start simple
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    </li>
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     <a class="reference internal nav-link" href="#transfer-learning">
-     39.1.2. Transfer learning
+     38.1.2. Transfer learning
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     <a class="reference internal nav-link" href="#evaluate-quality-using-model-metrics">
-     39.2.1. Evaluate quality using model metrics
+     38.2.1. Evaluate quality using model metrics
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#check-metrics-for-important-data-slices">
-     39.2.2. Check metrics for important data slices
+     38.2.2. Check metrics for important data slices
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#use-real-world-metrics">
-     39.2.3. Use real-world metrics
+     38.2.3. Use real-world metrics
     </a>
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+     38.2.4. Optimizing and satisfying metrics
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+     38.3.1. Data and feature debugging
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-     39.4.2. Tune hyperparameters
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+   38.5. Your turn! 🚀
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   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   39.7. Acknowledgments
+   38.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1830,15 +1830,15 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="model-training-evaluation">
-<h1><span class="section-number">39. </span>Model training &amp; evaluation<a class="headerlink" href="#model-training-evaluation" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">38. </span>Model training &amp; evaluation<a class="headerlink" href="#model-training-evaluation" title="Permalink to this headline">#</a></h1>
 <p>Machine Learning modeling, including model selection, training, evaluation and debugging, is very important, but only a small component of the entire Machine Learning pipeline. Some might even argue that it’s the easiest component. Details of specific algorithms won’t be discussed in this section. Instead, we will focus on giving an overview picture of how to choose the right model for the problem.</p>
 <section id="model-selection">
-<h2><span class="section-number">39.1. </span>Model selection<a class="headerlink" href="#model-selection" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">38.1. </span>Model selection<a class="headerlink" href="#model-selection" title="Permalink to this headline">#</a></h2>
 <p>Once problems are framed as one of the common Machine Learning tasks, the typical approaches to solve them can usually be located correspondingly. You should first figure out the category of the problem. Is it supervised or unsupervised? Or regression vs. classification? Does it require generation or only prediction? If it is the former, the models will have to be much harder to learn the latent space of the data.</p>
 <p>Note that the model selection also highly depends on how the business problem is defined. For the same problem area, such as a house price prediction task, different targets may result in choosing different Machine Learning models. It can be regression if the required output is raw numbers. But if the goal is to quantize the income into different brackets and predict the bracket, it becomes a classification problem. Similarly, unsupervised learning could be used to learn labels for the data, which could be then used for supervised learning.</p>
 <p>Keep in mind that there could be many ways to frame a problem, and the better one could only be known after the chosen models are trained and evaluated. Even though there are hundreds of ways to select and train a Machine Learning model, it is always a good practice to start with simple data, simple feature engineering, and simple model. Besides, transfer learning could be leveraged to reduce the training time in the context of the neural network. Furthermore, AutoML helps to further save time spent from feature engineering to HPO tuning. We will discuss these three useful tricks for selecting models in detail.</p>
 <section id="start-simple">
-<h3><span class="section-number">39.1.1. </span>Start simple<a class="headerlink" href="#start-simple" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.1.1. </span>Start simple<a class="headerlink" href="#start-simple" title="Permalink to this headline">#</a></h3>
 <p>When searching for a solution, the first goal is to find an effective simple approach for the task. This serves three major purposes.</p>
 <ul class="simple">
 <li><p>Focus on resolving the confidence about if the model could solve the problem, as additional complexity and potential bugs are avoided.</p></li>
@@ -1864,21 +1864,21 @@ <h3><span class="section-number">39.1.1. </span>Start simple<a class="headerlink
 <p>Overall, there is no need to pursue the state-of-the-art approach or the most optimized performance at the very beginning. But it is necessary to keep the eyes on the trap of increasingly complex heuristics.</p>
 </section>
 <section id="transfer-learning">
-<h3><span class="section-number">39.1.2. </span>Transfer learning<a class="headerlink" href="#transfer-learning" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.1.2. </span>Transfer learning<a class="headerlink" href="#transfer-learning" title="Permalink to this headline">#</a></h3>
 <p><a class="reference external" href="https://en.wikipedia.org/wiki/Transfer_learning">Transfer learning</a> is a research problem in Machine Learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.</p>
 <p>Using a large amount of data and tackling a completely new Machine Learning problem can be very challenging sometimes. Transfer learning could be the starting point if the simplified solution does not work or perform well. It allows utilizing knowledge(model) acquired (trained) for one task to solve other similar tasks.</p>
 <p>What’s more, transfer learning is very commonly adopted in hot areas such as computer vision and natural language processing. It usually gives significantly better performance than training a simple model. And it is even a rare case to train a model from scratch in such areas. Instead, researchers and data scientists prefer starting from a pre-trained model that already learned general features and how to classify objects.</p>
 <figure class="align-default" id="id1">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/transfer-learning.jpeg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/transfer-learning.jpeg" />
 <figcaption>
-<p><span class="caption-number">Fig. 39.1 </span><span class="caption-text"><a class="reference external" href="https://www.geeksforgeeks.org/ml-introduction-to-transfer-learning">Transfer learning</a></span><a class="headerlink" href="#id1" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 38.1 </span><span class="caption-text"><a class="reference external" href="https://www.geeksforgeeks.org/ml-introduction-to-transfer-learning">Transfer learning</a></span><a class="headerlink" href="#id1" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Traditionally, there are three major categories of transfer learning strategies and techniques based on the characteristics of the problem and the data. <strong>Inductive transfer learning</strong> is used if the domains are the same between the source and target, but the exact tasks are not. If the problems’ domains are even different, <strong>transductive transfer learning</strong> could be the choice. <strong>Unsupervised transfer learning</strong> is similar to inductive transfer learning, but for unsupervised tasks with unlabeled datasets both in the source and target.</p>
 <figure class="align-default" id="transfer-learning-strategies">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/transfer-learning-strategies.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/transfer-learning-strategies.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 39.2 </span><span class="caption-text"><a class="reference external" href="https://www.v7labs.com/blog/transfer-learning-guide,%20https://www.v7labs.com/blog/transfer-learning-guide">Transfer learning strategies</a></span><a class="headerlink" href="#transfer-learning-strategies" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 38.2 </span><span class="caption-text"><a class="reference external" href="https://www.v7labs.com/blog/transfer-learning-guide,%20https://www.v7labs.com/blog/transfer-learning-guide">Transfer learning strategies</a></span><a class="headerlink" href="#transfer-learning-strategies" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Under the deep learning context, there are multiple levels of complexity in using a pre-trained model. The most straightforward way is to use the pre-trained model directly, but may not be applicable mostly. An idea here is to leverage the pre-trained model’s weighted layers to extract features while retraining the last layer. One step further, a more engaging technique is to fine-tune and train all layers after starting with only the feature layers. If it still does not work, fully training all the layers will be the fallback.</p>
@@ -1888,7 +1888,7 @@ <h3><span class="section-number">39.1.2. </span>Transfer learning<a class="heade
 </div>
 </section>
 <section id="automl">
-<h3><span class="section-number">39.1.3. </span>AutoML<a class="headerlink" href="#automl" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.1.3. </span>AutoML<a class="headerlink" href="#automl" title="Permalink to this headline">#</a></h3>
 <p>Automated Machine Learning(AutoML) provides methods and processes to make Machine Learning available for non-Machine Learning experts, to improve the efficiency of Machine Learning and accelerate research on Machine Learning. Designing and tuning Machine Learning systems is a labor and time-intensive task, and also requires extensive expertise. AutoML is focused on automating the model selection and training process.</p>
 <p>As it is named, AutoML helps automate many aspects of Machine Learning model developments and training. It consists of a broader group of methodologies listed here:</p>
 <ul class="simple">
@@ -1906,13 +1906,13 @@ <h3><span class="section-number">39.1.3. </span>AutoML<a class="headerlink" href
 </section>
 </section>
 <section id="model-evaluation">
-<h2><span class="section-number">39.2. </span>Model evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">38.2. </span>Model evaluation<a class="headerlink" href="#model-evaluation" title="Permalink to this headline">#</a></h2>
 <p>In practice, it is challenging to detect if the model is properly learning without under-fitting or overfitting. Ideally, before moving to production, it is always necessary to evaluate the trained model to make sure that everything is working properly. A common approach is to divide the dataset into three parts - training set, validation set and test set.</p>
 <p>The model is trained by using only the train set, and the validation set is used to track the progress and conclude to optimize the model. Then the test set is used to evaluate the performance of the model. Using completely new data allows us to get an unbiased opinion on how well the algorithm works.</p>
 <figure class="align-default" id="recommended-method-of-deviding-the-dataset">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/recommended-method-of-deviding-the-dataset.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/recommended-method-of-deviding-the-dataset.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 39.3 </span><span class="caption-text"><a class="reference external" href="https://towardsdatascience.com/preventing-deep-neural-network-from-overfitting-953458db800a">Recommended method of dividing the data set</a></span><a class="headerlink" href="#recommended-method-of-deviding-the-dataset" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 38.3 </span><span class="caption-text"><a class="reference external" href="https://towardsdatascience.com/preventing-deep-neural-network-from-overfitting-953458db800a">Recommended method of dividing the data set</a></span><a class="headerlink" href="#recommended-method-of-deviding-the-dataset" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>There is no strict heuristic about how to split the dataset, especially when working with big data. The split ratios depend greatly on the specific problem and data volume. Generally speaking, the train vs. validation vs. test split should allow for:</p>
@@ -1922,7 +1922,7 @@ <h2><span class="section-number">39.2. </span>Model evaluation<a class="headerli
 </ul>
 <p>However, while evaluating a Machine Learning model can seem daunting, model metrics show where to start. The following sections discuss how to evaluate performance using metrics.</p>
 <section id="evaluate-quality-using-model-metrics">
-<h3><span class="section-number">39.2.1. </span>Evaluate quality using model metrics<a class="headerlink" href="#evaluate-quality-using-model-metrics" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.2.1. </span>Evaluate quality using model metrics<a class="headerlink" href="#evaluate-quality-using-model-metrics" title="Permalink to this headline">#</a></h3>
 <p>To evaluate your model’s quality, commonly-used metrics are:</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://developers.google.com/machine-learning/crash-course/descending-into-ml/training-and-loss">loss</a></p></li>
@@ -1959,16 +1959,16 @@ <h3><span class="section-number">39.2.1. </span>Evaluate quality using model met
 <p>However, since the real world does not always go as what is imagined, it may be the case to try many metrics before finding one that could be satisfied with, and the metrics may change alongside the development, or even after getting the model into production.</p>
 </section>
 <section id="check-metrics-for-important-data-slices">
-<h3><span class="section-number">39.2.2. </span>Check metrics for important data slices<a class="headerlink" href="#check-metrics-for-important-data-slices" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.2.2. </span>Check metrics for important data slices<a class="headerlink" href="#check-metrics-for-important-data-slices" title="Permalink to this headline">#</a></h3>
 <p>After having a high-quality model, the model might still perform poorly on subsets of the data. For example, the unicorn predictor must predict well both in the Sahara desert and in New York City, and at all times of the day. However, there is less training data for the Sahara desert. Therefore, it is necessary to track model quality specifically for the Sahara desert. Such subsets of data, like the subset corresponding to the Sahara desert, are called <strong>data slices</strong>. Data slices should be separately monitored where performance is especially important or where the model might perform poorly.</p>
 <p>Use the understanding of the data to identify data slices of interest. Then compare model metrics for data slices against the metrics for the entire data set. Checking that the model performs across all data slices helps remove bias. For more, see <a class="reference external" href="https://developers.google.com/machine-learning/crash-course/fairness/evaluating-for-bias">Fairness: Evaluating for Bias</a>.</p>
 </section>
 <section id="use-real-world-metrics">
-<h3><span class="section-number">39.2.3. </span>Use real-world metrics<a class="headerlink" href="#use-real-world-metrics" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.2.3. </span>Use real-world metrics<a class="headerlink" href="#use-real-world-metrics" title="Permalink to this headline">#</a></h3>
 <p>Model metrics do not necessarily measure the real-world impact of the model. For example, the AUC could be increased by changing a hyperparameter, but how did the change affect the user experience? To measure real-world impact, separate metrics need to be defined. Measuring real-world impact helps compare the quality of different iterations of the model.</p>
 </section>
 <section id="optimizing-and-satisfying-metrics">
-<h3><span class="section-number">39.2.4. </span>Optimizing and satisfying metrics<a class="headerlink" href="#optimizing-and-satisfying-metrics" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.2.4. </span>Optimizing and satisfying metrics<a class="headerlink" href="#optimizing-and-satisfying-metrics" title="Permalink to this headline">#</a></h3>
 <p>As models are getting bigger and more resource-intensive, how to scale the mode training becomes more and more important. Thinking beyond the above model metrics, the model utilitarian performance should also be considered. It includes the training speed, inference speed, model size, model stability, etc.</p>
 <p>To the given example <a class="reference external" href="https://github.com/ajaymache/machine-learning-yearning">ng2017mlyearning</a> below, both model accuracy and running time are important to decide which is the best classifier. It may not be natural to derive a single metric from them. Instead, a more real-world thinking-based strategy could be applied. The running time is important, but mostly it could be acceptable once under a certain value, such as 100ms. Whenever this condition is satisfied and the running time is good enough for production, then accuracy needs to be optimized with the best effort. Here, the running time is the satisfying metric and the accuracy is the optimizing metric.</p>
 <figure class="align-default">
@@ -1979,7 +1979,7 @@ <h3><span class="section-number">39.2.4. </span>Optimizing and satisfying metric
 </section>
 </section>
 <section id="model-debugging-improvement">
-<h2><span class="section-number">39.3. </span>Model debugging &amp; improvement<a class="headerlink" href="#model-debugging-improvement" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">38.3. </span>Model debugging &amp; improvement<a class="headerlink" href="#model-debugging-improvement" title="Permalink to this headline">#</a></h2>
 <p>Once the model is working, the next step is to optimize the model’s quality for production readiness. Both debugging and optimizing are critical in the Machine Learning pipeline.</p>
 <p><strong>How is Machine Learning debugging different?</strong> Before diving into any particular Machine Learning debugging method, it is important to understand what differentiates debugging Machine Learning models from traditional software programs. Unlike the latter, an Machine Learning model with poor quality usually does not imply the presence of a bug. Instead, There could be many reasons to cause a model not to perform well. So that to debug poor performance in a model, a broader range of potential causes need to be investigated compared to traditional programming.</p>
 <p>For example, here are a few causes for poor model performance:</p>
@@ -1993,7 +1993,7 @@ <h2><span class="section-number">39.3. </span>Model debugging &amp; improvement<
 </ul>
 <p>Debugging Machine Learning models is complicated by the time it takes to run your experiments. Given the longer iteration cycles, and the larger error space, debugging Machine Learning models is uniquely challenging.</p>
 <section id="data-and-feature-debugging">
-<h3><span class="section-number">39.3.1. </span>Data and feature debugging<a class="headerlink" href="#data-and-feature-debugging" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.3.1. </span>Data and feature debugging<a class="headerlink" href="#data-and-feature-debugging" title="Permalink to this headline">#</a></h3>
 <p>Low-quality data will significantly affect your model’s performance. It’s much easier to detect low-quality data at input instead of guessing at its existence after the model predicts badly. Monitor the data by following the advice in this section.</p>
 <p><strong>Validate input data using rules.</strong></p>
 <p>To monitor the data, one approach is to write rules that the data must satisfy, and continuously check the data against the expected <a class="reference external" href="#data-quality">data quality</a>. This collection of rules is defined by following these steps:</p>
@@ -2028,7 +2028,7 @@ <h3><span class="section-number">39.3.1. </span>Data and feature debugging<a cla
 </ol>
 </section>
 <section id="model-debugging">
-<h3><span class="section-number">39.3.2. </span>Model debugging<a class="headerlink" href="#model-debugging" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.3.2. </span>Model debugging<a class="headerlink" href="#model-debugging" title="Permalink to this headline">#</a></h3>
 <p>After debugging the data, follow these steps to continue debugging the model.</p>
 <p><strong>Check that the model can predict labels.</strong></p>
 <p>Before debugging the model, try to determine whether the features encode predictive signals. Linear correlations could be found between individual features and labels by using correlation matrices.</p>
@@ -2078,7 +2078,7 @@ <h3><span class="section-number">39.3.2. </span>Model debugging<a class="headerl
 </table>
 </section>
 <section id="interpreting-loss-curves">
-<h3><span class="section-number">39.3.3. </span>Interpreting loss curves<a class="headerlink" href="#interpreting-loss-curves" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.3.3. </span>Interpreting loss curves<a class="headerlink" href="#interpreting-loss-curves" title="Permalink to this headline">#</a></h3>
 <p>Machine learning would be a breeze if all our <a class="reference external" href="https://developers.google.com/machine-learning/crash-course/descending-into-ml/training-and-loss">loss curves</a> looked like this the first time we trained our model:</p>
 <figure class="align-default">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/metric-curve-ideal.svg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/metric-curve-ideal.svg" /></figure>
@@ -2145,25 +2145,25 @@ <h3><span class="section-number">39.3.3. </span>Interpreting loss curves<a class
 </section>
 </section>
 <section id="model-optimization">
-<h2><span class="section-number">39.4. </span>Model optimization<a class="headerlink" href="#model-optimization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">38.4. </span>Model optimization<a class="headerlink" href="#model-optimization" title="Permalink to this headline">#</a></h2>
 <p>Once the model is working, it’s time to optimize the model’s quality. Follow the steps below.</p>
 <section id="add-useful-features">
-<h3><span class="section-number">39.4.1. </span>Add useful features<a class="headerlink" href="#add-useful-features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.4.1. </span>Add useful features<a class="headerlink" href="#add-useful-features" title="Permalink to this headline">#</a></h3>
 <p>The model performance could be improved by adding features that encode information not yet encoded by the existing features. Correlation matrices could be used to find linear correlations between individual features and labels. To detect nonlinear correlations between features and labels, the model must be trained with and without the feature, or combination of features, and check for an increase in model quality. The feature’s inclusion must be justified by an increase in model quality.</p>
 </section>
 <section id="tune-hyperparameters">
-<h3><span class="section-number">39.4.2. </span>Tune hyperparameters<a class="headerlink" href="#tune-hyperparameters" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.4.2. </span>Tune hyperparameters<a class="headerlink" href="#tune-hyperparameters" title="Permalink to this headline">#</a></h3>
 <p>Values of hyperparameters make your model work. However, these hyperparameter values can still be tuned. The values could be tuned manually by trial and error, but manual tuning is time-consuming. Instead, consider using an AutoML hyperparameter tuning service, such as <a class="reference external" href="https://cloud.google.com/ml-engine/docs/tensorflow/hyperparameter-tuning-overview">Google Cloud Machine Learning hyperparameter tuning</a>, <a class="reference external" href="https://github.com/awslabs/autogluon">Auto Gluon</a>, etc. With different sets of hyperparameters, the same model may perform drastically differently on the same dataset. Keep in mind that not all hyperparameters are created equal. A model could be more sensitive to one hyperparameter.</p>
 </section>
 <section id="tune-model-depth-and-width">
-<h3><span class="section-number">39.4.3. </span>Tune model depth and width<a class="headerlink" href="#tune-model-depth-and-width" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">38.4.3. </span>Tune model depth and width<a class="headerlink" href="#tune-model-depth-and-width" title="Permalink to this headline">#</a></h3>
 <p>While debugging the model, its depth and width are increased to improve the model performance In contrast, during model optimization, the mode depth and width could be either increased or decreased depending on the goals. If the model quality is adequate, then try reducing overfitting and training time by decreasing depth and width. Specifically, try halving the width at each successive layer. Since the model quality will also decrease, it is always a trade-off to balance quality with overfitting and training time.</p>
 <p>Conversely, if the goal is to have higher model quality, then try increasing depth and width. Remember that increases in depth and width are practically limited by accompanying increases in training time and overfitting. To understand overfitting.</p>
 <p>Since depth and width are hyperparameters, hyperparameter tuning could be used to optimize depth and width.</p>
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">39.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">38.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Understanding the challenges in Machine Learning debugging by completing the <a class="reference internal" href="../assignments/machine-learning-productionization/counterintuitive-challenges-in-ml-debugging.html"><span class="doc std std-doc">Counterintuitive Challenges in ML Debugging</span></a>.</p>
 <p>Apply the debugging concepts learned by completing the following:</p>
 <ul class="simple">
@@ -2172,13 +2172,13 @@ <h2><span class="section-number">39.5. </span>Your turn! 🚀<a class="headerlin
 </ul>
 </section>
 <section id="self-study">
-<h2><span class="section-number">39.6. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">38.6. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="https://blog.statsbot.co/machine-learning-algorithms-183cc73197c">Machine Learning Algorithms: Which One to Choose for Your Problem</a> by Daniil Korbut, Stats and Bots, 2017.</p></li>
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">39.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">38.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Google for creating the open-source course <a class="reference external" href="https://developers.google.com/machine-learning/testing-debugging">Testing and Debugging in Machine Learning</a> which is licensed under <a class="reference external" href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a>. It contributes to the majority of <a class="reference external" href="#model-evaluation">Model evaluation</a>, <a class="reference external" href="#model-deugging-improvement">Model debugging &amp; improvement</a>, <a class="reference external" href="#model-optimization">Model optimization</a>, and assignments.</p>
 <p>Thanks to <a class="reference external" href="https://github.com/chiphuyen">chiphuyen</a> for creating the <a class="reference external" href="https://huyenchip.com/machine-learning-systems-design/toc.html">Machine Learning Systems Design</a> which inspires some of the contents in this section.</p>
 </section>
@@ -2221,13 +2221,13 @@ <h2><span class="section-number">39.7. </span>Acknowledgments<a class="headerlin
         <i class="fas fa-angle-left"></i>
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-            <p class="prev-next-title"><span class="section-number">38. </span>Data engineering</p>
+            <p class="prev-next-title"><span class="section-number">37. </span>Data engineering</p>
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">40. </span>Model deployment</p>
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diff --git a/machine-learning-productionization/overview.html b/machine-learning-productionization/overview.html
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-     42.20. Convolutional Neural Network
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@@ -1577,61 +1577,61 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-     36.2.2. Data processing
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+   35.3. Model training &amp; evaluation
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-     36.3.1. Model algorithm
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-     36.3.2. Model testing
+     35.3.2. Model testing
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+   35.5. Maintenance
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-   36.6. Your turn! 🚀
+   35.6. Your turn! 🚀
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@@ -1655,61 +1655,61 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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+     35.2.1. Data ingestion
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+   35.5. Maintenance
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+   35.6. Your turn! 🚀
   </a>
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   </a>
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@@ -1734,7 +1734,7 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="overview">
-<h1><span class="section-number">36. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">35. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h1>
 <p>Moving Machine Learning models into production is as important as building them, sometimes even harder. Maintaining data quality and model accuracy over time are just a few of the challenges. To achieve end-to-end system productionization as a whole, the various components and designs need to be identified, from defining a problem to serving the model as a service.</p>
 <p>This chapter combines the foundational concepts of Machine Learning with the functional expertise of modern software development and engineering to help you develop production-ready Machine Learning knowledge.</p>
 <p>Productionization of a Machine Learning solution is not a one-time thing. It is always under improving one-time through the iterative process continuously.</p>
@@ -1753,12 +1753,12 @@ <h1><span class="section-number">36. </span>Overview<a class="headerlink" href="
 </ul>
 <figure class="align-default" id="machine-learning-lifecycle">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/machine-learning-lifecycle.svg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/machine-learning-lifecycle.svg" /><figcaption>
-<p><span class="caption-number">Fig. 36.1 </span><span class="caption-text">Machine Learning Lifecycle</span><a class="headerlink" href="#machine-learning-lifecycle" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 35.1 </span><span class="caption-text">Machine Learning Lifecycle</span><a class="headerlink" href="#machine-learning-lifecycle" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>In the below sections, we will walk through the Machine Learning lifecycle components with a real-world example.</p>
 <section id="problem-framing">
-<h2><span class="section-number">36.1. </span>Problem framing<a class="headerlink" href="#problem-framing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">35.1. </span>Problem framing<a class="headerlink" href="#problem-framing" title="Permalink to this headline">#</a></h2>
 <p>To bring a Machine Learning solution to production successfully, the first step is to define a valuable business objective and translate the objective into a Machine Learning solvable problem.</p>
 <p><strong><a class="reference external" href="https://en.wikipedia.org/wiki/COVID-19">COVID-19</a> Projections</strong> is an artificial intelligence solution to accurately forecast infections, deaths, and recovery timelines of the COVID-19/coronavirus pandemic in the US and globally. By the end of April 2020, it was cited by the Centers for Disease Control &amp; Prevention (CDC) as one of the first models to “help inform public health decision-making”.</p>
 <blockquote>
@@ -1804,9 +1804,9 @@ <h2><span class="section-number">36.1. </span>Problem framing<a class="headerlin
 </ol>
 </section>
 <section id="data-engineering">
-<h2><span class="section-number">36.2. </span>Data engineering<a class="headerlink" href="#data-engineering" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">35.2. </span>Data engineering<a class="headerlink" href="#data-engineering" title="Permalink to this headline">#</a></h2>
 <section id="data-ingestion">
-<h3><span class="section-number">36.2.1. </span>Data ingestion<a class="headerlink" href="#data-ingestion" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">35.2.1. </span>Data ingestion<a class="headerlink" href="#data-ingestion" title="Permalink to this headline">#</a></h3>
 <p>COVID-19 Projections Death Forecasting model uses the daily death total provided by <a class="reference external" href="https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series">Johns Hopkins CSSE</a>, which is considered by experts to be the “gold standard” reference data. It sometimes uses US testing data from the <a class="reference external" href="https://covidtracking.com/">COVID Tracking Project</a> in our research and graphs. Below is a piece of sample data from CSSE.</p>
 <div class="cell tag_output_scroll docutils container">
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@@ -1988,7 +1988,7 @@ <h3><span class="section-number">36.2.1. </span>Data ingestion<a class="headerli
 <p>The above data is publicly available, and able to be automatically and easily sourced by a web crawler. There are also other ingestion approaches, including synthetic data simulation and manual collecting.</p>
 </section>
 <section id="data-processing">
-<h3><span class="section-number">36.2.2. </span>Data processing<a class="headerlink" href="#data-processing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">35.2.2. </span>Data processing<a class="headerlink" href="#data-processing" title="Permalink to this headline">#</a></h3>
 <p>Typical data processing includes data cleaning, labeling, feature engineering, and augmentation.</p>
 <p>For example, because the CSSE raw data may be noisy, a smoothing algorithm is first to smooth the data. For example, if a state reports 0 death on one day and 300 deaths the next day, the data is smoothed to show 150 deaths on each day. <a class="reference external" href="http://matlab.cheme.cmu.edu/2011/10/30/smooth-transitions-between-discontinuous-functions/#:~:text=Sigmoid%20functions,-A%20sigmoid%20function&amp;text=There%20is%20no%20formal%20justification,or%20from%201%20to%20zero.">Sigmoid Function</a> is used for this process as shown in below <a class="reference external" href="https://github.com/youyanggu/yyg-seir-simulator/blob/b511187a2d4273c92235fdb79017e7a6367e2f4c/region_model.py#L9">code snippet</a>.</p>
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@@ -2021,22 +2021,22 @@ <h3><span class="section-number">36.2.2. </span>Data processing<a class="headerl
 </section>
 </section>
 <section id="model-training-evaluation">
-<h2><span class="section-number">36.3. </span>Model training &amp; evaluation<a class="headerlink" href="#model-training-evaluation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">35.3. </span>Model training &amp; evaluation<a class="headerlink" href="#model-training-evaluation" title="Permalink to this headline">#</a></h2>
 <section id="model-algorithm">
-<h3><span class="section-number">36.3.1. </span>Model algorithm<a class="headerlink" href="#model-algorithm" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">35.3.1. </span>Model algorithm<a class="headerlink" href="#model-algorithm" title="Permalink to this headline">#</a></h3>
 <p>A <a class="reference external" href="https://en.wikipedia.org/wiki/Hyperparameter_optimization#Grid_search">Grid Search</a> based approach is used as the model algorithm, which is similar to the traditional way of performing hyperparameter optimization. Grid Search builds a model on each parameter combination possible. It iterates through every different combination and stores a model for each of them.</p>
 <p>The algorithm is built on top of an SEIR(susceptible-exposed-infectious-recovered) simulator. SEIR is a modified <a class="reference external" href="https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology">SIR</a> epidemiology model which is applied to the mathematical modeling of infectious diseases.</p>
 <figure class="align-default" id="seir-model">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/SEIR.jpeg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/SEIR.jpeg" />
 <figcaption>
-<p><span class="caption-number">Fig. 36.2 </span><span class="caption-text">SEIR Model</span><a class="headerlink" href="#seir-model" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 35.2 </span><span class="caption-text">SEIR Model</span><a class="headerlink" href="#seir-model" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The simulator generates infection and death data according to the pre-defined static parameters. By comparing the simulated data with real-world data, it gets the simulator performance under a certain parameter setting.</p>
 <figure class="align-default" id="death-forecasting-model-overview">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/death-forecasting-model-overview.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/death-forecasting-model-overview.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 36.3 </span><span class="caption-text"><a class="reference external" href="https://covid19-projections.com/model-details/">Death Forecasting Model Overview</a></span><a class="headerlink" href="#death-forecasting-model-overview" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 35.3 </span><span class="caption-text"><a class="reference external" href="https://covid19-projections.com/model-details/">Death Forecasting Model Overview</a></span><a class="headerlink" href="#death-forecasting-model-overview" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The training of the Death Forecasting model is basically a Grid Search process. It is found that the brute-force Grid Search method that iterates through the entire parameter space is the most effective in finding an optimal set of parameters. So if there are <span class="math notranslate nohighlight">\(10\)</span> values for one parameter and <span class="math notranslate nohighlight">\(10\)</span> values for another parameter. Then there are <span class="math notranslate nohighlight">\(100\)</span> different parameter combinations for those two parameters. For parameters not able to be estimated as lacking data, the values are considered all equally, resulting in a wider confidence interval.</p>
@@ -2047,41 +2047,41 @@ <h3><span class="section-number">36.3.1. </span>Model algorithm<a class="headerl
 <p>Based on such a strategy, the Grid Search algorithm searches the optimized SEIR simulator parameters from thousands of different combinations. Then the simulator with the optimized parameters could be used to predict the future death data.</p>
 </section>
 <section id="model-testing">
-<h3><span class="section-number">36.3.2. </span>Model testing<a class="headerlink" href="#model-testing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">35.3.2. </span>Model testing<a class="headerlink" href="#model-testing" title="Permalink to this headline">#</a></h3>
 <p>To evaluate the result, the trained Death Forecasting model has been compared with existing Machine Learning approaches, such as the popular <a class="reference external" href="https://covid19.healthdata.org/">model</a> developed by the <a class="reference external" href="https://en.wikipedia.org/wiki/Institute_for_Health_Metrics_and_Evaluation">Institute for Health Metrics and Evaluation (IHME)</a>. The latter is commonly referred to by the White House and the media. The detailed result could be found on the <a class="reference external" href="https://covid19-projections.com/about/#historical-performance">covid19-projections website</a>.</p>
 <figure class="align-default" id="evaluation-between-different-machine-learning-model-s-covid-19-death-projections">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/covid19-model-performance-evaluation.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/covid19-model-performance-evaluation.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 36.4 </span><span class="caption-text">Evaluation Between Different Machine Learning Model’s COVID-19 Death Projections</span><a class="headerlink" href="#evaluation-between-different-machine-learning-model-s-covid-19-death-projections" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 35.4 </span><span class="caption-text">Evaluation Between Different Machine Learning Model’s COVID-19 Death Projections</span><a class="headerlink" href="#evaluation-between-different-machine-learning-model-s-covid-19-death-projections" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The evaluation is more focused on accuracy. There are other <a class="reference external" href="https://mlinproduction.com/testing-machine-learning-models-deployment-series-07/">Test-driven Machine Learning development</a> methods, including unit testing, integration testing, etc.</p>
 <figure class="align-default" id="the-machine-learning-test-pyramid">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/test-pyramid.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/test-pyramid.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 36.5 </span><span class="caption-text">The <a class="reference external" href="https://martinfowler.com/articles/cd4ml.html">Machine Learning Test Pyramid</a> from Martin Fowler</span><a class="headerlink" href="#the-machine-learning-test-pyramid" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 35.5 </span><span class="caption-text">The <a class="reference external" href="https://martinfowler.com/articles/cd4ml.html">Machine Learning Test Pyramid</a> from Martin Fowler</span><a class="headerlink" href="#the-machine-learning-test-pyramid" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 </section>
 <section id="deployment">
-<h2><span class="section-number">36.4. </span>Deployment<a class="headerlink" href="#deployment" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">35.4. </span>Deployment<a class="headerlink" href="#deployment" title="Permalink to this headline">#</a></h2>
 <p>The model training itself is handled manually. Every day, raw daily projections for all 50 US states and select international countries will be uploaded onto the COVID-19 Projections <a class="reference external" href="https://pages.github.com/">GitHub Pages</a>. This will trigger the website building process, which is managed by Github Action and fully CI/CD.</p>
 <figure class="align-default" id="covid-19-projection-github-action">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/covid19-projection-github-action.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/covid19-projection-github-action.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 36.6 </span><span class="caption-text">COVID-19 Projection Github Action</span><a class="headerlink" href="#covid-19-projection-github-action" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 35.6 </span><span class="caption-text">COVID-19 Projection Github Action</span><a class="headerlink" href="#covid-19-projection-github-action" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The Machine Learning model could be also deployed on an embedded device or as a service. You could refer to <a class="reference external" href="https://aws.amazon.com/greengrass/ml/">AWS Greengrass</a> and <a class="reference external" href="https://aws.amazon.com/pm/sagemaker/">AWS SageMaker</a> to learn more.</p>
 </section>
 <section id="maintenance">
-<h2><span class="section-number">36.5. </span>Maintenance<a class="headerlink" href="#maintenance" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">35.5. </span>Maintenance<a class="headerlink" href="#maintenance" title="Permalink to this headline">#</a></h2>
 <p><a class="reference external" href="https://www.forbes.com/sites/forbestechcouncil/2021/09/23/model-drift-in-data-analytics-what-is-it-so-what-now-what/?sh=54ce17194862">Model drift</a> refers to the degradation of model performance due to changes in data and relationships between input and output variables. In order to deal with model drift, continuous model monitoring is the key.</p>
 <p>The covid19-projection is a part-time project. During the author actively worked on it, the past performance is evaluated weekly. The <a class="reference external" href="https://covid19-projections.com/historical-performance/">historical performance</a> along with other models are presented in the <a class="reference external" href="https://github.com/reichlab/covid19-forecast-hub">COVID-19 Forecast Hub</a>, which is a website that maintains the authoritative, up-to-date record for forecasts of COVID-19 cases, deaths, and hospitalizations in the US. The latest forecasts from these models were sent to the CDC weekly and presented on the CDC COVID-19 Forecasting page. Also, the evaluation results were used to guide the model iteration for better consistency and accuracy.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">36.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">35.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>It is time to start your own Machine Learning project!</p>
 <p>In this session/assignment, you should come up with a Machine Learning Project idea. Everyone should, in the next session, present their idea with:</p>
 <ul class="simple">
@@ -2113,7 +2113,7 @@ <h2><span class="section-number">36.6. </span>Your turn! 🚀<a class="headerlin
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 </section>
 <section id="self-study">
-<h2><span class="section-number">36.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">35.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="https://ml-ops.org/">Machine Learning Operations</a></p></li>
 <li><p><a class="reference external" href="https://docs.microsoft.com/en-us/azure/machine-learning/concept-model-management-and-deployment">MLOps: Model management, deployment, lineage, and monitoring with Azure Machine Learning</a></p></li>
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-            <p class="prev-next-title"><span class="section-number">35. </span>Object detection</p>
+            <p class="prev-next-title"><span class="section-number">34. </span>Object detection</p>
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+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     42.4. Data Science introduction
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-     42.6. NumPy and Pandas
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-     42.20. Convolutional Neural Network
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@@ -1575,32 +1575,32 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
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   <a class="reference internal nav-link" href="#is-machine-learning-the-best-choice">
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+   36.1. Is Machine Learning the best choice?
   </a>
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+   36.2. How easy to obtain the data?
   </a>
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+   36.3. What models to use?
   </a>
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   <a class="reference internal nav-link" href="#how-to-put-a-model-into-production">
-   37.4. How to put a model into production?
+   36.4. How to put a model into production?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   37.5. Your turn! 🚀
+   36.5. Your turn! 🚀
   </a>
  </li>
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   <a class="reference internal nav-link" href="#self-study">
-   37.6. Self study
+   36.6. Self study
   </a>
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 </ul>
@@ -1624,32 +1624,32 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#is-machine-learning-the-best-choice">
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+   36.1. Is Machine Learning the best choice?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#how-easy-to-obtain-the-data">
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+   36.2. How easy to obtain the data?
   </a>
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   <a class="reference internal nav-link" href="#what-models-to-use">
-   37.3. What models to use?
+   36.3. What models to use?
   </a>
  </li>
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   <a class="reference internal nav-link" href="#how-to-put-a-model-into-production">
-   37.4. How to put a model into production?
+   36.4. How to put a model into production?
   </a>
  </li>
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   <a class="reference internal nav-link" href="#your-turn">
-   37.5. Your turn! 🚀
+   36.5. Your turn! 🚀
   </a>
  </li>
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   <a class="reference internal nav-link" href="#self-study">
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+   36.6. Self study
   </a>
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 </ul>
@@ -1674,7 +1674,7 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="problem-framing">
-<h1><span class="section-number">37. </span>Problem framing<a class="headerlink" href="#problem-framing" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">36. </span>Problem framing<a class="headerlink" href="#problem-framing" title="Permalink to this headline">#</a></h1>
 <p><strong><a class="reference external" href="https://developers.google.com/machine-learning/problem-framing/problem-framing">Problem framing</a></strong> is the process of analyzing a problem to isolate the individual elements that need to be addressed to solve it. It helps you to determine the feasibility of your project, and clearly defines the project goals and success criteria. Under our context, problem framing is to dive deep into the business requirements to figure out a potential Machine Learning based technical solution.</p>
 <blockquote>
 <div><p>“Formal problem framing is the critical beginning for solving an ML problem, as it forces us to better understand both the problem and the data in order to design and build a bridge between them. “
@@ -1687,7 +1687,7 @@ <h1><span class="section-number">37. </span>Problem framing<a class="headerlink"
 </ul>
 <p>Before rushing into the Machine Learning implementation, let’s frame the problem by answering the below questions first.</p>
 <section id="is-machine-learning-the-best-choice">
-<h2><span class="section-number">37.1. </span>Is Machine Learning the best choice?<a class="headerlink" href="#is-machine-learning-the-best-choice" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">36.1. </span>Is Machine Learning the best choice?<a class="headerlink" href="#is-machine-learning-the-best-choice" title="Permalink to this headline">#</a></h2>
 <p>Machine Learning is not universally applicable. Similar to any other technology, it could only be used to solve certain problems.</p>
 <p><strong>What are the existing solutions?</strong> Machine Learning may be considered either when a problem is new, or existing non-Machine Learning solutions are not optimized. If it is the former, try solving the problem manually using a <a class="reference external" href="https://en.wikipedia.org/wiki/Heuristic">heuristic</a> approach. Otherwise, the existing non-Machine Learning solution could be leveraged as a benchmark to help you make the decision.</p>
 <p><strong>Is the error tolerable?</strong> Achieving 100% accuracy in Machine Learning predictions is challenging. Occasionally, the slight possibility of false negatives may be unacceptable. Consider Deep Learning’s success in expediting MRI reconstruction compared to conventional methods. Despite the significant enhancement in image quality, the issue of false-negative reconstructions persists and warrants improvement.</p>
@@ -1701,13 +1701,13 @@ <h2><span class="section-number">37.1. </span>Is Machine Learning the best choic
 </div>
 </section>
 <section id="how-easy-to-obtain-the-data">
-<h2><span class="section-number">37.2. </span>How easy to obtain the data?<a class="headerlink" href="#how-easy-to-obtain-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">36.2. </span>How easy to obtain the data?<a class="headerlink" href="#how-easy-to-obtain-the-data" title="Permalink to this headline">#</a></h2>
 <p>It is no doubt that data is the driving force of Machine Learning. Make sure you have the data required to train a model, before initiating the Machine Learning execution. Regardless of the challenge to gather data from your own company or real customers, the options below could be helpful.</p>
 <p><strong><a class="reference external" href="https://en.wikipedia.org/wiki/Synthetic_data">Synthetic data</a></strong>, referring to any production data relevant to a given situation that is not obtained through direct measurement, is typically generated by computer simulation. In today’s AI landscape, synthetic data is increasingly gaining importance and is even projected to surpass real data by 2030 (Gartner Inc.). <a class="reference external" href="https://en.wikipedia.org/wiki/Generative_adversarial_network">Generative Adversarial Networks</a>(GANs) stand out as one of the most popular frameworks for generating synthetic data. For instance, <a class="reference external" href="https://blogs.nvidia.com/blog/2020/04/23/ford-ai-data/">Ford has combined gaming engines and GANs to create synthetic data for AI training for self-driving</a>. Other approaches to generating synthetic data include statistical distribution fitting, rule-based procedural generation, benchmarks, simulations, and more.</p>
 <figure class="align-default" id="synthetic-data-will-completely-overshadow-real-data-in-ai-models">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/gartner-chart.jpeg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/gartner-chart.jpeg" />
 <figcaption>
-<p><span class="caption-number">Fig. 37.1 </span><span class="caption-text">Synthetic Data Will Completely Overshadow Real Data in AI Models(<a class="reference external" href="https://www.gartner.com/en/documents/4002912">https://www.gartner.com/en/documents/4002912</a>)</span><a class="headerlink" href="#synthetic-data-will-completely-overshadow-real-data-in-ai-models" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 36.1 </span><span class="caption-text">Synthetic Data Will Completely Overshadow Real Data in AI Models(<a class="reference external" href="https://www.gartner.com/en/documents/4002912">https://www.gartner.com/en/documents/4002912</a>)</span><a class="headerlink" href="#synthetic-data-will-completely-overshadow-real-data-in-ai-models" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="admonition seealso">
@@ -1726,12 +1726,12 @@ <h2><span class="section-number">37.2. </span>How easy to obtain the data?<a cla
 </div>
 </section>
 <section id="what-models-to-use">
-<h2><span class="section-number">37.3. </span>What models to use?<a class="headerlink" href="#what-models-to-use" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">36.3. </span>What models to use?<a class="headerlink" href="#what-models-to-use" title="Permalink to this headline">#</a></h2>
 <p>This is one of the most widely discussed topics when people approach a Machine Learning solution. There are three main categories of machine learning: supervised learning, unsupervised learning, and reinforcement learning. The cheat sheet from Microsoft could be a helpful quick reference.</p>
 <figure class="align-default" id="machine-learning-algorithm-cheat-sheet">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/machine-learning-algorithm-cheat-sheet.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/machine-learning-algorithm-cheat-sheet.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 37.2 </span><span class="caption-text"><a class="reference external" href="https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet">Machine Learning Algorithm Cheat Sheet</a></span><a class="headerlink" href="#machine-learning-algorithm-cheat-sheet" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 36.2 </span><span class="caption-text"><a class="reference external" href="https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet">Machine Learning Algorithm Cheat Sheet</a></span><a class="headerlink" href="#machine-learning-algorithm-cheat-sheet" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="admonition seealso">
@@ -1753,17 +1753,17 @@ <h2><span class="section-number">37.3. </span>What models to use?<a class="heade
 </div>
 </section>
 <section id="how-to-put-a-model-into-production">
-<h2><span class="section-number">37.4. </span>How to put a model into production?<a class="headerlink" href="#how-to-put-a-model-into-production" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">36.4. </span>How to put a model into production?<a class="headerlink" href="#how-to-put-a-model-into-production" title="Permalink to this headline">#</a></h2>
 <p>Machine Learning inference is the process of inputting data into a model to calculate an output, which is also referred to as “putting a Machine Learning model into production”. There are two major types of inferences that may impact the feasibility of productionize the trained model.</p>
 <p><strong>Batch inference</strong> means that multiple predictions are requested periodically. The <a class="reference external" href="https://covid19-projections.com/">COVID-19 Projections</a> project is typically a batch inference example, which is mentioned in the previous section. Another good example is Facebook News feed generation, which is a complex and time-consuming artificial intelligent process. The News feeds are pre-generated regularly and stored in the cache. Then the web application could request and present the cached feeds in runtime.</p>
 <p><strong>Online inference</strong>, on the contrary, is the process to handle the prediction in real-time, synchronized, or from continuous data streaming. The online inference is suitable for more latency-sensitive user scenarios such as search engines and autonomous driving. And it requires speeding up both the inferring and input data sampling. It should be noted that the inference is usually just part of the end-to-end workflow of serving a request from the client side. It will be more challenging if the reference is on the critical path.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">37.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">36.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Apply the knowledge of this section to refine <a class="reference external" href="overview.html#your-turn">your own Machine Learning project proposal</a>.</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">37.6. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">36.6. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
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     <link rel="index" title="Index" href="../../genindex.html" />
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-    <link rel="prev" title="16.2. Gradient descent" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html" />
+    <link rel="next" title="16.1. Introduction to clustering" href="introduction-to-clustering.html" />
+    <link rel="prev" title="15.2. Gradient descent" href="../../ml-fundamentals/parameter-optimization/gradient-descent.html" />
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+<h1><span class="section-number">16. </span>Clustering models for Machine Learning<a class="headerlink" href="#clustering-models-for-machine-learning" title="Permalink to this headline">#</a></h1>
 <p>Clustering is a machine learning task where it looks to find objects that resemble one another and group these into groups called clusters.  What differs clustering from other approaches in machine learning, is that things happen automatically, in fact, it’s fair to say it’s the opposite of supervised learning.</p>
 <p>Nigeria’s diverse audience has diverse musical tastes, let’s look at some music popular in Nigeria. This dataset includes data about various songs’ ‘danceability’ score, ‘acousticness’, loudness, ‘speechiness’, popularity and energy. It will be interesting to discover patterns in this data!</p>
 <figure class="align-default" id="a-turntable">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/turntable.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/turntable.png" />
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+<p><span class="caption-number">Fig. 16.1 </span><span class="caption-text">A turntable photographed by Marcela Laskoski on Unsplash</span><a class="headerlink" href="#a-turntable" title="Permalink to this image">#</a></p>
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-<li class="toctree-l1"><a class="reference internal" href="k-means-clustering.html">17.2. K-Means clustering</a></li>
+<li class="toctree-l1"><a class="reference internal" href="introduction-to-clustering.html">16.1. Introduction to clustering</a></li>
+<li class="toctree-l1"><a class="reference internal" href="k-means-clustering.html">16.2. K-Means clustering</a></li>
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diff --git a/ml-advanced/clustering/introduction-to-clustering.html b/ml-advanced/clustering/introduction-to-clustering.html
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+    <link rel="next" title="16.2. K-Means clustering" href="k-means-clustering.html" />
+    <link rel="prev" title="16. Clustering models for Machine Learning" href="clustering-models-for-machine-learning.html" />
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-     41.14. Try it in Excel
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-     41.21. Soda profits
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+   16.1.3. Clustering algorithms
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  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-cluster-your-data">
-   17.1.4. Exercise - cluster your data
+   16.1.4. Exercise - cluster your data
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exercise-data-distribution">
-     17.1.4.1. Exercise - data distribution
+     16.1.4.1. Exercise - data distribution
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
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+   16.1.5. Your turn! 🚀
   </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
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+   16.1.6. Self study
   </a>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   17.1.7. Acknowledgments
+   16.1.7. Acknowledgments
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  </li>
 </ul>
@@ -1687,10 +1687,10 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="introduction-to-clustering">
-<h1><span class="section-number">17.1. </span>Introduction to clustering<a class="headerlink" href="#introduction-to-clustering" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">16.1. </span>Introduction to clustering<a class="headerlink" href="#introduction-to-clustering" title="Permalink to this headline">#</a></h1>
 <p>Clustering is a type of Unsupervised Learning that presumes that a dataset is unlabelled or that its inputs are not matched with predefined outputs. It uses various algorithms to sort through unlabeled data and provide groupings according to patterns it discerns in the data.</p>
 <section id="introduction">
-<h2><span class="section-number">17.1.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.1.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>In a professional setting, clustering can be used to determine things like market segmentation, determining what age groups buy what items, for example. Another use would be anomaly detection, perhaps to detect fraud from a dataset of credit card transactions. Or you might use clustering to determine tumors in a batch of medical scans.</p>
 <p>Think a minute about how you might have encountered clustering ‘in the wild’, in a banking, e-commerce, or business setting.</p>
 <div class="admonition note">
@@ -1700,7 +1700,7 @@ <h2><span class="section-number">17.1.1. </span>Introduction<a class="headerlink
 <p>Alternately, you could use it for grouping search results - by shopping links, images, or reviews, for example. Clustering is useful when you have a large dataset that you want to reduce and on which you want to perform more granular analysis, so the technique can be used to learn about data before other models are constructed.</p>
 </section>
 <section id="getting-started-with-clustering">
-<h2><span class="section-number">17.1.2. </span>Getting started with clustering<a class="headerlink" href="#getting-started-with-clustering" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.1.2. </span>Getting started with clustering<a class="headerlink" href="#getting-started-with-clustering" title="Permalink to this headline">#</a></h2>
 <p>Scikit-learn offers a large array of methods to perform clustering. The type you choose will depend on your use case. According to the documentation, each method has various benefits. Here is a simplified table of the methods supported by Scikit-learn and their appropriate use cases:</p>
 <table class="colwidths-auto table">
 <thead>
@@ -1751,7 +1751,7 @@ <h2><span class="section-number">17.1.2. </span>Getting started with clustering<
 <figure class="align-default" id="flat-vs-nonflat-geometry-infographic">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/flat-nonflat.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/flat-nonflat.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 17.2 </span><span class="caption-text">Flat vs Nonflat Geometry Infographic by Dasani Madipalli</span><a class="headerlink" href="#flat-vs-nonflat-geometry-infographic" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 16.2 </span><span class="caption-text">Flat vs Nonflat Geometry Infographic by Dasani Madipalli</span><a class="headerlink" href="#flat-vs-nonflat-geometry-infographic" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p><strong>Distances</strong></p>
@@ -1763,7 +1763,7 @@ <h2><span class="section-number">17.1.2. </span>Getting started with clustering<
 <p>Data that is ‘noisy’ is considered to be ‘dense’. The distances between points in each of its clusters may prove, on examination, to be more or less dense, or ‘crowded’ and thus this data needs to be analyzed with the appropriate clustering method. This article demonstrates the difference between using K-Means clustering vs. HDBSCAN algorithms to explore a noisy dataset with uneven cluster density.</p>
 </section>
 <section id="clustering-algorithms">
-<h2><span class="section-number">17.1.3. </span>Clustering algorithms<a class="headerlink" href="#clustering-algorithms" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.1.3. </span>Clustering algorithms<a class="headerlink" href="#clustering-algorithms" title="Permalink to this headline">#</a></h2>
 <p>There are over 100 clustering algorithms, and their use depends on the nature of the data at hand. Let’s discuss some of the major ones:</p>
 <ul class="simple">
 <li><p><strong>Hierarchical clustering</strong>. If an object is classified by its proximity to a nearby object, rather than to one farther away, clusters are formed based on their members’ distance to and from other objects. Scikit-learn’s agglomerative clustering is hierarchical.</p></li>
@@ -1771,7 +1771,7 @@ <h2><span class="section-number">17.1.3. </span>Clustering algorithms<a class="h
 <figure class="align-default" id="hierarchical-clustering-infographic">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/hierarchical.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/hierarchical.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 17.3 </span><span class="caption-text">Hierarchical clustering Infographic by Dasani Madipalli</span><a class="headerlink" href="#hierarchical-clustering-infographic" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 16.3 </span><span class="caption-text">Hierarchical clustering Infographic by Dasani Madipalli</span><a class="headerlink" href="#hierarchical-clustering-infographic" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul class="simple">
@@ -1780,7 +1780,7 @@ <h2><span class="section-number">17.1.3. </span>Clustering algorithms<a class="h
 <figure class="align-default" id="centroid-clustering-infographic">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/centroid.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/centroid.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 17.4 </span><span class="caption-text">Centroid clustering Infographic by Dasani Madipalli</span><a class="headerlink" href="#centroid-clustering-infographic" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 16.4 </span><span class="caption-text">Centroid clustering Infographic by Dasani Madipalli</span><a class="headerlink" href="#centroid-clustering-infographic" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul class="simple">
@@ -1790,7 +1790,7 @@ <h2><span class="section-number">17.1.3. </span>Clustering algorithms<a class="h
 </ul>
 </section>
 <section id="exercise-cluster-your-data">
-<h2><span class="section-number">17.1.4. </span>Exercise - cluster your data<a class="headerlink" href="#exercise-cluster-your-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.1.4. </span>Exercise - cluster your data<a class="headerlink" href="#exercise-cluster-your-data" title="Permalink to this headline">#</a></h2>
 <p>Clustering as a technique is greatly aided by proper visualization, so let’s get started by visualizing our music data. This exercise will help us decide which of the methods of clustering we should most effectively use for the nature of this data.</p>
 <ol class="simple">
 <li><p>Open the <em>notebook.ipynb</em> file in this folder.</p></li>
@@ -2294,7 +2294,7 @@ <h2><span class="section-number">17.1.4. </span>Exercise - cluster your data<a c
 <p>Is there any convergence in this dataset around a song’s perceived popularity and danceability? A FacetGrid shows that there are concentric circles that line up, regardless of genre. Could it be that Nigerian tastes converge at a certain level of danceability for this genre?</p>
 <p>Try different datapoints (energy, loudness, speechiness) and more or different musical genres. What can you discover? Take a look at the <code class="docutils literal notranslate"><span class="pre">df.describe()</span></code> table to see the general spread of the data points.</p>
 <section id="exercise-data-distribution">
-<h3><span class="section-number">17.1.4.1. </span>Exercise - data distribution<a class="headerlink" href="#exercise-data-distribution" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">16.1.4.1. </span>Exercise - data distribution<a class="headerlink" href="#exercise-data-distribution" title="Permalink to this headline">#</a></h3>
 <p>Are these three genres significantly different in the perception of their danceability, based on their popularity?</p>
 <ol class="simple">
 <li><p>Examine our top three genres data distribution for popularity and danceability along a given x and y axis.</p></li>
@@ -2344,11 +2344,11 @@ <h3><span class="section-number">17.1.4.1. </span>Exercise - data distribution<a
 </section>
 <hr class="docutils" />
 <section id="your-turn">
-<h2><span class="section-number">17.1.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.1.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">17.1.6. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.1.6. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>Before you apply clustering algorithms, as we have learned, it’s a good idea to understand the nature of your dataset.There are several resources available.</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html">right clustering algorithm</a></p></li>
@@ -2356,7 +2356,7 @@ <h2><span class="section-number">17.1.6. </span>Self study<a class="headerlink"
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">17.1.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.1.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">Machine Learning for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2398,13 +2398,13 @@ <h2><span class="section-number">17.1.7. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
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-            <p class="prev-next-title"><span class="section-number">17. </span>Clustering models for Machine Learning</p>
+            <p class="prev-next-title"><span class="section-number">16. </span>Clustering models for Machine Learning</p>
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-        <p class="prev-next-title"><span class="section-number">17.2. </span>K-Means clustering</p>
+        <p class="prev-next-title"><span class="section-number">16.2. </span>K-Means clustering</p>
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diff --git a/ml-advanced/clustering/k-means-clustering.html b/ml-advanced/clustering/k-means-clustering.html
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-    <title>17.2. K-Means clustering &#8212; Ocademy Open Machine Learning Book</title>
+    <title>16.2. K-Means clustering &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="18. Getting started with ensemble learning" href="../ensemble-learning/getting-started-with-ensemble-learning.html" />
-    <link rel="prev" title="17.1. Introduction to clustering" href="introduction-to-clustering.html" />
+    <link rel="next" title="17. Getting started with ensemble learning" href="../ensemble-learning/getting-started-with-ensemble-learning.html" />
+    <link rel="prev" title="16.1. Introduction to clustering" href="introduction-to-clustering.html" />
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-     17.2.4.2. Elbow method
+     16.2.4.2. Elbow method
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-display-the-clusters">
-   17.2.5. Exercise - display the clusters
+   16.2.5. Exercise - display the clusters
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#variance">
-   17.2.6. Variance
+   16.2.6. Variance
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   17.2.7. Your turn! 🚀
+   16.2.7. Your turn! 🚀
   </a>
  </li>
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   <a class="reference internal nav-link" href="#self-study">
-   17.2.8. Self study
+   16.2.8. Self study
   </a>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   17.2.9. Acknowledgments
+   16.2.9. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1717,7 +1717,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="k-means-clustering">
-<h1><span class="section-number">17.2. </span>K-Means clustering<a class="headerlink" href="#k-means-clustering" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">16.2. </span>K-Means clustering<a class="headerlink" href="#k-means-clustering" title="Permalink to this headline">#</a></h1>
 <p>In this section, you will learn how to create clusters using Scikit-learn and the Nigerian music dataset you imported earlier. We will cover the basics of K-Means for Clustering. Keep in mind that, as you learned in the earlier section, there are many ways to work with clusters and the method you use depends on your data. We will try K-Means as it’s the most common clustering technique. Let’s get started!</p>
 <p>Terms you will learn about:</p>
 <ul class="simple">
@@ -1727,13 +1727,13 @@ <h1><span class="section-number">17.2. </span>K-Means clustering<a class="header
 <li><p>Variance</p></li>
 </ul>
 <section id="introduction">
-<h2><span class="section-number">17.2.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.2.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>K-Means Clustering is a method derived from the domain of signal processing. It is used to divide and partition groups of data into ‘k’ clusters using a series of observations. Each observation works to group a given datapoint closest to its nearest ‘mean’, or the center point of a cluster.</p>
 <p>The clusters can be visualized as Voronoi diagrams, which include a point (or ‘seed’) and its corresponding region.</p>
 <figure class="align-default" id="voronoi-diagram">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/voronoi.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/voronoi.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 17.5 </span><span class="caption-text">voronoi diagram infographic by Jen Looper</span><a class="headerlink" href="#voronoi-diagram" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 16.5 </span><span class="caption-text">voronoi diagram infographic by Jen Looper</span><a class="headerlink" href="#voronoi-diagram" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The K-Means clustering process executes in a three-step process):</p>
@@ -1748,11 +1748,11 @@ <h2><span class="section-number">17.2.1. </span>Introduction<a class="headerlink
 <p>One drawback of using K-Means includes the fact that you will need to establish ‘k’, that is the number of centroids. Fortunately the  ‘elbow method’ helps to estimate a good starting value for ‘k’. You’ll try it in a minute.</p>
 </section>
 <section id="prerequisite">
-<h2><span class="section-number">17.2.2. </span>Prerequisite<a class="headerlink" href="#prerequisite" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.2.2. </span>Prerequisite<a class="headerlink" href="#prerequisite" title="Permalink to this headline">#</a></h2>
 <p>You will work in this section’s <em>notebook.ipynb</em> file that includes the data import and preliminary cleaning you did in the last section.</p>
 </section>
 <section id="exercise-preparation">
-<h2><span class="section-number">17.2.3. </span>Exercise - preparation<a class="headerlink" href="#exercise-preparation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.2.3. </span>Exercise - preparation<a class="headerlink" href="#exercise-preparation" title="Permalink to this headline">#</a></h2>
 <p>Start by taking another look at the songs data.</p>
 <div class="cell tag_hide-input docutils container">
 <div class="cell_input docutils container">
@@ -1943,11 +1943,11 @@ <h2><span class="section-number">17.2.3. </span>Exercise - preparation<a class="
 </div>
 </section>
 <section id="silhouette-score">
-<h2><span class="section-number">17.2.4. </span>Silhouette score<a class="headerlink" href="#silhouette-score" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.2.4. </span>Silhouette score<a class="headerlink" href="#silhouette-score" title="Permalink to this headline">#</a></h2>
 <p>Look for a silhouette score closer to 1. This score varies from -1 to 1, and if the score is 1, the cluster is dense and well-separated from other clusters. A value near 0 represents overlapping clusters with samples very close to the decision boundary of the neighboring clusters.</p>
 <p>Our score is <strong>.53</strong>, so right in the middle. This indicates that our data is not particularly well-suited to this type of clustering, but let’s continue.</p>
 <section id="exercise-build-a-model">
-<h3><span class="section-number">17.2.4.1. </span>Exercise - build a model<a class="headerlink" href="#exercise-build-a-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">16.2.4.1. </span>Exercise - build a model<a class="headerlink" href="#exercise-build-a-model" title="Permalink to this headline">#</a></h3>
 <ol class="simple">
 <li><p>Import <code class="docutils literal notranslate"><span class="pre">KMeans</span></code> and start the clustering process.</p></li>
 </ol>
@@ -1974,7 +1974,7 @@ <h3><span class="section-number">17.2.4.1. </span>Exercise - build a model<a cla
 </div>
 </section>
 <section id="elbow-method">
-<h3><span class="section-number">17.2.4.2. </span>Elbow method<a class="headerlink" href="#elbow-method" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">16.2.4.2. </span>Elbow method<a class="headerlink" href="#elbow-method" title="Permalink to this headline">#</a></h3>
 <p>Previously, you surmised that, because you have targeted 3 song genres, you should choose 3 clusters. But is that the case?</p>
 <ol class="simple">
 <li><p>Use the ‘elbow method’ to make sure.</p></li>
@@ -2001,7 +2001,7 @@ <h3><span class="section-number">17.2.4.2. </span>Elbow method<a class="headerli
 </section>
 </section>
 <section id="exercise-display-the-clusters">
-<h2><span class="section-number">17.2.5. </span>Exercise - display the clusters<a class="headerlink" href="#exercise-display-the-clusters" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.2.5. </span>Exercise - display the clusters<a class="headerlink" href="#exercise-display-the-clusters" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p>Try the process again, this time setting three clusters, and display the clusters as a scatterplot:</p></li>
 </ol>
@@ -2046,12 +2046,12 @@ <h2><span class="section-number">17.2.5. </span>Exercise - display the clusters<
 <figure class="align-default" id="problem-models">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/problems.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/clustering/problems.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 17.6 </span><span class="caption-text">roblem models by Scikit-learn</span><a class="headerlink" href="#problem-models" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 16.6 </span><span class="caption-text">roblem models by Scikit-learn</span><a class="headerlink" href="#problem-models" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="variance">
-<h2><span class="section-number">17.2.6. </span>Variance<a class="headerlink" href="#variance" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.2.6. </span>Variance<a class="headerlink" href="#variance" title="Permalink to this headline">#</a></h2>
 <p>Variance is defined as “the average of the squared differences from the Mean” . In the context of this clustering problem, it refers to data that the numbers of our dataset tend to diverge a bit too much from the mean.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -2059,11 +2059,11 @@ <h2><span class="section-number">17.2.6. </span>Variance<a class="headerlink" hr
 </div>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">17.2.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.2.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">17.2.8. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.2.8. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>You can use this tool to visualize sample data points and determine their centroids.</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/">such as this one</a></p></li>
@@ -2071,7 +2071,7 @@ <h2><span class="section-number">17.2.8. </span>Self study<a class="headerlink"
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">17.2.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">16.2.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">Machine Learning for Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2113,13 +2113,13 @@ <h2><span class="section-number">17.2.9. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">17.1. </span>Introduction to clustering</p>
+            <p class="prev-next-title"><span class="section-number">16.1. </span>Introduction to clustering</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../ensemble-learning/getting-started-with-ensemble-learning.html" title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">18. </span>Getting started with ensemble learning</p>
+        <p class="prev-next-title"><span class="section-number">17. </span>Getting started with ensemble learning</p>
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diff --git a/ml-advanced/ensemble-learning/bagging.html b/ml-advanced/ensemble-learning/bagging.html
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>18.1. Bagging &#8212; Ocademy Open Machine Learning Book</title>
+    <title>17.1. Bagging &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="18.2. Random forest" href="random-forest.html" />
-    <link rel="prev" title="18. Getting started with ensemble learning" href="getting-started-with-ensemble-learning.html" />
+    <link rel="next" title="17.2. Random forest" href="random-forest.html" />
+    <link rel="prev" title="17. Getting started with ensemble learning" href="getting-started-with-ensemble-learning.html" />
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-     41.14. Try it in Excel
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-     41.49. Linear Regression Implementation from Scratch
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 <p>In previous sections, we explored different classification algorithms as well as techniques that can be used to properly validate and evaluate the quality of your models.</p>
 <p>Now, suppose that we have chosen the best possible model for a particular problem and are struggling to further improve its accuracy. In this case, we would need to apply some more advanced machine learning techniques that are collectively referred to as <em>ensembles</em>.</p>
 <p>An <em>ensemble</em> is a set of elements that collectively contribute to a whole. A familiar example is a musical ensemble, which blends the sounds of several musical instruments to create harmony, or architectural ensembles, which are a set of buildings designed as a unit. In ensembles, the (whole) harmonious outcome is more important than the performance of any individual part.</p>
 <section id="ensembles">
-<h2><span class="section-number">18.1.1. </span>Ensembles<a class="headerlink" href="#ensembles" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.1.1. </span>Ensembles<a class="headerlink" href="#ensembles" title="Permalink to this headline">#</a></h2>
 <p><a class="reference external" href="https://en.wikipedia.org/wiki/Condorcet%27s_jury_theorem">Condorcet’s jury theorem</a> (1784) is about an ensemble in some sense. It states that, if each member of the jury makes an independent judgment and the probability of the correct decision by each juror is more than 0.5, then the probability of the correct decision by the whole jury increases with the total number of jurors and tends to one. On the other hand, if the probability of being right is less than 0.5 for each juror, then the probability of the correct decision by the whole jury decreases with the number of jurors and tends to zero.</p>
 <p>Let’s write an analytic expression for this theorem:</p>
 <ul class="simple">
@@ -1699,7 +1699,7 @@ <h2><span class="section-number">18.1.1. </span>Ensembles<a class="headerlink" h
 <p>A similar idea for error reduction was adopted in the field of Machine Learning.</p>
 </section>
 <section id="bootstrapping">
-<h2><span class="section-number">18.1.2. </span>Bootstrapping<a class="headerlink" href="#bootstrapping" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.1.2. </span>Bootstrapping<a class="headerlink" href="#bootstrapping" title="Permalink to this headline">#</a></h2>
 <p><em>Bagging</em> (also known as <a class="reference external" href="https://en.wikipedia.org/wiki/Bootstrap_aggregating">Bootstrap aggregation</a>) is one of the first and most basic ensemble techniques. It was proposed by <a class="reference external" href="https://en.wikipedia.org/wiki/Leo_Breiman">Leo Breiman</a> in 1994. Bagging is based on the statistical method of <a class="reference external" href="https://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29">bootstrapping</a>, which makes the evaluation of many statistics of complex models feasible.</p>
 <p>The bootstrap method goes as follows. Let there be a sample <span class="math notranslate nohighlight">\(\large X\)</span> of size <span class="math notranslate nohighlight">\(\large N\)</span>. We can make a new sample from the original sample by drawing <span class="math notranslate nohighlight">\(\large N\)</span> elements from the latter randomly and uniformly, with replacement. In other words, we select a random element from the original sample of size <span class="math notranslate nohighlight">\(\large N\)</span> and do this <span class="math notranslate nohighlight">\(\large N\)</span> times. All elements are equally likely to be selected, thus each element is drawn with the equal probability <span class="math notranslate nohighlight">\(\large \frac{1}{N}\)</span>.</p>
 <p>Let’s say we are drawing balls from a bag one at a time. At each step, the selected ball is put back into the bag so that the next selection is made equiprobably i.e. from the same number of balls <span class="math notranslate nohighlight">\(\large N\)</span>. Note that, because we put the balls back, there may be duplicates in the new sample. Let’s call this new sample <span class="math notranslate nohighlight">\(\large X_1\)</span>.</p>
@@ -1707,7 +1707,7 @@ <h2><span class="section-number">18.1.2. </span>Bootstrapping<a class="headerlin
 <figure class="align-default" id="process-of-bootstrap">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Process_of_bootstrap.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Process_of_bootstrap.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Process_of_bootstrap.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 18.1 </span><span class="caption-text">Process of bootstrap</span><a class="headerlink" href="#process-of-bootstrap" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 17.1 </span><span class="caption-text">Process of bootstrap</span><a class="headerlink" href="#process-of-bootstrap" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>For our example, we’ll use the familiar <code class="docutils literal notranslate"><span class="pre">telecom_churn</span></code> dataset. Previously, when we discussed feature importance, we saw that one of the most important features in this dataset is the number of calls to customer service. Let’s visualize the data and look at the distribution of this feature.</p>
@@ -1913,7 +1913,7 @@ <h2><span class="section-number">18.1.2. </span>Bootstrapping<a class="headerlin
 </div>
 </div>
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-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.legend.Legend at 0x7f4adf2b37c0&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.legend.Legend at 0x7f6aa00320d0&gt;
 </pre></div>
 </div>
 <img alt="../../_images/bagging_10_1.png" src="../../_images/bagging_10_1.png" />
@@ -1978,7 +1978,7 @@ <h2><span class="section-number">18.1.2. </span>Bootstrapping<a class="headerlin
 <p>For the interpretation of confidence intervals, you can address this <a class="reference external" href="https://www.graphpad.com/guides/prism/7/statistics/stat_more_about_confidence_interval.htm?toc=0&amp;printWindow">concise note</a> or any course on statistics. It’s not correct to say that a confidence interval contains 95% of values. Note that the interval for the loyal customers is narrower, which is reasonable since they make fewer calls (0, 1 or 2) in comparison with the churned clients who call until they are fed up and decide to switch providers.</p>
 </section>
 <section id="id1">
-<h2><span class="section-number">18.1.3. </span>Bagging<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.1.3. </span>Bagging<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
 <p>Now that you’ve grasped the idea of bootstrapping, we can move on to <em>bagging</em>.</p>
 <p>Suppose that we have a training set <span class="math notranslate nohighlight">\(\large X\)</span>. Using bootstrapping, we generate samples <span class="math notranslate nohighlight">\(\large X_1, \dots, X_M\)</span>. Now, for each bootstrap sample, we train its own classifier <span class="math notranslate nohighlight">\(\large a_i(x)\)</span>. The final classifier will average the outputs from all these individual classifiers. In the case of classification, this technique corresponds to voting:
 $<span class="math notranslate nohighlight">\(\large a(x) = \frac{1}{M}\sum_{i = 1}^M a_i(x).\)</span>$</p>
@@ -1986,7 +1986,7 @@ <h2><span class="section-number">18.1.3. </span>Bagging<a class="headerlink" hre
 <figure class="align-default" id="illustration-of-bagging">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Illustration_of_Bagging.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Illustration_of_Bagging.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Illustration_of_Bagging.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 18.2 </span><span class="caption-text">Illustration of Bagging</span><a class="headerlink" href="#illustration-of-bagging" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 17.2 </span><span class="caption-text">Illustration of Bagging</span><a class="headerlink" href="#illustration-of-bagging" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Let’s consider a regression problem with base algorithms <span class="math notranslate nohighlight">\(\large b_1(x), \dots , b_n(x)\)</span>. Assume that there exists an ideal target function of true answers <span class="math notranslate nohighlight">\(\large y(x)\)</span> defined for all inputs and that the distribution <span class="math notranslate nohighlight">\(\large p(x)\)</span> is defined. We can then express the error for each regression function as follows:</p>
@@ -2026,7 +2026,7 @@ <h2><span class="section-number">18.1.3. </span>Bagging<a class="headerlink" hre
 <figure class="align-default" id="comparison-of-bagging-and-tree">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Comparison_of_Bagging_and_Tree.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Comparison_of_Bagging_and_Tree.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Comparison_of_Bagging_and_Tree.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 18.3 </span><span class="caption-text">Comparison of Bagging and Tree</span><a class="headerlink" href="#comparison-of-bagging-and-tree" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 17.3 </span><span class="caption-text">Comparison of Bagging and Tree</span><a class="headerlink" href="#comparison-of-bagging-and-tree" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The error for the decision tree:</p>
@@ -2040,7 +2040,7 @@ <h2><span class="section-number">18.1.3. </span>Bagging<a class="headerlink" hre
 <p>The example above is unlikely to be applicable to any real work. This is because we made a strong assumption that our individual errors are uncorrelated. More often than not, this is way too optimistic for real-world applications. When this assumption is false, the reduction in error will not be as significant. In the following sections, we will discuss some more sophisticated ensemble methods, which enable more accurate predictions in real-world problems.</p>
 </section>
 <section id="out-of-bag-error">
-<h2><span class="section-number">18.1.4. </span>Out of bag error<a class="headerlink" href="#out-of-bag-error" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.1.4. </span>Out of bag error<a class="headerlink" href="#out-of-bag-error" title="Permalink to this headline">#</a></h2>
 <p>In case of Random Forest, there is no need to use cross-validation or hold-out samples in order to get an unbiased error estimation. Why? Because, in ensemble techniques, the error estimation takes place internally.</p>
 <p>Random trees are constructed using different bootstrap samples of the original dataset. Approximately 37% of inputs are left out of a particular bootstrap sample and are not used in the construction of the <span class="math notranslate nohighlight">\(\large k\)</span>-th tree.</p>
 <p>This is easy to prove. Suppose there are <span class="math notranslate nohighlight">\(\large \ell\)</span> examples in our dataset. At each step, each data point has equal probability of ending up in a bootstrap sample with replacement, probability <span class="math notranslate nohighlight">\(\large\frac{1}{\ell}.\)</span> The probability that there is no such bootstrap sample that contains a particular dataset element (i.e. it has been omitted <span class="math notranslate nohighlight">\(\large \ell\)</span> times) equals <span class="math notranslate nohighlight">\(\large (1 - \frac{1}{\ell})^\ell\)</span>. When <span class="math notranslate nohighlight">\(\large \ell \rightarrow +\infty\)</span>, it becomes equal to the <a class="reference external" href="https://en.wikipedia.org/wiki/List_of_limits">Second Remarkable Limit</a> <span class="math notranslate nohighlight">\(\large \frac{1}{e}\)</span>. Then, the probability of selecting a specific example is <span class="math notranslate nohighlight">\(\large \approx  1 - \frac{1}{e} \approx 63\%\)</span>.</p>
@@ -2048,18 +2048,18 @@ <h2><span class="section-number">18.1.4. </span>Out of bag error<a class="header
 <figure class="align-default" id="principle-of-oobe">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Principle_of_OOBE.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Principle_of_OOBE.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/bagging/Principle_of_OOBE.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 18.4 </span><span class="caption-text">Principle of OOBE</span><a class="headerlink" href="#principle-of-oobe" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 17.4 </span><span class="caption-text">Principle of OOBE</span><a class="headerlink" href="#principle-of-oobe" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The top part of the figure above represents our original dataset. We split it into the training (left) and test (right) sets. In the left image, we draw a grid that perfectly divides our dataset according to classes. Now, we use the same grid to estimate the share of the correct answers on our test set. We can see that our classifier gave incorrect answers in those 4 cases that have not been used during training (on the left). Hence, the accuracy of our classifier is <span class="math notranslate nohighlight">\(\large \frac{11}{15}*100\% = 73.33\%\)</span>.</p>
 <p>To sum up, each base algorithm is trained on <span class="math notranslate nohighlight">\(\large \approx 63\%\)</span> of the original examples. It can be validated on the remaining <span class="math notranslate nohighlight">\(\large \approx 37\%\)</span>. The Out-of-Bag estimate is nothing more than the mean estimate of the base algorithms on those <span class="math notranslate nohighlight">\(\large \approx 37\%\)</span> of inputs that were left out of training.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">18.1.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.1.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">18.1.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.1.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/kashnitsky">Yury Kashnitsky</a> for creating the open-source content <a class="reference external" href="https://www.kaggle.com/code/kashnitsky/topic-5-ensembles-part-1-bagging/notebook">Ensembles and random forest</a>. They inspire the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2101,13 +2101,13 @@ <h2><span class="section-number">18.1.6. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
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-            <p class="prev-next-title"><span class="section-number">18. </span>Getting started with ensemble learning</p>
+            <p class="prev-next-title"><span class="section-number">17. </span>Getting started with ensemble learning</p>
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+        <p class="prev-next-title"><span class="section-number">17.2. </span>Random forest</p>
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diff --git a/ml-advanced/ensemble-learning/feature-importance.html b/ml-advanced/ensemble-learning/feature-importance.html
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     <link rel="index" title="Index" href="../../genindex.html" />
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+    <link rel="next" title="18. Introduction to Gradient Boosting" href="../gradient-boosting/introduction-to-gradient-boosting.html" />
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-     19.4. XGBoost + k-fold CV + Feature Importance
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-     41.9. Analyzing text about Data Science
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-     41.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.21. Soda profits
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-     41.35. Tell a story
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-     41.36. Explore a planetary computer dataset
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   <a class="reference internal nav-link" href="#sklearn-random-forest-feature-importance">
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 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="feature-importance">
-<h1><span class="section-number">18.3. </span>Feature importance<a class="headerlink" href="#feature-importance" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">17.3. </span>Feature importance<a class="headerlink" href="#feature-importance" title="Permalink to this headline">#</a></h1>
 <p>It’s quite often that you want to make out the exact reasons of the algorithm outputting a particular answer. Or at the very least to find out which input features contributed most to the result. With Random Forest, you can obtain such information quite easily.</p>
 <section id="intuition">
-<h2><span class="section-number">18.3.1. </span>Intuition<a class="headerlink" href="#intuition" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.3.1. </span>Intuition<a class="headerlink" href="#intuition" title="Permalink to this headline">#</a></h2>
 <p>From the picture below, it is intuitively clear that, in our credit scoring problem, <em>Age</em> is much more important than <em>Income</em>. This can be formally explained using the concept of <em>information gain</em>.</p>
 <figure class="align-default" id="example-of-credit-scoring-problem">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/feature-importance/Example_of_credit_scoring_problem.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/feature-importance/Example_of_credit_scoring_problem.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/feature-importance/Example_of_credit_scoring_problem.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 18.7 </span><span class="caption-text">Example of credit scoring problem</span><a class="headerlink" href="#example-of-credit-scoring-problem" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 17.7 </span><span class="caption-text">Example of credit scoring problem</span><a class="headerlink" href="#example-of-credit-scoring-problem" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>In the case of many decision trees or a random forest, the closer the mean position of a feature over all the trees to the root, the more significant it is for a given classification or regression problem. Gains in the splitting criterion, such as the <em>Gini impurity</em>, obtained at each optimal split in every tree is a measure of importance that is directly associated with the splitting feature. The value of this score is distinct for each feature and accumulates over all the trees.</p>
 <p>Let’s go a little deeper into the details.</p>
 <p>There exist a lot of methods to assess feature importances. Leo Breinman in his works suggested to evaluate the importance of a variable by measuring decrease of accuracy of the forest when the variable is randomly permuted or decrease of impurity of a nodes where the given variable is used for splitting. The former method is often called <strong>permutation importance</strong>. The latter method is used in <code class="docutils literal notranslate"><span class="pre">sklearn</span></code>.</p>
 <section id="permutation-importance">
-<h3><span class="section-number">18.3.1.1. </span>Permutation importance<a class="headerlink" href="#permutation-importance" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">17.3.1.1. </span>Permutation importance<a class="headerlink" href="#permutation-importance" title="Permalink to this headline">#</a></h3>
 <p>Inspired by this <a class="reference external" href="https://www.researchgate.net/publication/5231126_Conditional_Variable_Importance_for_Random_Forests">article</a>.
 The average reduction in accuracy caused by a variable is determined during the calculation of the out-of-bag error. The greater the reduction in accuracy due to an exclusion or permutation of the variable, the higher its <em>importance score</em>. For this reason, variables with a greater average reduction in accuracy are generally more significant for classification.</p>
 <p>The rationale for calculating permutation importance is the following: By randomly permuting the predictor variable <span class="math notranslate nohighlight">\(X_j\)</span>, its original association with the response <span class="math notranslate nohighlight">\(Y\)</span> is broken. When the permuted variable <span class="math notranslate nohighlight">\(X_j\)</span>, together with all the others non-permuted variables, is used the response for the out-of-bag observations, the prediction <em>accuracy</em> decreases substantially if the original <span class="math notranslate nohighlight">\(X_j\)</span> was associated with response. Thus, as a measure of variable importance, the difference in prediction accuracy before and after permuting is used.</p>
@@ -1728,7 +1728,7 @@ <h3><span class="section-number">18.3.1.1. </span>Permutation importance<a class
 </section>
 </section>
 <section id="illustrating-permutation-importance">
-<h2><span class="section-number">18.3.2. </span>Illustrating permutation importance<a class="headerlink" href="#illustrating-permutation-importance" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.3.2. </span>Illustrating permutation importance<a class="headerlink" href="#illustrating-permutation-importance" title="Permalink to this headline">#</a></h2>
 <p>Let’s assume that we have a toy dataset with 10 instances. Target variable can be either <strong>‘N’</strong> or <strong>‘P’</strong>.</p>
 <div class="math notranslate nohighlight">
 \[\begin{split}\begin{array}{c|c|c|c|c|c|c|c|c|c}
@@ -1798,7 +1798,7 @@ <h2><span class="section-number">18.3.2. </span>Illustrating permutation importa
 <p>The above mentioned steps are to be done for each out-of-bag sample <span class="math notranslate nohighlight">\(\overline{\mathfrak{B}}^{(t)}\)</span>. To get not normalized <em>permutation importance</em> we sum all computed differences and divide by the number of trees. Normalization is done by dividing <em>not normalized permutation importance</em> by standard error.</p>
 </section>
 <section id="sklearn-random-forest-feature-importance">
-<h2><span class="section-number">18.3.3. </span>Sklearn Random Forest Feature Importance<a class="headerlink" href="#sklearn-random-forest-feature-importance" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.3.3. </span>Sklearn Random Forest Feature Importance<a class="headerlink" href="#sklearn-random-forest-feature-importance" title="Permalink to this headline">#</a></h2>
 <p>Inspired by <a class="reference external" href="https://medium.com/&#64;srnghn/the-mathematics-of-decision-trees-random-forest-and-feature-importance-in-scikit-learn-and-spark-f2861df67e3">this</a> article.
 Sklearn library uses another approach to determine feature importance. The rationale for that method is that the more gain in information the node (with splitting feature <span class="math notranslate nohighlight">\(X_j\)</span>) provides, the higher its importance.</p>
 <p>The average reduction in the Gini impurity – or MSE for regression – represents the contribution of each feature to the homogeneity of nodes and leaves in the resulting Random Forest model. Each time a selected feature is used for splitting, the Gini impurity of the child nodes is calculated and compared with that of the original node.</p>
@@ -2060,7 +2060,7 @@ <h2><span class="section-number">18.3.3. </span>Sklearn Random Forest Feature Im
 <p>Voila!</p>
 </section>
 <section id="practical-example">
-<h2><span class="section-number">18.3.4. </span>Practical example<a class="headerlink" href="#practical-example" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.3.4. </span>Practical example<a class="headerlink" href="#practical-example" title="Permalink to this headline">#</a></h2>
 <p>Let’s consider the results of a survey given to visitors of hostels listed on <a class="reference external" href="http://Booking.com">Booking.com</a> and <a class="reference external" href="http://TripAdvisor.com">TripAdvisor.com</a>. Our features here are the average ratings for different categories including service quality, room condition, value for money, etc. Our target variable is the hostel’s overall rating on the website.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2132,11 +2132,11 @@ <h2><span class="section-number">18.3.4. </span>Practical example<a class="heade
 <p>The picture above shows that, more often than not, customers pay great attention to staff and the price-quality ratio. This couple of factors affects the resulting overall rating the most. The difference between these two features and other features is not very large, so we can conclude that exclusion of any of these features will lead to a reduction of model’s accuracy. However, based on our analysis, we can recommend hostel owners to focus primarily on staff training and price-to-quality ratio.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">18.3.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.3.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">18.3.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.3.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/kashnitsky">Yury Kashnitsky</a> for creating the open-source content <a class="reference external" href="https://www.kaggle.com/code/kashnitsky/topic-5-ensembles-part-3-feature-importance/notebook">Ensembles and random forest</a>. They inspire the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2178,13 +2178,13 @@ <h2><span class="section-number">18.3.6. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">18.2. </span>Random forest</p>
+            <p class="prev-next-title"><span class="section-number">17.2. </span>Random forest</p>
         </div>
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     <a class='right-next' id="next-link" href="../gradient-boosting/introduction-to-gradient-boosting.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">19. </span>Introduction to Gradient Boosting</p>
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     <meta charset="utf-8" />
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     <link rel="index" title="Index" href="../../genindex.html" />
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-     41.14. Try it in Excel
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     <a class="reference internal" href="../../assignments/project-plan-template.html">
-     41.5. Project Plan​ Template
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-introduction.html">
-     41.6. Python programming introduction
+     40.6. Python programming introduction
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-basics.html">
-     41.7. Python programming basics
+     40.7. Python programming basics
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     <a class="reference internal" href="../../assignments/prerequisites/python-programming-advanced.html">
-     41.8. Python programming advanced
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     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html">
-     41.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html">
-     41.10. Data Science scenarios
+     40.10. Data Science scenarios
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/write-a-data-ethics-case-study.html">
-     41.11. Write a data ethics case study
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     <a class="reference internal" href="../../assignments/data-science/lines-scatters-and-bars.html">
-     41.12. Lines, scatters and bars
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/apply-your-skills.html">
-     41.13. Apply your skills
+     40.13. Apply your skills
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     <a class="reference internal" href="../../assignments/data-science/try-it-in-excel.html">
-     41.14. Try it in Excel
+     40.14. Try it in Excel
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/dive-into-the-beehive.html">
-     41.15. Dive into the beehive
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/build-your-own-custom-vis.html">
-     41.16. Build your own custom vis
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/classifying-datasets.html">
-     41.17. Classifying datasets
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     <a class="reference internal" href="../../assignments/data-science/small-diabetes-study.html">
-     41.18. Small diabetes study
+     40.18. Small diabetes study
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
-     41.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
-     41.20. Displaying airport data
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/soda-profits.html">
-     41.21. Soda profits
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     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
-     41.22. Analyzing COVID-19 papers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     41.23. Estimation of COVID-19 pandemic
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
-     41.24. Data processing in Python
+     40.24. Data processing in Python
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
-     41.25. Evaluating data from a form
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-preparation.html">
-     41.26. Data preparation
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     <a class="reference internal" href="../../assignments/data-science/analyzing-data.html">
-     41.27. Analyzing data
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     <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     41.28. NYC taxi data in winter and summer
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     <a class="reference internal" href="../../assignments/data-science/matplotlib-applied.html">
-     41.29. Matplotlib applied
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     <a class="reference internal" href="../../assignments/data-science/tell-a-story.html">
-     41.35. Tell a story
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     <a class="reference internal" href="../../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     41.36. Explore a planetary computer dataset
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html">
-     41.37. Exploring for answers
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     <a class="reference internal" href="../../assignments/data-science/market-research.html">
-     41.38. Market research
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     <a class="reference internal" href="../../assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">
-     41.39. Low code/no code Data Science project on Azure ML
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     41.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     41.41. Data Science in the cloud: The “Azure ML SDK” way
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
-     41.42. Machine Learning overview - assignment 1
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     41.43. Machine Learning overview - assignment 2
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     41.44. Regression with Scikit-learn
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     41.45. Linear regression - California Housing
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     41.46. Linear Regression Metrics
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     41.47. Loss Function
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     41.48. Gradient descent
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     41.49. Linear Regression Implementation from Scratch
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     41.50. ML logistic regression - assignment 1
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     41.51. ML logistic regression - assignment 2
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
-     41.52. ML neural network - Assignment 1
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     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
-     41.53. Regression tools
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-     41.54. Managing data
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-     41.55. Exploring visualizations
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-     41.56. Try a different model
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-     41.57. Create a regression model
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-and-polynomial-regression.html">
-     41.58. Linear and polynomial regression
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-     41.59. Retrying some regression
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     <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html">
-     41.60. Pumpkin varieties and color
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     <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html">
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-     41.62. Explore classification methods
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html">
-     41.63. Kernel method assignment 1
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     41.65. Support Vector Machines (SVM) - Classification
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     41.70. Dropout and Batch Normalization
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/regularized-linear-models.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
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     <a class="reference internal" href="../../assignments/machine-learning-productionization/debugging-in-classification.html">
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
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     <a class="reference internal" href="../../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
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     <a class="reference internal" href="../../assignments/deep-learning/gan/art-by-gan.html">
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-     18.2.3.1. Practice with random forests in a real problem
+     17.2.3.1. Practice with random forests in a real problem
     </a>
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-   18.2.6. Extremely Randomized Trees
+   17.2.6. Extremely Randomized Trees
   </a>
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-   18.2.7. Similarities between Random Forest and k-Nearest Neighbors
+   17.2.7. Similarities between Random Forest and k-Nearest Neighbors
   </a>
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+   17.2.10. Your turn! 🚀
   </a>
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   </a>
  </li>
 </ul>
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                             <ul class="visible nav section-nav flex-column">
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   </a>
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   </a>
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     <a class="reference internal nav-link" href="#practice-with-random-forests-in-a-real-problem">
-     18.2.3.1. Practice with random forests in a real problem
+     17.2.3.1. Practice with random forests in a real problem
     </a>
    </li>
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   </a>
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   </a>
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   </a>
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-   18.2.7. Similarities between Random Forest and k-Nearest Neighbors
+   17.2.7. Similarities between Random Forest and k-Nearest Neighbors
   </a>
  </li>
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   <a class="reference internal nav-link" href="#transformation-of-a-dataset-into-a-high-dimensional-representation">
-   18.2.8. Transformation of a dataset into a high-dimensional representation
+   17.2.8. Transformation of a dataset into a high-dimensional representation
   </a>
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   <a class="reference internal nav-link" href="#pros-and-cons-of-random-forests">
-   18.2.9. Pros and cons of random forests
+   17.2.9. Pros and cons of random forests
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#pros">
-     18.2.9.1. Pros:
+     17.2.9.1. Pros:
     </a>
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+     17.2.9.2. Cons:
     </a>
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-   18.2.10. Your turn! 🚀
+   17.2.10. Your turn! 🚀
   </a>
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+   17.2.11. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1764,7 +1764,7 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="random-forest">
-<h1><span class="section-number">18.2. </span>Random forest<a class="headerlink" href="#random-forest" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">17.2. </span>Random forest<a class="headerlink" href="#random-forest" title="Permalink to this headline">#</a></h1>
 <p><a class="reference external" href="https://en.wikipedia.org/wiki/Leo_Breiman">Leo Breiman</a> managed to apply bootstrapping not only in statistics but also in machine learning. He, along with Adel Cutler, extended and improved the random forest algorithm <a class="reference external" href="http://ect.bell-labs.com/who/tkh/publications/papers/odt.pdf">proposed by Tin Kam Ho</a>. They combined the construction of uncorrelated trees using <a class="reference external" href="https://en.wikipedia.org/wiki/Predictive_analytics#Classification_and_regression_trees_.28CART.29">CART</a>, bagging, and the <a class="reference external" href="https://en.wikipedia.org/wiki/Random_subspace_method">random subspace method</a>.</p>
 <p>Decision trees are a good choice for the base classifier in bagging because they are quite sophisticated and can achieve zero classification error on any sample. The random subspace method reduces the correlation between the trees and thus prevents overfitting. With bagging, the base algorithms are trained on different random subsets of the original feature set.</p>
 <p>The following algorithm constructs an ensemble of models using the random subspace method:</p>
@@ -1837,7 +1837,7 @@ <h1><span class="section-number">18.2. </span>Random forest<a class="headerlink"
 </div></div>
 </div>
 <section id="algorithm">
-<h2><span class="section-number">18.2.1. </span>Algorithm<a class="headerlink" href="#algorithm" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.2.1. </span>Algorithm<a class="headerlink" href="#algorithm" title="Permalink to this headline">#</a></h2>
 <p>The algorithm for constructing a random forest of <span class="math notranslate nohighlight">\(\large N\)</span> trees goes as follows:</p>
 <ul class="simple">
 <li><p>For each <span class="math notranslate nohighlight">\(\large k = 1, \dots, N\)</span>:</p>
@@ -1860,7 +1860,7 @@ <h2><span class="section-number">18.2.1. </span>Algorithm<a class="headerlink" h
 <p>You can see random forest as bagging of decision trees with the modification of selecting a random subset of features at each split.</p>
 </section>
 <section id="comparison-with-decision-trees-and-bagging">
-<h2><span class="section-number">18.2.2. </span>Comparison with Decision Trees and Bagging<a class="headerlink" href="#comparison-with-decision-trees-and-bagging" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.2.2. </span>Comparison with Decision Trees and Bagging<a class="headerlink" href="#comparison-with-decision-trees-and-bagging" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Disable warnings in Anaconda</span>
@@ -2045,7 +2045,7 @@ <h2><span class="section-number">18.2.2. </span>Comparison with Decision Trees a
 <p>Now, let’s investigate some parameters which can help us increase the model accuracy.</p>
 </section>
 <section id="parameters">
-<h2><span class="section-number">18.2.3. </span>Parameters<a class="headerlink" href="#parameters" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.2.3. </span>Parameters<a class="headerlink" href="#parameters" title="Permalink to this headline">#</a></h2>
 <p>The <a class="reference external" href="http://scikit-learn.org/stable/">scikit-learn library</a> implements random forests by providing two estimators: <code class="docutils literal notranslate"><span class="pre">RandomForestClassifier</span></code> and <code class="docutils literal notranslate"><span class="pre">RandomForestRegressor</span></code>.</p>
 <p>The full list of random forest parameters for regression is shown below:</p>
 <ul class="simple">
@@ -2079,7 +2079,7 @@ <h2><span class="section-number">18.2.3. </span>Parameters<a class="headerlink"
 <li><p><code class="docutils literal notranslate"><span class="pre">max_depth</span></code> — the maximum depth of the tree.</p></li>
 </ul>
 <section id="practice-with-random-forests-in-a-real-problem">
-<h3><span class="section-number">18.2.3.1. </span>Practice with random forests in a real problem<a class="headerlink" href="#practice-with-random-forests-in-a-real-problem" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">17.2.3.1. </span>Practice with random forests in a real problem<a class="headerlink" href="#practice-with-random-forests-in-a-real-problem" title="Permalink to this headline">#</a></h3>
 <p>In this example we will look at predicting customer churn. This is a classification problem, so we will use accuracy for model evaluation.</p>
 <p>First, let’s build a simple classifier which we will use as a baseline. For the sake of simplicity, we will use only numeric features.</p>
 <div class="cell docutils container">
@@ -2387,7 +2387,7 @@ <h3><span class="section-number">18.2.3.1. </span>Practice with random forests i
 </section>
 </section>
 <section id="variance-and-decorrelation">
-<h2><span class="section-number">18.2.4. </span>Variance and Decorrelation<a class="headerlink" href="#variance-and-decorrelation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.2.4. </span>Variance and Decorrelation<a class="headerlink" href="#variance-and-decorrelation" title="Permalink to this headline">#</a></h2>
 <p>Let’s write the variance of a random forest as</p>
 <div class="math notranslate nohighlight">
 \[\large Var f(x) = \rho(x)\sigma^2(x)\]</div>
@@ -2409,12 +2409,12 @@ <h2><span class="section-number">18.2.4. </span>Variance and Decorrelation<a cla
 <figure class="align-default" id="comparison-of-tree-and-random-forest">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/random-forest/Comparison_of_Tree_and_Random_Forest.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/random-forest/Comparison_of_Tree_and_Random_Forest.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/random-forest/Comparison_of_Tree_and_Random_Forest.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 18.5 </span><span class="caption-text">Comparison of Tree and Random Forest</span><a class="headerlink" href="#comparison-of-tree-and-random-forest" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 17.5 </span><span class="caption-text">Comparison of Tree and Random Forest</span><a class="headerlink" href="#comparison-of-tree-and-random-forest" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="bias">
-<h2><span class="section-number">18.2.5. </span>Bias<a class="headerlink" href="#bias" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.2.5. </span>Bias<a class="headerlink" href="#bias" title="Permalink to this headline">#</a></h2>
 <p>Just as in bagging, the bias of a random forest is the same as the bias of a single tree <span class="math notranslate nohighlight">\(\large T(x,\Theta(Z))\)</span>:</p>
 <div class="math notranslate nohighlight">
 \[\begin{split} \large \begin{array}{rcl} Bias &amp;=&amp; \mu(x) - E_Z \, f_{rf}(x) \\
@@ -2422,13 +2422,13 @@ <h2><span class="section-number">18.2.5. </span>Bias<a class="headerlink" href="
 <p>In absolute value, the bias is usually higher than that of an unprunned tree because randomization and sample space reduction impose their own restrictions on the model. Therefore, the improvements in prediction accuracy obtained by bagging and random forests are solely the result of variance reduction.</p>
 </section>
 <section id="extremely-randomized-trees">
-<h2><span class="section-number">18.2.6. </span>Extremely Randomized Trees<a class="headerlink" href="#extremely-randomized-trees" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.2.6. </span>Extremely Randomized Trees<a class="headerlink" href="#extremely-randomized-trees" title="Permalink to this headline">#</a></h2>
 <p>Extremely Randomized Trees employ a greater degree of randomization at the cut-point choice when splitting a tree node. As in random forests, a random subset of features is used. But, instead of the search for the optimal thresholds, their values are selected at random for each possible feature, and the best one among these randomly generated thresholds is used as the best rule to split the node. This usually trades off a slight reduction in the model variance with a small increase of the bias.</p>
 <p>In the <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> library, there are 2 implementations of Extremely Randomized Trees: <a class="reference external" href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html#sklearn.ensemble.ExtraTreesClassifier">ExtraTreesClassifier</a> and <a class="reference external" href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html#sklearn.ensemble.ExtraTreesRegressor">ExtraTreesRegressor</a>.</p>
 <p>This method should be used if you have greatly overfit with random forests or gradient boosting.</p>
 </section>
 <section id="similarities-between-random-forest-and-k-nearest-neighbors">
-<h2><span class="section-number">18.2.7. </span>Similarities between Random Forest and k-Nearest Neighbors<a class="headerlink" href="#similarities-between-random-forest-and-k-nearest-neighbors" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.2.7. </span>Similarities between Random Forest and k-Nearest Neighbors<a class="headerlink" href="#similarities-between-random-forest-and-k-nearest-neighbors" title="Permalink to this headline">#</a></h2>
 <p>The random forest method is similar to the nearest neighbors technique. Random forests predictions are based on labels of alike examples from the training set. The more often these examples appear in the same leaf of a tree, the higher their similarity. Let’s prove this formally.</p>
 <p>Let’s consider a regression problem with the quadratic loss function. Let <span class="math notranslate nohighlight">\(\large T_n(x)\)</span> be the number of the leaf of the <span class="math notranslate nohighlight">\(\large n\)</span>-th tree in a random forest with input <span class="math notranslate nohighlight">\(\large x\)</span>.
 The algorithm response for the input vector <span class="math notranslate nohighlight">\(\large x\)</span> equals the averaged response over all the examples of the training sample that fall into the leaf <span class="math notranslate nohighlight">\(\large T_n(x)\)</span>. This can be written as</p>
@@ -2447,20 +2447,20 @@ <h2><span class="section-number">18.2.7. </span>Similarities between Random Fore
 <figure class="align-default" id="similarity-between-random-forests-and-knn">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/random-forest/Comparison_of_Tree_and_Random_Forest.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/random-forest/Comparison_of_Tree_and_Random_Forest.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/ensemble-learning/random-forest/Comparison_of_Tree_and_Random_Forest.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 18.6 </span><span class="caption-text">Similarity between random forests and knn</span><a class="headerlink" href="#similarity-between-random-forests-and-knn" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 17.6 </span><span class="caption-text">Similarity between random forests and knn</span><a class="headerlink" href="#similarity-between-random-forests-and-knn" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="transformation-of-a-dataset-into-a-high-dimensional-representation">
-<h2><span class="section-number">18.2.8. </span>Transformation of a dataset into a high-dimensional representation<a class="headerlink" href="#transformation-of-a-dataset-into-a-high-dimensional-representation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.2.8. </span>Transformation of a dataset into a high-dimensional representation<a class="headerlink" href="#transformation-of-a-dataset-into-a-high-dimensional-representation" title="Permalink to this headline">#</a></h2>
 <p>Random forests are mostly used in supervised learning, but there is a way to apply them in the unsupervised setting.</p>
 <p>Using the <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> method <a class="reference external" href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomTreesEmbedding.html#sklearn.ensemble.RandomTreesEmbedding">RandomTreesEmbedding</a>, we can transform our dataset into a high-dimensional, <a class="reference external" href="https://en.wikipedia.org/wiki/Sparse_matrix">sparse</a> representation. We first build extremely randomized trees and then use the index of the leaf containing the example as a new feature.</p>
 <p>For example, if the input appears in the first leaf, we assign <span class="math notranslate nohighlight">\(1\)</span> as the feature value; if not, we assign <span class="math notranslate nohighlight">\(0\)</span>. This is a so-called <em>binary coding</em>. We can control the number of features and the sparseness of data by increasing or decreasing the number of trees and their depth. Because nearby data points are likely to fall into the same leaf, this transformation provides an implicit nonparametric estimate of their density.</p>
 </section>
 <section id="pros-and-cons-of-random-forests">
-<h2><span class="section-number">18.2.9. </span>Pros and cons of random forests<a class="headerlink" href="#pros-and-cons-of-random-forests" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.2.9. </span>Pros and cons of random forests<a class="headerlink" href="#pros-and-cons-of-random-forests" title="Permalink to this headline">#</a></h2>
 <section id="pros">
-<h3><span class="section-number">18.2.9.1. </span>Pros:<a class="headerlink" href="#pros" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">17.2.9.1. </span>Pros:<a class="headerlink" href="#pros" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>High prediction accuracy; will perform better than linear algorithms in most problems; the accuracy is comparable with that of boosting.</p></li>
 <li><p>Robust to outliers, thanks to random sampling.</p></li>
@@ -2478,7 +2478,7 @@ <h3><span class="section-number">18.2.9.1. </span>Pros:<a class="headerlink" hre
 </ul>
 </section>
 <section id="cons">
-<h3><span class="section-number">18.2.9.2. </span>Cons:<a class="headerlink" href="#cons" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">17.2.9.2. </span>Cons:<a class="headerlink" href="#cons" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>In comparison with a single decision tree, Random Forest’s output is more difficult to interpret.</p></li>
 <li><p>There are no formal p-values for feature significance estimation.</p></li>
@@ -2492,11 +2492,11 @@ <h3><span class="section-number">18.2.9.2. </span>Cons:<a class="headerlink" hre
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">18.2.10. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.2.10. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">18.2.11. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">17.2.11. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/kashnitsky">Yury Kashnitsky</a> for creating the open-source content <a class="reference external" href="https://www.kaggle.com/code/kashnitsky/topic-5-ensembles-part-2-random-forest/notebook">Ensembles and random forest</a>. They inspire the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2538,13 +2538,13 @@ <h2><span class="section-number">18.2.11. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">18.1. </span>Bagging</p>
+            <p class="prev-next-title"><span class="section-number">17.1. </span>Bagging</p>
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     <link rel="index" title="Index" href="../../genindex.html" />
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-<h1><span class="section-number">19.2. </span>Gradient boosting example<a class="headerlink" href="#gradient-boosting-example" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">18.2. </span>Gradient boosting example<a class="headerlink" href="#gradient-boosting-example" title="Permalink to this headline">#</a></h1>
 <section id="what-is-gradient-boosting">
-<h2><span class="section-number">19.2.1. </span>What is gradient boosting?<a class="headerlink" href="#what-is-gradient-boosting" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.2.1. </span>What is gradient boosting?<a class="headerlink" href="#what-is-gradient-boosting" title="Permalink to this headline">#</a></h2>
 <p>If you’re inside the world of machine learning, it’s for sure you have heard about gradient boosting algorithms such as xgboost or lightgbm. Indeed, gradient boosting represents the state-of-start for a lot of machine learning task, but how does it work? We’ll try to answer this question specifically for the case of gradient boosting for trees which is the most popular case up today</p>
 <p>For me, understanding gradient boosting is all about undestarding its link to gradient descent. Remember gradient descent is the algorithm to minimize a loss function <span class="math notranslate nohighlight">\(L(\theta)\)</span> by sustracting the gradient to the parameters</p>
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 <p>This equation would be the <em>gradient descent</em> version of trees. Maybe this is nothing new for you, but I hope it gives you a better or new way of seeing gradient boosting.</p>
 </section>
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+<h2><span class="section-number">18.2.2. </span>Implementing Gradient boosting<a class="headerlink" href="#implementing-gradient-boosting" title="Permalink to this headline">#</a></h2>
 <p>Now let’s get our hands on some data and see a woking example</p>
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+<h2><span class="section-number">18.2.3. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">19.2.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.2.4. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Alexis Moraga for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/senoratiramisu/understanding-gradient-boosting">Understanding Gradient Boosting</a>. It inspires the majority of the content in this chapter.</p>
 </section>
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-     42.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/numpy-and-pandas.html">
-     42.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-visualization.html">
-     42.7. Data visualization
+     41.7. Data visualization
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-lifecycle.html">
-     42.8. Data Science lifecycle
+     41.8. Data Science lifecycle
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-in-the-cloud.html">
-     42.9. Data Science in the cloud
+     41.9. Data Science in the cloud
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/data-science/data-science-in-real-world.html">
-     42.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/ml-overview.html">
-     42.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/linear-regression.html">
-     42.12. Linear Regression
+     41.12. Linear Regression
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    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression.html">
-     42.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/logistic-regression-condensed.html">
-     42.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/neural-network.html">
-     42.15. Neural Network
+     41.15. Neural Network
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-fundamentals/build-an-ml-web-app.html">
-     42.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/unsupervised-learning.html">
-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/kernel-method.html">
-     42.18. Kernel method
+     41.18. Kernel method
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/model-selection.html">
-     42.19. Model Selection
+     41.19. Model Selection
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1577,78 +1577,78 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction-and-history-of-boosting">
-   19.1.1. Introduction and history of boosting
+   18.1.1. Introduction and history of boosting
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#history-of-gbm">
-     19.1.1.1. History of GBM
+     18.1.1.1. History of GBM
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#gbm-algorithm">
-   19.1.2. GBM algorithm
+   18.1.2. GBM algorithm
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#ml-problem-statement">
-     19.1.2.1. ML problem statement
+     18.1.2.1. ML problem statement
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#functional-gradient-descent">
-     19.1.2.2. Functional gradient descent
+     18.1.2.2. Functional gradient descent
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#friedmans-classic-gbm-algorithm">
-     19.1.2.3. Friedman’s classic GBM algorithm
+     18.1.2.3. Friedman’s classic GBM algorithm
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#step-by-step-example-how-gbm-works">
-     19.1.2.4. Step-By-Step example: How GBM Works
+     18.1.2.4. Step-By-Step example: How GBM Works
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loss-functions">
-   19.1.3. Loss functions
+   18.1.3. Loss functions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#regression-loss-functions">
-     19.1.3.1. Regression loss functions
+     18.1.3.1. Regression loss functions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-loss-functions">
-     19.1.3.2. Classification loss functions
+     18.1.3.2. Classification loss functions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#weights">
-     19.1.3.3. Weights
+     18.1.3.3. Weights
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   19.1.4. Conclusion
+   18.1.4. Conclusion
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   19.1.5. Your turn! 🚀
+   18.1.5. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   19.1.6. Acknowledgments
+   18.1.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1672,78 +1672,78 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction-and-history-of-boosting">
-   19.1.1. Introduction and history of boosting
+   18.1.1. Introduction and history of boosting
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#history-of-gbm">
-     19.1.1.1. History of GBM
+     18.1.1.1. History of GBM
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#gbm-algorithm">
-   19.1.2. GBM algorithm
+   18.1.2. GBM algorithm
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#ml-problem-statement">
-     19.1.2.1. ML problem statement
+     18.1.2.1. ML problem statement
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#functional-gradient-descent">
-     19.1.2.2. Functional gradient descent
+     18.1.2.2. Functional gradient descent
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#friedmans-classic-gbm-algorithm">
-     19.1.2.3. Friedman’s classic GBM algorithm
+     18.1.2.3. Friedman’s classic GBM algorithm
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#step-by-step-example-how-gbm-works">
-     19.1.2.4. Step-By-Step example: How GBM Works
+     18.1.2.4. Step-By-Step example: How GBM Works
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loss-functions">
-   19.1.3. Loss functions
+   18.1.3. Loss functions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#regression-loss-functions">
-     19.1.3.1. Regression loss functions
+     18.1.3.1. Regression loss functions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#classification-loss-functions">
-     19.1.3.2. Classification loss functions
+     18.1.3.2. Classification loss functions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#weights">
-     19.1.3.3. Weights
+     18.1.3.3. Weights
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusion">
-   19.1.4. Conclusion
+   18.1.4. Conclusion
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   19.1.5. Your turn! 🚀
+   18.1.5. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   19.1.6. Acknowledgments
+   18.1.6. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1768,11 +1768,11 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="gradient-boosting">
-<h1><span class="section-number">19.1. </span>Gradient Boosting<a class="headerlink" href="#gradient-boosting" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">18.1. </span>Gradient Boosting<a class="headerlink" href="#gradient-boosting" title="Permalink to this headline">#</a></h1>
 <figure class="align-default" id="boosting">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Boosting.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Boosting.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Boosting.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.2 </span><span class="caption-text">Boosting</span><a class="headerlink" href="#boosting" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.2 </span><span class="caption-text">Boosting</span><a class="headerlink" href="#boosting" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Today we are going to have a look at one of the most popular and practical machine learning algorithms: gradient boosting.
@@ -1781,7 +1781,7 @@ <h1><span class="section-number">19.1. </span>Gradient Boosting<a class="headerl
 <iframe src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/Gradient-boosting/index.html" width="105%" height="700px;" style="border:none;"  scrolling="yes"></iframe>
 A demo of Gradient Boosting. <a href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/Gradient-boosting/index.html">[source]</a>
 </p><section id="introduction-and-history-of-boosting">
-<h2><span class="section-number">19.1.1. </span>Introduction and history of boosting<a class="headerlink" href="#introduction-and-history-of-boosting" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.1.1. </span>Introduction and history of boosting<a class="headerlink" href="#introduction-and-history-of-boosting" title="Permalink to this headline">#</a></h2>
 <p>Almost everyone in machine learning has heard about gradient boosting. Many data scientists include this algorithm in their data scientist’s toolbox because of the good results it yields on any given (unknown) problem.</p>
 <p>Furthermore, XGBoost is often the standard recipe for <a class="reference external" href="https://github.com/dmlc/xgboost/blob/master/demo/README.md#usecases">winning</a> <a class="reference external" href="http://blog.kaggle.com/tag/xgboost/">ML competitions</a>. It is so popular that the idea of stacking XGBoosts has become a meme. Moreover, boosting is an important component in <a class="reference external" href="https://en.wikipedia.org/wiki/Learning_to_rank#Practical_usage_by_search_engines">many recommender systems</a>; sometimes, it is even considered a <a class="reference external" href="https://yandex.com/company/technologies/matrixnet/">brand</a>.
 Let’s look at the history and development of boosting.</p>
@@ -1794,14 +1794,14 @@ <h2><span class="section-number">19.1.1. </span>Introduction and history of boos
 <figure class="align-default" id="initial-iterative-process">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Initial_iterative_process.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Initial_iterative_process.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Initial_iterative_process.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.3 </span><span class="caption-text">Initial iterative process</span><a class="headerlink" href="#initial-iterative-process" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.3 </span><span class="caption-text">Initial iterative process</span><a class="headerlink" href="#initial-iterative-process" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The size of point corresponds to its weight, which was assigned for an incorrect prediction.  On each iteration, we can see that these weights are growing – the stumps cannot cope with this problem. Although, if we take a weighted vote for the stumps, we will get the correct classifications:</p>
 <figure class="align-default" id="weighting-the-stump">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Weighting_the_stump"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Weighting_the_stump" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Weighting_the_stump" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.4 </span><span class="caption-text">Weighting the stump</span><a class="headerlink" href="#weighting-the-stump" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.4 </span><span class="caption-text">Weighting the stump</span><a class="headerlink" href="#weighting-the-stump" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Pseudocode:</p>
@@ -1827,11 +1827,11 @@ <h2><span class="section-number">19.1.1. </span>Introduction and history of boos
 <figure class="align-default" id="comparison-between-various-boost-function">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Comparison_between_various_boost_function.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Comparison_between_various_boost_function.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Comparison_between_various_boost_function.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.5 </span><span class="caption-text">Comparison between various boost function</span><a class="headerlink" href="#comparison-between-various-boost-function" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.5 </span><span class="caption-text">Comparison between various boost function</span><a class="headerlink" href="#comparison-between-various-boost-function" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="history-of-gbm">
-<h3><span class="section-number">19.1.1.1. </span>History of GBM<a class="headerlink" href="#history-of-gbm" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.1.1.1. </span>History of GBM<a class="headerlink" href="#history-of-gbm" title="Permalink to this headline">#</a></h3>
 <p>It took more than 10 years after the introduction of GBM for it to become an essential part of the data science toolbox.<br />
 GBM was extended to apply to different statistics problems: GLMboost and GAMboost for strengthening already existing GAM models, CoxBoost for survival curves, and RankBoost and LambdaMART for ranking.<br />
 Many realizations of GBM also appeared under different names and on different platforms: Stochastic GBM, GBDT (Gradient Boosted Decision Trees), GBRT (Gradient Boosted Regression Trees), MART (Multiple Additive Regression Trees), and more. In addition, the ML community was very segmented and dissociated, which made it hard to track just how widespread boosting had become.</p>
@@ -1841,14 +1841,14 @@ <h3><span class="section-number">19.1.1.1. </span>History of GBM<a class="header
 </section>
 </section>
 <section id="gbm-algorithm">
-<h2><span class="section-number">19.1.2. </span>GBM algorithm<a class="headerlink" href="#gbm-algorithm" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.1.2. </span>GBM algorithm<a class="headerlink" href="#gbm-algorithm" title="Permalink to this headline">#</a></h2>
 <section id="ml-problem-statement">
-<h3><span class="section-number">19.1.2.1. </span>ML problem statement<a class="headerlink" href="#ml-problem-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.1.2.1. </span>ML problem statement<a class="headerlink" href="#ml-problem-statement" title="Permalink to this headline">#</a></h3>
 <p>We are going to solve the problem of function approximation in a general supervised learning setting. We have a set of features <span class="math notranslate nohighlight">\( \large x \)</span> and target variables <span class="math notranslate nohighlight">\(\large y, \large \left\{ (x_i, y_i) \right\}_{i=1, \ldots,n}\)</span> which we use to restore the dependence <span class="math notranslate nohighlight">\(\large y = f(x) \)</span>. We restore the dependence by approximating <span class="math notranslate nohighlight">\( \large \hat{f}(x) \)</span> and by understanding which approximation is better when we use the loss function <span class="math notranslate nohighlight">\( \large L(y,f) \)</span>, which we want to minimize: <span class="math notranslate nohighlight">\( \large y \approx \hat{f}(x), \large \hat{f}(x) = \underset{f(x)}{\arg\min} \ L(y,f(x)) \)</span></p>
 <figure class="align-default" id="find-function">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Find_function.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Find_function.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Find_function.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.6 </span><span class="caption-text">Find function</span><a class="headerlink" href="#find-function" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.6 </span><span class="caption-text">Find function</span><a class="headerlink" href="#find-function" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>At this moment, we do not make any assumptions regarding the type of dependence <span class="math notranslate nohighlight">\( \large f(x) \)</span>, the model of our approximation <span class="math notranslate nohighlight">\( \large \hat{f}(x) \)</span>, or the distribution of the target variable (<span class="math notranslate nohighlight">\( \large y \)</span>). We only expect that the function <span class="math notranslate nohighlight">\( \large L(y,f) \)</span> is differentiable. Our formula is very general; let’s define it for a particular data set with a population mean <span class="math notranslate nohighlight">\( \large \hat {f}(x) \)</span>. Our expression for minimizing the loss of the data is the following:</p>
@@ -1875,12 +1875,12 @@ <h3><span class="section-number">19.1.2.1. </span>ML problem statement<a class="
 <figure class="align-default" id="iteration-steps">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Iteration_steps.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Iteration_steps.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Iteration_steps.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.7 </span><span class="caption-text">Iteration steps</span><a class="headerlink" href="#iteration-steps" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.7 </span><span class="caption-text">Iteration steps</span><a class="headerlink" href="#iteration-steps" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="functional-gradient-descent">
-<h3><span class="section-number">19.1.2.2. </span>Functional gradient descent<a class="headerlink" href="#functional-gradient-descent" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.1.2.2. </span>Functional gradient descent<a class="headerlink" href="#functional-gradient-descent" title="Permalink to this headline">#</a></h3>
 <p>Let’s imagine for a second that we can perform optimization in the function space and iteratively search for the approximations <span class="math notranslate nohighlight">\(\large \hat{f}(x)\)</span> as functions themselves. We will express our approximation as a sum of incremental improvements, each being a function. For convenience, we will immediately start with the sum from the initial approximation <span class="math notranslate nohighlight">\(\large \hat{f_0}(x)\)</span>:<span class="math notranslate nohighlight">\(\large \hat{f}(x) = \sum_{i = 0}^M \hat{f_i}(x)\)</span></p>
 <p>Nothing has happened yet; we have only decided that we will search for our approximation <span class="math notranslate nohighlight">\(\large \hat{f}(x)\)</span> not as a big model with plenty of parameters (as an example, neural network), but as a sum of functions, pretending we move in functional space.</p>
 <p>In order to accomplish this task, we need to limit our search by some function family <span class="math notranslate nohighlight">\(\large \hat{f}(x) = h(x, \theta)\)</span>. There are a few issues here – first of all, the sum of models can be more complicated than any model from this family; secondly, the general objective is still in functional space. Let’s note that, on every step, we will need to select an optimal coefficient <span class="math notranslate nohighlight">\(\large \rho \in \mathbb{R}\)</span>. For step <span class="math notranslate nohighlight">\(\large t\)</span>, the problem is the following:</p>
@@ -1898,12 +1898,12 @@ <h3><span class="section-number">19.1.2.2. </span>Functional gradient descent<a
 <figure class="align-default" id="regression-for-you">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Regression_for_you.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Regression_for_you.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Regression_for_you.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.8 </span><span class="caption-text">Regression for you</span><a class="headerlink" href="#regression-for-you" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.8 </span><span class="caption-text">Regression for you</span><a class="headerlink" href="#regression-for-you" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="friedmans-classic-gbm-algorithm">
-<h3><span class="section-number">19.1.2.3. </span>Friedman’s classic GBM algorithm<a class="headerlink" href="#friedmans-classic-gbm-algorithm" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.1.2.3. </span>Friedman’s classic GBM algorithm<a class="headerlink" href="#friedmans-classic-gbm-algorithm" title="Permalink to this headline">#</a></h3>
 <p>We can now define the classic GBM algorithm suggested by Jerome Friedman in 1999. It is a supervised algorithm that has the following components:</p>
 <ul class="simple">
 <li><p>dataset <span class="math notranslate nohighlight">\(\large \left\{ (x_i, y_i) \right\}_{i=1, \ldots,n}\)</span>;</p></li>
@@ -1931,12 +1931,12 @@ <h3><span class="section-number">19.1.2.3. </span>Friedman’s classic GBM algor
 </ol>
 </section>
 <section id="step-by-step-example-how-gbm-works">
-<h3><span class="section-number">19.1.2.4. </span>Step-By-Step example: How GBM Works<a class="headerlink" href="#step-by-step-example-how-gbm-works" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.1.2.4. </span>Step-By-Step example: How GBM Works<a class="headerlink" href="#step-by-step-example-how-gbm-works" title="Permalink to this headline">#</a></h3>
 <p>Let’s see an example of how GBM works. In this toy example, we will restore a noisy function <span class="math notranslate nohighlight">\(\large y = cos(x) + \epsilon, \epsilon \sim \mathcal{N}(0, \frac{1}{5}), x \in [-5,5]\)</span>.</p>
 <figure class="align-default" id="regression-problem">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Regression_problem.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Regression_problem.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Regression_problem.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.9 </span><span class="caption-text">Regression problem</span><a class="headerlink" href="#regression-problem" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.9 </span><span class="caption-text">Regression problem</span><a class="headerlink" href="#regression-problem" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>This is a regression problem with a real-valued target, so we will choose to use the mean squared error loss function. We will generate 300 pairs of observations and approximate them with decision trees of depth 2. Let’s put together everything we need to use GBM:</p>
@@ -1953,7 +1953,7 @@ <h3><span class="section-number">19.1.2.4. </span>Step-By-Step example: How GBM
 <figure class="align-default" id="current-approximation-and-every-tree">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Current_approximation_and_every_tree.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Current_approximation_and_every_tree.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Current_approximation_and_every_tree.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.10 </span><span class="caption-text">Current approximation and every tree</span><a class="headerlink" href="#current-approximation-and-every-tree" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.10 </span><span class="caption-text">Current approximation and every tree</span><a class="headerlink" href="#current-approximation-and-every-tree" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>By the second iteration, our trees have recovered the basic form of the function. However, at the first iteration, we see that the algorithm has built only the “left branch” of the function (<span class="math notranslate nohighlight">\(\large x \in [-5, -4]\)</span>). This was due to the fact that our trees simply did not have enough depth to build a symmetrical branch at once, and it focused on the left branch with the larger error. Therefore, the right branch appeared only after the second iteration.</p>
@@ -1961,17 +1961,17 @@ <h3><span class="section-number">19.1.2.4. </span>Step-By-Step example: How GBM
 <figure class="align-default" id="how-gbm-works">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/How_GBM_works.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/How_GBM_works.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/How_GBM_works.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.11 </span><span class="caption-text">How GBM Works</span><a class="headerlink" href="#how-gbm-works" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.11 </span><span class="caption-text">How GBM Works</span><a class="headerlink" href="#how-gbm-works" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 </section>
 <section id="loss-functions">
-<h2><span class="section-number">19.1.3. </span>Loss functions<a class="headerlink" href="#loss-functions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.1.3. </span>Loss functions<a class="headerlink" href="#loss-functions" title="Permalink to this headline">#</a></h2>
 <p>If we want to solve a classification problem instead of regression, what would change? We only need to choose a suitable loss function <span class="math notranslate nohighlight">\(\large L(y, f)\)</span>. This is the most important, high-level moment that determines exactly how we will optimize and what characteristics we can expect in the final model.</p>
 <p>As a rule, we do not need to invent this ourselves – researchers have already done it for us. Today, we will explore loss functions for the two most common objectives: regression <span class="math notranslate nohighlight">\(\large y \in \mathbb{R}\)</span> and binary classification <span class="math notranslate nohighlight">\(\large y \in \left\{-1, 1\right\}\)</span>.</p>
 <section id="regression-loss-functions">
-<h3><span class="section-number">19.1.3.1. </span>Regression loss functions<a class="headerlink" href="#regression-loss-functions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.1.3.1. </span>Regression loss functions<a class="headerlink" href="#regression-loss-functions" title="Permalink to this headline">#</a></h3>
 <p>Let’s start with a regression problem for <span class="math notranslate nohighlight">\(\large y \in \mathbb{R}\)</span>. In order to choose the appropriate loss function, we need to consider which of the properties of the conditional distribution <span class="math notranslate nohighlight">\(\large (y|x)\)</span> we want to restore. The most common options are:</p>
 <ul class="simple">
 <li><p><span class="math notranslate nohighlight">\(\large L(y, f) = (y - f)^2\)</span> a.k.a. <span class="math notranslate nohighlight">\(\large L_2\)</span> loss or Gaussian loss. It is the classical conditional mean, which is the simplest and most common case. If we do not have any additional information or requirements for a model to be robust, we can use the Gaussian loss.</p></li>
@@ -1982,7 +1982,7 @@ <h3><span class="section-number">19.1.3.1. </span>Regression loss functions<a cl
 <figure class="align-default" id="asymmetric-function">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Asymmetric_function.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Asymmetric_function.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Asymmetric_function.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.12 </span><span class="caption-text">Asymmetric function</span><a class="headerlink" href="#asymmetric-function" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.12 </span><span class="caption-text">Asymmetric function</span><a class="headerlink" href="#asymmetric-function" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Let’s use loss function <span class="math notranslate nohighlight">\(\large L_q\)</span> on our data. The goal is to restore the conditional 75%-quantile of cosine. Let us put everyting together for GBM:</p>
@@ -2000,7 +2000,7 @@ <h3><span class="section-number">19.1.3.1. </span>Regression loss functions<a cl
 <figure class="align-default" id="standard-line-search">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Standard_line_search.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Standard_line_search.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Standard_line_search.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.13 </span><span class="caption-text">Standard line search</span><a class="headerlink" href="#standard-line-search" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.13 </span><span class="caption-text">Standard line search</span><a class="headerlink" href="#standard-line-search" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>We can observe that, on each iteration, <span class="math notranslate nohighlight">\(\large r_{i} \)</span> take only 2 possible values, but GBM is still able to restore our initial function.</p>
@@ -2010,7 +2010,7 @@ <h3><span class="section-number">19.1.3.1. </span>Regression loss functions<a cl
 <figure class="align-default" id="functions-and-relevant-gbm">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Functions_and_relevant_GBM.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Functions_and_relevant_GBM.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Functions_and_relevant_GBM.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.14 </span><span class="caption-text">Functions and relevant GBM</span><a class="headerlink" href="#functions-and-relevant-gbm" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.14 </span><span class="caption-text">Functions and relevant GBM</span><a class="headerlink" href="#functions-and-relevant-gbm" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>In this example, we used splines as the base algorithm. See, it does not always have to be trees for boosting?</p>
@@ -2019,7 +2019,7 @@ <h3><span class="section-number">19.1.3.1. </span>Regression loss functions<a cl
 </spoiler></p>
 </section>
 <section id="classification-loss-functions">
-<h3><span class="section-number">19.1.3.2. </span>Classification loss functions<a class="headerlink" href="#classification-loss-functions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.1.3.2. </span>Classification loss functions<a class="headerlink" href="#classification-loss-functions" title="Permalink to this headline">#</a></h3>
 <p>Now, let’s look at the binary classification problem <span class="math notranslate nohighlight">\(\large y \in \left\{-1, 1\right\}\)</span>. We saw that GBM can even optimize non-differentiable loss functions. Technically, it is possible to solve this problem with a regression <span class="math notranslate nohighlight">\(\large L_2\)</span> loss, but it wouldn’t be correct.</p>
 <p>The distribution of the target variable requires us to use log-likehood, so we need to have different loss functions for targets multiplied by their predictions:  <span class="math notranslate nohighlight">\(\large y \cdot f\)</span>. The most common choices would be the following:</p>
 <ul class="simple">
@@ -2029,14 +2029,14 @@ <h3><span class="section-number">19.1.3.2. </span>Classification loss functions<
 <figure class="align-default" id="adaboost-and-logistic">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/AdaBoost_and_logistic.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/AdaBoost_and_logistic.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/AdaBoost_and_logistic.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.15 </span><span class="caption-text">AdaBoost and logistic</span><a class="headerlink" href="#adaboost-and-logistic" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.15 </span><span class="caption-text">AdaBoost and logistic</span><a class="headerlink" href="#adaboost-and-logistic" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Let’s generate some new toy data for our classification problem. As a basis, we will take our noisy cosine, and we will use the sign function for classes of the target variable. Our toy data looks like the following (jitter-noise is added for clarity):</p>
 <figure class="align-default" id="toy-data">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Toy_data.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Toy_data.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Toy_data.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.16 </span><span class="caption-text">Toy data</span><a class="headerlink" href="#toy-data" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.16 </span><span class="caption-text">Toy data</span><a class="headerlink" href="#toy-data" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>We will use logistic loss to look for what we actually boost. So, again, we put together what we will use for GBM:</p>
@@ -2052,25 +2052,25 @@ <h3><span class="section-number">19.1.3.2. </span>Classification loss functions<
 <figure class="align-default" id="initialization-of-algorithm">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Initialization_of_algorithm.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Initialization_of_algorithm.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Initialization_of_algorithm.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.17 </span><span class="caption-text">Initialization of algorithm</span><a class="headerlink" href="#initialization-of-algorithm" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.17 </span><span class="caption-text">Initialization of algorithm</span><a class="headerlink" href="#initialization-of-algorithm" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Our optimal initial approximation is around -0.273. You could have guessed that it was negative because it is more profitable to predict everything as the most popular class, but there is no formula for the exact value. Now let’s finally start GBM, and look what actually happens under the hood:</p>
 <figure class="align-default" id="result-of-gbm">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Result_of_GBM.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Result_of_GBM.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Result_of_GBM.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.18 </span><span class="caption-text">Result of GBM</span><a class="headerlink" href="#result-of-gbm" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.18 </span><span class="caption-text">Result of GBM</span><a class="headerlink" href="#result-of-gbm" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The algorithm successfully restored the separation between our classes. You can see how the “lower” areas are separating because the trees are more confident in the correct prediction of the negative class and how the two steps of mixed classes are forming. It is clear that we have a lot of correctly classified observations and some amount of observations with large errors that appeared due to the noise in the data.</p>
 </section>
 <section id="weights">
-<h3><span class="section-number">19.1.3.3. </span>Weights<a class="headerlink" href="#weights" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.1.3.3. </span>Weights<a class="headerlink" href="#weights" title="Permalink to this headline">#</a></h3>
 <p>Sometimes, there is a situation where we want a more specific loss function for our problem. For example, in financial time series, we may want to give bigger weight to large movements in the time series; for churn prediction, it is more useful to predict the churn of clients with high LTV (or lifetime value: how much money a client will bring in the future).</p>
 <figure class="align-default" id="statistical-learning">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Statistical_learning.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Statistical_learning.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Statistical_learning.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.19 </span><span class="caption-text">Statistical learning</span><a class="headerlink" href="#statistical-learning" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.19 </span><span class="caption-text">Statistical learning</span><a class="headerlink" href="#statistical-learning" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>The statistical warrior would invent their own loss function, write out the gradient for it (for more effective training, include the Hessian), and carefully check whether this function satisfies the required properties. However, there is a high probability of making a mistake somewhere, running up against computational difficulties, and spending an inordinate amount of time on research.</p>
@@ -2090,14 +2090,14 @@ <h3><span class="section-number">19.1.3.3. </span>Weights<a class="headerlink" h
 <figure class="align-default" id="asymmetric-weight-function">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Asymmetric_weight_function.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Asymmetric_weight_function.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Asymmetric_weight_function.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.20 </span><span class="caption-text">Asymmetric weight function</span><a class="headerlink" href="#asymmetric-weight-function" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.20 </span><span class="caption-text">Asymmetric weight function</span><a class="headerlink" href="#asymmetric-weight-function" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>With these weights, we expect to get two properties: less detailing for negative values of <span class="math notranslate nohighlight">\(\large x\)</span> and the form of the function, similar to the initial cosine. We take the other GBM’s tunings from our previous example with classification including the line search for optimal coefficients. Let’s look what we’ve got:</p>
 <figure class="align-default" id="adjusted-result-of-gbm">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Adjusted_result_of_GBM.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Adjusted_result_of_GBM.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Adjusted_result_of_GBM.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.21 </span><span class="caption-text">Adjusted result of GBM</span><a class="headerlink" href="#adjusted-result-of-gbm" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.21 </span><span class="caption-text">Adjusted result of GBM</span><a class="headerlink" href="#adjusted-result-of-gbm" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>We achieved the result that we expected. First, we can see how strongly the pseudo-residuals differ; on the initial iteration, they look almost like the original cosine. Second, the left part of the function’s graph was often ignored in favor of the right one, which had larger weights. Third, the function that we got on the third iteration received enough attention and started looking similar to the original cosine (also started to slightly overfit).</p>
@@ -2105,36 +2105,36 @@ <h3><span class="section-number">19.1.3.3. </span>Weights<a class="headerlink" h
 </section>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">19.1.4. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.1.4. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>Today, we learned the theory behind gradient boosting. GBM is not just some specific algorithm but a common methodology for building ensembles of models. In addition, this methodology is sufficiently flexible and expandable – it is possible to train a large number of models, taking into consideration different loss-functions with a variety of weighting functions.</p>
 <p>Practice and ML competitions show that, in standard problems (except for image, audio, and very sparse data), GBM is often the most effective algorithm (not to mention stacking and high-level ensembles, where GBM is almost always a part of them).  Also, there are many adaptations of GBM <a class="reference external" href="https://arxiv.org/abs/1603.04119">for Reinforcement Learning</a> (Minecraft, ICML 2016). By the way, the Viola-Jones algorithm, which is still used in computer vision, <a class="reference external" href="https://en.wikipedia.org/wiki/Viola%E2%80%93Jones_object_detection_framework#Learning_algorithm">is based on AdaBoost</a>.</p>
 <p>In this article, we intentionally omitted questions concerning GBM’s regularization, stochasticity, and hyper-parameters. It was not accidental that we used a small number of iterations <span class="math notranslate nohighlight">\(\large M = 3\)</span> throughout. If we used 30 trees instead of 3 and trained the GBM as described, the result would not be that predictable:</p>
 <figure class="align-default" id="good-fit">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Good_fit.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Good_fit.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Good_fit.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.22 </span><span class="caption-text">Good fit</span><a class="headerlink" href="#good-fit" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.22 </span><span class="caption-text">Good fit</span><a class="headerlink" href="#good-fit" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="overfitting">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Overfitting.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Overfitting.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Overfitting.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.23 </span><span class="caption-text">Overfitting</span><a class="headerlink" href="#overfitting" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.23 </span><span class="caption-text">Overfitting</span><a class="headerlink" href="#overfitting" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="gradient-boosting-interactive-playground">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Gradient_boosting_interactive_playground.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Gradient_boosting_interactive_playground.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/gradient_boosting/Gradient_boosting_interactive_playground.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.24 </span><span class="caption-text">Gradient Boosting Interactive Playground</span><a class="headerlink" href="#gradient-boosting-interactive-playground" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.24 </span><span class="caption-text">Gradient Boosting Interactive Playground</span><a class="headerlink" href="#gradient-boosting-interactive-playground" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>You can go to the <a class="reference external" href="http://arogozhnikov.github.io/2016/07/05/gradient_boosting_playground.html">Gradient Boosting Interactive Playground</a> to learn more.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">19.1.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.1.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">19.1.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.1.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/kashnitsky">Yury Kashnitsky</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/kashnitsky/topic-10-gradient-boosting/notebook">Gradient Boosting</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2176,13 +2176,13 @@ <h2><span class="section-number">19.1.6. </span>Acknowledgments<a class="headerl
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-            <p class="prev-next-title"><span class="section-number">19. </span>Introduction to Gradient Boosting</p>
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+<h1><span class="section-number">18. </span>Introduction to Gradient Boosting<a class="headerlink" href="#introduction-to-gradient-boosting" title="Permalink to this headline">#</a></h1>
 <p>Gradient Boosting is a popular machine learning algorithm that is used for both regression and classification tasks. It is a type of ensemble method that combines multiple weak learners into a single strong learner by sequentially fitting new models to the errors made by previous models.</p>
 <p>The idea behind gradient boosting is to use a loss function to measure the difference between the predicted and actual values, and then fit a new model to the residual errors. In each iteration, the model is trained on the errors made by the previous models, and the final prediction is obtained by summing the predictions of all models.</p>
 <p>The main advantage of gradient boosting is that it can handle complex data patterns and can achieve high accuracy on a wide range of tasks. However, it is computationally expensive and can be sensitive to overfitting if not properly tuned.</p>
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 <figcaption>
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+<p><span class="caption-number">Fig. 18.1 </span><span class="caption-text">Gradient Boosting Process Diagram</span><a class="headerlink" href="#gradient-boosting" title="Permalink to this image">#</a></p>
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+            <p class="prev-next-title"><span class="section-number">17.3. </span>Feature importance</p>
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+     40.13. Apply your skills
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+     40.14. Try it in Excel
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     <a class="reference internal" href="../../slides/ml-fundamentals/build-an-ml-web-app.html">
-     42.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/unsupervised-learning.html">
-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/kernel-method.html">
-     42.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/ml-advanced/model-selection.html">
-     42.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1577,157 +1577,157 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction-to-xgboost-algorithm">
-   19.4.1. Introduction to XGBoost Algorithm
+   18.4.1. Introduction to XGBoost Algorithm
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#evolution-of-tree-based-algorithms">
-     19.4.1.1. Evolution of tree-based algorithms
+     18.4.1.1. Evolution of tree-based algorithms
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#main-features-of-xgboost">
-     19.4.1.2. Main features of XGBoost
+     18.4.1.2. Main features of XGBoost
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#bagging-vs-boosting">
-   19.4.2. Bagging Vs Boosting
+   18.4.2. Bagging Vs Boosting
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#bagging">
-     19.4.2.1. Bagging
+     18.4.2.1. Bagging
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#boosting">
-     19.4.2.2. Boosting
+     18.4.2.2. Boosting
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#xgboost-algorithm-intuition">
-   19.4.3. XGBoost algorithm intuition
+   18.4.3. XGBoost algorithm intuition
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#gradient-boosting">
-     19.4.3.1. Gradient boosting
+     18.4.3.1. Gradient boosting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#gradient-boosted-trees">
-     19.4.3.2. Gradient Boosted Trees
+     18.4.3.2. Gradient Boosted Trees
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#extreme-gradient-boosting-xgboost">
-     19.4.3.3. Extreme gradient boosting (XGBoost)
+     18.4.3.3. Extreme gradient boosting (XGBoost)
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#implementing-xgboost-in-python">
-   19.4.4. Implementing XGBoost in Python
+   18.4.4. Implementing XGBoost in Python
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#load-libraries">
-     19.4.4.1. Load libraries
+     18.4.4.1. Load libraries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#read-dataset">
-     19.4.4.2. Read dataset
+     18.4.4.2. Read dataset
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#eda">
-     19.4.4.3. EDA
+     18.4.4.3. EDA
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#shape-of-dataset">
-       19.4.4.3.1. Shape of dataset
+       18.4.4.3.1. Shape of dataset
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#preview-dataset">
-       19.4.4.3.2. Preview dataset
+       18.4.4.3.2. Preview dataset
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#summary-of-dataset">
-       19.4.4.3.3. Summary of dataset
+       18.4.4.3.3. Summary of dataset
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#summary-statistics-of-dataset">
-       19.4.4.3.4. Summary statistics of dataset
+       18.4.4.3.4. Summary statistics of dataset
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#check-for-missing-values">
-       19.4.4.3.5. Check for missing values
+       18.4.4.3.5. Check for missing values
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#declare-feature-vector-and-target-variable">
-     19.4.4.4. Declare feature vector and target variable
+     18.4.4.4. Declare feature vector and target variable
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#split-data-into-separate-training-and-test-set">
-     19.4.4.5. Split data into separate training and test set
+     18.4.4.5. Split data into separate training and test set
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#train-the-xgboost-classifier">
-     19.4.4.6. Train the XGBoost Classifier
+     18.4.4.6. Train the XGBoost Classifier
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#make-predictions-with-xgboost-classifier">
-     19.4.4.7. Make predictions with XGBoost classifier
+     18.4.4.7. Make predictions with XGBoost classifier
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#check-accuracy-score">
-     19.4.4.8. Check accuracy score
+     18.4.4.8. Check accuracy score
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#k-fold-cross-validation-using-xgboost">
-   19.4.5. k-fold Cross Validation using XGBoost
+   18.4.5. k-fold Cross Validation using XGBoost
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#feature-importance-with-xgboost">
-   19.4.6. Feature importance with XGBoost
+   18.4.6. Feature importance with XGBoost
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#results-and-conclusion">
-   19.4.7. Results and conclusion
+   18.4.7. Results and conclusion
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   19.4.8. Your turn! 🚀
+   18.4.8. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   19.4.9. Acknowledgments
+   18.4.9. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1751,157 +1751,157 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction-to-xgboost-algorithm">
-   19.4.1. Introduction to XGBoost Algorithm
+   18.4.1. Introduction to XGBoost Algorithm
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#evolution-of-tree-based-algorithms">
-     19.4.1.1. Evolution of tree-based algorithms
+     18.4.1.1. Evolution of tree-based algorithms
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#main-features-of-xgboost">
-     19.4.1.2. Main features of XGBoost
+     18.4.1.2. Main features of XGBoost
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#bagging-vs-boosting">
-   19.4.2. Bagging Vs Boosting
+   18.4.2. Bagging Vs Boosting
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#bagging">
-     19.4.2.1. Bagging
+     18.4.2.1. Bagging
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#boosting">
-     19.4.2.2. Boosting
+     18.4.2.2. Boosting
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#xgboost-algorithm-intuition">
-   19.4.3. XGBoost algorithm intuition
+   18.4.3. XGBoost algorithm intuition
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#gradient-boosting">
-     19.4.3.1. Gradient boosting
+     18.4.3.1. Gradient boosting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#gradient-boosted-trees">
-     19.4.3.2. Gradient Boosted Trees
+     18.4.3.2. Gradient Boosted Trees
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#extreme-gradient-boosting-xgboost">
-     19.4.3.3. Extreme gradient boosting (XGBoost)
+     18.4.3.3. Extreme gradient boosting (XGBoost)
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#implementing-xgboost-in-python">
-   19.4.4. Implementing XGBoost in Python
+   18.4.4. Implementing XGBoost in Python
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#load-libraries">
-     19.4.4.1. Load libraries
+     18.4.4.1. Load libraries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#read-dataset">
-     19.4.4.2. Read dataset
+     18.4.4.2. Read dataset
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#eda">
-     19.4.4.3. EDA
+     18.4.4.3. EDA
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#shape-of-dataset">
-       19.4.4.3.1. Shape of dataset
+       18.4.4.3.1. Shape of dataset
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#preview-dataset">
-       19.4.4.3.2. Preview dataset
+       18.4.4.3.2. Preview dataset
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#summary-of-dataset">
-       19.4.4.3.3. Summary of dataset
+       18.4.4.3.3. Summary of dataset
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#summary-statistics-of-dataset">
-       19.4.4.3.4. Summary statistics of dataset
+       18.4.4.3.4. Summary statistics of dataset
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#check-for-missing-values">
-       19.4.4.3.5. Check for missing values
+       18.4.4.3.5. Check for missing values
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#declare-feature-vector-and-target-variable">
-     19.4.4.4. Declare feature vector and target variable
+     18.4.4.4. Declare feature vector and target variable
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#split-data-into-separate-training-and-test-set">
-     19.4.4.5. Split data into separate training and test set
+     18.4.4.5. Split data into separate training and test set
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#train-the-xgboost-classifier">
-     19.4.4.6. Train the XGBoost Classifier
+     18.4.4.6. Train the XGBoost Classifier
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#make-predictions-with-xgboost-classifier">
-     19.4.4.7. Make predictions with XGBoost classifier
+     18.4.4.7. Make predictions with XGBoost classifier
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#check-accuracy-score">
-     19.4.4.8. Check accuracy score
+     18.4.4.8. Check accuracy score
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#k-fold-cross-validation-using-xgboost">
-   19.4.5. k-fold Cross Validation using XGBoost
+   18.4.5. k-fold Cross Validation using XGBoost
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#feature-importance-with-xgboost">
-   19.4.6. Feature importance with XGBoost
+   18.4.6. Feature importance with XGBoost
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#results-and-conclusion">
-   19.4.7. Results and conclusion
+   18.4.7. Results and conclusion
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   19.4.8. Your turn! 🚀
+   18.4.8. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   19.4.9. Acknowledgments
+   18.4.9. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1926,11 +1926,11 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="xgboost-k-fold-cv-feature-importance">
-<h1><span class="section-number">19.4. </span>XGBoost + k-fold CV + Feature Importance<a class="headerlink" href="#xgboost-k-fold-cv-feature-importance" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">18.4. </span>XGBoost + k-fold CV + Feature Importance<a class="headerlink" href="#xgboost-k-fold-cv-feature-importance" title="Permalink to this headline">#</a></h1>
 <p>As we all know that a lot of competitions were won using only one algorithm - <strong>XGBoost</strong>. It is one of the most popular machine learning algorithm these days. It works well for both types of tasks - regression and classification.</p>
 <p>So, in this kernel, we will discuss XGBoost and develop a simple baseline XGBoost model with Python.</p>
 <section id="introduction-to-xgboost-algorithm">
-<h2><span class="section-number">19.4.1. </span>Introduction to XGBoost Algorithm<a class="headerlink" href="#introduction-to-xgboost-algorithm" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.4.1. </span>Introduction to XGBoost Algorithm<a class="headerlink" href="#introduction-to-xgboost-algorithm" title="Permalink to this headline">#</a></h2>
 <ul>
 <li><p><strong>XGBoost</strong> stands for <strong>Extreme Gradient Boosting.</strong></p></li>
 <li><p>It is a performant machine learning library based on the paper <a class="reference external" href="https://statweb.stanford.edu/~jhf/ftp/trebst.pdf">Greedy Function Approximation: A Gradient Boosting Machine, by Friedman</a></p></li>
@@ -1945,7 +1945,7 @@ <h2><span class="section-number">19.4.1. </span>Introduction to XGBoost Algorith
 </li>
 </ul>
 <section id="evolution-of-tree-based-algorithms">
-<h3><span class="section-number">19.4.1.1. </span>Evolution of tree-based algorithms<a class="headerlink" href="#evolution-of-tree-based-algorithms" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.1.1. </span>Evolution of tree-based algorithms<a class="headerlink" href="#evolution-of-tree-based-algorithms" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>The tree based algorithms have evolved over the years.</p></li>
 <li><p>XGBoost belongs to a family of tree-based algorithms.</p></li>
@@ -1954,12 +1954,12 @@ <h3><span class="section-number">19.4.1.1. </span>Evolution of tree-based algori
 <figure class="align-default" id="id1">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Evolution_of_tree_based_algorithms.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Evolution_of_tree_based_algorithms.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Evolution_of_tree_based_algorithms.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.26 </span><span class="caption-text">Evolution of tree-based algorithms</span><a class="headerlink" href="#id1" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.26 </span><span class="caption-text">Evolution of tree-based algorithms</span><a class="headerlink" href="#id1" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="main-features-of-xgboost">
-<h3><span class="section-number">19.4.1.2. </span>Main features of XGBoost<a class="headerlink" href="#main-features-of-xgboost" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.1.2. </span>Main features of XGBoost<a class="headerlink" href="#main-features-of-xgboost" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>The primary reasons we should use this algorithm are its <strong>accuracy</strong>, <strong>efficiency</strong> and <strong>feasibility</strong>.</p></li>
 <li><p>It is a linear model and a <a class="reference external" href="https://en.wikipedia.org/wiki/Decision_tree_learning">tree learning</a> algorithm that does parallel computations on a single machine.</p></li>
@@ -1975,7 +1975,7 @@ <h3><span class="section-number">19.4.1.2. </span>Main features of XGBoost<a cla
 </section>
 </section>
 <section id="bagging-vs-boosting">
-<h2><span class="section-number">19.4.2. </span>Bagging Vs Boosting<a class="headerlink" href="#bagging-vs-boosting" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.4.2. </span>Bagging Vs Boosting<a class="headerlink" href="#bagging-vs-boosting" title="Permalink to this headline">#</a></h2>
 <ul>
 <li><p>To know bagging and boosting, we need to know ensemble methods.</p></li>
 <li><p>Ensemble methods combine several decision trees to produce better predictive performance than utilizing a single decision tree.</p></li>
@@ -1991,7 +1991,7 @@ <h2><span class="section-number">19.4.2. </span>Bagging Vs Boosting<a class="hea
 </li>
 </ul>
 <section id="bagging">
-<h3><span class="section-number">19.4.2.1. </span>Bagging<a class="headerlink" href="#bagging" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.2.1. </span>Bagging<a class="headerlink" href="#bagging" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Bagging (or Bootstrap Aggregation), is a simple and very powerful ensemble method.</p></li>
 <li><p>Bagging is the application of the Bootstrap procedure to a high-variance machine learning algorithm, typically decision trees.</p></li>
@@ -2003,12 +2003,12 @@ <h3><span class="section-number">19.4.2.1. </span>Bagging<a class="headerlink" h
 <figure class="align-default" id="process-of-bagging">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Process_of_bagging.webp"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Process_of_bagging.webp" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Process_of_bagging.webp" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.27 </span><span class="caption-text">Process of Bagging</span><a class="headerlink" href="#process-of-bagging" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.27 </span><span class="caption-text">Process of Bagging</span><a class="headerlink" href="#process-of-bagging" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="boosting">
-<h3><span class="section-number">19.4.2.2. </span>Boosting<a class="headerlink" href="#boosting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.2.2. </span>Boosting<a class="headerlink" href="#boosting" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Boosting is a sequential process, where each subsequent model attempts to correct the errors of the previous model. The succeeding models are dependent on the previous model.</p></li>
 <li><p>In this technique, learners are learned sequentially with early learners fitting simple models to the data and then analyzing data for errors. In other words, we fit consecutive trees (random sample) and at every step, the goal is to solve for net error from the prior tree.</p></li>
@@ -2019,13 +2019,13 @@ <h3><span class="section-number">19.4.2.2. </span>Boosting<a class="headerlink"
 <figure class="align-default" id="process-of-boosting">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Process_of_boosting.webp"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Process_of_boosting.webp" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Process_of_boosting.webp" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.28 </span><span class="caption-text">Process of Boosting</span><a class="headerlink" href="#process-of-boosting" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.28 </span><span class="caption-text">Process of Boosting</span><a class="headerlink" href="#process-of-boosting" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 </section>
 <section id="xgboost-algorithm-intuition">
-<h2><span class="section-number">19.4.3. </span>XGBoost algorithm intuition<a class="headerlink" href="#xgboost-algorithm-intuition" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.4.3. </span>XGBoost algorithm intuition<a class="headerlink" href="#xgboost-algorithm-intuition" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>XGBoost is a powerful and lightning fast machine learning library. It’s commonly used to win some professional level competitions.</p></li>
 <li><p>It’s an intimidating algorithm, especially because of the number of large parameters that XGBoost provides.</p></li>
@@ -2033,7 +2033,7 @@ <h2><span class="section-number">19.4.3. </span>XGBoost algorithm intuition<a cl
 <li><p>So, in this section, we will discuss - <strong>gradient boosting</strong>, <strong>gradient boosted trees</strong> and <strong>XGBoost</strong>. The purpose of this section is to clarify these concepts.</p></li>
 </ul>
 <section id="gradient-boosting">
-<h3><span class="section-number">19.4.3.1. </span>Gradient boosting<a class="headerlink" href="#gradient-boosting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.3.1. </span>Gradient boosting<a class="headerlink" href="#gradient-boosting" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.</p></li>
 <li><p>It builds the model in a stage-wise fashion like other boosting methods do and it generalizes them by allowing optimization of an arbitrary differentiable loss function.</p></li>
@@ -2059,7 +2059,7 @@ <h3><span class="section-number">19.4.3.1. </span>Gradient boosting<a class="hea
 </ul>
 </section>
 <section id="gradient-boosted-trees">
-<h3><span class="section-number">19.4.3.2. </span>Gradient Boosted Trees<a class="headerlink" href="#gradient-boosted-trees" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.3.2. </span>Gradient Boosted Trees<a class="headerlink" href="#gradient-boosted-trees" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Gradient boosted trees consider the special case where the simple model h is a decision tree.</p></li>
 <li><p>It can be depicted with the following diagram which is taken from XGBoost’s documentation.</p></li>
@@ -2067,7 +2067,7 @@ <h3><span class="section-number">19.4.3.2. </span>Gradient Boosted Trees<a class
 <figure class="align-default" id="id2">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Gradient_boosted_trees.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Gradient_boosted_trees.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/Gradient_boosted_trees.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.29 </span><span class="caption-text">Gradient Boosted Trees</span><a class="headerlink" href="#id2" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.29 </span><span class="caption-text">Gradient Boosted Trees</span><a class="headerlink" href="#id2" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul class="simple">
@@ -2078,7 +2078,7 @@ <h3><span class="section-number">19.4.3.2. </span>Gradient Boosted Trees<a class
 <figure class="align-default" id="new-tree-minimizing-loss">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/New_tree_minimizing_loss.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/New_tree_minimizing_loss.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost_k_fold_cv_feature_importance/New_tree_minimizing_loss.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.30 </span><span class="caption-text">New Tree minimizing loss</span><a class="headerlink" href="#new-tree-minimizing-loss" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.30 </span><span class="caption-text">New Tree minimizing loss</span><a class="headerlink" href="#new-tree-minimizing-loss" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <ul class="simple">
@@ -2098,7 +2098,7 @@ <h3><span class="section-number">19.4.3.2. </span>Gradient Boosted Trees<a class
 </ul>
 </section>
 <section id="extreme-gradient-boosting-xgboost">
-<h3><span class="section-number">19.4.3.3. </span>Extreme gradient boosting (XGBoost)<a class="headerlink" href="#extreme-gradient-boosting-xgboost" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.3.3. </span>Extreme gradient boosting (XGBoost)<a class="headerlink" href="#extreme-gradient-boosting-xgboost" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>XGBoost is one of the fastest implementations of gradient boosted trees. It does this by tackling one of the major inefficiencies of gradient boosted trees.</p></li>
 <li><p>Consider the case where there are thousands of features, and therefore thousands of possible splits. Now, if we consider the potential loss for all possible splits to create a new branch we have thousands of potential splits and losses.</p></li>
@@ -2109,9 +2109,9 @@ <h3><span class="section-number">19.4.3.3. </span>Extreme gradient boosting (XGB
 </section>
 </section>
 <section id="implementing-xgboost-in-python">
-<h2><span class="section-number">19.4.4. </span>Implementing XGBoost in Python<a class="headerlink" href="#implementing-xgboost-in-python" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.4.4. </span>Implementing XGBoost in Python<a class="headerlink" href="#implementing-xgboost-in-python" title="Permalink to this headline">#</a></h2>
 <section id="load-libraries">
-<h3><span class="section-number">19.4.4.1. </span>Load libraries<a class="headerlink" href="#load-libraries" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.4.1. </span>Load libraries<a class="headerlink" href="#load-libraries" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -2123,7 +2123,7 @@ <h3><span class="section-number">19.4.4.1. </span>Load libraries<a class="header
 </div>
 </section>
 <section id="read-dataset">
-<h3><span class="section-number">19.4.4.2. </span>Read dataset<a class="headerlink" href="#read-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.4.2. </span>Read dataset<a class="headerlink" href="#read-dataset" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="s1">&#39;https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/wholesale_customers_data.csv&#39;</span>
@@ -2135,9 +2135,9 @@ <h3><span class="section-number">19.4.4.2. </span>Read dataset<a class="headerli
 </div>
 </section>
 <section id="eda">
-<h3><span class="section-number">19.4.4.3. </span>EDA<a class="headerlink" href="#eda" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.4.3. </span>EDA<a class="headerlink" href="#eda" title="Permalink to this headline">#</a></h3>
 <section id="shape-of-dataset">
-<h4><span class="section-number">19.4.4.3.1. </span>Shape of dataset<a class="headerlink" href="#shape-of-dataset" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">18.4.4.3.1. </span>Shape of dataset<a class="headerlink" href="#shape-of-dataset" title="Permalink to this headline">#</a></h4>
 <ul class="simple">
 <li><p>I will start off by checking the shape of the dataset.</p></li>
 </ul>
@@ -2156,7 +2156,7 @@ <h4><span class="section-number">19.4.4.3.1. </span>Shape of dataset<a class="he
 <p>We can see that there are 440 instances and 8 attributes in the dataset.</p>
 </section>
 <section id="preview-dataset">
-<h4><span class="section-number">19.4.4.3.2. </span>Preview dataset<a class="headerlink" href="#preview-dataset" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">18.4.4.3.2. </span>Preview dataset<a class="headerlink" href="#preview-dataset" title="Permalink to this headline">#</a></h4>
 <div class="cell tag_output-scoll docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
@@ -2263,7 +2263,7 @@ <h4><span class="section-number">19.4.4.3.2. </span>Preview dataset<a class="hea
 </ul>
 </section>
 <section id="summary-of-dataset">
-<h4><span class="section-number">19.4.4.3.3. </span>Summary of dataset<a class="headerlink" href="#summary-of-dataset" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">18.4.4.3.3. </span>Summary of dataset<a class="headerlink" href="#summary-of-dataset" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
@@ -2292,7 +2292,7 @@ <h4><span class="section-number">19.4.4.3.3. </span>Summary of dataset<a class="
 </div>
 </section>
 <section id="summary-statistics-of-dataset">
-<h4><span class="section-number">19.4.4.3.4. </span>Summary statistics of dataset<a class="headerlink" href="#summary-statistics-of-dataset" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">18.4.4.3.4. </span>Summary statistics of dataset<a class="headerlink" href="#summary-statistics-of-dataset" title="Permalink to this headline">#</a></h4>
 <div class="cell tag_output-scoll docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
@@ -2423,7 +2423,7 @@ <h4><span class="section-number">19.4.4.3.4. </span>Summary statistics of datase
 </div>
 </section>
 <section id="check-for-missing-values">
-<h4><span class="section-number">19.4.4.3.5. </span>Check for missing values<a class="headerlink" href="#check-for-missing-values" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">18.4.4.3.5. </span>Check for missing values<a class="headerlink" href="#check-for-missing-values" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
@@ -2448,7 +2448,7 @@ <h4><span class="section-number">19.4.4.3.5. </span>Check for missing values<a c
 </section>
 </section>
 <section id="declare-feature-vector-and-target-variable">
-<h3><span class="section-number">19.4.4.4. </span>Declare feature vector and target variable<a class="headerlink" href="#declare-feature-vector-and-target-variable" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.4.4. </span>Declare feature vector and target variable<a class="headerlink" href="#declare-feature-vector-and-target-variable" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;Channel&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
@@ -2619,7 +2619,7 @@ <h3><span class="section-number">19.4.4.4. </span>Declare feature vector and tar
 </div>
 </section>
 <section id="split-data-into-separate-training-and-test-set">
-<h3><span class="section-number">19.4.4.5. </span>Split data into separate training and test set<a class="headerlink" href="#split-data-into-separate-training-and-test-set" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.4.5. </span>Split data into separate training and test set<a class="headerlink" href="#split-data-into-separate-training-and-test-set" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># split X and y into training and testing sets</span>
@@ -2632,7 +2632,7 @@ <h3><span class="section-number">19.4.4.5. </span>Split data into separate train
 </div>
 </section>
 <section id="train-the-xgboost-classifier">
-<h3><span class="section-number">19.4.4.6. </span>Train the XGBoost Classifier<a class="headerlink" href="#train-the-xgboost-classifier" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.4.6. </span>Train the XGBoost Classifier<a class="headerlink" href="#train-the-xgboost-classifier" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>In order to train the XGBoost classifier, we need to know different parameters that XGBoost provides.</p></li>
 <li><p>We will discuss these parameters in later sections.</p></li>
@@ -2709,7 +2709,7 @@ <h3><span class="section-number">19.4.4.6. </span>Train the XGBoost Classifier<a
 </div>
 </section>
 <section id="make-predictions-with-xgboost-classifier">
-<h3><span class="section-number">19.4.4.7. </span>Make predictions with XGBoost classifier<a class="headerlink" href="#make-predictions-with-xgboost-classifier" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.4.7. </span>Make predictions with XGBoost classifier<a class="headerlink" href="#make-predictions-with-xgboost-classifier" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># make predictions on test data</span>
@@ -2721,7 +2721,7 @@ <h3><span class="section-number">19.4.4.7. </span>Make predictions with XGBoost
 </div>
 </section>
 <section id="check-accuracy-score">
-<h3><span class="section-number">19.4.4.8. </span>Check accuracy score<a class="headerlink" href="#check-accuracy-score" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.4.4.8. </span>Check accuracy score<a class="headerlink" href="#check-accuracy-score" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># compute and print accuracy score</span>
@@ -2742,7 +2742,7 @@ <h3><span class="section-number">19.4.4.8. </span>Check accuracy score<a class="
 </section>
 </section>
 <section id="k-fold-cross-validation-using-xgboost">
-<h2><span class="section-number">19.4.5. </span>k-fold Cross Validation using XGBoost<a class="headerlink" href="#k-fold-cross-validation-using-xgboost" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.4.5. </span>k-fold Cross Validation using XGBoost<a class="headerlink" href="#k-fold-cross-validation-using-xgboost" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>To build more robust models with XGBoost, we should always perform k-fold cross validation.</p></li>
 <li><p>In this way, we ensure that the original training dataset is used for both training and validation.</p></li>
@@ -2850,7 +2850,7 @@ <h2><span class="section-number">19.4.5. </span>k-fold Cross Validation using XG
 </div>
 </section>
 <section id="feature-importance-with-xgboost">
-<h2><span class="section-number">19.4.6. </span>Feature importance with XGBoost<a class="headerlink" href="#feature-importance-with-xgboost" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.4.6. </span>Feature importance with XGBoost<a class="headerlink" href="#feature-importance-with-xgboost" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>XGBoost provides a way to examine the importance of each feature in the original dataset within the model.</p></li>
 <li><p>It involves counting the number of times each feature is split on across all boosting trees in the model.</p></li>
@@ -2879,7 +2879,7 @@ <h2><span class="section-number">19.4.6. </span>Feature importance with XGBoost<
 </ul>
 </section>
 <section id="results-and-conclusion">
-<h2><span class="section-number">19.4.7. </span>Results and conclusion<a class="headerlink" href="#results-and-conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.4.7. </span>Results and conclusion<a class="headerlink" href="#results-and-conclusion" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>In this kernel, we implement XGBoost with Python and Scikit-Learn to classify the customers from two different channels as Horeca (Hotel/Retail/Café) customers or Retail channel (nominal) customers.</p></li>
 <li><p>The y labels contain values as 1 and 2. We have converted them into 0 and 1 for further analysis.</p></li>
@@ -2889,11 +2889,11 @@ <h2><span class="section-number">19.4.7. </span>Results and conclusion<a class="
 </ul>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">19.4.8. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.4.8. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">19.4.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.4.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/prashant111">Prashant Banerjee</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/prashant111/xgboost-k-fold-cv-feature-importance">XGBoost + k-fold CV + Feature Importance</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2935,13 +2935,13 @@ <h2><span class="section-number">19.4.9. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">19.3. </span>XGBoost</p>
+            <p class="prev-next-title"><span class="section-number">18.3. </span>XGBoost</p>
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">20. </span>Unsupervised learning</p>
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 <section class="tex2jax_ignore mathjax_ignore" id="xgboost">
-<h1><span class="section-number">19.3. </span>XGBoost<a class="headerlink" href="#xgboost" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">18.3. </span>XGBoost<a class="headerlink" href="#xgboost" title="Permalink to this headline">#</a></h1>
 <section id="what-is-xgboost">
-<h2><span class="section-number">19.3.1. </span>What is XGBoost<a class="headerlink" href="#what-is-xgboost" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.3.1. </span>What is XGBoost<a class="headerlink" href="#what-is-xgboost" title="Permalink to this headline">#</a></h2>
 <p><strong>XGBoost</strong> is the leading model for working with standard tabular data (the type of data you store in Pandas DataFrames, as opposed to more exotic types of data like images and videos). XGBoost models dominate many famous competitions.</p>
 <p>To reach peak accuracy, XGBoost models require more knowledge and  <em>model tuning</em> than techniques like Random Forest. After this section, you’ill be able to</p>
 <ul class="simple">
@@ -1753,7 +1753,7 @@ <h2><span class="section-number">19.3.1. </span>What is XGBoost<a class="headerl
 <figure class="align-default" id="gradient-boosted-decision-trees">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost/Gradient_boosted_decision_trees.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost/Gradient_boosted_decision_trees.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-advanced/xgboost/Gradient_boosted_decision_trees.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 19.25 </span><span class="caption-text">Gradient Boosted Decision Trees</span><a class="headerlink" href="#gradient-boosted-decision-trees" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 18.25 </span><span class="caption-text">Gradient Boosted Decision Trees</span><a class="headerlink" href="#gradient-boosted-decision-trees" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>We go through cycles that repeatedly builds new models and combines them into an <strong>ensemble</strong> model.  We start the cycle by calculating the errors for each observation in the dataset.  We then build a new model to predict those.  We add predictions from this error-predicting model to the “ensemble of models.”</p>
@@ -1762,7 +1762,7 @@ <h2><span class="section-number">19.3.1. </span>What is XGBoost<a class="headerl
 <p>This process may sound complicated, but the code to use it is straightforward. We’ll fill in some additional explanatory details in the <strong>model tuning</strong> section below.</p>
 </section>
 <section id="example">
-<h2><span class="section-number">19.3.2. </span>Example<a class="headerlink" href="#example" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.3.2. </span>Example<a class="headerlink" href="#example" title="Permalink to this headline">#</a></h2>
 <p>We will start with the data pre-loaded into <strong>train_X</strong>, <strong>test_X</strong>, <strong>train_y</strong>, <strong>test_y</strong>.</p>
 <div class="cell docutils container">
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Mean Absolute Error : 18213.708668664385
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 </pre></div>
 </div>
 </div>
 </div>
 </section>
 <section id="model-tuning">
-<h2><span class="section-number">19.3.3. </span>Model Tuning<a class="headerlink" href="#model-tuning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.3.3. </span>Model Tuning<a class="headerlink" href="#model-tuning" title="Permalink to this headline">#</a></h2>
 <section id="n-estimators-and-early-stopping-rounds">
-<h3><span class="section-number">19.3.3.1. </span><code class="docutils literal notranslate"><span class="pre">n_estimators</span> <span class="pre">and</span> <span class="pre">early_stopping_rounds</span></code><a class="headerlink" href="#n-estimators-and-early-stopping-rounds" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.3.3.1. </span><code class="docutils literal notranslate"><span class="pre">n_estimators</span> <span class="pre">and</span> <span class="pre">early_stopping_rounds</span></code><a class="headerlink" href="#n-estimators-and-early-stopping-rounds" title="Permalink to this headline">#</a></h3>
 <p>XGBoost has a few parameters that can dramatically affect your model’s accuracy and training speed.  The first parameters you should understand are: <code class="docutils literal notranslate"><span class="pre">n_estimators</span> <span class="pre">and</span> <span class="pre">early_stopping_rounds</span></code>.</p>
 <p><code class="docutils literal notranslate"><span class="pre">n_estimators</span></code> specifies how many times to go through the modeling cycle described above.</p>
 <p>In the <a class="reference external" href="http://i.imgur.com/2q85n9s.png">underfitting vs overfitting graph</a>, n_estimators moves you further to the right.  Too low a value causes underfitting, which is inaccurate predictions on both training data and new data. Too large a value causes overfitting, which is accurate predictions on training data, but inaccurate predictions on new data (which is what we care about). You can experiment with your dataset to find the ideal.  Typical values range from 100-1000, though this depends a lot on the <code class="docutils literal notranslate"><span class="pre">learning</span> <span class="pre">rate</span></code> discussed below.</p>
@@ -1877,7 +1877,7 @@ <h3><span class="section-number">19.3.3.1. </span><code class="docutils literal
 <p>When using <code class="docutils literal notranslate"><span class="pre">early_stopping_rounds</span></code>, you need to set aside some of your data for checking the number of rounds to use.  If you later want to fit a model with all of your data, set <code class="docutils literal notranslate"><span class="pre">n_estimators</span></code> to whatever value you found to be optimal when run with early stopping.</p>
 </section>
 <section id="learning-rate">
-<h3><span class="section-number">19.3.3.2. </span>learning_rate<a class="headerlink" href="#learning-rate" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.3.3.2. </span>learning_rate<a class="headerlink" href="#learning-rate" title="Permalink to this headline">#</a></h3>
 <p>Here’s a subtle but important trick for better XGBoost models:</p>
 <p>Instead of getting predictions by simply adding up the predictions from each component model, we will multiply the predictions from each model by a small number before adding them in.  This means each tree we add to the ensemble helps us less.  In practice, this reduces the model’s propensity to overfit.</p>
 <p>So, you can use a higher value of <code class="docutils literal notranslate"><span class="pre">n_estimators</span></code> without overfitting.  If you use early stopping, the appropriate number of trees will be set automatically.</p>
@@ -1914,22 +1914,22 @@ <h3><span class="section-number">19.3.3.2. </span>learning_rate<a class="headerl
 </div>
 </section>
 <section id="n-jobs">
-<h3><span class="section-number">19.3.3.3. </span><code class="docutils literal notranslate"><span class="pre">n_jobs</span></code><a class="headerlink" href="#n-jobs" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">18.3.3.3. </span><code class="docutils literal notranslate"><span class="pre">n_jobs</span></code><a class="headerlink" href="#n-jobs" title="Permalink to this headline">#</a></h3>
 <p>On larger datasets where runtime is a consideration, you can use parallelism to build your models faster.  It’s common to set the parameter <code class="docutils literal notranslate"><span class="pre">n_jobs</span></code> equal to the number of cores on your machine.  On smaller datasets, this won’t help.</p>
 <p>The resulting model won’t be any better, so micro-optimizing for fitting time is typically nothing but a distraction. But, it’s useful in large datasets where you would otherwise spend a long time waiting during the <code class="docutils literal notranslate"><span class="pre">fit</span></code> command.</p>
 <p>XGBoost has a multitude of other parameters, but these will go a very long way in helping you fine-tune your XGBoost model for optimal performance.</p>
 </section>
 </section>
 <section id="conclusion">
-<h2><span class="section-number">19.3.4. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.3.4. </span>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">#</a></h2>
 <p>XGBoost is currently the dominant algorithm for building accurate models on conventional data (also called tabular or strutured data).  Go apply it to improve your models!</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">19.3.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.3.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">19.3.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">18.3.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://www.kaggle.com/dansbecker">DanB</a> for creating the open-source course <a class="reference external" href="https://www.kaggle.com/code/dansbecker/xgboost/input?select=train.csv">XGBoost</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -1971,13 +1971,13 @@ <h2><span class="section-number">19.3.6. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">19.2. </span>Gradient boosting example</p>
+            <p class="prev-next-title"><span class="section-number">18.2. </span>Gradient boosting example</p>
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-        <p class="prev-next-title"><span class="section-number">19.4. </span>XGBoost + k-fold CV + Feature Importance</p>
+        <p class="prev-next-title"><span class="section-number">18.4. </span>XGBoost + k-fold CV + Feature Importance</p>
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+   20.2. Support Vector Machines: Maximizing the
    <em>
     Margin
    </em>
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+   20.8. {figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svr1.jpeg
   </a>
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@@ -1673,12 +1673,12 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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+   20.1. Motivating Support Vector Machines
   </a>
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-   21.2. Support Vector Machines: Maximizing the
+   20.2. Support Vector Machines: Maximizing the
    <em>
     Margin
    </em>
@@ -1686,68 +1686,68 @@ <h2> Contents </h2>
  </li>
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-   21.4. Beyond linear boundaries: Kernel SVM
+   20.4. Beyond linear boundaries: Kernel SVM
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+   20.5. Hinge loss
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+   20.6. RBF Kernel
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-   21.7. Support Vector Regression (SVR)
+   20.7. Support Vector Regression (SVR)
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-   21.8. {figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svr1.jpeg
+   20.8. {figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svr1.jpeg
   </a>
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 width: 90%
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-   21.10. SVM v.s. logistic regression
+   20.10. SVM v.s. logistic regression
   </a>
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   <a class="reference internal nav-link" href="#svr-v-s-linear-regression">
-   21.11. SVR v.s. linear regression
+   20.11. SVR v.s. linear regression
   </a>
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-   21.12. Support Vector Machine Summary
+   20.12. Support Vector Machine Summary
   </a>
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   <a class="reference internal nav-link" href="#optional-lets-dive-into-the-math-of-svm">
-   21.13. [Optional] Let’s dive into the math of SVM …
+   20.13. [Optional] Let’s dive into the math of SVM …
   </a>
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   <a class="reference internal nav-link" href="#your-turn">
-   21.14. Your turn! 🚀
+   20.14. Your turn! 🚀
   </a>
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   <a class="reference internal nav-link" href="#acknowledgement">
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 </ul>
@@ -1788,7 +1788,7 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="kernel-method">
-<h1><span class="section-number">21. </span>Kernel method<a class="headerlink" href="#kernel-method" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">20. </span>Kernel method<a class="headerlink" href="#kernel-method" title="Permalink to this headline">#</a></h1>
 <p>SVMs are a powerful and flexible class of algorithms used for classification and regression. In this section, we will explore the intuition behind SVMs and their use in classification problems.
 To start with, let’s understand the basic concept of SVMs.
 Support Vector Machines (SVMs) are supervised learning algorithms that can be used for classification and regression tasks. SVMs try to find the best decision boundary that separates data points of different classes. The decision boundary is chosen such that it maximizes the margin between the data points of different classes.
@@ -1845,7 +1845,7 @@ <h1><span class="section-number">21. </span>Kernel method<a class="headerlink" h
 </div></div>
 </div>
 <section id="motivating-support-vector-machines">
-<h2><span class="section-number">21.1. </span>Motivating Support Vector Machines<a class="headerlink" href="#motivating-support-vector-machines" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.1. </span>Motivating Support Vector Machines<a class="headerlink" href="#motivating-support-vector-machines" title="Permalink to this headline">#</a></h2>
 <p>In this section we will consider differentiated classification: rather than modelling each category, we simply find a line or curve or flowform that separates these categories from each other.</p>
 <div class="cell docutils container">
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-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.collections.PathCollection at 0x7f2d442e81f0&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.collections.PathCollection at 0x7f2c3a3dd190&gt;
 </pre></div>
 </div>
 <img alt="../_images/kernel-method_9_1.png" src="../_images/kernel-method_9_1.png" />
@@ -1889,7 +1889,7 @@ <h2><span class="section-number">21.1. </span>Motivating Support Vector Machines
 Obviously we can’t simply “draw a line between classes”, but we do need to think a bit deeper.</p>
 </section>
 <section id="support-vector-machines-maximizing-the-margin">
-<h2><span class="section-number">21.2. </span>Support Vector Machines: Maximizing the <em>Margin</em><a class="headerlink" href="#support-vector-machines-maximizing-the-margin" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.2. </span>Support Vector Machines: Maximizing the <em>Margin</em><a class="headerlink" href="#support-vector-machines-maximizing-the-margin" title="Permalink to this headline">#</a></h2>
 <p>SVM proposes a new solution algorithm which finds the nearest straight line by applying an appropriate boundary fill to each line. The following is an example of an improvement to the SVM algorithm.</p>
 <div class="cell docutils container">
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@@ -1917,17 +1917,17 @@ <h2><span class="section-number">21.2. </span>Support Vector Machines: Maximizin
 <p>In SVM, the best models are those that maximise this margin, and SVM is an optimal model that achieves the maximum margin and can effectively use the boundary information in the dataset to predict the distribution of new data points, thereby improving prediction accuracy. Because of its excellent fitting ability, SVM can also be used to handle large amounts of unbalanced data.</p>
 </section>
 <section id="fitting-a-support-vector-machine">
-<h2><span class="section-number">21.3. </span>Fitting a support vector machine<a class="headerlink" href="#fitting-a-support-vector-machine" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.3. </span>Fitting a support vector machine<a class="headerlink" href="#fitting-a-support-vector-machine" title="Permalink to this headline">#</a></h2>
 <figure class="align-default" id="svm-for-linear-classification-2d-case">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svm2d.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svm2d.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svm2d.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 21.1 </span><span class="caption-text">SVM for Linear classification : 2D case. <a class="reference external" href="https://online.stat.psu.edu/stat555/node/102/">source</a></span><a class="headerlink" href="#svm-for-linear-classification-2d-case" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 20.1 </span><span class="caption-text">SVM for Linear classification : 2D case. <a class="reference external" href="https://online.stat.psu.edu/stat555/node/102/">source</a></span><a class="headerlink" href="#svm-for-linear-classification-2d-case" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="svm-for-linear-classification-3d-case">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svm3d.gif"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svm3d.gif" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svm3d.gif" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 21.2 </span><span class="caption-text">SVM for Linear classification : 3D case. <a class="reference external" href="https://www.youtube.com/watch?v=3liCbRZPrZA">source</a></span><a class="headerlink" href="#svm-for-linear-classification-3d-case" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 20.2 </span><span class="caption-text">SVM for Linear classification : 3D case. <a class="reference external" href="https://www.youtube.com/watch?v=3liCbRZPrZA">source</a></span><a class="headerlink" href="#svm-for-linear-classification-3d-case" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Now we will use a linear kernel and set the C parameter to a large size:</p>
@@ -2044,7 +2044,7 @@ <h2><span class="section-number">21.3. </span>Fitting a support vector machine<a
 </div>
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 <script type="application/vnd.jupyter.widget-view+json">
-{"version_major": 2, "version_minor": 0, "model_id": "64e7e8ca2509413dbcdf78bc6e970021"}
+{"version_major": 2, "version_minor": 0, "model_id": "7bdfe7c317ce4e388aed07b1f420b60b"}
 </script><div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;function __main__.plot_svm(N=10, ax=None)&gt;
 </pre></div>
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 <section id="beyond-linear-boundaries-kernel-svm">
-<h2><span class="section-number">21.4. </span>Beyond linear boundaries: Kernel SVM<a class="headerlink" href="#beyond-linear-boundaries-kernel-svm" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.4. </span>Beyond linear boundaries: Kernel SVM<a class="headerlink" href="#beyond-linear-boundaries-kernel-svm" title="Permalink to this headline">#</a></h2>
 <figure class="align-default" id="kernel-svm-visualization">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svm1.gif"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svm1.gif" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svm1.gif" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 21.3 </span><span class="caption-text">Kernel SVM visualization (with a polynomial kernel). <a class="reference external" href="https://www.youtube.com/embed/3liCbRZPrZA">source</a></span><a class="headerlink" href="#kernel-svm-visualization" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 20.3 </span><span class="caption-text">Kernel SVM visualization (with a polynomial kernel). <a class="reference external" href="https://www.youtube.com/embed/3liCbRZPrZA">source</a></span><a class="headerlink" href="#kernel-svm-visualization" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Kernels is an “easy algorithm” that saves us from the tedious calculations in a high-dimensional space. If support vector machines and kernels are combined to project data into a high-dimensional space, making it possible to handle non-linear relationships with linear classifiers, the power of SVMs can be enormous.</p>
@@ -2102,111 +2102,111 @@ <h2><span class="section-number">21.4. </span>Beyond linear boundaries: Kernel S
 </div>
 <div class="cell_output docutils container">
 <script type="application/vnd.jupyter.widget-view+json">
-{"version_major": 2, "version_minor": 0, "model_id": "72ea5954d9fe43a0be3cd622a5389617"}
-</script><div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;function __main__.plot_3D(elev=30, azim=30, X=array([[-2.00291268e-01, -1.23126783e-01],
-       [-1.38022175e-02,  1.78452137e-01],
-       [ 1.02838885e+00, -4.62397985e-01],
-       [-4.44485092e-02,  1.11028097e+00],
-       [ 7.18558254e-01,  9.37094537e-01],
-       [ 1.25727901e-01,  6.89772530e-02],
-       [ 6.17354007e-01, -6.31880804e-01],
-       [ 1.06219932e+00,  3.18411890e-01],
-       [ 1.10083700e+00, -1.92192059e-01],
-       [ 8.40813682e-01, -3.84450362e-01],
-       [-5.67045040e-01,  9.46218571e-01],
-       [-9.76828713e-01,  5.28717967e-01],
-       [ 1.78121047e-01,  3.88719012e-02],
-       [-6.50586228e-01, -7.24781279e-01],
-       [-1.06766070e-01,  1.98531037e-01],
-       [-1.05338672e-03, -2.02362479e-01],
-       [ 5.02949978e-01,  7.76141218e-01],
-       [ 1.31091104e-01, -3.72588580e-02],
-       [-5.19241486e-01,  7.83569439e-01],
-       [-4.51683190e-02, -1.17568402e-01],
-       [-1.26296329e-01, -1.23185314e-02],
-       [ 7.95805291e-02, -1.03239142e+00],
-       [-1.37904344e-01, -9.34530856e-01],
-       [-5.76242813e-02,  1.46696983e-01],
-       [-1.27466210e-01,  5.76904671e-02],
-       [-8.85858547e-01, -2.49167872e-01],
-       [ 2.50855627e-01, -1.59941920e-01],
-       [ 7.43858488e-01,  6.61282562e-01],
-       [ 1.01247427e-01,  6.03322373e-02],
-       [ 4.67384787e-01,  8.10648780e-01],
-       [-8.14621803e-01, -5.90112463e-01],
-       [ 2.14412923e-01,  7.69890941e-02],
-       [-1.04042494e+00,  2.51615447e-01],
-       [-8.44715899e-02,  7.19266805e-02],
-       [ 1.86293303e-01,  1.71314224e-01],
-       [ 1.76660210e-01, -2.08017957e-01],
-       [ 9.51639764e-01,  4.05793172e-01],
-       [-3.69498372e-02, -2.36876037e-01],
-       [-1.15420402e-01, -1.98762528e-02],
-       [-2.23570038e-01, -1.06173465e-02],
-       [ 8.95515928e-01,  1.20535775e-01],
-       [-4.34037914e-01, -1.01147142e+00],
-       [ 4.46953710e-01, -8.26247274e-01],
-       [-1.39035899e-01,  6.29521454e-02],
-       [-1.88462145e-01, -8.53705390e-02],
-       [ 1.00126302e+00, -3.00366528e-01],
-       [-1.10481150e-01, -6.00663650e-02],
-       [-8.49581248e-03, -4.20195753e-02],
-       [ 2.49908270e-02, -6.11350646e-02],
-       [ 9.81311260e-01, -6.81544149e-03],
-       [ 7.11042226e-01, -9.90276895e-01],
-       [-4.79749113e-01, -9.43331742e-01],
-       [-1.96788658e-02,  3.77080619e-02],
-       [ 1.46617022e-01,  9.39053017e-01],
-       [ 1.69729217e-01,  2.98410884e-02],
-       [ 1.68148252e-02, -2.98172655e-01],
-       [ 4.14159992e-02,  9.28732563e-02],
-       [-6.33136884e-02,  2.34679361e-01],
-       [ 3.08635543e-02, -7.80265324e-02],
-       [-8.09497148e-01, -7.23696749e-01],
-       [-9.20094079e-01,  6.97210258e-01],
-       [ 5.51888557e-02,  6.36697268e-02],
-       [-9.91292590e-01, -1.38589378e-02],
-       [-1.00093038e+00, -2.03466583e-01],
-       [ 6.07619858e-01, -8.42349600e-01],
-       [ 8.06970356e-01, -4.37132055e-01],
-       [ 5.71655876e-01,  6.14754038e-01],
-       [-2.20691250e-02,  3.34624124e-03],
-       [-5.95317547e-01,  7.70128136e-01],
-       [-9.08755948e-03, -9.12467983e-01],
-       [ 2.49545934e-01, -8.65942540e-01],
-       [-6.46743725e-02, -2.74594295e-02],
-       [ 2.05427934e-01, -7.99351662e-01],
-       [-1.68922065e-01,  1.00270203e+00],
-       [ 1.81786143e-01,  9.18244333e-01],
-       [ 1.04685555e+00,  5.51134204e-02],
-       [-8.10395091e-01,  6.31827093e-01],
-       [-5.98415945e-01,  8.13217919e-01],
-       [-1.01470786e-02,  8.12924588e-03],
-       [-1.05063225e+00, -3.81956467e-01],
-       [ 7.48046354e-01,  5.97282185e-01],
-       [-7.83010480e-02, -6.51059499e-02],
-       [ 5.34643901e-02,  2.43376803e-02],
-       [-9.76144155e-01, -3.96938666e-01],
-       [-1.22694238e-01, -2.84440371e-02],
-       [-2.40458859e-02, -7.49911445e-01],
-       [-2.40397180e-01,  9.00658480e-01],
-       [-1.11339718e+00,  1.13587105e-01],
-       [ 1.27532473e-01, -1.88020392e-01],
-       [ 7.55546574e-02, -1.29731035e-01],
-       [-5.68482305e-02, -1.07122071e-01],
-       [ 1.06930683e-01, -1.36285877e-01],
-       [ 7.24596268e-02, -1.51890359e-01],
-       [-2.86855706e-01, -5.27668102e-02],
-       [ 1.43415103e-01, -4.96257172e-02],
-       [ 1.10446036e-01, -8.56026048e-02],
-       [ 1.44261454e-01,  1.49331647e-02],
-       [ 2.65844747e-02,  9.44468068e-02],
-       [ 3.52600299e-02, -1.61718032e-01],
-       [ 6.55673048e-02, -4.77516704e-02]]), y=array([1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0,
-       0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1,
-       1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0,
-       0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0,
-       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]))&gt;
+{"version_major": 2, "version_minor": 0, "model_id": "e8d5064d974b43e092a05905f081074e"}
+</script><div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;function __main__.plot_3D(elev=30, azim=30, X=array([[ 0.11837111, -0.03203905],
+       [ 0.12970811, -1.11256221],
+       [-0.50409485,  0.7991212 ],
+       [-0.72989295, -0.60055976],
+       [-0.70025349,  0.90247824],
+       [ 0.24392057,  0.05184432],
+       [-1.17002276, -0.02014995],
+       [ 0.47982751, -0.74048836],
+       [ 0.98270926, -0.40842819],
+       [ 0.81307859,  0.45405909],
+       [ 0.83885964, -0.42451973],
+       [ 0.04279497,  0.14551352],
+       [ 0.06437443,  0.06546047],
+       [ 0.15623371, -0.31632589],
+       [ 0.18313996, -0.08364448],
+       [-0.0030183 ,  0.04686915],
+       [-0.88028578,  0.532819  ],
+       [-0.42058207, -0.98373818],
+       [-0.00263838,  0.09248294],
+       [ 0.02523009,  0.23085278],
+       [ 0.21245994,  0.00394056],
+       [-0.2694445 , -0.85134893],
+       [ 0.52325357, -0.70273021],
+       [ 0.03335592,  0.0422233 ],
+       [ 0.00865885,  0.10951954],
+       [-1.18139011, -0.16726197],
+       [ 0.28605566,  1.20763581],
+       [ 0.35445552, -0.87762971],
+       [-0.07959425, -0.09365631],
+       [-0.11629632,  0.17092844],
+       [-0.12805125, -0.00286549],
+       [-1.14513159, -0.24697832],
+       [ 0.02260507,  0.00272024],
+       [-0.02507292,  0.08505536],
+       [ 0.97643601,  0.07987102],
+       [-0.02339582, -0.21052205],
+       [ 0.71825627,  0.86944853],
+       [ 0.07631985, -0.03775774],
+       [ 0.42699313, -0.96804875],
+       [ 0.02230899,  0.05807794],
+       [ 0.88690704,  0.48682712],
+       [-0.60045911, -0.79041412],
+       [ 0.74578274,  0.91184328],
+       [ 1.12823526,  0.04520138],
+       [ 0.14580674,  0.0727919 ],
+       [-0.50947303,  0.63351259],
+       [ 0.05396526,  0.02709499],
+       [ 0.26464122,  0.85523786],
+       [ 0.04537166, -0.08886532],
+       [-0.00871596,  0.01764704],
+       [-0.49072614, -0.52268546],
+       [-0.96991739,  0.46423349],
+       [-0.01530302,  0.12858834],
+       [-0.12024455, -0.11154268],
+       [-0.223299  ,  1.15322542],
+       [-0.38169681,  0.97886024],
+       [ 0.19420722,  0.87059259],
+       [-0.0016215 ,  1.15203074],
+       [-0.05923185,  0.10452554],
+       [-0.00539065, -0.00646125],
+       [-0.09334799,  0.0862223 ],
+       [ 0.0230368 , -0.37978209],
+       [ 0.79888363, -0.64446565],
+       [-0.09667992, -0.0537957 ],
+       [ 0.01665839,  0.20541158],
+       [ 0.07324116, -0.02666396],
+       [-0.00371103, -0.12373911],
+       [ 0.13356913, -0.07065824],
+       [-0.09047764,  0.07384053],
+       [ 0.89850093,  0.15146639],
+       [ 0.04829626,  0.14596582],
+       [-0.08559119, -0.97526445],
+       [-0.19804126,  1.1196651 ],
+       [ 1.11037586,  0.36611865],
+       [ 0.19373186, -0.20912239],
+       [-0.45097522, -1.01775786],
+       [-0.32505229,  1.1755452 ],
+       [ 0.00938661,  0.11285819],
+       [-0.11608994,  0.09930851],
+       [ 1.01964571, -0.2813047 ],
+       [-0.25504546,  0.04543063],
+       [ 0.10152314,  0.08272095],
+       [-0.87898966, -0.48385257],
+       [-0.79378229, -0.47716821],
+       [ 0.38252217,  0.68679926],
+       [-0.05639158, -0.05225847],
+       [-0.19008824, -0.86332183],
+       [-0.07145021,  0.05680205],
+       [-0.95197068,  0.00879897],
+       [ 0.04477681,  0.08715884],
+       [ 0.85692156, -0.2580265 ],
+       [-0.18163865,  0.18257323],
+       [ 0.56121819, -0.89225434],
+       [-0.14069707,  0.06850504],
+       [ 0.08698948, -0.00401784],
+       [-1.01465911,  0.3396538 ],
+       [-0.88714939,  0.63621048],
+       [-0.155571  , -0.03980251],
+       [ 0.13891479, -0.19425922],
+       [ 1.09765538, -0.02026493]]), y=array([1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0,
+       0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0,
+       1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1,
+       1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1,
+       0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0]))&gt;
 </pre></div>
 </div>
 </div>
@@ -2239,7 +2239,7 @@ <h2><span class="section-number">21.4. </span>Beyond linear boundaries: Kernel S
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.collections.PathCollection at 0x7f2d41dde7c0&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.collections.PathCollection at 0x7f2c37ed18e0&gt;
 </pre></div>
 </div>
 <img alt="../_images/kernel-method_39_1.png" src="../_images/kernel-method_39_1.png" />
@@ -2248,11 +2248,11 @@ <h2><span class="section-number">21.4. </span>Beyond linear boundaries: Kernel S
 <p>Currently, for linearly divisible data, we usually use a linear support vector machine. However, if the data is non-linear, such as a classification problem, we can transform it into a linearly divisible problem, thus improving the generalisation of the model. In this case, kernel tricks are used to transform linearly divisible problems into non-linearly divisible problems.</p>
 </section>
 <section id="hinge-loss">
-<h2><span class="section-number">21.5. </span>Hinge loss<a class="headerlink" href="#hinge-loss" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.5. </span>Hinge loss<a class="headerlink" href="#hinge-loss" title="Permalink to this headline">#</a></h2>
 <p>An alternative to cross-entropy for binary classification problems is the hinge loss function. It is mainly studied for the Support Vector Machine (SVM) model. It is designed to perform binary classification of target values which in the set {-1, 1}</p>
 </section>
 <section id="rbf-kernel">
-<h2><span class="section-number">21.6. </span>RBF Kernel<a class="headerlink" href="#rbf-kernel" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.6. </span>RBF Kernel<a class="headerlink" href="#rbf-kernel" title="Permalink to this headline">#</a></h2>
 <div class="cell tag_hide-input docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
@@ -2280,21 +2280,21 @@ <h2><span class="section-number">21.6. </span>RBF Kernel<a class="headerlink" hr
 </div>
 </section>
 <section id="support-vector-regression-svr">
-<h2><span class="section-number">21.7. </span>Support Vector Regression (SVR)<a class="headerlink" href="#support-vector-regression-svr" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.7. </span>Support Vector Regression (SVR)<a class="headerlink" href="#support-vector-regression-svr" title="Permalink to this headline">#</a></h2>
 <p>SVR regression (Squeeze-and-VectorRegression), also known as Squeeze Regression, is an optimisation problem. The basic idea is to find a regression plane such that the variables in all data sets are closest to a point on this plane, so that all points on the plane are correlated.
 Objective function:
 In SVR regression, the objective function is to minimise the two-parametric number of weights while keeping the points in each training set as far away as possible from the support vector on one side of their own category.</p>
 </section>
 <section id="figure-https-static-1300131294-cos-ap-shanghai-myqcloud-com-images-svm-svr1-jpeg">
-<h2><span class="section-number">21.8. </span>{figure} <a class="reference external" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svr1.jpeg">https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svr1.jpeg</a><a class="headerlink" href="#figure-https-static-1300131294-cos-ap-shanghai-myqcloud-com-images-svm-svr1-jpeg" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.8. </span>{figure} <a class="reference external" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svr1.jpeg">https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/svm/svr1.jpeg</a><a class="headerlink" href="#figure-https-static-1300131294-cos-ap-shanghai-myqcloud-com-images-svm-svr1-jpeg" title="Permalink to this headline">#</a></h2>
 </section>
 <section id="name-support-vector-regression-width-90">
-<h2><span class="section-number">21.9. </span>name: support vector regression
+<h2><span class="section-number">20.9. </span>name: support vector regression
 width: 90%<a class="headerlink" href="#name-support-vector-regression-width-90" title="Permalink to this headline">#</a></h2>
 <p>An illustration of support vector regression</p>
 </section>
 <section id="svm-v-s-logistic-regression">
-<h2><span class="section-number">21.10. </span>SVM v.s. logistic regression<a class="headerlink" href="#svm-v-s-logistic-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.10. </span>SVM v.s. logistic regression<a class="headerlink" href="#svm-v-s-logistic-regression" title="Permalink to this headline">#</a></h2>
 <p>Similarities: Both are linear classification algorithms
 Differences:</p>
 <ol class="simple">
@@ -2309,12 +2309,12 @@ <h2><span class="section-number">21.10. </span>SVM v.s. logistic regression<a cl
 <p><a class="reference external" href="https://www.geeksforgeeks.org/differentiate-between-support-vector-machine-and-logistic-regression/">https://www.geeksforgeeks.org/differentiate-between-support-vector-machine-and-logistic-regression/</a></p>
 </section>
 <section id="svr-v-s-linear-regression">
-<h2><span class="section-number">21.11. </span>SVR v.s. linear regression<a class="headerlink" href="#svr-v-s-linear-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.11. </span>SVR v.s. linear regression<a class="headerlink" href="#svr-v-s-linear-regression" title="Permalink to this headline">#</a></h2>
 <p>The problem of overfitting is a very tricky one in the field of statistics. In this case, many machine learning methods, such as least squares (OLSE), will perform poorly. Support vector machines (SVR), on the other hand, can minimise the overfitting problem. SVR allows for non-linear fitting when there is enough data for training.
 A final issue to consider is OLSE linear regression. While linear regression is effective for most problems, it is not always effective for some special cases. For example, in OLSE linear regression, there is some bias in fitting the variables linearly because the model is not hyperplane. In contrast, SVR allows for non-linear fitting problems.</p>
 </section>
 <section id="support-vector-machine-summary">
-<h2><span class="section-number">21.12. </span>Support Vector Machine Summary<a class="headerlink" href="#support-vector-machine-summary" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.12. </span>Support Vector Machine Summary<a class="headerlink" href="#support-vector-machine-summary" title="Permalink to this headline">#</a></h2>
 <p>The basic model of SVM: is defined as a linear classifier with maximum interval on the feature space. Specifically, when linearly separable, it finds the optimal classification hyperplane for both classes of samples in the original space. When linearly indistinguishable, relaxation variables are added and samples from a low-dimensional input space are mapped to a higher-dimensional space via a non-linear mapping to make them linearly distinguishable, so that the optimal classification hyperplane can be found in that feature space.</p>
 <p>Advantages of SVM:</p>
 <ol class="simple">
@@ -2327,7 +2327,7 @@ <h2><span class="section-number">21.12. </span>Support Vector Machine Summary<a
 </ol>
 </section>
 <section id="optional-lets-dive-into-the-math-of-svm">
-<h2><span class="section-number">21.13. </span>[Optional] Let’s dive into the math of SVM …<a class="headerlink" href="#optional-lets-dive-into-the-math-of-svm" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.13. </span>[Optional] Let’s dive into the math of SVM …<a class="headerlink" href="#optional-lets-dive-into-the-math-of-svm" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
@@ -2355,18 +2355,18 @@ <h2><span class="section-number">21.13. </span>[Optional] Let’s dive into the
 </div>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">21.14. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.14. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>You can follow this <a class="reference internal" href="../assignments/ml-advanced/kernel-method/kernel-method-assignment-1.html"><span class="doc std std-doc">assignment</span></a> to practise Support Vector Machines with examples.</p>
 </section>
 <section id="acknowledgement">
-<h2><span class="section-number">21.15. </span>Acknowledgement<a class="headerlink" href="#acknowledgement" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">20.15. </span>Acknowledgement<a class="headerlink" href="#acknowledgement" title="Permalink to this headline">#</a></h2>
 <p>Thanks for <a class="reference external" href="https://jakevdp.github.io/PythonDataScienceHandbook/05.07-support-vector-machines.html">jakevdp</a>, for which the code part is licenced under MIT licence.</p>
 </section>
 </section>
 
 
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yWcBqBfJ5VANYfPNbMfe//xl6bZ3ZVRJRCchmFxGLxaAoEQwPD0HXdQCFu33cbjc0TTN2pq2urjGxUioHDCy0evk8vO89BMdPD0PQl3aMXL7mDMBx+Eeo6VEx86OfQa/ymlcnEZWEn/70JxgY6DO+bmlphSSF4fcH4XQ6zSuMyhIDC62a84ffh/Mnj570cSGfh1VR4P78P2PhDvaQItpKpqePQZYj2L17DzyewvqTYDCE2dk4RDEMUZTg9VabXCWVMwYWWjXXl/4PdIsVgpY/6fcIWh7ur34ZCx/9GGC3b2B1RLTRFhaSUFUFqipjcnISAOB2u7F373kAgFBIhChKXJdC64KBhVZH12F/7jenDCvLLNPHYO3vQ36ppwcRbR65XA59fTHIsoyhoQFoWuHysNVqRWenD42NTcb3Lq9PIVoPDCy0OroOaGfQ6XRpbQsRlb/lxbJAIbA88cRjxp4pzc3bIIphBAJBuN1us0qkLYCBhVbHYkE+EIS1JwrhuDevE9FdLuSXdqUkovI1MxOHosiIx+N405uuBgC4XC7s2LELdrsdoigZfX6Iio2BhVYt9d4PoPJv/+aU36NbrUjfdAvATZ+IylIqlUJPTxSKEsH4+JgxHo/HUVtbCCcXX3ypWeXRFsbAQquWvukWuL/ypcJZlvxr17LoViv0qios/F8f2fjiiGjNdF3H+PgYXnzxdxgY6Ed+aX5bLBa0t3dCkrjzLJmPgYVWr7ISM9/9IaoP3QT7C7+DbrMBuRyEpc3jtJZWzD70bWidPrMrJaLT0HUd+XweNlvhz0AymURvbwwA0NDQCFGUEAqJqKjwmFkmkYGBhc6I3tyMmcNPwP7sM3B+51uwTk7A0VCH+auuQerAnwA2/koRlbJEIrG0RX4EwaCICy7YB0EQ4PN14bzzzkcwGEJ9fYPZZRK9Bv+60JkTBGT3X4zs/oths1ngqPUgG08CuTO4i4iINkwmk0EsFoUsRzA6OmKM9/bGcMEF+wAUtsvft2+/WSUSnRYDCxHRJvbkk49DUWTjNmRBENDW1g5RDMPv95tcHdHqMbAQEW0Suq4jHp9GbW2dsbuspunI5XKora2DJIURCnEBLZUnBhYiojI3Pz8PVVWgKBFMTx/DddfdiKamZgDA3r3nYefOXWhsbOIW+VTWGFiIiMpQNruIWCwGVZUxNDRo7EZrs9kwPX3MCCzLe6cQlTsGFiKiMjM9fQyPPPJtZLNZY6ylpRWiKCEQCMHpdJpYHVFxMLAQEZW46eljmJubg8/XBQCoqamFw+FARUUFRDEMUZTg9VabWyRRkTGwEBGVoFRqwViXMjk5CY/Hg3e+8y9gsVhgsVjwtrfdgMrKSq5LoS2DgYWIqERks1n09/dBUSIYHByAttQh3WKxoKmpGZlMGm53BQCgqqrKzFKJNhwDCxFRifjtb5/FkSMvGl83NTVDFMMIBkNwu93mFUZUAhhYiIhMMDMTh6rK6OzsQnPzNgBAICCip6fH2C+Fd/gQvYqBhYhog6TTaUSjKhQlgvHxMQDA/HzSCCxNTU145zvfxXUpRCfAwEJEVES6rqOvrxeKEkF/fx/y+TyAwhb5HR0+486f5TEiOjEGFiKiInvmmf9EIjELAGhoaIQoSgiFRFRUeEyujKh8MLAQnUwyCedPfwzL5AQ0bzUWD74JekOD2VVRCUskElBVGYOD/Xj3u28FUDhrsnv3HszNzUGSwqiv5+8Q0VowsBD9sXweFf/4P+H+P1+AJZmEbrFA0DToNhvS19+E5P/8HPRK3lJKBZlMBrFYFIoiY2RkGABgsQjo7e1FQ0MrAOCcc/aYWSLRpsDAQnQ8XUfVbR+E85FvQ1jqzSIs7YUh5HJwffubsL3yMma+/2PAw9P5W9n09DSef/436OvrRS6XA1A4m9LW1o7t28Po7OzEwkLO5CqJNg8GFqLjOA4/Ctd3Hz7p40I+D9sffo+K+/4FCx/92AZWRmbTdR25XA52u90Yi0ZVAEBtbR0kKYxgUERVVRVsNgucTicDC9E6YmAhOo77S/dBt1ohLN3JcSKCpsH9wBex8NcfBWycQptdMjkPRSlskV9XV4crr7wKAFBXV4eLLno92tra0djYxDt8iIqM77ZEy3Qd9l89fcqwsswyOQFrXy/ywdAGFEYbLZtdRG9v4VbkoaFB6EuXB5PJeeRyOdiWguq5577OzDKJykI2m0VfXwyKIuPmm6+D1epa0/NseGBJJpO4+uqrMT4+ju985zs455xzNroEopNbRVgxLC4Wrw4yzW9/+2scOfICstmsMdbS0gpRlOD3B42wQkQnp+s6RkaGoSgyYrEoFpfeL7PZLFyuMgksX/jCF4yNk4hKiiAg39UNa1+vseD2ZHS7A1pHxwYVRsU0PT2NqqpK2O0OAIDNZkc2m0V1dTVEMQxRlOD1VptcJVF5iMfjUJQIFEXG/PycMe71VkMURbhca++JtaGBpaenB//2b/+Gj33sY/jUpz61kS9NtCrpd78Pnk/991N+j261In3djdCrvBtUFa23VGoB0agKWY5gcnICBw5cCUkKAwDC4TC2bduGbdtauC6FaBVSqZTRcmJiYtwYdzqdCASCCIUktLS0QhAEWCyWNb/OhgaWz3zmM7j55pvR3d29kS9LtGrpWw7B/eX7YRkaPOFaFt1ihe5yIfXXt5tQHZ2NXC5nbJE/ODgAbel2dYvFYuxCCwBudwXc7gqzyiQqC7lcDv39fVCUCAYG+lfMp46OTohiGF1d3et6CXXDAsvhw4ehKAruuecevPzyy+vynFbr2pMarY/lY7BpjkVtDeb+4zCqbnwbrJGjxh1DxuZxdbWY/8Z3IEhSya1Y33THYh1lMhk89NDXkE6njbGWlm3Grchu99pPU58Ij0Xp4LFYP7quY2xsDLIcQU+PumI+bdvWDEnajmAwhIqKkwf+szlpuSHvualUCnfffTduv/12VFZWrtvzer3r+yZDa7epjkWtBPzhJeAnP4Hw4IPA8DCE2lrg+uthueEGeNe4YGyjbKpjsUbxeBwjIyPYuXPn0ogHHR2tmJ2dxfbt27Fz507U1dUVvQ4ei9LBY7F2MzMzeOWVV3D06FHMzMwAKASPxsY6Yz7V19cXvY4NCSz33nsv6uvrcd11163r8yYSKeTz2ro+J50Zq9UCr9e9OY/F/jcU/ne8VB5IJc2p5zQ29bFYhXQ6baxLGRsbhcViQU1No9Fg8JJLDsDtdhvrUuLx4h3HrX4sSgmPxdpkMhljPo2OjhjjdrsdgUAQoiihra3dWJOy2vlUXe1e8zqWogeW4eFhPPDAA/j85z+PubnCiuGFhQXj/5PJJDxr3OI8n9eQy/EXsBTwWJSOrXQs8vk8Bgb6oSgR9Pf3GXcgCoKA1tZ2JJNpOByFT9YOhwv5vA7g1HeArW99W+dYlDoei9PL5/MYHOyHoijo71/ZcqK9vQOhkAS/32/cUadpMNaurNZpbsA8paIHlqGhIWSzWXzgAx94zWOHDh3Cnj178PDDJ98KnYjoZGT5KJ588gnj6/r6BoiihFBIhMezfpefiTYrXdcxOTkBRZERjapIpRaMx+rq6iGKYYRC4rou51irogeW7du34+tf//qKsaNHj+Kzn/0s7rrrLm4cR0SrkkgkoKoyqqtrEFzaYdjvD+J3v3sefn8AoiihoaHR5CqJysPc3BxUVYaiyIjHp43xiooKhEIiRDGM+vqGkrq1v+iBxev1Yt++fSd8bOfOncctiiMiWmlxcRGxWBSyHMHIyDAAoLl5mxFYXC4XbrnlUEm9qRKVqsJ86oGiFObTcssJm82Grq5uiGIYHR2dZ7VXSjGV2p2ZREQYHByALB9Fb29sxXX01tY2iGIYuq4bIYVhhejkNE3D0NAgVFVGLBZDLvdqy4nW1jZIUhjd3QE4nU4Tq1wdUwLLvn37IMuyGS9NRGXgpZeOoL+/DwBQW1u7dB1dQlVVlbmFEZWJY8emIMsRRKMKkslX7+Cpqakx5pPXW167dfMMCxGZJpmch6oqUFUFb3rTm4030J07dy31HpHQ2NjEsyhEq7CwkISqKlAUGVNTk8a4y+VCIBCCJG1HU1P5zicGFiLaUNnsInp7C1vkDw0NGtfRVVXG6153AQDA5+uGz8cWHkSnk81m0dcXg6LIGBwcMOaT1WqFz9cFUQyjs9MHq9VqcqVnj4GFiDZEMjmPX//6WcRiUWSzr15Hb2lphShK8PuDJlZHVD50XcfIyDAURUYsFsXi4qLx2LZtLRBFCYFACK4S35X7TDGwEFHRLC4uwuEobDLlcDiMsLJ8uUcUJVRX15hbJFGZiMfjUJQIFEXG/PycMV5V5YUkSQiFJNTU1JpYYXExsBDRukqlFowtvfP5PG688e0QBAF2uwOXXPIGVFdXY9u2lrK9jk60kVKpFKJRFYoSwcTEuDHudDoRCAQRCkloaWndEvOJgYWIztqpWs3PzSXg9VYDAMLh7WaWSVQWlueTqsro7+9bMZ86OjohimH4fF2w2+0mV7qxGFiI6KwcPfoyfvWrp5HJZIyxxsYmSFIYwWAIbvfJW80TUYGu6xgfH4OiRNDTE0U6nTYea2xsNLbI38rziYGFiM7I7OwMbDab0aunosKDTCaDyspKY3+Huro6k6skKg+JxCwURYaiRDA7O2uMV1ZWIhQqrPOqq6s3scLSwcBCRKeVTqfR06NCUWSMjY3i3HNfh4suej0AoKOjE3/6p29Da2vblriOTnS2MpmMMZ9GR0eMcbvdvtQXK4zW1raS3SLfLAwsRHRC+XweAwP9UBQZ/f29yOfzAApb4S8svNrR1WKxoK2t3awyicpCPp/H0NAAZLkwn45vOdHe3gFRDKO7uxt2u8PkSksXAwsRvYau6/j2t7+BeDxujNXXN0AUJYRConE5iIhOTtd1TE5OGLs5p1KvBv26unpjXUplJefTajCwEBHm5ubQ1xfDrl27IQjCUqPBdmQyGeM6ekNDo9llEpWF+fl5qKoMWY4gHp82xt3uCoRCojGfeAn1zDCwEG1RJ2o139DQiJaWVgDAvn37cckll/E6OtEqZLOLiMViUJQIhoeHjC3ybTYburq6IYphdHR0cj6dBQYWoi1kudW8okTQ29u7otX8H69DKYd280Rm0jQNw8NDUJQIYrHYivnU0tIKSQrD7w9yLq0TBhaiLWRsbBQ/+tEPjK9ra2sRCkll2WqeyCzT08cgyxGoqoxkMmmM19TULF1CDXM+FQEDC9EmlUzOQ1UVAMDevecBKHzqa2xsQnPzNohiuKxbzRNtpIWFJFRVgaLImJqaNMZdLhcCgRAkKYympmbOpyJiYCHaRLLZLBRFxtGjR41W8y6XG+ecswdWqxWCIOC6627kmyrRKmSzWfT390KWZQwNDRhb5FutVvh8XQiFJHR2+mCz8U/pRuB/ZaJNYGxsFLL8CkZGBpBIJKFphQV/y63mlxcAAmBYIToFXdcxMjIMVVXQ06NicXHReGz5zGQgEITb7Taxyq2JgYWoTOm6boSP3t4YIpGjcLsd8HqrEQwWbp2srq4xt0iiMhGPx6GqMhRFxtxcwhivqvJCFAu39tfU1JpYITGwEJWRVGoB0agKWY7gwgsvQmenDwAgSWHk81lceOF5cLurkc/rp3kmIkqlUohGVaiqjPHxMWPc4XAgEAghFBLZcqKEMLAQlbjlVvOKEsHAQL9xHV1RIkZgqaurxxvfeAC1tR7E40kADCxEJ5LL5ZZaThTm03LLCYvFgvb2TkiSBJ+vG3a73eRK6Y8xsBCVqFwuh6ef/iV6eqLIZDLGeGNjEyQpjGAwZGJ1ROVD13WMjo7i6NGj6OlRkU6njccaGxshioX5VFHhMbFKOh0GFqISkk6n4XK5ABTuRBgbG0Mmkzmu1XwYdXV1JldJVB4SiVn09KgYHIxhdHTCWIzu8XgQCkmQpDDq6upNrpJWi4GFyGTpdBqxWBSKIuPYsSkcOvQe2O12CIKAiy56PaxWK1vNE61SJpNBLBaFLEcwOjoCi0WA2+2AzWZHd7cfohjmfCpTDCxEJsjn8xgc7DdazS9fRxcEAePjY2hv7wAA+HxdJlZJVB7y+TyGhgaM+ZTL5QAU5lN7ewcuuOBcNDa2QhD4J6+c8egRbbCBgX489tjPkE6njLG6unpIUqHVvMfDVvNEp6PrOqamJqEoMlRVQSq1YDxWW1u3NJ8k1NR4jcXouZxmYsV0thhYiIpsbm4O2WzWWHtSU1ODdDqFiorlVvNh1Nc38NZJolWYn59f2iI/gunpY8a42708nyQ0NDRyPm1CDCxERbC4uIhYrAeKEsHIyDB8vi5cffU1AACvtxpve9v1aGpq5nV0olXIZhcRi8WgKBEMDw8ZOzfbbDZ0dXVDFCW0t3fCarWaXCkVEwML0TrRNA1DQ4NQlAh6e3tXtJrP5/PQNM0IKNu2tZhVJlFZ0DQNw8NDUFUZPT09K+ZTS0srJCkMvz8Ip9NpYpW0kRhYiNbJ4cM/Qn9/n/F1bW0tQiEJoZDEVvNEqzQ9fQyyHIGqykgmk8Z4TU3N0q39ErzeahMrJLMwsBCtQTJZuI4eDu8w9k3p6PBhfHwcwWAIohhGU1MTr6MTrcLCQhKqqkBVZUxOThrjLpcLgUAIkhRGU1Mz59MWx8BCtErZbBZ9fTEoiozBwQHoug6Hw4EdO3YBAMLh7dixYyevoxOtQjabRX9/L2RZxtDQgNFywmq1orPTB1EMo7PTB5uNf6aogL8JtHXk8xASs9CdLqCiYlU/stxqXlFkxGLRFa3mt21rQcVxz8PeI0SnVtgifwSKIqOnR10xn5qbt0EUwwgEgnC73SZWSaWKgYU2PcvIMNz33wvXg1+FZaltfPa885F6/4eQedv1wCnu1EmlFvAf//Hvxl0JXm+10Wq+urpmI8onKnszM3EoigxFkTG3NAcBoKrKa8ynmppaEyukcsDAQpua7aUjqP7zt0KYn4OwtJssANhe/B28f/k+pH96GHNf+CJgtRqt5mdnZ3DJJZcBACoqPAgGQ7Db7RDFMLZta+F1dKJVSKVS6OmJQlEiGB8fM8YdDgcCgRBEUUJLSyvnE60aAwttXskkqm9622vCCgAIy9fL//27GGluxpH9F2NgoB+apkEQBOzZcy6qqqoAAAcPvmnDSycqR/l8HgMD/VCUCPr7+4yWExaLBe3tnZAkCT5fNy+f0powsNCm5Xrk2xCmpnCiz2+TFgtetNuh2O1IffdhLDQ1A1ar0Wre4XBseL1E5UjXdUxMjEOWI+jpUZFOp43HludTMBhCRYXHxCppM2BgoU3L+Z1vAYIALK0/0QEjvIxbLPj9UiipWljATqsV/pvewVbzRKuUSMwu9fGRMTMzY4x7PB5jv5T6+gbzCqRNh4GFNi3L1CTSug7VbscrNhuCuRzOzxZ2ywzlchjOZhHOZtGRz2O+rh4ZhhWiU8pkMojFopDlCEZHR4xxm80Ov98PUQyjra2dLSeoKIoeWJ588kl88YtfRDQaxfz8PJqbm3Hw4EHcdtttxhoBovWUz+cxONiPp1xuDFRVYXn1SlYQjMDiBPCm405d60uNCYloJU3TMDg4AEWJoK+vF7lcDgAgCALa2tohimH4/X7Y7byMSsVV9MAyMzOD3bt349Zbb0VNTQ1UVcU999wDVVXxwAMPFPvlaYv51a+eRiRyFOl0CrZgCI6+GBryeezM5RDOZk/4M5q3GouXvnFjCyUqYbquY2pqcumSj4JUasF4rLa2DpIURjAo8kMnbaiiB5Zrr712xdf79u2Dw+HAJz7xCYyPj6O5ubnYJdAmtrCQXLGYL5FIIJ1OoaKiAqEbb8a+XzyOpuQCLFr+hD+vCwJS7/8QsLS9PtFWNj9faDmhKBFMTx8zxt3uCoRCIkIhEY2NbDlB5jBlDUtNTQ2AwtbMRGdqcXERsVgPVFXG8PAQbrrpFtTWFjadOvfc8xAOb0dHRycsFgts3/gOcMO10BcWVtzarFssgKZh8eprsPDRj5n1r0Jkumx2EbFYDKoqY2ho0Ngk0WazwefrhiRJaG/vZMsJMt2GBZZ8Po9cLodoNIrPf/7zOHDgANrb2zfq5anMLbeaV5QIYrHYilbzo6PDRmBpalp5xi533vmIP/403P/n83D/24MQFgqntvPhHUh94C+RvukdAN+IaYvRNG2p5UQEsVjPig+PLS2tEEUJgUAITqfTxCqJVhL05ThdZJdddhnGx8cBAJdeein++Z//eUUflrVIJFLI57X1KI/WyGq1wOt1F/VYTE1N4Yc//P5rWs1L0valVvPe1T3R4iKEY8cAlxN6TW3hludNZCOOBa1OqR6LY8eOQVEiUBQZ8/Pzxnh1dQ1EUYIkhVFdXW1iheuvVI/FVlVd7V7zXWQbFlgikcjS1udR3HvvvWhvb8dXvvIVnmak10gmk5idnUVraysAIJfL4b777oMgCJAkCTt27EBLC7fIJ1qNhYUFHD16FK+88gomJiaMcafTacyn1lZukU+lb8MCy/EikQiuvfZa/NM//ROuuuqqNT8PE7P51uvTSzabRV9fL2Q5gsHBAVRVeXHLLbcab6JTU5Oora1jwD0FfpIsHWYfi+X5pCiy0XICKGyR7/N1QZLC8Pm6YLNt/q24zD4WtNLZnGEx5bdVkiTY7XYMDAyc1fPk8xpyOf4CloK1HAtd15euo8uIxaIrWs07HE7Mzy8YbeZrauqh6+DxXgXOi9KxkcdC13WMjo5AUWT09Kgr5lNTU7OxRf7ynAK21nzivCgNZ3OKxJTAcuTIEWSzWS663eKeeeY/8fvfv2h8XVXlhSRJCIXYap5otWZm4lBVGbIsY24uYYxXVlZBksIIhSRjUTpROSt6YLntttuwa9cuSJIEl8uFSCSCL3/5y5AkCQcPHiz2y1OJKKxfUtHW1mb06+nq6kYk8gpbzROdoXQ6jWhUhaJEMD4+Zow7HA74/UGIooTW1jbOJ9pUih5Ydu/ejUcffRT3338/dF1HW1sbbrjhBrz3ve9lR9xNLpfLrWg1r2kadu/ei4svvhQA0NrahkOH3sNW80SrkM/nV8yn/NK+QoIgoKOjE6IooavLz/lEm1bRA8sHPvABfOADHyj2y1CJ0HUd4+NjUJQIenqir2k1X1//aoNBQRD45kp0CrquY2JiHIoiIxpVkU6njMcaGhohihKCwRA8nkoTqyTaGJt/iThtKF3X8ZOfPIqFpQ3allvNS1LYuBRERKeWSCSgqjIUJYKZmRlj3OPxIBgUIUlh1Nc3mFcgkQkYWGjNMpkMZLkHx46N4ZJLDgAo3Da5Y8dOzM3NQRTDaG1tY6t5olXIZDKIxaJQFBkjI8PGuM1mh9/vhyiG0dbWzvlEWxYDC52RfD6PoaEByLKM/v5eaFoebrcD3d0iWloKd31dcMFFJldJVB40TcPg4AAUJYK+vl7kcjkAhculra1tEMUw/P4A1/sRgYGFVimRmMVLL/3+Na3mGxsbcP75565Ym0JEJ6frOqamJqGqMlRVMS6fAkBtbR0kKYxgUERVVZWJVRKVHgYWOild143bIlOptLFnynKreVGUsG1bM+rqKhGPJ7kpE9EpzM/PQ1UVKEoE09PHjHGXy23Mp8bGJt6KTHQSDCy0wnKreUWJwOv14g1vKKxNaWpqwu7de9He3oH29g5ji3y+uRKdXDa7CFXtgaJEMDQ0iOVOKDabDT5fNyRJQnt7J1tOEK0CAwtB0zQMDw8ttZqPIZcrtJp3Op245JI3wGq1QhAEY/8UIjq5wrqUITz7bB9+//uXkcm8ukV+S0srRFFCIBCC0+k0sUqi8sPAssUdOfICjhx5Aclk0hirqamBKIYRCon85Ee0StPTx6AoMlRVxsJCEm63A9lsFtXV1RDFMERRgtdbbXaZRGWLgWWLWVhIwul0GUFkcTGDZDIJl8uFQCAESQqjqamZl3qIViGVWjDWpUxOThrjbrcLu3efg7a2bjQ0cF0K0XpgYNkCstks+vt7IcsyhoYGcOWVV8HvDwAAwuGdqK9vRGenb0u0mic6W7lcDn19vVCUCAYHB6BphcXmFosFPl8XQiEJgYAfjY3VXIxOtI74F2qT0nUdIyPDUFXlNa3mJybGjcBSVVXF2yeJTkPXdYyOjkBVZfT0RJHJZIzHmpqaIYphBIMhuN1uAIDNxs3diNYbA8smlE6n8Z3vfGtFq/mqKi8kSUIoJKGmhq3miVZjdnYGihKBoihIJGaN8crKKoiiBFEMo7aW84loIzCwbAKpVAqTkxPo7PQBAFwuF1wuFzKZNAKBEERRQktLK6+jE61COp1GT48KRZExNjZqjNvtdmM+tba2cT4RrUImk0FPj4q+vl686U1vPqvnYmApU7lczmg1PzDQD0EQcOjQe4xbJQ8e/BN4PJXshky0Cvl83phP/f19yOfzAAr7DHV0dEIUJXR1+TmfiFYhn89jcLAfiqKgv//VlhODg/1obDxnzc/LwFJGdF3H+PgYFEVGT4+KdDptPNbY2Ihkct4ILLzsQ3Rquq5jYmICihJBNKoinU4Zj9XXN0AUJYRCIjyeShOrJCofiUQCv//9i4hG1RUtXOrq6iGKhTtQzwYDSxl5+eU/4KmnfmF87fF4EApJkKQw6urYy4doNRKJBKLRwq3I8XjcGK+oqEAoJEEUJTQ0NJpYIVH50DTN6CCey2Xx0ktHABRauIiiCFEMo76+YV0uoTKwlKjlVvOVlVXo6OgEAHR3d+PXv34G3d2FVvOtrW1sNU+0CouLi4jFopDlCEZGho1xm82O7u5uiGIY7e0dnE9Eq1CYT4WWEx6PB1dc8ScACmdS9u49D62tbejo6Fz3+cTAUkLy+TyGhgYgy7Jx3a+jo9MILB5PJd71rvdyvxSiVdA0DUNDg1CUCHp7Y8Z1dEEQ0NraBlGU4PcH4XA4TK6UqPSdrIWLzWbHZZdljfVd+/dfXLQa+JevBExOTixt6a2suO5XW1uH9vaOFV2TGVaITm1qahKKEoGqKlhYOH4+1S61nJC49xDRGfj971/Eiy/+7iQtXKQNW4zOv34l4Jln/tM4Te12Vxit5hsaGnnrJNEqJJPzS1vkyzh2bMoYd7ncCIVEhEISmpq4RT7RaiwsJGG3O4wgks/njRYuwWDh75MZLVwYWDZQNruIWCwGVZVxxRVXwu2uAABs374TFRUVEEW2midarWx2Eb29hS3yh4YGoes6AMBqtcLn64YkSejo8HE+Ea1CNptFX18MiiJjcHAAV1xxJUIhCQAgioUNRzs7zZ1PDCxFpmkaRkaGl6779SCbLVz3U1UFu3fvBYClHTMlE6skKg/LLSdkOYJYLGrMJwBoaWlFKCQiEAjB5XKZWCVReVieT4oiIxaLrmjhMjk5YQQWj6cS3d3m397PwFIkqdQCXnzxBaiqvOK633Kr+a4uv4nVEZWX6enppXUpMubn541xr7faCPzV1TXmFUhUZrLZRXzrW99Y0cKlMJ/Ekm3hwsCyjo6/Hx0Q8NJLR5DP5+FyuRAIhCBJYVOu+xGVo1RqAdGoClmOYHJywhh3Op0IBIIQxTC2bWvhfCJahVQqhYmJMfh83QAAu92ByspKLC5mymY+MbCcpWw2i/7+XsiyjGx2EX/2Z9cBANxuNy64YB+qq2vQ2enj3T1Eq5DL5dDXV1iXMjg4AE3TAAAWiwWdnT6IYhg+XxfnE9Eq5HI59Pf3GS1cAODQofcYXcUPHDgIt7uibFpOcNavwXKr+eUt8o+/7jc3N2fcMnnuua8zq0SisqHrOsbGRqEoEfT0RJHJZIzHmpqaIYoSgsGQsUidiE7u1RYuhfl0ohYuy4HF6602q8w1YWA5Q6oq49e/fnbFdb+qKu9S3xHu70C0WrOzM1CUCBRFQSIxa4xXVlYt3dofRl1dnYkVEpUfWT6KJ554zPi6srLSaDlR7i1cGFhOI5VKQRAE464DQRAwN5eAw+GA3x9kq3miM5BOp9HTo0JRZIyNjRrjdrsdfn8QkhTmfCJapUwmg54eFRUVFcaNHD5fN5xOJ7q6ujddCxcGlhPI5XJGq/mBgX6cf/6FOO+88wEUfhmuvPJN8Pm6y+a6H5GZ8vn80nwqtJzI5/MACuG/o6MToiihq8vP+US0Cidq4dLS0moEFrfbjXe9672bcv8hBpYlhVbz45DlCHp61BXX/SYnJ41/ttvtCAZFM0okKhuF+TQBRYkgGlWRTqeMx+rrG5YuoYrweMzf24GoHExOTkCWC/Pp+BYudXX18Pm6V7Rw2YxhBWBgAVB4c/3ud7+1Iph4PB7jul99fYOJ1RGVj7m5OaiqDEWJIB6PG+MVFRXGfGpoaDSxQqLy9JvfPGvc6eN2Vxj7pWylFi5bMrBkMhkMDg4gGAwBKJyarqurRzw+A7/fD1EMo62tfdNc9yMqpkKr+ShkOWL0xAIKXVy7uwvX0dvbOzifiFZhuYWLokTwxjdeYdzIEQ7vgMPhgCiG0dHRuSXn05YJLJqmYXBwAIoSQV9f4bpfTU2N8Wlv3779uPTSN8BuZ6t5otPRNA1DQ4NQlAh6e3uNVvMA0NbWDlGU4PcH4XBwPhGdjqZpGB4eWmrhEjPmUzSqGNtjBAJBBAJBM8s03aYOLLquL7Wal6GqyorrfrW1dSvWqfBaOtHpFeZTBKqqYGHh+PlUa7Sa5639RKuTSqXw4ou/e00Ll5qaGohiGIFAyMTqSs+mDiwjI8P4wQ++Z3ztdlcgGAxBFCU0NrLVPNFqJJPzUFUFiiLj2LEpY9zlciMUKlxHb2rifCJajXw+byyKtVqt+MMfXkIul2ULl1XYNIFl+bqfrusIh7cDALZta0FlZRWam7dBkiS0t3du2tXTROspm11Eb28vVLXQal7XdQCFN1ifrxuSJKGjw9xW80Tl4vgWLqnUAq6//iYAgMPhwL59F6GqyovOTs6n0ynrwHL8dfRYrAfZbBaVlZWQpDAEQYDVasU73nErfwmIVmG51bwsRxCLRZHNvrouZdu2FoiihEAgZGyiSEQntzyfFEVGLBZd0cJlZiZudEPevXuvSRWWn7INLKlUCg8++FUkEnPGWHV1NUQxjFwuZ2xCxbBCdGrT09NLtyLLmJ9/dT4VWs0XbkWurq4xr0CiMhONKnj22V+9poWLJBVauCyHFTozZRtYMpkM5ufn4XQ6l9alhNHcvI3X/YhWIZVaQDSqQpYjmJycMMadTmfZtJonKhWpVGFjxOWmgjabzWjhEggU1k22tLRyPp2lsg0sdrsdV131ZrS1dbLVPNEq/HGreU3TAAAWiwWdnT6EQhK6uro5n4hW4Y9buOzdex4uvPAiAEBHhw9XXnkVfL4utpxYR0V/Z/rxj3+MH/zgB3j55ZeRSCTg8/lw66234rrrrjurtOnxeBAIBJHLaetYLdHmcnyr+WhURSaTMR5rbGyCJIURDIbgdleYWCVReTh+PvX0RFdsjTE9fcz4Z6vVamxMSuun6IHlq1/9Ktra2nDHHXegtrYWzzzzDD7xiU9gbGwMt912W7FfnmhLmp2dWdp/SMbs7KwxXllZaeyXUldXZ2KFROVF13U88si3MTExboxVVlYaLSfq6upNrG5rKHpguffee1e8Me7fvx8zMzP4yle+gv/yX/7LltxemKgY0uk0fv/7Hvz2ty9gePjVLfLtdjv8/iAkqdBqntfRiU4vk8mgv78PoZAIQRAgCAIaGhoQj0/D7w9AFAvziX/DNk7RA8uJPsVt374dDz/8MBYWFlBZyR1midYqn89jcLAfsixjcLAPDocVqdQiBEFAR0cnRLGwLoUtJ4hOL5/PY2hoALIso7+/0MLF6/Vi27YWAMAFF+zD619/CeeTSUxZXff888+jubn5rMOK1cpka7blY8BjsXF0XTdazRdaThTuULBYBDQ0NKOrq9BzhO0mzMN5UTpOdyyWW7jIcgSKsrKFS0NDPTQtD5ut8LNeL9tOnK2zOcG74YHlueeew6OPPoqPfexjZ/1cXq97HSqi9cBjUXyJRAJHjx7FK6+8gunpaWO8oaEW4XAYO3bsQFNTk4kV0h/jvCgdJzsWw8PD+I//eMT4ur6+ZsV84iXU0rGhgWVsbAy333479u3bh0OHDp318yUSKeTzvEvITFarBV6vm8eiSBYXFxGLRSHLEQwPDxtb5NtstqXr6JLRan75EySPhfk4L0rH8ccinU6jp6cHuVwOu3adAwBwu6vhdFagoaERkhRGR8erLVxmZhZO9dS0BtXV7jWv+9mwwJJIJPD+978fNTU1uOeee9ZloVI+r/G25hLBY7F+jm850dvba7SaB4DW1jZIUhh+fxAOh2Pp+2HsqQLwWJQSHgvzaZqGvr4+/OY3v0M02oNcLgu3uwKhUNgIJjfc8A7jb5Kug8esiJY+c63JhgSWdDqND37wg5ibm8O3vvUttp8nOoFjx6YgyxFEo8oJW82HQhK8Xq+JFRKVj+npacjyUfT0KNC0LFKpRWiajpqaGoRCEjTt1a7JvNOnPBQ9sORyOXzkIx9BLBbDQw89hObm5mK/JFHZWFhIQlUVKIqMqalJY9zlchstJ3gdnejMRSIv48iRF2GxCKit9SIQkBAMSmhqauZ8KlNFDyx33XUXnnjiCdxxxx2Yn5/Hiy++aDy2Y8cO47Q20VaRzWbR1xeDosgYHBww1qVYrVb4fF0QxTBbzROtUjabRX9/L2RZxp49e9He3gEAEMUwEokEtm/fjr17d2JuLsNLPWWu6IHl6aefBgDcfffdr3nsscceQ3t7e7FLIDLdqVrNNzdvgySFEQiE4HK5TKySqDzouo7R0REoioyeHtWYT263ywgsDQ2NuOqqt8Bmsyz1x8qc4hmpHBQ9sDz++OPFfgmikhWPx6EoESiKjPn5OWPc662GKIpsNU90BnK5HH73u+egKDLm5hLGeFWVF6JY2CKfNi+2ZSVaZ6lUCtGoCkWJrOg74nQ6EQgEIYphbNvWwuvoRKuQy+WMDuJWqxWxWBRzcwk4HA4EAiGIooSWllbOpy2AgYVoHeRyOfT39xmt5pdvM7ZYLEtb5IfR1dVtvPES0cnlcjkMDPRDUSIYGxvDLbccgt1uhyAIOP/8fQB0+HzdsNvtZpdKG4jvnkRrdHyr+WhURSbz6jXyxsYmSFIYwWAIbneFiVUSlQdd1zExMQ5ZjqCnR0U6nTYeGxkZhs/XBQAIBkMmVUhmY2AhOkOzszNQFBmqKmN2dtYYf7XVfPiETT+J6MSGh4fwy18+gZmZGWPM4/EszScJ9fUN5hVHJYOBhWgV0uk0YrEoFEXG6OiIMW632+H3ByGKElvNE61SJpNBJpMxNkL0eDyYmZmBzWaH3++HKIbR1tbO+UQrMLAQnUQ+n8fgYD9kWcbAQB9yuRwAQBAEtLd3QBTD6O7uZqt5olXI5/MYGhqAosjo6+tFZ6cPb3rTmwEANTW1uPrqt6CtrZ3ziU6KgYXoOLquY3JyYumSj4J0OmU8VldXv7RFvojKykoTqyQqD7quY2pq0phPqdSrzQQTiQQ0TTPOonR1+c0qk8oEAwsRgLm5OaiqDEWJIB6PG+MVFRUIhUSIYhj19Q28dZJoFXRdhyAI+PnPf4JoVDXGC00HRYRCIhob2XKCzgwDC21Zi4uLiMV6oCgRjIwMG1vk22w2dHV1QxQLreZ5HZ3o9LLZRcRiMXR1dRstV7Zta0FfXy98vm5IkoT29k62nKA1Y2ChLUXTNAwNDUJVZcRiMeRyWeOx1tY2SFIY3d0BOJ1OE6skKg+api21nIigp6cHuVwWl19+BcLhHQAASdoOUQxzPtG6YGChLeHYsSnIcgTRqIJkMmmM19TULK1LkYw7Fojo1Kanj0GWI1BVecV8qq6uhsXy6hkUNrel9cTAQpvWwkISqqpAUWRMTU0a4y6XC4FACJK0HU1NvI5OdCYWFpJ4+OFvGJdQC/Op0HKiuXkb5xMVDQMLbSrZbBZ9fTEoiozBwQHjTdVqtcLn64IohtHZ6eN1dKJVyGaz6O/vRTw+jQsuuAgAUFHhQUeHD1arxZhPbDlBG4G/ZVT2dF1fuo4uIxaLGq3mgcKiP1GUEAiE4HK5TKySqDzouo7R0REoioyeHhWLi4sQBAHbt+8ybud/85uv4ZkU2nAMLFS24vE4VFWGLEcwPz9njFdVeSFJEkIhCTU1tSZWSFQ+EolZyPJRyLKMubmEMV5ZWQVJCsNieTWgMKyQGRhYqKykUilEoyoUJYKJiXFjnK3mic7O8PAQnnvutwAK8+n4lhOcT1QKGFio5B3far6/vw+apgEALBYLOjo6IYph+HxdbDVPtAr5fN6YT21tHdi16xwAgN8fRF9fL4LBELq6/JxPVHIYWKgk6bqO8fGxpf0doitazTc2Nhpb5LvdFSZWSVQedF3HxMQ4ZDmCnh7VmE9zc3NGYHE6nbj66mvMLJPolBhYqKQkErNQlMIW+bOzs8Z4ZWWl0Wq+rq7exAqJyssLL/wOkcjLmJmZMcY8Ho8xn4jKBQMLmS6TyaCnR4WiyBgdHTHG7XY7/P4ARDGM1tY2bpFPtArZ7OKKjscTE2OYmZmBzWaH3++HKIbR1tbO+URlh4GFTLHcal6WZfT39yKXywEo3H3Q1tYOUQzD7/ez1TzRKmiahsHBAShKBH19fbjxxptRXV0DANi9ey98vm74/QHuPEtljYGFNoyu65icnICqKq9pNV9XV2+sS1ne64GITk7XdUxNTUJRZESjChYWXp1PfX292LPnXABAS0srWlpazSqTaN0wsFDRzc3NIRpVIMsRxOPTxvhyq3lRlNDQ0MhbJ4lWKR6P46c//TGmp48ZYy6X25hPjY1NJlZHVBwMLFQUy63mFSWC4eEhY4t8m82Grq5uiGIY7e0d3CKfaBWy2UXMzc0ZC84rKysxPz8Hm80Gn68boigubZfP+USbFwMLrRtN0zA8PARFiSAWiyGXyxqPtbS0QpLC8PuDbDVPtAqapi21nIggFutBZWUlbrrpFgiCALvdjquvvgb19Q2cT7RlMLDQWZucnMRvfvMCIpGjK1rN19TULK1LkeD1ek2skKh8TE8fg6LIUFUZ8/PzxrimaUilFlBR4QEAtLa2mVUikSkYWGhNFhaSUFUF0aiCZHIWqdQiNE1fajUfgiSF0dTUzHUpRGfgN795Fs8//1vja6fTiWAwBFEMo7l5G+cTbWkMLLRqy63mZVnG0NAANE2DxSLA43HB7w8gEBDZap5olXK5HPr6etHQ0GA06WxpaYXFYoHP14VQSILP18X5RLSEM4FOSdf1pevoMmKxKBYXF43Hmpu3Yfv27bjggr1IpzXkcpqJlRKVPl3XMTo6AlWV0dMTRSaTwZ49e/H6118KAGhra8ehQ+9mywmiE2BgoROKx+NQVRmKsrLVfFWVF6JY2NK7pqYWNpsFbrcb6XTyFM9GtLXNzs5AUSJQFAWJxPEtJ6qMNSlAoaEnwwrRiTGwkCGVSiEaVaGqMsbHx4xxh8OBQCAEUZTQ0tLK6+hEZ0DTNHzve981Nkq02+3GfGptbeN8IlolBpYtLpfLGa3mBwb6kc/nARQ+6bW3d0KSJPh83Ww1T7QK+XweAwP9GBrqx9ve9lYAhbkkihLi8WmIooSuLj/nE9EaMLBsQSdrNQ8AjY2NEMUwgsHQilPVRHRihfk0AUWJIBpVkU6nYLEIOP/8vaiqKmz0tn//xTyTQnSWGFi2kERi1tjf4USt5iUpbOykSbSVWCw9cLu/Dqv1KAA7stn9SKdvga7XnvRnFhaSOHr0FShKZMV8qqioQDi8HV6vF0sbPJdxWMnAZnsBgpCCpnUgnw+aXRBtYQwsm1wmk0EsFoUsRzA6OmKM22x2BAIBiGIYra1tbDVPW1QOlZV/A7f7S9B1K4A8AAEOxw/h8dyFubn/F5nMO0/4k4lEAr/5zbMACvPJ7/cjFJLQ3t4Bh8OGmhoP4vFyXYyeQkXFP8Lt/hIslhljNJu9EMnkx5HNXmFeabRlMbBsQvl8HkNDA1AUGX19vcjlcgAKn/La2tohimH4/X7Y7Ww1T1tbZeXfwOX6MgBAEPJLo4XTIrqegdf7XzAz40Q0egFUVYbbXYGLLy7cgtzcvA2SFEZrazv8/gAcjs0yn1KoqflT2Gy/hSCs3KrAZnsO1dV/jrm5e5HJvMOk+mirYmDZJI5vNa+qinFHAgDU1tZBkgpb5FdWVppYJVHpsFqjcLu/dMLHdB2YnLTg5ZftOHr0TkxNfRiAAKfTiX379sNms0EQBBw4cOXGFr0BPJ7PnjCsAIAgaNB1oKrqw8hmL4am+UyokLYqBpYyNz8/D1WVIcsRxOPTxrjbXYFQSEQoJKKxsamMr6ETFYfL9TXouvW4MysFf/iDDc8/78DU1HLn43lUVg6hu/sahELSJu+InILL9eUThpVlglAIdG73V5BM/t3GlUZbHgNLGcpmFxGLxaAoEQwPD0FfWtlns9nQ1dUNUZTQ3t65yd9Yic6O1foKgDwWFwGbDVhexhWPWzA1ZYXFoiMQyGHHDg2NjS1YXLzM1Ho3gt3+a1gsc6f9PkHIw+H4AQMLbSgGljKhaRqGh4eWWs3HkMtljcdaWlohSWH4/UG2midaBV3XMTioQ1VdUFUb3vzmFILBwpmWXbuyqK7WEQpl4XYDum5BMrk19k0RhIXTf5PxveW6oJjKVdEDS39/P7785S/jyJEjUFUVfr8fP/zhD4v9spvG9PQxyHIEqiojmXz1DaKmpgahUGGLfK+32sQKicrH9PQ0FCUCVVWQyVTAbrdDEIC+PpsRWGprddTWvvqBQBA0ZLP7zSp5Q2la26q+T9ct0LSOIldDtFLRA4uqqnjyySexZ88eaJpmXL6gk1tYSEJVFaiqjMnJSWPc5XIhEAhBksJoamrmuhSiVcpkMviP//h3TE5OGGNO5/nYs+cx7NiRQmvridds6LoVudwu5HKv26hSTZXL7UYuF4bVKkMQTvVerSGV+ouNKosIwAYElgMHDuDgwYMAgDvuuAN/+MMfiv2SZSmbzaK/vxeyLGNoaACaVngDtVqt6Oz0QRTD6Oz0sdU80SrkcjkcOzaF5uZtAACn0wld12GxWIz55PN1obKyBlVVf4UTfY4q7MviwPz8PRtbvKkEJJN/i+rqQyf9Dl23QtPakMn8+QbWRbQBgYUbkp2crusYGRmGqiro6VGxuLhoPNbcvA2iGEYgEITb7TaxSqLyoOs6xsZGoSgR9PREkc9reNe73mPsj/LGNx5AZWXlim7I6fRfQNfd8HjuhNU6vhRSdAiChnx+O+bmvoBcbq85/0ImWVz8M8zP3w2P5+MALMZdVLouANChaa2YmfkPAOwqTRurrD+uW63lGYZmZuKQ5QgURUYikTDGa2qqIYqFLfJrak6+JXgpWT4G5XosNpOteixmZ2cgyxHIsoxEYtYYr6qqQjKZQEVFEwCgpWXbCX8+n387EokbYLf/FBZLYWv+XO71yOdfB0DAWk5qlvuxyGZvQyJxAE7nF+Fw/AhACprWiUzmvVhcvAkWiwfl8lm03I/FZnM2KxnKOrB4veVz5iGVSkGWZbzyyisYHR01xqurKyFJErZv34729vayXZdSTsdis9tKx+IPf/gDfvKTnxhfe70eiKKIHTt2oKOj4wzn0w3rXl95H4sLlv5XYLEUbv/2lGlP1PI+FgSUeWBJJFLI50++wZHZcrkc+vv7IMsRDAz0I58vnFq1WCzo6OiEJIXR1dVttJqfmVn9LYWlwmq1wOt1l/yx2Ao2+7HI5/Po7++Dy+VCa2vhbhavtwGZTA7t7R2QpDC6u/0lMZ82+7EoJzwWpaW62r3mpSJlHVjyeQ25XGn9AhZazY9DliPo6VGRTqeNxxobGyGKYQSDIVRUvPoxpdT+HdaiFI/FVrWZjkVhPk1AUSKIRlWk0yl0dvrwlre0AADcbg9uvfU9cLlcxs+U0r/7ZjoW5Y7HojSczY3CZR1YSkkikYCqyq9pNe/xeIz9UurrG8wrkKiMJBIJRKMKFCWCeDxujFdUVKCurh66rhuXe44PK0S0eTGwnIVMJoNYLApZjmB0dMQYX241L4phtLW1804pojP0+OM/M+aUzWZHd3c3RDGM9vYOzieiLarogSWVSuHJJ58EAAwPD2N+fh6HDx8GAFx44YWoq6srdgnrStM0DA4OQFEi6OvrRS6XAwAIgoC2tnaIYhh+vx92+2ZpNU9UPJqmYWhoEKoq4/Wvv9S4hV+SwrBYLEvzKWDcmkxEW5egF3nr2aGhIVxxxRUnfOzrX/869u3bt+bnjseTG3JNUtd1TE1NQlFkqKqCVOrVxXy1tXWQpDBCIQmVlZVFr6XU2GwW1NZ6NuxY0MmV07EozKfCFvkLC4X5dMklb8A55+wGgBWXfMpROR2LzY7HorTU1XnWfIt50c+wtLe3Q5blYr9MUczPz0NVC9fRp6ePGeNudwVCIRGiKKGhobGs31iJNkomk8HRoy9DUWQcOzZljLtcbgSDIbS2thpjnFNE9Me4huWPZLOLiMViUFUZQ0ODRu8jm80Gn68bkiShvb0TVqvV5EqJSt/xZ0o0LY9f//pX0DQNVqvVmE8dHT7OJyI6LQYWFK6jj4wMQ1EiiMV6kM2+2qm1paUVoighEAjB6XSaWCVReVhuOSHLEaRSKbzlLW8FUDgzuXfveaisrEQgENpUd/cIwjSczu/Aah2ArjuRz/8JgBNfCieitdnSgWV6+tjSuhQZ8/Pzxnh1dTVEMQxRlOD1VptYIVH5mJ6eXrq1X8b8/JwxnkjMGvNo3779ZpVXJHl4PJ+B230PgCwKb6k6BOEfAeyCxfIAgLCpFRJtFlsusKRSC8a6lMnJSWPc5XIhEAhCFMNobt7Ga+hEq9TXF8Nzz/0Wk5MTxpjT6TTmU1WV18Tqiquy8r/B5XoAgrB870L2uEePoqrqIGZmnkA+HzKjPKJNZUsElmw2i/7+PihKBIODA9C0wkpxi8UCn68LohhGZ6cPtrV0OSPaYnK5HHRdM27dz2azmJycgMViQWenD6GQhK6ubpPmUx52+y9gtQ5C191YXLwcut5UlFey2Z6H2/3lU9YiCEl4PP8dicTDRamBaCvZtH+hdV3H6OgIFEVGT4+KxcVF47GmpmZji/zlfR+I6OR0XcfY2CgUJYKenijOPfd1OPfc1wEAurr8uOSSy5bmU4VpNbpcX0NFxf+A1TpmjOm6FZnM2zA//4/Q9fp1fr0vQ9dtEITcSb9HEPJwOH4Ci2UImta+rq9PtNVsusAyMxOHqsqQZRlzcwljvLKyytgvpba21sQKicrH7OwMFKWwLiWRmDXGh4YGjcBit9txzjl7zCoRAFBR8Tl4PJ95TZ8SQcjD6fwebLYXMDPz83UNLXb7r04ZVl6tQYfN9gIWF7dqYCmENofj5wDS0LQupNPvYICjM7YpAks6nUY0qkJVZYyNjRrjDocDfn8QoiihtbWN61KIVknXdfzoRz/A4OCAMWa32+H3ByFJYaNbcimwWl+Gx/MZAMCJprgg5GG19sHj+TTm5/+/dXzlM9lzs6j7c5Ysm+3X8HrfBat1BLq+/OdGR0XF/0A6/W7Mz/8jALuZJVIZKdvAous6YrEeHD16FP39fcjn8wAKG051dPggiiK6ul5tNU9EJ5fP5zE2Noq2tsKnXkEQ4HK5luZTJ0SxsC6lFFtOuN1fWtWlGZfr35BM3gVdX587/3K5c2G1DqzqLEsut2tdXrOc2GxHUFPzViwvRP7j/04u11cgCHOYm/sSAH6YpNMr28CSSCTw4x//CJpW+OTS0NAIUZQQDIbg8Wy9LfKJzpSu65icnDBaTqTTKdx00ztQV1e4bHLhhRdh//6LS34+ORw/X+WlmTRstueRzR5Yl9dNp98Hl+u7p/weXbcim70UmuZfl9csJx7PnQCyEIT8CR8XBB0u17eRSn0AudzaW7TQ1lG2gUXXdXg8Hvj9IUhSGPX1DWaXRFQW5ubmlvZLiSAejxvjFRUVmJtLGIGlfPYgyqz6OwVh9d97Otns65FOvw1O5/chCCfqUWMBYEcy+el1e81yYbH0wOF48rTfp+s2uN1fwtwcAwudXtkGFo/Hg0OH3g2NvayIVm10dATf//4jK1pOdHf7EQpJ6OjohMWytqZkZsrnRVgskyf9JL/ye4Pr+MoC5ua+CF2vgsv1IAoBpTBeOOPTjLm5B5HLmbsg2Qw220ur+j5ByMFme77I1dBmUbaBxW63w2JZNPZUIaKVNE3D0NAgstksAoHCH+qmpma4XG7U1tZCksLw+4NwOEpvXcqZSKffA4fjl6f8Hl23IJe7sAgbuDkwP/8vWFj4v+FyPQSrtR+67kY+fyUqK69HPp8BsBXfo85kTQrXr9DqlG1gIaITm5qawiuvvAJVlbGwsICqKi/8/gAEQYDVasU73nFr2YeU42Uyb0U2uwc22x9OeJZF1wUAApLJTxStBk3zYWHhb42vbTYLCm+v63cJqpzkcnuh68JxOwCfWGGNDy8H0eowsBBtAsnkPGKxKAYHYxgcHDEWo7tcbvh8XcjlssYdPpsprBTYMTv776iuvh52+/PQdSsEIb+0J4sAwIFE4gFks5eaXOfWoWk+LC4ehMPx+Ckv1QlCHun0+zawMipnDCxEm8Bzz/0GkcgrcLsdsFqt6OrqgiRJ6OjwwWq1ml1e0el6PWZmHoPd/jhcrgdhtfZC1yuwuPhmpNO3rPsut3R6yeT/hN1+OYDUSc98pdN/gVzuvI0vjsoSAwtRGdF1HSMjw1AUGTt27EJzczMAQBTDmJmJ44ILzkVTUztsts12FmU1LMhmDyKbPWh2IQQgn5cwM/MTeL1/AZtNXdo4TgCQB2BFKnUbkslPmlwllRMGFqIyEI/HoSgRKIqM+fk5AIDVajUCS0tLK6677gbU1noQjyeRy23FhZ5UavL5cxCPPwe7/Wk4HI8BSEHTfEinb+RZLzpjDCxEJSqXy+Ho0VegKBFMTIwb406nE4FAYYt8otInIJu9BNnsJWYXQmWOgYWohOi6bvS8slqtePHF5zE/Pw+LxbK0RX4YXV3dsNk4dYloa+G7HpHJdF3H+PgYFCWCkZFh3HjjO2CxWCAIAs4993xoWh6hkAi3u8LsUomITMPAQmSSRGIWshyBqsqYnZ01xoeGBtHZ6QMA7Np1jlnlERGVFAYWog02NjaKZ599BqOjI8aY3W6H3x+AKIbR2tpmYnVERKWJgYWoyPL5PBYXF+F2uwEU+veMjo5AEAS0t3dAFMPo7u42NnYjIqLXYmAhKgJd1zE5OQFFkaGqCnw+Hw4cuBIAUF/fgDe84XJ0dnahsrLS5EqJiMoDAwvROpqbm4OqylAUGfH4tDE+NjYKTdOMxbQ7duwysUoiovLDwEK0Tn7xi8cRibwCvdDEBjabDV1d3RDFMDo6OmGxWEyukIiofDGwEK2BpmkYHh7Ctm0tsNvtAICqqirouo7W1jZIUhjd3QE4nU6TKyUi2hwYWIjOwLFjU5DlCKJRBclkEldeeRWCwRAAYMeOXQiFJHi9XpOrJCLafBhYiE5jYSEJVVWgKDKmpiaNcZfLjcXFjPG12+027gQiIqL1xcBCdArJ5Dz+9V+/Bk0rNBO0Wq3w+bogimF0dvpgtVpNrpCIaGtgYCFaous6RkaGMT09jXPO2Q0A8Hgq0djYBEEQIIoSAoEQXC6XyZUSEW09DCy05cXjcShKBIoiY35+DlarFaGQaASTt771z4yFtUREZA4GFtqSUqkUolEVihLBxMS4Me5wOBAIhJDL5YwxhhUiIvMxsNCWpKoynn76KQCAxWJBR0cnRDEMn6+LAYWIqAQxsNCmpus6xsfHoCgRtLa2IRgUAQDBYAiqqiAUEhEMhlBR4TG5UiIiOhUGFtqUEolZKIoMRYlgdnYWQGGtynJgqajw4LrrbjSzRCIiOgMMLLSpRCKvIBI5itHREWPMbrfD7w9AFMMmVkZERGeDgYXKmq7rEATB+FpVFYyOjkAQBLS3d0AUw+ju7obd7jCxSiIiOlsMLFR2dF3H5OQEVFVBNKrg+utvgsdTCQDYvXsP2ts7EQqJqKysNLlSIiJaLxsSWHp6evCZz3wGL7zwAjweD6699lp85CMfgcPBT720enNzc4hGFchyBPH4tDHe0xPF7t17AQA+Xzd8vm6TKiQiomIpemCZnZ3Fu971LnR1deGee+7B+Pg47r77bqTTaXzyk58s9svTJjAzE8dTTz2J4eEh6LoOALDZbOjq6oYohtHe3mFyhUREVGxFDyzf/OY3kUwm8S//8i+oqakBAOTzedx111344Ac/iObm5mKXQGVG0zQsLCwYl3Tc7gqMjY1C13W0tLRCksLw+4NwOp0mV0pERBul6IHll7/8Jfbv32+EFQC4+uqr8alPfQpPP/00/vzP/7zYJVCZmJ4+BlmOQFVlVFR4cP31NwEAnE4nDhy4Eo2NjfB6q02ukoiIzFD0wBKLxXDdddetGPN6vWhsbEQsFjur57ZaLWf183T2lo/BWo/FwkISilJYlzI1NWmM67qGxcU0KioqAACSJJ59sZvc2R4LWj88FqWDx6K0HHdT5xkremBJJBLwer2vGa+urjY29Forr9d9Vj9P62ctx+LZZ5/Fr371K2iaBgDweFzw+/3YsWMHuru7YbPxJra14LwoHTwWpYPHovyV9V+ERCKFfF4zu4wtzWq1wOt1n/ZY6LqOkZFhVFfXGGtTbDY3ksk0mpu3QZLCCAZDcLsLbypzcxkAmY34V9g0VnssqPh4LEoHj0Vpqa52w2JZ29muogcWr9eLubm514zPzs6iuvrs1iPk8xpyOf4CloKTHYt4PA5VlaEoMubmEjj//AtxwQX7AABtbZ246aZbUFNTa3w/j+fZ47woHTwWpYPHojQs3ei5JkUPLH6//zVrVebm5jA5OQm/31/slycTpFIp9PREoSgRjI+PGeOFfXde/W212WwrwgoREdHJFD2wXHbZZbjvvvtWrGU5fPgwLBYLLr744mK/PG0wTdPwzW8+hHQ6BQCwWCzo6OiEKIbh83XBbrebXCEREZWjogeWm2++GQ8++CA+/OEP44Mf/CDGx8fxuc99DjfffDP3YClzuq5jdHQUr7wyju3b9wAoBJTubj+mpiYgioV1KRUVHpMrJSKiclf0wFJdXY2vfe1r+PSnP40Pf/jD8Hg8uP7663H77bcX+6WpSBKJWSiKDEWJYG4uAbfbgYaGFtTVNQIALrnkMt7hQ0RE62pD/qoEAgF89atf3YiXoiLJZDKIxaKQ5QhGR0eMcYfDgR07dsBme/VSD8MKERGtN/5loVWZmBjHL37xOABAEAS0tbVDFMMQxSCammoRjye5Ap+IiIqGgYVW0HUdU1OTUBQZbrcb5513PgCgra0d7e0daG/vRCgkHreXCnePJCKi4mNgIQDA/Pw8VFWGLEcQj08DACoqKrB373mwWCywWCx461v/zNwiiYhoy2Jg2eJisR68/PJLGB4egr60o4/NZoPP1w1JkkyujoiIqICBZYvRNA2CIEBY6kA1MjKMoaFBAEBLSyskKQy/Pwin02lmmURERCswsGwR09PHIMsRqKqMK674E7S1tQMAtm/fAZfLBVGU4PWeXasEIiKiYmFg2cQWFpKIRlUoSgSTk5PGeE+PagSW+voG1Nc3mFUiERHRqjCwbELpdBqPPfYzDA0NQNMKtxpbrVZ0dvogimF0dvpMrpCIiOjMMLBsArquI5GYRXV1DQDA6XRidjYOTdPQ3LwNoZCEYDAEt9ttbqFERERrxMBSxmZm4ktb5MtYXMzg0KH3wGazQRAEvOENB1BR4UFtLbshExFR+WNgKTOpVAo9PVEoSgTj42PGuMPhwPT0MTQ1FRpKLq9RISIi2gwYWMqIosj4xS8eQz6fB1DojNze3glJkuDzdcNut5/mGYiIiMoTA0uJ0nUdExPjsFqtaGgodEFubGxCPp9HY2MjRDGMYDCEigqPyZUSEREVHwNLiUkkElBVGYoSwczMDAKBEP7kT64CANTW1uLtb38namq4LoWIiLYWBpYSkMlkEItFIcsRjI6OGOM2mx0OhwO6rhs70zKsEBHRVsTAUgJ+9KMfGAtoBUFAW1s7RDEMv98Pu91hcnVERETmY2DZQLquY2pqEqoq44IL9hlhxO8PYHFxEZIURigkobKy0uRKiYiISgsDywaYn5+HqipQlAimp48BAOrrGyFJYQDA7t17sWfPucZlHyIiIlqJgaVIstksYrEeKEoEw8ND0HUdAGCz2eDzdaO6+tVGgxaLxawyiYiIygIDS5Gk02k8/vjPjK9bWlohihICgRCcTqeJlREREZUfBpZ1MD19DLIcQSaTwRvfeAAAUFVVhXB4B6qqqiCKErze6tM8CxEREZ0MA8sapVILxrqUyclJAIVLOxdeuM/YzO3yy68ws0QiIqJNg4HlDA0PD+HIkRcwODgATdMAAFarFZ2dPohiGA4HL/cQERGtNwaW09B1HZqmwWq1Aih0SO7v7wMANDU1G1vku91uE6skIiLa3BhYTmJmJg5VlSHLMvbuPQ+7dp0DAAgEQkgmkwiFJNTWctdZIiKijcDAcpx0Oo1oVIWiRIydZwEgFosagcXlcuHCCy8yq0QiIqItiYEFhcs+P//5T9DbG0M+nwdQWEDb3t4JSZLg83WbXCEREdHWtiUDi67riMenUVdXD6DQvyebzSKfz6OhoRGiKCEUEo27fYiIiMhcWyqwJBIJqKoMRYlgZmYGt9xyyNgf5cILL8K+fftRX99gcpVERET0xzZ9YMlkMojFopDlCEZHR4xxm82OqakpI7A0NDSaVSIRERGdxqYOLKOjI/jhD7+PXC4HoHDpp7W1DaIYht8fgMPhMLlCIiIiWo1NE1h0XcfU1CQWFxfR1tYOAGhoaIAgCKitrYMkhREMiqiqqjK5UiIiIjpTZR9Yksl5KEphi/zp6WOora3DTTe9A4IgwG534KabbkFlZSUEQTC7VCIiIlqjsg0si4uL+MEP/h0DAwPQdR0AYLPZUFdXj1wuC7u9cLmHZ1SIiIjKX9kGloWFBQwOFsJKS0srRFFCIBCC08lePkRERJtN2QaWQmfkixAIhIw7fYiIiGhzKtvAUlVVhQsuuBC5nGZ2KURERFRkFrMLWCsuoiUiIto6yjawEBER0dbBwEJEREQlr+iB5emnn8ZHP/pRHDx4EJIk4e///u+L/ZJERES0yRQ9sDz11FOIRCK44IIL4PV6i/1yREREtAkV/S6hv/mbv8Edd9wBAPj1r39d7JcjIiKiTajoZ1gsFi6TISIiorNTtvuwAEB1tRtLu/KTSZbvLuexMB+PRengsSgdPBalxWJZ+5YkZR1YePamdPBYlA4ei9LBY1E6eCzK3xkHlrm5OUxMTJz2+zo6OuBwONZUFBEREdHxzjiwHD58GHfeeedpv+/RRx9FIBBYU1FERERExzvjwHLDDTfghhtuKEYtRERERCfEi3pERERU8oq+6HZ4eBgvvfQSACCVSmFgYACHDx8GAFx11VXFfnkiIiLaBARdL+6NXo888gg+/vGPn/AxWZaL+dJERES0SRQ9sBARERGdLa5hISIiopLHwEJEREQlj4GFiIiISh4DCxEREZU8BhYiIiIqeQwsREREVPLKPrA8/fTT+OhHP4qDBw9CkiT8/d//vdklbQk9PT1497vfjb179+Liiy/G5z73OSwuLppd1pbU39+PT37yk7j22muxY8cOXHPNNWaXtCX9+Mc/xl/+5V/isssuw969e3HttdfiO9/5DrhzhDmefPJJvPOd78RFF12EXbt24YorrsBnP/tZzM3NmV3alpdMJnHZZZdBkiRjY9nVKPpOt8X21FNPIRKJ4IILLsDs7KzZ5WwJs7OzeNe73oWuri7cc889GB8fx9133410Oo1PfvKTZpe35aiqiieffBJ79uyBpmn8A2mSr371q2hra8Mdd9yB2tpaPPPMM/jEJz6BsbEx3HbbbWaXt+XMzMxg9+7duPXWW1FTUwNVVXHPPfdAVVU88MADZpe3pX3hC19APp8/8x/Uy1w+nzf++fLLL9fvuusuE6vZGu677z597969ejweN8a++c1v6tu3b9fHxsbMK2yLOn4OfOxjH9Pf8pa3mFjN1nXs2LHXjN155536eeedt+IYkXm+9a1v6aIo8n3KRNFoVN+7d6/+jW98QxdFUf/973+/6p8t+0tCFkvZ/yuUnV/+8pfYv38/ampqjLGrr74amqbh6aefNq+wLYpzoDTU1dW9Zmz79u2Yn5/HwsKCCRXRH1t+z8pms+YWsoV95jOfwc0334zu7u4z/lm+09EZi8Vi8Pv9K8a8Xi8aGxsRi8VMqoqo9Dz//PNobm5GZWWl2aVsWfl8HplMBi+//DI+//nP48CBA2hvbze7rC3p8OHDUBQFH/7wh9f082W/hoU2XiKRgNfrfc14dXU11xERLXnuuefw6KOP4mMf+5jZpWxpl19+OcbHxwEAl156Kf7X//pfJle0NaVSKdx99924/fbb1xzgSy6wzM3NYWJi4rTf19HRAYfDsQEVERGdmbGxMdx+++3Yt28fDh06ZHY5W9r999+PVCqFaDSKe++9Fx/60Ifwla98BVar1ezStpR7770X9fX1uO6669b8HCUXWA4fPow777zztN/36KOPIhAIbEBF9Me8Xu8Jbw2cnZ1FdXW1CRURlY5EIoH3v//9qKmpwT333MM1RiYLh8MAgHPPPRfnnHMOrr32WvzsZz/DVVddZXJlW8fw8DAeeOABfP7znzf+diyv61pYWEAymYTH4znt85RcYLnhhhtwww03mF0GnYLf73/NWpW5uTlMTk6+Zm0L0VaSTqfxwQ9+EHNzc/jWt76Fqqoqs0ui40iSBLvdjoGBAbNL2VKGhoaQzWbxgQ984DWPHTp0CHv27MHDDz982ucpucBCpe+yyy7Dfffdt2Ity+HDh2GxWHDxxRebXB2ROXK5HD7ykY8gFovhoYceQnNzs9kl0R85cuQIstksF91usO3bt+PrX//6irGjR4/is5/9LO666y6cc845q3qesg8sw8PDxk55qVQKAwMDOHz4MADwlF+R3HzzzXjwwQfx4Q9/GB/84AcxPj6Oz33uc7j55pv5Jm2CVCqFJ598EkBhPszPzxtz4MILLzzh7ba0/u666y488cQTuOOOOzA/P48XX3zReGzHjh1cc7fBbrvtNuzatQuSJMHlciESieDLX/4yJEnCwYMHzS5vS/F6vdi3b98JH9u5cyd27ty5qucRdL28t8V85JFH8PGPf/yEj8myvMHVbB09PT349Kc/jRdeeAEejwfXXnstbr/9dr4pm2BoaAhXXHHFCR/7+te/ftI3ClpfBw4cwPDw8Akfe+yxx/ipfoPdf//9ePTRRzEwMABd19HW1oYrr7wS733ve3mbeQn49a9/jUOHDuE73/nOqs+wlH1gISIios2Py9eJiIio5DGwEBERUcljYCEiIqKSx8BCREREJY+BhYiIiEoeAwsRERGVPAYWIiIiKnkMLERERFTyGFiIiIio5DGwEBERUcljYCEiIqKS9/8DQLeYnUD1XxEAAAAASUVORK5CYII="}}]}}, 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j2nOu68Vnl5Nb2z7cms+jvfQicoJgOqlpqG8vR6xdi1ywYOQXXRftqScRa9ZANErpne8ia+gTChw6qT1XDXfRIgrHfYLQNVd5hcdvzwl8U1O/UOW/9GVkV1dLzqEegUN5b1BKSaHg7R8FAoeAeggK0npMNfVcucWSSESmZ7i1fPdfZ3tNSHfdBr+UGL+7lfAvfoH62qtgO6AI5Ny5FI49ntJZZ1YVOEy3eq4Rst//ASKbwbz1Vu9n5Dje61dVEILCpz9D7oyvt/gsagscvDang6KIQOAQ0BDBHNJ6SiikEY2OP9wKkErlMAytvcOtUpLccw/U119DhsdvsYl8Hnf+Agb+9e9Kay/8s4sIX3C+d5EOh73Pu64nBnBd7H0+iH7jDWRLTkXgMB3DrVNGSrR/P0boyivQnngchIK6154UTjyJ7BZbtekUXKLRCKFQiL6+AebM6aJUcgAwTZ1sNk+pZFeGaAG6uxP096ewbYKiNE10+hxSUJDWM+odbi233YC2D7eGLvkV0e99B6nrFZnzCFwXYVnkTj2d/OlnAKA++QRdRxwGrouMxcY+xrIQ+QL2d7+D9rVTyWZzRCLhjm3PNUokEiIcDjEwkGrj6m6sgwNQubkJHBw6j04vSIGoYT2hUfWcqiqVdlyz1HP1Ujz2WOyttkaUSlAqrVOVSQm2jSiVcJYspfDJT1UeE7ruWiiVkOPJwA0DkKiXX45wbOLxaMeo55rBdEdUqKqClLIypxVEVARMhqAgrQeEQpOLhrCsEuGw0TT1XL3IWJzU9TdS2nVXhL8aEoWC97fjYO+4I6kbb0LOmVN5jH7vvZV9lHEJh1Hefhv50ksA5PPFpq8mxMAAoct+Q9eB+9O90w4kd3sv0W9+A/Wpp5r6PNWYvogKbzC2VPJsgoKIioDJErTsZjHN8J4DsG2XYtFqqfdcVaREe/IJjD/8AaW/DzeZxDrwIOxd3j2m8HTvuD0il6u6QhJCeHsWpRLk8wze+ntie+6BEKKpERXqyy8R+9zJqG8u885P1z2RRslGhkzyp/8XhU99umnPV41EIoaqKpV9m1ajqgrxeBRVVclkcpWC1Ns7QFdXHCEE/f2e512p5DA0lKanJ0mpZAcCh2mg01t2QUGahYxWz+VyhTHec7mctzoIh9ep53I5Tz1XtgrK570o8Gg0BHiZPNlstukrombQddABqM8+M0LuLBAIpSyLBpnNgiIY+MeDKAsXkkzGyWbzTYmoEJk0iY8djPrGG8hk98i9LykRaU/ynPnphVj7fWTKzzceiqKMiBtvFeXCEwqZlEo2jmNjGOYIgUMmk2Xu3CTptPc7Ewgcpp9OL0hBy26WMdn23HjDrZGIWdkbSKczHVmMAApHHeV94HhKLyG8YgTgSonrOmBZWB/aFzlv3qQdHIajvPEG4f+7gPinP0ni0I+jvfgiMhobK8QQApnogpJF6NJf1XZamCKu65LPF/wVb2ve3qGQSXd3AsPQyWSyDA2l/cHY+hwcikXP/zCRCBwcAkYSrJBmCa2MhigWi3R1dVEseo7OnYhIp+j6+EdRX3sN4nHQNKSUlYFRkU4jo1FS196As8MO3mMmG1HhuoR/djGh31zqScqFQKRSXktQVZGJhG/8OqrQFQqIksXQ7/6As9XWTXvt1ejuTuA4LqlUpmnH1DSVWCyCpmkUCt7vQvnyEURUzAyCFVJAS2mH95zjSPL5PKGQiVpNhu2jLFuGfs/d6Pfdi1i7to0/BVCSSdwbb0Jssw2k08iBARgaQgwOesWoq4vMxT+vFCNYF1ERCplo49kUVSF02W8I//xn4Lq48+bhzpvnuZH7KxIxNIQYqrKHo2tg2yi9vVN+vRPRTIGD57QQIZlMICUMDqYqK6Lh3zPcwaGsrgsEDgGNEDg1zGDGG25tRTREPl/ENL0oh6Ghkatl9emnifzkx+j//CeiVEIKIBSmuP/+5P/r67gbbtSyn4EQEA77w62JGEN/vB1x++2Yt92KsnIlMh7H2u8jFA85FDl37pjHNxpRIYaGCP36UqQiRuxXSUXxflJ+TIZIDSHjMVCGFXDHAUUdX5reRCyrhGWViEbDw/4vGycUMolEvD3ETCZLoWDV+O4goiJgagQtuxnIdCW36rpGV1ecdDpLsehdmLRHHyH+qZO8VYhhevM+UiKKRXBs3I02InX9Tbgbb9z0n0OzvOdUVa1b4GDefBPR//kG7tw5I/aKRH+/t/JRlHWWPnPnImPrWiRioB9n48UM3Xn3+MaxTWQqAoda7blajOfgEAgcOoOgZRfQNKY7GqK8MR2N+kKAYpHYV7/iFaN4AkIh74KsqshIBBmNoS5fTvSs5nqrNTsaohGBg/LWW6CIMcIFGY9XVkcV0cJwGyI/y6h4/CfaUoxgcgKHetpztR/fWERFIHAIGE5QkGYIzVbPTXa4NZvN4a3GQhh3/tVri0Wi1QdSFQXXMNEffhj1pRen/DPwVH+tiYaoO6LC0KtnEuk67sKFI4uS60Ahj+jvQxQKFA/6KIUTTpzyuTZCIw4O1dRztu00/JyNRFQEDg4Bwwn2kDqcaU1urYLregKHSCSMeOjB8kmO/wDThEwa/Z//xNly8safrY6GKAscJoqoKO38LkKqCsWityIcfoxYDGfRIpTeXkTOU+2Jko2z9TYUjj2O4lFHV/fmazHZbJ5EIlZxYh/NZNtz41FPREU+X6zsMeXzhYrAIYioWL8JClKHUi0aYvRwa9ujIXzKAgfDsSeeqSln9hQmJxdvZzREPQIHe9ddcbbYEu3553ANo6Ksq2AYYBjYW25F5vz/Q0ajuIsXj/2+NjKewGH0cOvgYGpSK6LqBAKHgMYJWnYdSKe052qRzeZQFi/2Lhy1ipLjIFyJs9Giho7fyvZcLTKZHKqqEAqZ1b9BUcie833cuT0oa9ciMplKi05ksyhr1+LOmUP2R+fhbLcd7tKl01qMypSTW8uKuWa158ZDCIHjOOTzBWKxKPm81zqMx2OkUhk0TSUcDlX2mAxD9z820TSC1t16SqCy6yBaqp7r60e/4w7U5cuRoRClvfeeUgsNILHiLfS990YCMlR970Vk0shkNwP/fBDqlDtPd3JrNBrGNE0GBobGvTCqLzxP+PyfoD/ysDcci/czKL3nveRPPQ1nu+3aecp1UY6ocBynae25iRkbUTEwkMIwdMJhM4ioaDOdrrILWnYdQEvbc0MZ9J/9jO6LLkQMDXl361KCEJR2353M/14waUl2ZtFiug8/HHHttWDb3l6SriNVzVsx5PMgJfkvfLGuYtQpya25nKdc9PY8qjs4OFtvQ+aSX6O8+iraiy94n9tyS5zNt2jNSbku2r8fQ3v8cYRdwlm0MdY+H4Rq2U9VEEKgKErl7+a258anLHAo7xuVBQ59fYOEQmZF4NDT010ROPT0JAmHjcDBYT0kKEjTTKuHWyPfP5fwzy4GQJb3PPxcIf2BB+g65KMM/eFPkxpelatWY2samhBe6wq8/SJF8QqTaVL44pcofPbk8Q/iumgvvkCoZGFusBB3iy0YGkpPa3JrvQIHAHezzbA226yl56M++yzR730H9YXnESUbqXht0sjcHqwPfQhniy1A07Hf8Q6crbcZ8/jhw60jZeCtL0iBwCGgEYKW3TTRjuFW9ZlnSe6/HxLpB9SNwnURpRLFQw8jc+HFDZ2/WLWKxEknoL7yMsI0vdmjdBpRKIArkZEwmXN+gHXccdUP4LqYt9xM+MYbUF9/zRsk1TRKW29D4RMnUNr3w7WzjdpAOT6hmREVjaK+8Dzxkz+D0tuL25X0lH1CIAYHUVavAtv24trDYaRpYr/zXeTOPAtny63GVc+1O6JCSomqKnR3d5FOZ1EUJYiomCY6vWUXFKQ208xoiNHtudHRENGz/pvQ1VcjDX38i7tlga4z8MhjyHnz6n4dsa98CeMvf8ZNdiN0DVVRcF2J68/oKP19uAsWMPjXu8e261yX6I9+QOiG6xGA29WFq6hezHhqCHSd/Fe+OiIRdjpoxMGhJlKiPv886quvAOBstjnONtvUVXBjnz8Z42/34S7cYJ1XXmoIZcUKT0zhqxidpZtAqYSSTuEuWIhzzTWYO+9EqWSTzeZGtOfaFVExnMDBoTPo9IIUtOzaSDu95wD0hx4C6da+8GkaolhEe+pJSh/8UF2vQ3nrLfT77vPuzDXNmwGV0sseciRSCNyuJMrq1Rh33oF1yGGVxwoBsfvuwbjpRohGsWOxdSI9XUdGo4j+PsI/uxh7p52wd35ngz/l5jHcwaFYtCa1+a8+9RThn1+M9vRTCF+cIcNh7B13JP/FL+Nsv/24j1VefQX90Ue86IqyUs9xUFat8tqumub9QEslRDqFnNuDjEZQ16xBPedsMldcSaE4du5ouINDoWC1ZZ+u7OBQ9kNMpbIjimIsFqG3d7Cyx9TbO1BxcOjvzwCBwGF9YPr1qOsBmqaQTIaJxz05diqVxbJsYrEw0eg6ibbrOiQS6+TeQ0NZX9q9zirIsmzi8QihkEE+X2BwMOUXoyozRTX2Psbg1H9R0h95GFHIew4NPtL1LtblDCI0DVwX/cEHK99jGDrJrgTGjTcgbRs7GquqGJfdcyCfx7j11vrPv0XU7eBQBe2Jx4mdfqpXVCJRnMWLcRYvRkbC6A89SOy0r6E++cT4j3/+eUQ+P8KMVaRSYDtj4tpFoYCqKqiahkwkcB95lNLjT9Z8XfU6ODSLwMEhYCKCgtRChIB43Jw27zl7u+29k5hgTghV9TbG66VQqLSKyki8vQIvLrzyE6hcKCvecytW4D7+BG60hjpMCGQ4hHH/31oaZlcPk42owLaJ/OiHKH19OIuXIGOxys9MxuI4S5ai9PYS+fGPKqGC9eBJzOXIVa8QKP6/HcfFNUOIYgHt6adqHquZERX1EERUBExEUJBaRHm41TR1pJQoiqi059o13FosCwrGu+BJiXAcrPftibvJJnW/NnfDDSutohGf91dJivCVfIpA3XTTEcOt2d5+cB3kRBY6quYdv5FVXosoFi1KJbuh1YT2yCMor72Ks2B+9ZapEDjz56O+/Arav/5V9RjO5pt78125YdLzcQq0a5o4juutJMo3CxP87IY7OLQP4Q8fqyMcHMrnEo9HyWZzlSHasvQ/Hg8Fq6T1gKAgNZlq7bl02lM3maandEulcs1vz1Wh9L49sT60L8JxvIv78De06yKKFjISIf/1/27oNZbetyfuhhuhpMeqtFzXRQhQCnmEaaIddSS5XIGBgRSlko07Zw4yFPLUeDUQhQJuTw/o7bl7n4gJHRxGoT37jPdzH2dgGIBwGGHbaM8+U/XLzlZbY7/znSipocr/nTT95/dXozgOCIGbSKx7YLGIVBTPJaKO1zXcwaHVBA4OAbUIClKTmCgaQkpZUTQlEpGmt+eqoiikf/5LigcdjJASUSoh8nlEoYCwLNyeHlJXXoO9086NvVjD8IZdFcUbth1+kZASmcuh5PO4BxzIwKIlI50WYjGs/fZHyefGb8c5Djg21iGHNnZeLaSRiArAc/quB8H4K1ghyJ3yNdzuOYjVq6BYRHQnEaoKrou0PS9Bd84c0H1Zv5Qog4O4m2xKadfdJj7NSURUTJUgoiJgPAKVXRNoRD03/GLmunJS6rmGiETI/PIS8i++gHnjjSjL34JwGGvvvbH2P3CMY3W9FI88CjE4SPiiC1H6+ryMIEVBuK6nvDvoIKzzzquq4CoefTTGXX9FWbUSd/6CkQ7Yto26ehXO4sUUD/7o5F5zi6jHwaGMu8h3vyg7WFTDb3nWcspwdtyRzIUXEf/ed1GXvQGOg6vrnpu4ouD2zEP29Pjf7CD6+5Ehk9wpX6vbWbz8urzikKnrMVMlcHAIqEYwhzQFpjLcGouF0TTvgpHLFZFS1p3c2kkor75K9He3Yjz6KNgl7C22JPuxQ9DfvxeRWJTBwTROlRWA9tBDRL9xJsraNaAoSE1HlLwUWnfxEjI/Ob+q68B0Y5oG8bj3umo5OIhMmsQRhyMGB7wZoiooK1ci58xh6KZbxrVWqgy3Sol1553YDz2MLBbRnn8e7cknvNZn+S0sFNw5c8id9Q2sgw5u6HUZhk4i4bXNphJ53ghSunR1xVEUpeLgkM3mcRyHrq44/f1Dvq+hN0QbDof8j1MEzZ3J0elzSEFBmgTNGm4tFktomoqqKr4CaexwayczkfeclzoqGRqq/rsj1q7F+MvtGHfeiZIawu2Zh3XAgVj77ecl0HYo9To4mDfdSPgnPwZVxZ3bs27F4jhe3Lnrkv+vr1M87PAxjx0eDWHbNplMboz3nLJ8Ocaf/ug5XWga9jt2wDrgQGRicj+7wMFh9hMUpFnGeO25cNgc054LhQx/76joD7eaY9pz3veAogjfw60sWpg6ymuvob65DKkbODvugIw155dRCAiHw4TDJq7rksnkqnrP6bpGV1ecdDpLsWg15bk7gbodHKTEvPa3hH/za8TgIJ44HkDgJpMUTv6cF9o36sI63Hsul8tTKLTnZxc4OMx+goI0S2i191w83ry7U+2Rh4lc+FO0Rx4Bu+TNvsQTFA87jPxXT0Umk5M+dqPREPF4FF3XGBhIzSqFVD0RFWXEmjUYd/51nXXQ5ltgfXi/MVZNzU5unQzliIqBgVQbndaDiIp2ERSkGU67vOdUVSGZTEw5/0e/6y7iX/kS5HOeRFjXPYlwsQiOg7PtdqR+ex2yu7uh4042GkJRBMlkF8Wid4GdLQgh6O5OYFmlCQUO9RxrovZcO+nuTuA4bhsFDhLD0Cv7RtFoGFVV6evz2nWW5SlPy3tM+XyRnp4kmUw+EDg0SKcXpGBnsAbtTG51HE9+G4lMXn4rBgaInX4qFPLePkIo5O1baBoyGkVGIqjPPUvkB+fWf8wpJre6riSf95wO1DpVXzOBSTs4jGJ0cqsnlpi+YgSBg0PA9BEUpCpMl/ec5y82Od80APO2WxGpoXU2NWNfGGga5h//iOjtnfB4hqGTTHYRDpsjhlsbJZ/39tDa6ZvWDibj4FBG07x9qFgsgmWVGBhItW2vaCICB4eA6SIoSMOYaLi11d5zANlsDtM00CfhUKCXvd9qrLBkKITIZdEfeXjc7xnhPWfbDAykphwjns3m0HWt4lYxW2jUwUEIQSwWIZn0lHCDgynfXLSzLqqBg0PAdBAMxvq0OxpiPMp3hDFDJXvrLWiP/8fb+9l6G4of/VjNKHCRy02cseObrVaz7hmtnmtmcmt58r581ztbLiCNRFQMV89lMtmOWRFVw7VtivfeR/ilF5GZHPamm1La433jD/k2gSCiImC9L0jjqedisfAY9VwiER2jnotGQ7iuJJ3OIYQgHo9MebjVuvVWol//L+KrVo2YXYl+5/+R/Z9vUjzhpKqPcxYvQfvXo94qabzCZNtITcPZYMMRn25UPTcZstkcyWQXkUhoVgkcJnJw6AT1XCNoj/2LyA++j/rSS+A6RABXCNzFS8iddjqlD+3bsudu1MEhm83T3Z0gGjXJZkuBwGGGs96q7NqZ3NoI+t13kfjUSZ7ljK6DonoR5H7cOEIhe865VdNUtQceIPGJY5GGUT2yHBBDQ7ibbsbg3feCqk5aPTdZwmGTSCQ8roPDTKWag0OnqefqQXvsX8S/9EVIDSG7kohwCFVRcPr7UXp7kUJg77Y7heOOx9r3wxBu/j5TIw4O3vd7RrOBg8PEdLrKbr0sSK0YbnVdl2w2NzXvOcehe7f3oCxfjjQMhL8XNPy/SBSLyHCEgcf+M1a67bokjj8W7cEHvDTX4ftQUiKyWRCCzI9/Qumww+oabm0FEzk4zFSGOzhM13DrlHBdEocdgvr888gFCyoRFurKFV4EhvRujFAUZFcXzsaLyf7vTxo3552AiRwcVNV7XyiKguM49PUNBQ4OddLpBWm9up2YtuTWOtHvuxdl+dvIcjR1+WjDg/AMA1HIY95809gDKArpn/0C+z3v9Ry9UylEJoNIpxHpNGgauTO/AUcf3RT13GSZ7QKH7u5ER6rnJkJ75BHUV172BqeFANdFeXs5ZLOeUEZVKzc50jBQ33qL2Je/iOIP/DaL8QQOiYQX6qiqKq4rfbGDFggcZhHrxR7S6PZcOp0b055Lpz1JaSSyrj2XyXjtueHChtHtubLCbiqFqIz29NPr3viwLmxt9IuBcdNAZXc3qWuvR7/vPswbr/fcAQyT0p57UTr2WMI77tC29tx4zEaBQ9l9QwiBoihNFYS0C+25ZxGOU8lcEum0l1Bbjksflj4sSjbuvB6UtWsJX3452XPqn22rh9ECB8uyCYdNX0TidTSKxVIgcJhlzPqC1CnquUnjayKEEPVfuDWN0r77UtrX23wuq+cSLVDPTZbZJHAYrZ6LRMKYpjHtP+OGqbiG+6vzocER/x75sTdeIEMhjL/+hdwZ/zUlS6rxTqdQKBCNenli5f03z2S1O4iomIXM2oLUieq5ibC3f4fXo3ecdaskJIJy+04gpbvue+ugHeq5yVB2cIhEwhQK1owUOIynnpPS8/ArFKyaERWdhrPJJkghwLLAMBBFa2QxkhKBd48kfdGMDIUQ2SzK22/jNLEglfeNTNPbn3VdycDAED093RUHh7ISrzxEm88XKg4OhUKKmeCYHzCSWVeQZkp7rhqlD3wAd9FGnqhBUSoXg7KKCIEXOx4OUzziyJrHard6bjLk88VKS2YmCRxGq+cGB1Mj1HPeitt7XRNFVHQSpT33wl20Meqbb+LOn191dED6N0sy7m+Ol9vKehMuJaUS+oMPEOpdi9GdxN37/QyFvMHy7u6uEQ4Ovb0DFQeH/v5BwmGTeDzG0FDa/zgUCBxmILNK1NBO77mWoGlkzvkBaBrCsrzVUhnX9YZZhULurP8Z1xx1qt5z7WamCRzq9Z5r1MGhIzAM8qedjgyZiN61nlJTynWtvPIqtqcHRfPEDSKTwV24EGfpJpN/Xikxr76S7t3fS+K4YzBO+Qp84hOIrbbE+OopuP39gYPDesKsKEidrp5rhNKHP0z6kl/j9sxDlEoI20bYNlgWMh5Hnv8TCp/5bNXHNst7rp0MFzh08t1so95zwx0cOvl1jcba7yNkzz4XOWcuSBdc6c3E2bYfmd6DTCYRioCihXBdiocfOe7cWz1Ezv9fYt84C3XFCjAMZCjkCSuKRULXX0fXUUeQX70aKWVlrygUMlBVteLgUP49isejFItWReAAnXkjFlCdGT2H1KnDrU2hWMS44y9o//k3QkrsLbfCPuwwkos2HLMXNBPac7Xo5IiKqQy3NjOiou1kMhh33kHo8svRnnwCNA23p8dzkHcc1GwWCgWs97yX9C9+VdPSqhbRF54jtN+HvVWYYXpD4MNxHESpRP7zX6D03e8FERVTpNPnkGZsQerY4dYWMzxATUp34uFWy0J94QVEsYA7fwHukiXTc+IT0IkODs0Ybq3m4DCjcF3Ma39L6PLLvAFZxwEEsjuJetRRpL70FSyj8bakrmvEYhGUL38ZrrjCE0mMVzQKBejqou/Rx+jaYEFdDg69vQOEwyH/48DBoUxQkJqMp4oLo6oKtu34d9TNS25thXqu2XR3d+G6LoqijK+esyxCV1+FeeMNKKtWevtRuoG9yy7kP/lp7N12m56Tr0GnODg023tuuIPDjKVYRH/4IURfH4TDlHbdjfiSjRtOOR6unrOsEupWW6GsXIEM1XAV91dJg7f9Hvne947r4CCEoL/fWzGVSo4vDw8cHIbT6QVpxqjshrfnpJQ4jlMRJ4C3edmp6rlmoqoKruui6xqlUomhodzY9lyxSOyM0zDuvQep+IooVUUUi+j//Afav/9N9rvfwzro4Ol5EeOQzebo6opjmgbFYvvdDSZSz02WTCZHMhknFDIpFIpNONNpwDQp7f3+EZ/KZHJ0dyeIRELkchOPE4TDISKRUMVlwbJKdJdKntS8FuW5qJKNPczBoa9voKKuS6UyzJ2brAgcPJdwvfJxLlfEtmVQlDqcGbGOHa2eGxryrPvLF2LXlSiKQiwW7kz1XBMYrp5TFEGpZKMoStW9otDVV2Hcew9uogs5b57X99d1ZCyGu2AhWEUi53wPZfnyaXgl4zOdAodWJrfOVIHDRLiul3LsdSLGv5ToulYpXPl8kYGBoUpb3Nl0U8REq0/bBlXFXbK44uAQCBxmJx1dkOpVz5WloOCtlLylfIhEIoKqdoZ6bipUU89lMl7LIhwe1eqwLMybbkAqanUnZiGQc3sQQynMP/yuLeffCNlsDq8F255guHYlt3oriMmnAXcqXspx9TRgRVGIx6N0dcVxHJfBwRS53EjRSuETn/AEDePtG0qJcF2s938Ad6NF5U9Vos2llJXi4wUdUnFwUFWlUrRUVSUcNgIZeIfTkQVpMsmtjuMlt5ZVdbCuOMVi4YaTWzuBWsmtjuPdnUYiI+9O1eefR1m5Cpmo0Sv2/fL0v93X6pfQMGUHh1DIRFVb50fW7uRWKSXZbN6fj5kxnfK6yGbzGIaOYaxzlw+HQ3R3J9A0jVQqQyqV8TsRI7EOOAj7HTt40Sq2vW7mCbzZu2IRGQqRP/W0yqeFEFiWhWVZJBLRSvEpOziEwyFUVa0M0YKsODgIERSkTqbjClIzh1uLxZJv5SJ9+xF3xrXnag23enenI++6RTqFyOdQ1qxBWfYGyvK3PE+y0a09VUXkOktiXSaf99SQ1e66m0Er23O1KBYtSiW7Za9ruiinHEej4ZrtuaqYJqmrf4u98zu9mbuihcjnvT+WhUwkSP/6sioRF8IfPlZHODiUzyUej5LN5ipDtOVRiHg8FKySOpiOuVVrtfecYRi+v1ixo0PSGvWey2ZzJBIxzw/upZeInHs2IpVa586M59qM0Yuz0UYQ8otXycLZcMMaR55eWiFw6ITk1lkhcKhCLlegqytGV1fcFwpVXxFVQ86bx9Dv/oD+j79j3nwTyltvIWMxSh/ej+IhhyITiTGPGR1REQgcZgfTLvtu53CrJw2FwcHOkLAPZyrDrYlEDHVoEPejH0N59RUvCymfB1VbFxng2KDpOEuXeEbNA/1kfvTjjlPaDScej6Lrmj9zNfnC0WnJrZ7s2WRgYGhW3K2X1XNlW7uBgVQbB7Ml3d1JLMu7Oe3uTjAwkMIwdMJhk97eQbq6YqiqSm/vAN3dXtehvz+DEOtfREWny76ntWXXbu+5bNZb4neSv1gzvOey2RzKzTejvvYact583HnzQSheESpfJTQN7BKitxelrxd72+2wPvihFr6yqdMMgcN0tedqMVsEDqPbc/39g+MKHFpFIHCYXUxLy266oiFs2wv3ikRCFIvWtP9CNisawrEd3Gt+i6KpnuuyFsPdYANvINa2/R+FnwA6OEhpt93J/N9Pq6vwOoipRFR0QntuPMoCh5kYUQFjh1uHt+ey2TyJRKzSpWg1owUOZQeHIKJiZjItLbt43EDX9WnxnusEf7Gme88Vi3TvuQeKIpCJBG65d29ZiKFBbw/JdSuBakO3/wV3082a82LaQCMODp3WnqvFTHRwGD7cOtJmax2JRKxhB4epIKX0o+MDB4eJ6PSW3bSskNJpC/A2qhXFW/qXvedM0yAWa11y68i70/YKHMrJreFmJ7dqGlIRuLaDKgSynC5rGMh585Hz5nvPn8mAYyNjnf1LOZp6BQ6jk1tbMU/UTGaSwKHiPaco5PPFMfNEw2nEwUEMDqK+9CJIibPZ5sienobPLRA4zB6mXWXnutDfn6382zAgn/fac17hiTQ9uXVkgFp7BA4tTW5VVUrv3RXzrjtx53SjqApOtUKbyWDvtJPn3jCDGO7gYFmlMa23Tm7P1WK4g0MntJCrUas9Nx7DHRyGO6qMOO7KFYQvvgjjj39E5Lz3vwyFsD5yAPkvfwV36dKGzrPs4FAOfFxXfAoVB4fe3sHKHlNv70DFwaG/PwOsfwKHTqTj5pByOYts1sJxoFh0yOUsBgez/iBjGNdtznBruwQOtYZbm4l1+BFIw0AODgJ4eTXDyWZBERQPP2J8V+UOpprAod3Dra2gkwUO9Q63VqOmg8Obb5I4+kjM665FFAvIaBQZjSJKJcxbbyZxzJGoL77Q8PkGAoeZT8cVpOE4jksuZ+G6XnEqR3k3Y7h1uMChFcv1die3lt63J8VjjoV8HrlyJUqpBEhvH6l3LUo6hXXAgVgHf7Qlz99qRjs4dKJ6bjJ0ooNDw8Ot41DNwQEpiX39DG/WKJFAxmKeAlTTkLEYsqsLZfVqYqd+beww9wQEDg4zn44uSMNxXUmhYKFpWtNWNeU+eLPvTqcluVUIcv/13+TO+h+cRRvD4CDqqlWIwQHcefPJfe00sud833vzz1DyeU/w0g7vuXbSKQ4O9XjPNcJwB4cy6jPPoP3nP148ejVrKEVBRqOoL7+E9tBDk3jWwMFhJjOjrk7ZrLevtK7nPrXjNVvgMO3JrYpC8djjKB5xJOGnnyRaLJBXdAo77ug5fs9gyuq5sr+dlwra/NbndDHdAodq0RDNYLTAwfjH/d58XKxGwqyuQy6H8Y/7sffYo6HnCwQOM5sZVZCkhGy2SDweJhqNNEW23QyBQ8vUc5NF18m/cxf0RIywqlBok/y2VYxWz+m6N4VfKBRnzV3tdAkcGlHPTYbRAgfyhRG2VlUpfy0/uXMJBA4zlxnTsitTKNiUSrbfc2/OL85UBA7T0p6rk2w2Vz2iYoYwXjREuyMq2kU7BQ7Nbs/VYrjAwV240LuzrNU58Iuxu3DhpJ8zEDjMTGZcQQLIZAqVgK5mMBmBQ7vUc1NhvIiKTmci9Vy7IiraTbsEDlNRz02WssCBj30UGYkgstlxv1fkcmAYWAdNXoATCBxmJjPnKjUM2x4ucDCacsx6BQ7tVs9NlWoRFZ1Mveq5VkdUTBetFDg0Sz03GcqigsjGiyh84gTP0iqXG5l/JCUUCmBZFA85DHfjjaf4rIHAYaYxIwsSeAIH13X9WOipH2/k3Wn1u+5Obs/VIpvNYZqeXVOnMpnk1mw2h65rmGZzbko6Be8iqjRNTdrO9lwtMhmvhSy++U2KRx2NcBzE4CAilfL+DA4iLAvrwIPI/r9vT/n5RgscysnSZYGDpqkVgUMoZGAYuv+xiaYRFKVpYNrjJ6ZCKKQRj4cpFIpN86WrFlEx7eq5JtBuf7F6mar3XLMiKjqNZkVU1OM9104iYZNwJMxA/xDKww9h3nQj+mOPARJ7+x0oHn00pd338DzFmkYQUVGm073sZnRBAkgmPYfwwcFUUwYjNU2lqytONpunWCyOUM9lMrkZsSKqhqoqJJOJ5tsWTYHh6rlcLj+peSJFESSTXRSLnmXQbGGqJsCtVs81gvr005i33oJx7z2Qz6Mu2gjnyKMY2nc/5Ny5LX9+KSWGodPVFae/f6gyPtDX5xmuWpZFJpOjp6fbHyco0tOTJJPJk8/bs0oGHhSkFqNpgmQyiuM4TfOli0YjlcDAlnjPTRORSIhwONTmALWxNNt7Lhw2iUTCDA6mG4qo6HRM00s59vbR6rsRGu09l83mWi5YqIV59ZVEzj8fkc8jTQMUFVGyUFwXe/ES0hdehLPlVi0/DyldurriKIpSiajIZvM4jlMpVJ7fpOcSHg6H/I9TzOCdjTF0ekGa8T/pZgscVFVBVRX/rkh2pHpusky3wKFV3nOBwMFjOtRztdDvvovI//4YHAd3wQJk9xxkVxduzzzc+fNRl71B7JSvIFLtaCMHAoeZwIwvSNAcgcNo9Vw5g0kZbVI6w5kugUOrveeaL3BwUJRlaNo/0PW70LQHUZTlQHsv8PUIHKZTPTcuUhK68gqwSsg5c8YMwrpCwIIFqG8uw7jjLy0/nUDgMDOYFQWp7ODgtSsav0uupp7L5fId4S/WbMp3hLFYe1ZJk1HPTYbhERVT7fkLMYBp3oppXo+u34+m/QddvxfTvB7D+CNCtK99PdzBYfTr6hT1XDWU115Fe/opzzy1GhKk4nUizD/9sS3nVHZwKM8wlouPqqoVB4fy71E8HqVYtCoODu2+EVlfmRUFCSbn4DDRcGu7IiraTTscHKYjGqIxBwcXRVmJoixDUVYA5T2aDIbxFxTlRVx3I1x3a1x3M//veWjaU+j6XUD7/OaqOTh0WntuNEpfH5RsL+BsHFzXC5HU+nrbdl6Bg0NnM6O87CYikymQTEYn9KWr13tuuINDpwaoTYbhDg7FYvUAtakwXcmtZQeHSCRMoWCNI3CQqOqLqOrTKMrbCFEEdFx3Iba9A0IUUJTXcN3NGfv2iOI4m6JpL+A4W+M427b+RTHSBNi2HcJhsyPUc7WQ0Zjn5u04nlnqeN9XKqEkkxiG3pY242gHh7LAoezg0NUVJ58vVvaY8vlCxcGhUEgx1dibgNrMmhUS1CdwaHS4tVURFVPGdVGffhr9b/ehPfJww0aUrRA4tKs9V4uJBA6a9m8M43YUZTmuOw/H2RzHWYgQazGMP2EYvwMijH+vZiKlhqo+16JXUJ1Sya74wXVae64aztZb4yxdikjXECy4LjgO9n4fafP7a2KBgxd1Iunu7qp0XJLJyKy5Ke1UZtUKCcaPqJjscGuzIyqmjJQYd/yF0FVXoL78MlgWqCru/AUUDzucwokngVlfizGbzZFIxCgULEqlyd+djh5ubdZM2GTJZnN0dcUxTYNicV1BVJS30bSHcN0EUg6ffwnhuhsjxBoM4zFse5eax5cygaL0AQ7tcIYePtwqpcSySh3VnquKqlI85lii53wPshlvxTQcKVF61+LOmUP6IweQVJRKREWrGS5wiMejDA1lkFIyZ04XrivRNJV587qHnapLsVgiFDLQtHwQUdFCZtUKCcYKHJYte4MHHvgnXV3xSXvPdUqAGkDoqiuJfut/UJ97DjcaxV24EHfOHJTetYQvvpDomf/tFak6aIbAoROTW8cTOKjqKwiRHVWM1iFlF4C/p1QLB++t09q3TzX13HgCh06keNTRFA47HPJ5lNWrIZuFQgExNISyehUyHid77g9w5s+vRFS0ywRY03R0XUMIQTLpzeaoqorrOpRKJaSU9Pb2UyxaCKEwOJgKBA5tYMYPxo6Hrrv86le/5KqrrkJKyb33/g1Nm7zUebiDw3QEqAGozz1H/JMngnSrT7jnciiDg+S+fibF446v75iTdHBo9nBrsxnr4CAJhS4H8oCOEClAIqWJlD1IGQIkmnY/qroSy/o4UH2lqSgvYtvvpVT6UIvOffzh1qk6OLQd18X4/W2Err8e9aUXvTadaWJ98EMUjj8BZ7vtKt/a3Z3AcVxSqUzLTqc83hEKmTiOi2VZRCLh9cbBodMHY2ddy84rPndx8cUXsHbtWhYvXsypp54+pWIEnSFwMP/wO0Qui7vRRtW/IRKBoSHMW26meNTRdcWVNypw6LT23HiMFThYCDGAoryEEClfzCAAA9edi+tuguMsRcqNgBUIkULKeWOOK0Q/UoZxnNa4C0yU3DqyhWzV7eAwbSgK1iGHYX38UJQVb0OhgJzbg0wmx3xrNpsnkYi1TOBgmkZl1ZzLeQVGShdNUwOBQ4cwqwpSPp/nrLNO57HHHsUwTL70pS/zuc+dTKlkN2Vz3Uuh1P1f2Pbfner/+AfSNGumbbpdXahvvYWybBnuZpvVddxcroBpmkSjYdLp8XNqpks9N1ny+eKw1NBVqOrTqOpLSBljXbstj6pmEWIQcJHSxHG2QFH6kNLCdecDOlBEUdYARUql9+O6i5p6ro14z41MOe4ss9xxEQJ3o9o/s3ILuSwwaBaqqhKLhdH18v5xzpOceyfmx6x3jRA49PYOVAQO/f2DlRj0oaG0/3GIVKo4I1dJncysKkh9fb089dST7Lnn3pxyyulsuOGG6Lpe+UWc6qJm2gUOVtGT0tZCUcB1EVZjbcVaAodOb8/VoixwiETuQFFWAypSxvGKDICNovShqmtRlBVI2U2ptBeOswmKshZFeRMhbKTUcd0Nse2dcJztaNbd8ej2XL3zRJlMjmQyTihkTlsLuRV4xSHRFIHD6PZctfGO0Q4OfX0DleKTSmWYOzdZcXDwYtD1yse5XDEQODSZWVWQFi3amDvvvL+StlkWOMTjYaLRSFNWNSPvTtu7r+Yu2hjt3/+mVikQ+TwyZOLOX9DQsYcLHAYGvILUGe05iaK8jaq+jBADSGniuotxnM3w5Nm18QQOr2OaD2BZ8xEi5IsWdDyFnAUUESKPEEPY9gLARlVfx3G2oFTaC6/4hHDdhTTzLTNRe64Wwx0cZtOMnOu6FYFDoTD5Gblq7bnxKDs4rFtNl4tPoeLg0Ns7WBmi7e0dqAgc+vsztENpub4w61R2o6OfJ+PgMBHT5eBQ/OjHADm+is51EZks1j4fmpSt/3AHh85Qz+XR9dsxzavR9ftR1RfR9f9gmrcQCl2Norxe11GKxceBXv//vwi4CJFGUVahKCsQYgiQeCIHBSF0pIyiac+hKCtx3c39Fl1zilGzvOeqOTjMBrwZucmZ5aqqSldXjHg8SqlkMzAwVLMYlQkcHDqDWVeQqpHJFCr+Vc1guMChnct1a98PY2+3PcrqNVAc9SZzHJSVq5Bz51I87rhJHd9xXIpF73VN53Crh4th3I2uP4qUc3GcrXDdpf4g6+YI0Yth/LEOiTZImcJ1B1DVtUAM190Mx1mMlGGkjPn/hxZQQlX7UZTn0LQnEaIPTXsKIQaa8oqa7T03MuV4VjU7yGbzGIaOYdQnRhLCG15PJuMIMXy8o75iMdrBoVx8ygKHcDiEqqqVPSaQFYGDEEFBahbrRUFqSkSF6zJ8E2paHBxiMbI/OR97xx1R+vtR3n4bZfVq7+9Vq3AXLCDzw/Nwtt6m4UOXvefKq75SqdRy77laKMpyVPVpXHdjf89nOCquu4m/9/P4uMcQYgBVfQpVfQ4p1+C6aTStfAMhkFJHSgMpDcBECHDdeUi5AVImEKIfTXu67pVYLVrlPddJM3LNZLjAYSJM06C7u4tQyCSXyzM4WNt9ZXyCiIrpZnbdVtVgPAeHWoihIULXXEXo8stQ3nwTFIXS7ntQ+MxnsfY/YFoEDu6GG5G+/Er0f/wd4447UFatRMbjlPZ+P9Z+H6kqp52I0eo515UkEjF0XZ+Sg8NUUNWXEMLCdcdxi0bgugvQtBex7T2QMjnsazaa9i+/GL2Cqj6FogwBGVS1Hynn4roGQnirIm/eKIeUAikj/udKSBlGVV9A1/8GKEjZjetuxOi3jRApXzBhI2UY190Q8G582pHcur4KHGqr5xonEDhMP7N2MLYaoZBGPB6mUChOKHBQ3nqLro8fjPLWWyBlRVMlVRXhOBQOP5LMxT+ja04SIZhY4OC66Pf/DfPa36L/+zGQEmebbSgcfSzWR/av6YrcKmqp5xKJGKrqpWtOB6Z5A4ryBq67uMZ3lVCUNykWTxomw5Zo2oNo2kMoygBC9KIoAyjKywhhIUQRITQcJ4Ki5JBSBUSl+DnOVghhI8Sgv880iOsupFTaBYjgOJtSKn3If748mvYYqvoiQgz5anwFx1mAlDsTCr0L03SwrLfJ57PYdhQpe1ry8/KUeiYDA0Oz6m69WsrxaPVcNpub5IpoPCTd3Uksy6JQsOjuTjAwkMIwdMJhk97eQbq6YqiqSm/vAN3dXoZaf38GITpb4BAMxnYQhYJNKGRX7iTHXdU4DomjDkd5+23EqDe38B2kzVtuwl26hOz/fJOurgnuTkslol8/A/P3vwfpVqTb2kMPEXvoIUq77U7mV5cg44mmvdZa1KOey2ZzJJMJwuHQtCTmSqmzLhJiPMpecus6z0KsQdOeRgiJEH2+C4NAym4gB6gIYaEoOSCL16ozcN04XijfCiCCEDnAG5wUYg2q+jxSLkBR3kBVX6BQOB5VfQNNex7XnYfrbuafRwnTHMAw7sZ1nyCfz+G6vWiai6p6Bc1xdm76HJM3S2ZM24xcqyi/Lk9IkGlIPTdZygKH8mBsWeDQ1zdIKGRWBA49Pd0VgUNPT5Jw2JixDg6dwnqxhzScegQOxl13or38cqX4VENISeiXv8BOpScUOET+9zzM225F6hoykUBGo96fRAIZCqE/+ADRM06f8murh3rVc8MdHNrlLzYc113quymMv8+iKL1+MVi36lDVZUAGIXr9FVEWKUHKKFIuQMo5SBlBUUy8+zGJ63oFSFFyfrFaiRBDKEoWCPuODQIpNaRchKK8Rjj8czTtSRxnE6ScAyioqko02oVhbIhtv4Zt34Tj9OM4S3GcLZAygaY9jWH8oSn7UsNZHwQOyWS8YfXcZAgEDtPHtBWk5cvf4sc//j4nnXQse+/9Xj7xiSPrepyUkquvvoJDDz2QffbZg8997pM888zTdT9vPQIH86YbkRMNoAJKJoNx1501BQ5iYIDQNdd4q6JQleA4w0CaJsa996A+37pIg8lEQ7QioqJeHGdzXHe+P5jah6K8gqq+gKK87ie25hAig+PsSHm/BkCI1SjKSjTtKRRlJar6CoqyHCEyCJHBdTfAtpf4DgyL8VZIOYTI++IJCaQRYggpFbyCFAFiKMoAUiq47lJ/FbYWMBBCEA6HiEbD/hzNs7hun6/gc/EKn0DKhG85lPP3pZq7nzQbBQ5CePtwUkpUVW1YPTeFZ55Q4JDJBAKHZjNtBen111/loYceYNGiRSxdukndj7vmmiu57LJfcdRRx3LeeRcwd24Pp532Zd5+e3ndx8hmvYE7zzV57NeVlStrro7KSCFQ1qwZdXc6spAZf70DsllkpMZFwjTBtjFaEOU81eTWbDaHaRroNULWWoGUCWx7FzTtJQzjT2jaf1DVF9C0/2AYd6Drf8e2t8e2dxz2KBtVfQFVfRVwkDLpt+oSSBkGBv29pAFc10JVXYRQkTKG63b5lkJuZQ7JEzLYfqEKAZbfyvNWVkIMYRg2sVgKRXmTfP4lCoW3EOJNvwUYQYj+Ma/NdTdGVVeiqs1dJQH+RVSZFSnHw9Vz5baxrrdn9Tda4JDPFyrFJ5XKoGkqkUioEoNuGLr/sYmmERSlSTJta/s99tiLPfd8PwDnnvsdXnhh4tVBsVjkmmsu5+ijj+eoo7xZmx133JljjjmU6667hjPOOLOu557IwcHt7kYqCmIio1EpkV1d/rlVd3BQ1q4FRXiWPuMeSHj5MGvX1nX+9dIM77lqDg7tQIgUqvoijjMHIbpQlAE89ZuK6+r+DFEIbw/J9VdFD6FpT+Gp3XS82aJy8bDxxAt5vMLi+oar8/1CVELKhb6pah6vMIF3zzZ8JS3x2ogahjGEojyAbeexbRshJLpuoSir/ZWQjRAFf/DW8IsigIaUCoqysumps7PBwaGWem64g4NYvRrzz7ejrF2Dm0hgffgjuJtu2rTzCBwc2s+0FaTJ7Es888xTZLNZ9tlnne2/ruvsvfcHuP/++xo6Vi2Bg3XQwZh3/nXCY0hdx/rQvpV/l33ThgscZCSCv4kxvimq/7Waq6gGaLb33HQIHFT1Gd++Zxc8IUIKz2XB9veGVmOatyBEHyBQlAE07Wm8Vl7O3ztS/T2fATwBQ5evqtOx7SSqOoSqbokQDlKuArL+vlUJ0HDdBF7xyQFRPAshHcNQ0TQFx1mFZc3FcebjeeSBEGv9Yvo8XmFMoOtZyjHpjrPIl6ireKKM5jNTBQ4Tec9VBA6agnPq6Zg33YiwLN+/URL9wfexPrAPmf89H9nTHDVjIHBoLzNK1LBs2RsALF68dMTnlyzZhNWrV1EsNnaxHE/gUPz4Ibhz5yJrFE2pKBSPOho5Z07lc9UcHKwP7AOaDoUa52bbIASlfT7Y0PmPZqrtufFov8ChgKY97YsFvLtMKROAOmyuqBdFWUsodD2m+TsU5S0/22iRPwekoar9qOqLQAoI4xUzGylVhNBw3fnY9iZoWgwpFyHlfBxnI79dl0eIrD9jtApFWY6imIRC3QjRj21LXFfBceYy/E7YWwUJFOVNf89pvt8OVFCUV9H1f6MoaxDCGjU71TxmosCh3uHW7GAK44RPEPrtNWDbyFAIaZrIkIkEjHvuJnHUEYihoaacVyBwaC8zqiCl0ykMw8AcFdEdj8eRUpJONzYrNa7AIRwmde31EA6PETdIvGJk77wzmXO+P+aYowUO7qabYu21l3cnV21fynUR+TzOFltQ2uN9DZ3/cFrtPddOgcM6AcJwGXzKt/FJI+V8pJzniw0sHGepH0/+FIryvD+kauC6YV8QscYXIPTjFZoBoAAUcZx+XFdH0zK+pPtZvxAVEGIAIUoI4aAoWTQtg+O85EeIh/2Yil68lVv53PN+W1AiZcwvbpq/T7UQr9g+husaOE7z2kujmSkCh4a95267DXnHHaDr3t5reQUihCcQ0nW0l14k9OtLmniWgcChXcyogtQKxhM42O98FwP33EfxyKOQ+rpi5S5YQO7MbzB02x8gGh1zvGoCh+wPz8PZcktEJotIp6FUglIJkfX+7c5fQObin08cLVGFyajnJkv7BA4KXnts3R6eqi4HyqF53q+ttzejIYT0oyKW+2apGTw59wa4bhIpNcBBiBLr9pUchFiLqv4H1/0PqvoWilLwB2Jd306oCGTQtCyQxLZdHCeDlBIpF2FZ78d1N/IHaFegKCtQ1VcrEnNvH2r4ylggZRghVvlFtTVDsmU6WeAgXIdY7xqSa1chsrm61XOhq67wPtD16u0wVQUpCV1z9fgmxI2eayBwaBszYz3vE48nsCyLYrE4YpWUTqcRQhCPNz6FXEvg4G62OZkLLyb7/R+gLF+O1A3cpUsnLByjBQ5y3jxS199E+NeXYN5wA2LQM+uUkQjWxw4h/7nP4268cUPnPR3REO0SOEiZwHV7UJQ1vnVQHkVZ5bftytgIkcd1e1DV51GUtUgZQQgvltxzcejzi5BXjFw3ibdy2gAvB6nX34Mq4DgaipLEcRJAyJ9LMnFdgeMIbDuClFGEGPTbllFUtdePwdjUd2pII0QG256PEK6/2ur1h3AVvEHfMK67qa/Cay0dKXAoFIjdejPmjTfAm28iXRciEfSDP4pz/Am13wdSoj35pCc4qvEUUtdR+vpQli9vmsghEDi0hxlVkJYsWQrAm28uY4sttqx8ftmyN1iwYCGmWWXOpw4mcnCQsXjDhqWjBQ5yzhxyXz+T3Fe+ivr66+C6uIsXIxONuzNMZ3JrewQOGo7zDlT1j3izOl5mESQr3+GJFjS/cAz67Tv8dlkBb89I8Vcojr9vlPeHaIv+/k7cn2ly8FZkGX9gVkVRenCcbhxHQ4g3/Iu5iTeXFEJRcijKS7690RLfgTyNlMv857Z8h/LN8EQRLhDFdef7hWvqxqr10EkCB7VYpOuMU1H+9jdcwI1EQFEQ2SyhK6/AuPOvpH/2S5xtx1EeSumZHOOtOoQQCCHGL7SyuT/jQODQemZUQdp++x2IRqPcd9/dlYJk2zZ///t97LrrHlM6diZTIJmMNi14b7jAYcTdaTg8/huujOuiPfII2pNPIBwbZ/ESrH0+iNaVmPbk1uECh2Jx8gFqE2Hb26Eob6KqT/orHvAu6p7PHDi47kJU9S1fOafgFS8FL3CvXGhsygXHEz2EUJQ+vOLiya89gUMXiuKgqmEcR8WyuvGUdQBhVHUltr0ZUnYjZQjXLfjHNVGUV/CK6AaA7u8/pXHdTf0V1EgUZU3LBA2jGZlybGHbzfR8q4+Keu6i/4N778VJdCGHtRFlKIR0XZSVK4mdfipDv/uDtz80GkXB2WprtGee9kIqK8JVgRweW2nbyEQCd8ONmvw6RgocBgZS9PR0VwQO5UJV3mPK5wsVgUOhkKJZKcOzmWkrSIVCgYce+icAq1atJJvNct99dwOw007voru7m69+9QusWrWSG274HQCmaXL88Z/k8ssvIZnsZrPNNue2225iaGiIY445fkrnUxY4hMMmoZDRlFWHt8TXG7o7VZ98guh3v4P60ksI11+pSQkLF6L8139hH3PMNCW3rsO76zaJRsOk09kWPYuBZe2HpnX7YoaCbxXUjZTdfsT4SlT1LdbN++QqA6lS6n7h8WTeXlHT/P0ll3IB8+aTTDStC9fN4DghvII24D9G8aXgDlIaCJH1xQkJVPUp/1hhFOVNXHdDHCeJrj+LbW+G4yyp8roKeAasYwtVqxjZQm6vWW7Fe25oCHn99UhdH1GMKigK7ty5qMvewLj3Hqz9D6h6vMIJJxL7+hngOL7gSHjX+XI9cl0EkD/6WAi3QoAjfBfyrhECh97egYrAoa9vsOISPjSU9j8OkUoVg1XSBExbQRoY6Odb3xo5yFr+94UX/pLu7l1wHAdnlDLt+ONPBCTXX38Ng4MDbL75lpx//kVstNHUzSonE1FRi5F3pxNHVKhPP038C59D6e/HTSaRpolQFBTHgd5e3DPPJN/bh33CSVM7sSaQzeZIJGIUClYLIypC2PZe2PY7cZxN0fX7cJwNkNJLb/WUeHNR1VcQIuMPuHax7goVQkrTLygh38qnfEEWeK21LhQlg+umkLKEEBaqqgGrcF3bL0rlOSQHKCBEFhhEShtFGcRr8eX8FViXP2s0d9ggbJkCmraMUmlbXLexPcOp0u6IitHDrdZf7iDa14+cV0PIoevguOj33oO9007guLjz549wwi8eehih665Fe/xxpOYiNQ3h7ylJ20aUSrgbbEj+sye35HXVE1FRFjgEERWNs17FT9RDIxEV9dLVFWfCiAopiZ/4CfRHHsZdsAChqCiK1yN3pfQm0/v6IBxm8I9/Ri5Y0JRzmwrtjaiw0fW/oWn/xssc6kFV30bTHkRVXwMyvsWPiVdsTH/fKO27L3jFTVHeBDQ0LeK3554HlvnOD3P8odsSQpg4TgxvXymDbW+CZx9kAzre6qqcm6QjZRLLOpRS6QMIkcEw/oYQa/2i5O11gcC2t8S292Fs6GDraUdExXjREMattxD7xpm4CxeO/2DX9XLHdANUT0npJrspHnEkhU+cgJw/33uOgQFiX/4ixj/+vi4003c7sbfZhvSvfo27Sf12ZJNjZkZUBPETM4y6IyoaoJqDw2jU555De/IJ3EQCRdNQ/M1ax3Er/XHZ3Y2yZg3mn/5I4dOfmfJ5TZX2OjholErvx3Xn+ymuq/2Zn6Lv+L2F36LT/NkfbxBWUfrxCogO9KGqWVTVyz4qlUJ4zg6u37qzfLl3ASFsFKWI62bxik95xWwgRAEpQ0jZRdnTTlGWoaovYVmHIOVCisX5KMrrfrH0hmAdZws/32l63natFjjUioaQvh0XpZK3EhqO40CxiLJqFaJQAL2E290NgNLXS/jnF2Pe/kdSl1+Fu2QJsrub9G+vQ336acw//B6ldy1Gz1ycAw9iaMedx3dEaSKBwKE1BAWpCm0TOAxDffEFhGWhzJ0LQuC47tjvUxSQEvW5Z6d8Ts2gXQKHdWg4zg44zrZ48RI2hiEwjIfwZn4kMOTv/0TwhAxxpNRQlBSG8TKKksd1vYuxoliAi5QRf1Yph6Kk8Sx/vK975qqKr+oL+04PSYaP8Hn/LqKqz6Fpj2Hb78Nz9t7RdyPvDFolcKgnubW02+7IBQsRa9ess/WxbZT+fs9VYdjMkNR1r2ipKjIUAsdBeeMNYqd8mdRtv6/4QjrveAe5d7wDAMPQSSRiGKkMltV6z8VA4NAa1vvB2GrUE1HRKLUiKjRNJRoyhq2KnNotlU6YJ/GZnogKDSkX+vNEMYTIoyh9eN51XUhp+FlGOqXSbqjqhmiaDijYdthfBUk8wYOKlJ4c2xtotZCyAPTjujmEUPDMW5ehqq8gpc3It40XV1GOLdf1R/F88zqTZjo4COH9PieTcYRQag+3hkIUTjgR4bqV4XD1zTcR/f2eddbw4+bzKGvWrPu8qiKjUbTnnkV/6MGq51KekWvv72Hg4NBsgoI0DhNFVDRKNQeH4d5zcqutcDUdN1vjYua63uppq62nfkJNZLoiKlT1GRRlDY6zCMfZCddd6rfvNqVUehdCLCIcfguwKJXmYFnvxLZ3w7bfg+vOx3XnImUIIbIoyiqESPn7R+Vn0FDVDRAiWtmP8gIAe/EEDjlgEC/eYmOgbFW0uq0/h0ZphoNDvd5zwymceBKF4z4BRcubxSv7Ow6/OGuatwLyV08V9ZxhgOui33N3zdelKEplRq/VBA4OzScoSONQdnBQFIVotDl+YMPvTsd4z226BfZ226EMpcZdAYmhIWQ0hnXwwU05n2Yx3MGh+RRR1efR9T9imtdiGL9HVZ8DhtC0p3HdDX37nrxvFbQZsBmGsRhN68Z1l2PbfThOEikX+uq3OK7b4+/nGAhRqlwcPFVdGIgBNo4zhBAmEMHbh7JR1dXAEN7qahGuuxQpDbxAP4EQ0yfJr4fhDg6N7mU07D03HEUh943/IXfmWZ7biaJ4fzTNW24NN4IVAorFda08ITxJd2p8AY0XjlggHG5fynHZwaFs0lwuPqqqVhwcSiW7ssdULFoVB4daacjrK0FBqkGhYFMq2VWD9yZ/zILfcx/lPScEudPPQHYlEKvXjPThchy/teGQ/+xnmz7w1wyyWe/uNBxu3t2pEL2Y5g2Y5o3o+uMoynI07SlM80ZCod+gqq/6q5w5QA5NewbDWEEoZCFlDstajZRZvMC9DYcdt4i3fRryxQk6Qpj+LJPqf03Ha+ml8eIv4v7nNP8x830nhoWV+STva8kqcu/OI5crAPW3Whtqz01wIJFOIcNhnE02wVm6FHfOnIpKbsQTAqLgp+pKCUJBzpk74etyXbetprJlgUM47LXiysXHc9qnInBQVaVStFRVJRw2glXSKIKCNAHjRVQ0Srk9F4/H8Mw55RinBXuXd5P+6cW4S5cgBodQVq9GWb0GpbcXGYmSP/U0Cid/fqovqSU0P6Iig2H8CVV9FcdZ6ivUNsZxNsdxNkVVl6Oqj6Hr/0RVn0NVQdcVhFhGqfRvbHsZrtuN4/T4Euvh+h0FTx3npb8KEcJzeXBGfL1sOeQN2XrzTZ7UG/97yzcpNl7keRjHWewPznY2jURUTKY9VwuRy4FQKkVHRqNjC9K6E/X+LhZB07D233/C42ezeQxDxzDa00IOIiqaR6Cym4BmODiM9p4rFkt0dyeqym/t3XZj6A+3o99/P9pTTyBs3zroI/sjfSlsp9JMBwdNe8kP6Nucsb+mOq6bRNPeQggFTXsHQmjYtkOplPfl3y6uuxRF6fWthfJ4rTjwTFIlZdcE14373nLlcD7p/zH8Fp8AevDECiv873OBEl5URRbXjeO6G+A4O1Q5385kIgeHetRzk0H2zPN85lzXa9mpKjKR8NR2o4MsVdVzxs/lKe22G/ZOO094/OECh3Yo7jwCB4dmEAzG1oEQMGeO52vmDRXW97haya2maRCPR6fdBqjZlOW3Q0OZKTg4SEzzKhRlJa5bzX7HRdcfRdcfQ1Uj2PZ7sW13xMXSc/FW8Npswt9j2tD/nIOqvoyivOl/vtsXMwziGbl6KzxPzh0BCki5CMNwsO0XkFLDC96b7w/UJnDdTSiVdsdxdmImSXpV1YsvyWbzlRm58YZbm4WyaiVdH/lwxXMO8IZi16xBlEU9w1OUhYK9ww6kL/l1ZTh2wudQlBFu3O1ASpdoNEIoFKKvb4A5c7oolRwymSxz5yZJp7M4jlsZogXo7k7Q35/yMzpb/3vT6YOxQcuuDhoVONST3DpTAtQapTkCB9t3V4hV/aphZDDNLEIswbYLlEq9Y+7cpYyiKG9j21tRKu0J4Jug5vDC87rwVjneitdxFvpO4Io/+GriuT54ceaeHH8FijLHN01diuvOxXHegWUdTrF4JI6zMzOpGMFYgUOz23PVcBduQPHQw7zQyry/R6QouAsW4M6fj/StgqRp4myzLdnvfo/UNdfWXYwgEDjMVGZGb6EDqNfBoZFoiHocHGYiU3dwKLt3j1xhlaXKQvTiOBa2HUOI+QhhI8RKPFNVgRc54eK683CcHbHtdyLlfHT9z37QXwkpw9j2lqjqysoqx0uU9VCUIX8eSSBlN4ryKlLmEWIThHgXtj0fRRnyhRAp2uXe3QrKDg7d3QkURWlqe27c5/z6mYj+fsw77oBs1huGBUSpBNEoxYMOJvPD86Cra/LP4b8urzhkmnXqNQkcHKZG0LJrAE0TJJNRHMcZ4+BQqz1Xi2g0gmnqDAykZpXiJhIJEQ6HGBhITcrBQdfvQdf/geNsVblz13UN27YplV5HUf4DSBxnE1x3U4RYhaKsxVv9ePs5QgxQKn0E297FP2oOVX0FRVntS7TnIMQaNO1xhFjp2xGl8CLUB/y/JaD4BWwHhNgLw9iEbLbgv64cqvoWpdL7se3dmvbzaxfD23NCePsgbbs5chz0v9+PedONaE89CYD9jh0oHn4Epfd/YFIJyqMpt5BTbXJwAK9119UVR1GUioNDNpvHcRy6uuL09w9hGHpljykcDvkfp2h106rTW3bBCqkBqgkcpprcOpmIipnAVAUOjrMVmvY4ptmPri9CSkk+X8C2HYSIoig2IHxn7SSQxHGGDwwXUdWc34YrE/Gth0Y8E5DBNP+FZyMU9y2CkoA3l6SqK3CcBVjWAUAUTaPS0oIIrjsHTXsG234H3vzSzGC095xhGO1drasqpQ/sQ+kD+7TsKaZL4FAoFInHYxWbpmg0TF/fYCBwmIBgD6lByg4OhUKe8877AUcccShDQ4PecOtgumGBQjUHh9nCVBwcVHUpodBB6HqJUul5crnV2HYez727FylNXHcOUlabS5Go6nJcd+MJYx4U5Q10/WHfYFVFCAswcJxtKBaPwLb38JV+CRTFi54vFotomoque/dzUs5FiH5U9e2GX+d0MN5wazMcHDqRdjo4CCGIx6MkEnGklJimQTjsrT57errRNBVNU5k7N+nPOHo3BeuuAeu3g0OwQmoQ23a4/PIr+eUvf04qlWK77banWLRR1ckH+o2U307QGnUctH8/hrJiBZgm9s474y7cYMS3iDVrUF95BSFdnMVLcDdub/ZOmeECh4GB+u5OPeFIGNM0sKxdyGR0hPgPqvoGQvQCBq67OaXSPmjaC6jqK7juBv4eEAiRQVFWImU3pdL7WDcrNBZVfQnDuM2Xl28ExCnPJynKSjTNRcpuPKGCjqKswXUX+fJyG9M0sG3Hb//B6D2vTmO0em5oKD1CsDBc4DCeCfBMZLjAoVBonQmwt3/szRml01mKxWIloqJUKlXyw3Rdq9x8jp5xTCbj9PZ6LiDrI0FBaoBnnnmK88//ES+99CKJRIJvf/vb7Lff/k3xOq1H4GD89Q5Cl/wS9dXXoGQBAhmPU9rng+S+dirCsQld9hv0++5FSaUBiYxEKe26K4VPfmpaPPAaETiEwyEikRCuK4f1/DcDNkWIvor6TcoevHbdFmjaI6jqayjKKjwBQgjH2cr3q6vlaJFD1/+BFw3RjZd1hH+MKF7U+SqktPyZTQUvC8kFHIpFSSQSwzR1CoU8XtFqjhFvK6gVDTGcVkdUTBetFDhomkYsFkFVFQqFIrlcwR9+Hy5wKFAsWhiGTl/fID093ZUWf09PN/l8gVLJIZGIrtcCh0DUUCcDAwMccsj+2LbNQQd9jC996StsssmiqgKHyVJL4GDefBORH34fikXc7jlgmiAlIp1CZDK4SzdB6jrqW2/iJhLIri7fpiWNMjCAu2ABmR+ch7Nj++MQJhI46Lr3hlYUhXy+WHFGrxch1vq5R56c23NKqP1mVtXnMIzf47obomkP4w3BjpTge+3BcrEZBOYgpU5ZCq4oSzGMpeRyg0gpKBSOxUuW7RwmM9y6bkYu3bSIik6g2QKH4fvHpZJNJpMbk3DdaQKHThc1BAWpThzH4dZbb2Lbbbdnu+22ByAW8/rDE8m760UIQXd3Assqjbg7VVauIHHYIYh8HndelVmMUgntlVeQuoa9ww4jTSoBXBf1rbdwNtuc1NW/HRuQ1ga6u7uwbXuEwGFke65ENpvDcdozj6Hr96Fpj+I4m/uBf8uqWP5If5WkoutP+PHpC/CGbUsIkUPXY0g5n3T6YGx717acez1MdbjVSzkWVR0cZjLNSjke3p7LZvMUi9Xf/1JKVFWhu7uLdDpb+Z3v7R2o/Iz7+gaZN6+bUslhaChNT4+3v9QKgUOnF6RA1FAnqqpyxBFHV4oRtCeiAsD4058QQyncuT1VHyccBxzbGzSs1h9XFJwFC1Bef33cPJlWM1rgEA6H6O5OoGkaqVSGVCrTtmLk4VJeRbnuIqQM+5ZDo+/P8qjq27juXKALL7xP+CslDcdZg6paaNpmbTz32jRjuDUQOFRH0zSSSc/2q1gsMjCQGrcYQRBR0ShBQZoCrY6oKKP961/rrPqrIPK5ygmJ8fKUQiGEY6M98URTzrNR1oWWRejuThCJhMjniwwMDLVRjrsOzyHcBqRvwvoOpDRRlJUoSh+KMoiivOV74cV8xd0uuO58hJAIAY6ziGJxH2w7QSTy5rT3/KcUDTGKqURUdDKTdXBY577iqecGB9N1zxoGDg71ExSkKdKKiIps1kuhLN+dCqsISo2LwvA9gZpvEOGLIdqP9+aXKIqClJ6dUqN7Rc3EcTZByqSv3APXne974u2I687BdSOAiesuwbbfC5TNU99FqbQnpdKeOM6OSLmAYjEBPE0kMj3KqKZFQ4yi0YiKmUKjERVedlkXhqGTTmcZGkqP2SuaiCCioj6CgtQEmhVRUca2HQoFi0gk5C35N9vMi3Me5xdT6rrXaRICaY7TYvFbee78BU05x0Yot+dUVaNYtFBVpSnKxKkgZRLb3gVFSSHEGryh2LBfgHZAygXY9rtxnM3HiB3K0efrjmVSKmUIhbxVSjtppfdcIxEVM416Iioabc/VIoioqI+gIDWBsoODpmmEQs2R/pZXD9FoGOugg8E0x23HeXkyfmGKVC+KYmAANx6n9MEPNuX86kHXtTHtufKdeyfcddv2LpRKH0AIgaq+7P95yR+q3QjLOgjX7fED/WpRpFRScBytbWa5zWzP1WK2mwBX+z2cSnuuNsLfm1NHRFSsa2d7K6byHlM2m/c/Dq03q6SgIDWJVgoc2GUXrA/ui0gNIdLpkSsl20ZZuwZ3zhzcriSit3fk16VEDA6iZLNYHz+kLWmziqIQj0fp6orjOO6Y9txUHByai4Jtv5ti8Tgs62BKpb0plT5AsXgYxeJRfiDgdggxxFixw7CjKAM4zjZksza6rmGarZtHalV7rhbrk8ChGe258QgEDhMzu9bh00hZ4BCPh4lGI00ZKqw4OMSjDH73e6AqGHfdhVi5wrsy+QFn7rx55L75bZS33yJ02WWoy5YhNc2bQ7JKyGiEwuFHkv/il5rwSmtTfbh1JJNxcGglUsZxnHdU/ZrjbI3rPo6ivIHrLmX0fJOivIWU3dj2NkjpbUyX73qbfQGpd7i12awPDg627RCJhMcMtzabssDBNM3KXtHw3KZYLEJv72Blj6m3d6AicOjvzzDbHRxm7RzSsmVvcMEF5/HMM08RiUT5yEcO4LOf/eKEd+WHH34wq1atHPP5e+55AHO8/ZlhJJNhdF1rWvCepql0dfkBavkC6vPPY/zldpRlb0LIxH7Pe7E+vF8l6Ex5802Mv96B9uQT4EqcLbfE2m8/nK23oSlLt3FodLhVVRWSyQS5XGGSERXtQ1FexTD+7CvukkgZBoooSj9SzsGyPozjbON/ryCZ7KJY9Bzfm0GrklsbYbwZuSkfN5VCe+JxKBRxFy3C2aa1v6djnl8I5szpQggx7nBrs5FSYhh6ZTA2Gg2jqmrFwcGyLDKZXGWINp8v0tOTJJPJT9nBodPnkGZlQUqlUnziE0ey8caL+cQnPsnatWu4+OIL+PCH9+e00/675mMPP/xgtt56W44++vgRn99uu+3r+kWoFVExWTo5omIqw61TjahoJ0KsRdOeR1WfRQgLKXUcZ1tse2ukHDlQGw57Q5ODg1Nr97Q6ubVRmungIIaGCP/f+Zi33opIp7wWg6Zjv+Md5E/5qhc/0WKGD7cqirJeRFR0ekGalS273//+FnK5LN///o9JJLyAL8dxOP/8H3HCCZ+ip2dezcfPmTOH7bev3sKZiGoRFVOlUyMq6mnP1WKqERXtRMp5lErzKJV2B4p4vnXVV9v5fLHSkhkamtxNyXS152ox0gR48k4HYmiI+AnHoT31FGiaJ8pRFLAstP/8h/jnPkvmh+dhHXJoE89+HdW85+LxaNsjKjKZHN3dXSMEDr29A+t1RMWsFDU8/PCD7LLLeyrFCGCfffbFdV0effThlj9/uxwcpotq6rnJvpE7R+BQLxqeX13t881mc5MSOLRLPTdZmiFwCF9wPtpTTyFjMWQs5lldKQqEQshkF5RKRL/1TZQqrfOpUEs9186IivK5BAKHsczKgrRs2RssXrx0xOfi8Thz5/awbNkbEz7+zjvv4AMf2I19992TM844hVdffaWh52+Xg0O7mUg9NxmGCxxmE+XJ+/IqZyKmQz03Gabq4CCGhjBvu8UrQtVmm4TnYC+yGcxbbm7CGXtMpJ6brIPDVAgcHMYyKwtSOp0iFhvbK43H46RStVsN73vfXpx22tf5v//7Oaed9t8sX76cL37x07z99vKGzqEdDg4twXHQnngc446/oN95J8qbbwKt9Z7LZr2703C4PXen7SKbzQFiwrvuVg63toKpODho/34MkU4jwzUe6xcE/W9/m9wJDn++BoZbG3VwaAaBg8NIZuUe0lT42tf+q/LxjjvuzLvfvSvHHXcY1113DWeccWZDx8pkCiST0fqC9+pguINDK+S32gMPELr6Si/cz7IACYkEyp57Ik45hbyY1xK7H8fx7k7Lr6vTBQ714rqSfD5PJBKmULDGCBw6QT03Gcot5HI8dyMCB2FZ3lV4otWVEOs8GoeTzaI99yyUbJxNN0UuHO3QXn74yGiIegUm2WyeRCKGYeht2U8a7eBQFjiUHRy8LKViZY8pny9UHBwKhRQTxazMNGblCikeT5DNjg3hSqfTJHx5dL309PSwww478eKLzzd8Hq12cGgm+j13Ez33bLTnn8edOxd3880RW22FFonAn/9M6YtfpPBKY63LRvDuTjvDwaGZ5PNFHGfkXfdMac/VYrItZGfDjbxWXWmCi72UOEs3qfxTDA4S+fa3mLPzDnR99CC6Dvs4c3bZmfhJJ6A+88yIh05luLWWg0Pr8AQOn/3sZ7nggv8lm81jWQV6e/vWOweHWVmQlixZOmavKJPJ0NfXy5IlS9t6LjNB4CCGhgj/8hdQLOJssglqPI5u6CiKQimeoLR4CcrLrxC6/LKmPN94zDyBQ30MFzjMtPZcLSYjcHDe8Q7sbbZF5HLjGwFbFigKxUMPA0D099P10QMJX3qJ51Siqt4f18W48690HXwg2gMPNM17broEDo7jcMstt/DAAw/w+c9/ns9//rOsXdvLihVv8/jj/14vBA6zsiDtuuvuPPbYo6TT69pk9913N4qi8J73NBai1tu7lqeeeoJtttl2UucyEwQO+v1/Q1m9GhYtwjANVE3FcRwsq+S1zzQNd+5c9IceQlnxdlOesxqzWeDgva5Ix6rnJsOkBA5CkD/lq2AYiFRqbFGyLEQuh73LuynttTcA0bP+G/WVV7y9JV1fF8WiaaCqiGKBrpM/TVJXmuI9126Bg6daTfKNb3wDVVW54IKfsMsuu/Dmm29y/fXXcemll/K1r53Co48+ytq1a/n5zy9m+fK3Z6XAYVYWpI997DAikQhnnXU6jz76MLff/gd+9rOf8rGPHTpiBumrX/0CRx318cq/77rrDr773W9y551/4T//eYw//el3fOlLn0VR1DGDso3Q6QIH7cUXUVQF3d9YLVklnFEuEzKZRAwNob700pSfrxazTeBQbs/purddWyxaM649V4vJCBxKH9qX7Dnf9zK6hoYQg4OIVMr7u1DAfvd7SP/iV6BpKCtXYN7+J++B1YqDEN7n+/vJX31N07zn2iFwUBRRUa2qqsLixZty+OFH8vrrrxMKhdlggw244orL+cAHPoCiKPz0p+fz/PMvctVVV3LBBT9maCjDpZdeymWX/WrWrJJmZUFKJBL89Ke/QFU1zjrrdH75y4s5+OCP85WvnDbi+8rL5DIbbLARvb1rufDCn3DqqV/il7+8mK222oZf/eoyNpyiKWmrIyomSzgcIqyKinVKqWRX/+UuP0eLbVWGCxzaJb9tFaPbc7mcF3nQ7oiKVjLZiIrikUcxeNc95L92GvZOO+NsuRXW/geQvuTXpK69HjlnDgD6ffd50Su1fmb+74ny5z9P6bWMpp6IisniqVa7fIcTm4GBHNlskZNOOpk5c+Zy2WW/4bjjTsKyLK699jqOPPIoXn31VV5++UXe97738cADD/Cf//yLp556issvv4yHH36g6ec4HcxK66BOJRYzCIdNMplsbQcHy8K46060Z54G18Xeamusj+w/JlpiKv5iw73nrJ//AvXii3A223xc9ZPIpBGpFJkLLsTZdnLty0bo7u7Ctu2Od3CoRi31XDKZQEo5aQeHTqWrK44QYkoODtUIXXoJ0W9/a8zMkmCU/7plUdpzL1I33dLU508kYqiqZ+/TDHRdIxqNoGmqbwdVpFgcuYd4xx23c8453+bTn/4cTz/9JI8++jD/7/+dzYUXno9lFbn44p9x8smfZf78+Vx00UUcddRRbLjhhlx99Q2oau3i2enWQTP7FnSGUY/AQb/zTpLv35PYl79I6Fe/JHTpJcROP5XuPffAvPmmEd9bTeAgBgcxr7uW+Kc/SeLQjxM7+TMYt96CyHgXwGrDrdnd9kAmuhB9fdVPSkqU1Wuwt9nOM79sAzNR4FCPem6yDg6dTqsiKtwNN/Rc7UeNAowoRlJ6rveLFjX1uaF5AofR7blczmJgIDumGAHst98BfPe7P+Cggz7G1772X+i6zr/+9Qhf+MJXyOVyXHvt9RxzzCdYsWIFt99+B8cccxxvvvkmv/3t1VM6x04gWCG1mVBIIx4PUygUx6xq9LvuIv7lL4JV9Py9yneFjoPIZEDTyJ7zfYpHHT3icd7dKWTu/RvR/zrDs1wRwpfXer/w7tKllH72c8Lv2hnXlWSzuRFzFqFLfkXo2mtwY3Hk3Lnr+vWlEsrbb0M8Tvbb38F+1y6t++GMotl3p62kEe+5eDyKrmsdaZY7FTyTXZOBgaHmva5CgTk774AYGABjnCLuOOC6DN3yO+zdd2/O8w5jqibAZc9Hb+bIJpMpNjRU/uaby0gkukgkEpxyyudZvXo1V111PccddziDgwNceulVnH76V0ilUvz2tzexwQYbjnusTl8hBQVpGqgaUWHbJD+wN8pbbyK7uqq2zsTQEDKZZPAfDyCHOVFomkpX31rcjx8Ca1Z7MeXDeu7CsVHXroWlS8lfdwO5eJVZLNsmdPllmL//HWJokEpTRFFxN9qQ3JdOwd5jj+b+ICZgJkRUTGa4tRURFZ1AsyMqKsOtF1+EPOss7z2hqiPfG/7qqfTu95D63R9aFl3R3Z3AcVxSqbHzjeNRT3uuUYrFIqVSiVgsxj//+Xe++U3PVaavr5dvf/sbvP/9H+Scc3407uM7vSAFTg3TQDUHB/2++1DeXu7HkVd/U8lYDDE4iPHHP1I85tjK523bwf7NZWhr1+AsWFhZ3QhAURUUzcRduBBefx33uuvg5M+NPbimUfjsyVgHHoh+//2evFvTsbfZhtIe74NotOk/h4noZAeH0dEQQ0PpuueJJnJwmKlMxcFhNMOjIdInfx7t1dcJ/ebSke07P6TS3mln0pdd0dIcpUYcHBRF+JExnr1PLmeRyxXHHbtqBNM0K7ls73vfXtx99z/RNA0pJXfffSep1NDUn2QaCVZI08RogUP4p/9H+KcXVIL2xkOkUhSP/wTZ752z7pP5PMn9PoSayyHnzsVxXBRFVFRqruPiSulFnW+4EUN/vqOtIWhTpdMEDs2KhggEDmOpFg1RvkRpjz5C6Mor0P/xd0TJxt5ySwonnoR14EFQR3jmVKmnhTzV9txUkFIihGD58re47rqrefbZZ3jttVcwDBOQRCJRDjnk43zta1/DqNL+lFJy6aWXcu2119Lf308sFgMgl8uxaNEijjvuOI455piWvoZghTRNZLMWpqlXYqGngjI4gMjmcEMmqhAIX+DgunLEqkKaIUR/HxQKUMvcssPIZnMkEjEKBYvSRJYzLaTZ3nPZbI6urjimaUz5d6CTyGRyJJNxQiGTQqG+Yl2P95z9nveSec97W3HKdeHlF3mxK9781TrGtucKU27PNUp5/OP111/loYceYIsttuL1118FJOee+2PWrl3DRRedz+23387Q0BDRaJSPfexjlQJ16aWXcuGFF3LGGWdw22238dprryGE4Oyzz+aFF17gO9/5Dr/73e9YtmwZuVyOjTbaiC984Qt89KMfbdprCFR208RoBwd72+28L9S64DoOSIk9yjVCmiFQFcSw4uM4ztgWl+uAoo6/OdyhTLeDQ6u85xqNqJgpNOrgMBXvuXZSdnD4978f403fBb8R9Vy72GOPvbj11tsrIaMLFizkve/djb32+gBCCFatWsXZZ5/NqaeeyvXXX8++++7LjjvuyPnnn8+2227Lhz/8YV544QW+9a1v0dPTw+OPP84nP/lJDMPg9ddf5+yzz+ZXv/oVxxxzDJZlccUVV7DVVlvxuc9V2QpokKAgTSPDHRzkvh/C3XCjmh5fIptFdiWxDj54xOdDG26A8s53IjJZHL8IVbsQiHye0vv2rD1k2KFMl4NDq73n6o2omGlM6OAgJeYjDzPnlC8R3WQJojuJu+OOiJ/9zLMU6lByuQKnnXYap532VUBWHW6dbuFkuVX/8MMPMnduT+Xfv//9LTiO4w/Bl9h3330rBerEE09ESsmLL77IkUceCcB5551HMpnk/vvv58c//jGhUIitt96afffdl912240TTjiBvffem4suuohQKMS//vUvdt55Z/baay9OP/103n67cZuxoCBNMxUHh2SC7Le/u87ja/iGsOMghoZAVcmdeVZFYVdJbo2GsY48ClcRyKEU0pUoijLCmF4MDoJhVgwrZxrtdnBoV3JrWeAQCpnrjYODAJI/+j7xIw5D3HYbMpNBlkqoL71E9Dv/j67990NZtmx6TrwOjjzyKFauXMnVV1+J60pSqTxDQ/m27RXVy7Jlb1T2gcArULvvvjvz5s3jtdde4/rrr68UqJUrvXReVVVZu3YtW265JYlEgpdffpkVK1bwpz/9iVwux3HHHTfiOX784x+z/fbbI6Vk/vz5/PznP+fMM8/kpZde4ogjjqC/v7+hcw4K0jQzPKJCPfhA0hf9DHfBQkQ26/l7pdOITAY5Zy7Zc39A8ehjqg63pt+/D8XDj0DkcsiVKyGfR5Gu9/eqVYiSReFTn8betTFz2U6iHREV0xENUS2iYjZQzQQ4FDLpvuE61IsvRgLSMDxBgmEgw2GkYaAue4PECcd7rt8dRLk99+Uvf4kNN9yQ3/zmNzz11PPT2p6rRTqdQtPWDZYvW/YGm266KV1dXQwNDfH3v/+9UqBWrFiBqqq4rosQguOOO44tttgCy/8/cF2Xj3/841x99dVst9127LHHHpx++uncddddfPvb32aHHXZgyZIl7LbbbhxwwAFceuml9Pf387vf/a6hcw4KUgcw3MHB3m8/Bu//B5mLfkbh5M9R+PRnyf7v+Qz8/Z8Ujzxq/ORWRSH3P98i943/wdl0E2QqhdLbi5LL4WyzNdnvnUP+y1+ZUeq6arTSwWE6oyFmu4ODV+QTRA0NfvK/IKVXjEb/PioKUtdRX3kZ486/Ts9JV2G495yq6pxyyumUSiXOP//HM2a4OZ1OjciDe+211yoFqlDwzGR33313enp6uOKKK3jjjTc4+uh1Q/g333wzkUiE3/zmN5xwwgncfvvtbL/99my66aZjtggWLlzInDlzWLNmTUPnGKjsOoCywCEeDxONRshIsA46GOugdXtFw73n8vli9eRWRaF4zLEUjzgS9dlniUsH5nSTWrTEe+Pn856goQETzE5juMBhYKA5irtOSG4dLnCwrNKMuchNhOu6OI5LOByiVLLJ/OWvxFas8IrRePjDr+bNN414D0wH46nn9thjL3bddXcefvhBHn74AXbb7X3Tep7ViMcT2PbY98jQ0BBdXV2kUusKlGF4M1OLFy/mueee4/XXX+cPf/gDTz75ZOVxG220Ec8//zy77rorL7/8MvF4nCeeeIJCYezQ+uuvv05fXx+bbbZZQ+ccrJA6hPEiKqq15yaMEdc0nB13JLPLu1E32ID4by6la98P0v2+3ene7T1ET/0q2gMPjB+Q1uE0S+DQacmts03gUFbPKYrAdSWO4yBXr/YGWyfaB5QSxd/XmA4mUs8JITj11K+zZMnSKQ0At5IlS5aSyaxzlojHE/T19bF27Vo23XTTyueHhoZY6EfB27aNZVkIIdhyyy157bXXKnubO+ywA2vWrOHtt9/mwgsv5FOf+hSWZbFs1H6flJJzzjmH+fPnc+CBBzZ0zkFB6iCGR1SsWLGCP/zhd4TDxtj2XJ3Ip59GHnAgxvk/QVm5wttNLpUw//Jn4l84mfBPL5iRRakZAodOTG6dLQKHasmt2WyOUMhESSS8O4E6fu9kV1cbznYs40VDjD7ljTZaxG9/ezN77vn+aTnPidh1193p6+utjH8sWbKURx55BEVR2GOPPUgk1hWoXf295ZdeeolSyVuhP/vss9x5551E/JSBlStXMnfuXH71q1+x1VZbsdVWWwHQ39+PbdvYtk0qleKnP/0pDz/8MOedd17lsfUSODV0GLouueaaq7jkkksoFov89rfXsXTpJg0fR6RTJA47FHXZMsTCBUhFGTGXJFIphGWROef7WIcc2syX0DYm4+DQCe25iZipDg6jh1szmdyIeaKurjgim0XZfDPIZCA0zkrQdRFFi+y536dw0ifbdPat8Z6bDgqFAg899E/y+Tznnfd9hIBjjz2Bf/3rEZ577hmOOOII3nrrLR5//HE23XRTnn/+eTbaaCP6+/spFou4rks0GkVKb1VrGAalUolCocBXv/pVHnroIR599NGa53Duuedy+OGHN3zuwQqpg3jwwX9y6KGHcNFFF5FMJjn33HMnVYwAjDvuQH3zTdyeubhCoAiBGCYEl4kEuC6hK68YY+0/U2hE4NBp7blazESBQz3DrZlMDrUrgX3iSd5vYrVWl5SIooVMdrVtRKETh1unwsBAP9/61pl8//vfxbZLlEolrrzyNzz33DOV4daBgQGEEDz//PPMm+elaG+77bbMnTsXgGw2i67rhMNh0uk0If/moa+vjwMOOIDDDz8cVVX56Ec/ylVXXcXWW2/N0qVLURSFk08+eVLFCAJRQ0fgui7f/e43ueeeO1FVlRNOOImvfe0UVFWbtGuy8Zc/AxJ840UpJYoicIZdgN14HPXVV1CffRbnHe9o0qtpH/UKHJrlPdcuZpLAoZb33GjKDg7m2WdTeuIJ9Af+CaUSUte9OwbbRkiJjEZIX/LrCX0dm8F0es+1ig022JB//vOxql9LpdZw9tlns2zZMiKRCEIIlixZwhe+8AVuuOEG/v3vfzNv3jwMw+Dee+/lpptu4pvf/CbRaJRvf/vb/PznP+eGG25g/vz5fPWrX+Xkk0+uKOyWLVvGUUcdxemnnz7pcw9adh2AbdscddTH2XjjxXzta//F0qWbVI+oaIDEwQeivv6al20ECASK6rXtKv/ljoPS10/6kksp7blXM19S26gVUTET2nPj0ekRFRO152o9rrs7gZXOYF90EaHLL0dd4U30S13HOvAg8l/8Es42rU0lni3tuUYZHT/x6quvcvbZZ/P4448TiUTI5/Nst912fOELX2D16tX88Ic/JBQKoes69957LwAnnngiK1as4K677qoc4+CDDyYUCvHrX/96xL7unDlzWLx4cd3nF6yQOgBN07j55j+O0PJXi6hoBJlMIhynkqwpKa+SlHUXjlIJqaq4bbgTbRXVIiqmEg3RKXRyRMWIaIh0tiFj2EpERVeCwS+fwuBnTkZZ9gaiUMTdcENkd3frTpzWRkPMRDbbbDOuuOKKyr/LBepLX/oS0WiUww8/nFNPPXWEO7gn5V/3+/jkk0/iOA7ZbHaMG/ghhxzCD3/4w7rPJ1ghdTCxmEk4bFQiKhrBvOZqouecjdszd4R3naqqSOm5gIu1a3GXbsLQH2+fkf52wykLHCyrNKPacxPRSQKHRtpzEzGViIrJMhvbc43S6QF9gaihg8lmixUHh0YNFqwDD8JduADR2ztCtOC6ridwyGQRQOG442d8MQLI5wuYptFy77l20wkCByEEsViEZDKOlJLBwTTZbH5Ke1tlB4dQqPU5Rrq+Tobeyd5zAUFB6mhGR1Q09NjubjI/uQA5twdlzVrEwADkcpBOI1etQrGKFI48ekTy7EykrJ4rX2xc1+1Y9dxkmO6IilZFQzQaUTEZZpt6bn0gaNnNAKYicFBef53Q9ddh/PH3XrSFUHB23AH9058m+8F9KUwQx9zJjFbPWVZpXIHDTGY6BA7NbM+NR0XgYJUmrSYdj6A9V51Ob9kFBWkGoGmCZDKK4ziTEjgAUCigDPQjzRCyu5toLIpp6gwMpDpaVlyNWuq5SCREOBxiYCA1NqBwBhMOe0KCakmqzWSy6rnJUm6zDg6mm2LBs76q5+olKEgBTWEqAodqtPLutFWMVs9ls7mq6rnJODjMBFotcBiunstm822LVW+GwGG0ei6fL63X6rnx6PSCFMi+G2TZsje44ILzeOaZp4hEonzkIwfw2c9+cUK3ACkl11xzJbfddhODg4NsscWWfOUrp1Vihicimy1imhqRSJhi0ZryG60iv41HKRSKk5p1aieNDLdmszkSiRiFgkWpViT8DCObzdHVFcc0jaYWi3a052qRyeRIJuOEQiaFQuNClHa355Yvf4vrrruaZ599htdff5XFi5dw9dU3Tvi4qV4D1gcCUUMDpFIpTjnl89i2zbnn/piTT/4if/jDbVx00fkTPvaaa67ksst+xVFHHct5513A3Lk9nHbal3n77eV1PfdUBA7jUS1ArdOYTHLrcAeH2USzBQ4NqeekRHvkYSJnf4/of3+d8P+eh/riC1M+B5i8wGG61HOvv/4qDz30AIsWLWrI2muq14D1gaBl1wBXX305V111Gbfc8icSCc+J+Pe/v5Xzz/8Rt9zyJ3p65lV9XLFY5KMf/TCHHnokn/vclwAolUocc8yh7LrrHpxxxpl1n8NUHRxGo2kqXV1xstn8pO5OW0W97bnxqOXgMJNplsChkfac8sYbxL/8RdRnnva85sAbuFZVrH0+SPb8/5uyzU8jLeTpbs+5rltxIzj33O/wwgvPTbhCauY1YCp0essuWCE1wMMPP8guu7ynUowA9tlnX1zX5dFHHx73cc888xTZbJZ99vlQ5XO6rrP33h/g4YcfaOgchkdUNAPbdigUrErLoxNoRjREMyIqOpGpRlRUi4aoVYzEqlUkjj0a7amnQDcgEkVGYxCJglAw7/wr8U+dBMWp3cyUW8heHtj4Own1RkO0ksn8PjXzGjCbmT3v1DawbNkbLF68dMTn4vE4c+f2sGzZGzUfB4x57JIlm7B69SqKxfrv4G1bUiiU0DSNUKg5w5LlwL9odHpbXJNpz9UilyvgunLaX1ezyee9PZJGbkomO9wavvQSlLeXIyMRKJugegcE00SGQuiPPoL5pz9O5SUBtVvIM324tZnXgNlMUJAaIJ1OEYuNXfLG43FSqfEVQul0CsMwMM2RU+nxuHdxSKcba4dOxcGhGiPvTtvv2tDKaIhGIipmEo04OEx6uDWXw7zpRu8/aLxVgaYjAfPqqxp7AeOQyeR46KEHueOOPwOzZ7i12deA2UpQkIBiscCxxx7GscceNuJOJZUa4mMf24/Pf/5THWVuOZsEDq1Obl2fBQ6NtudGo769HJFOea26Wmga2vPPNSV92HEcrr/+Bs4++3s899wz096eC2gvQUECTDPE//zPd3n77eVccsnPK5//yU9+RCaT4Rvf+DaqqhKPJ8hmM2Men06nSdTY1I3HE1iWRXFUnz2dTiOEIB5vfKOxULApleymrmqy2RyqqrbFX6zZ7blaZLM5FEUhHB4noXSGks3mAEEkMvJ1Nct7Tta7VyKhKUt1nxNOOBEhBOed9yOKRWvGteeq0YprwGwkKEg+2223PcceewI33XQ9Tz75OPfddzf33HMnn/vcl1m8eAngZdKP3ivKZDL09fWyZMnScY9d/tqbby4b8flly95gwYKFmObkLpQzUeAwHcmt65PAoZnec+6ijZHdc8CqvaoSroO9085TLkrl9tx73/tujjnmGF5++WV+/evLZlx7rhqtugbMNmbPu7MJfOpTJ7PJJptyzjnf4Sc/+RE77fROjjji6MrXd911dx577NER/d777rsbRVF4z3t2Hfe422+/A9FolPvuu7vyOdu2+fvf72PXXfeY9PnONIFDq9tztZjtAod4PDKl9lxVTJPCsccCEsYran6xKhz/iSk91Wj13IknnkwymeQ3v7mU3t61Uzp2J9Cqa8BsI3BqGIau65x11v/jM585AcMw+cY3vj1ipfCxjx3GzTffwFlnnc4JJ3yKtWvX8LOf/ZSPfezQETNIX/3qF1i1aiU33PA7AEzT5PjjP8nll19CMtnNZpttzm233cTQ0BDHHHP8lM55Jjg4dEpy62x0cBBC4OZzGPfdh/Pss+SLFqWttkG+//2eKm6KFD5zMsYdd6C99JIXNW4Y3krIdaFYRDg21of2xfrI/pM6/ljvuQLFok00GuPzn/8KP/zh2Vxxxa8544yzpvxamkWhUOChh/4JwKpVK8lms5VCs9NO76K7u7ut14DZRFCQRvHIIw8BYFlFli9/iw033KjytUQiwU9/+gsuuODHnHXW6UQiUQ4++OOcfPIXRxzDcZwxbZLjjz8RkFx//TUMDg6w+eZbcv75F7HRRoumdL5lgUM8HiYajTTFl65YtAiFzEmn1ZbptOTW4QKHgYGZX5BCIZPI3XchvvVN5NtvowhByJWEkLiLNib7rf9HaZ8PTuk5ZDJJ6trriX39DPS/34/IZZFCQbguMmRSOOpost/5LtSYHapGPcmtBxxwMM899wybbLLZlF5DsxkY6Odb3xo5yFr+94UX/pLu7l3aeg2YTQRODcN45ZWX+exnT2DffT/CK6+8xODgIFdddQOxWGy6T21COs3BoRHvuXYyGxwcKt5zf/wDfOELyEIBmUigmCZSSmTRQqRTyFCIzE8vovTBD0180DpQX3kZ/e67EOk0smcexf0PQC5c2PBxgmiI6aPTnRqCguRj2zYnn3wi6XSaK6+8jhUrVlSK0ze+8e3pPr0JaUpExSi8O9jGIio6pT1Xi7ZFVEiJ+vTTGHfegbJ2LTIapbT7HpT22ttrfTXIiGiIVBpll10Qa9Ygu7tBCIQivBae64IrEQP9uBsvZvDOu5vSvpsqQTTE9NPpBSlo2flceeVvePnll/jpT39BJBJl88234KSTPsOll/6CD3zgg+y22/um+xRrUhY4hMMGoZDRlIiKXC6PaepEo+EJW4Gd1p6rRS5XwDRNotFwyyIqxOAg0W+cif7gA4iihe/+hnnjDbibbkbmR+fhbLNt3ccb7j2XTmeRN99CbM1qZKKrom6TrkSoAkVRcKWLjCdQlr+Fft99lD784YmfREqUN95ApFLIeT24w9rVU6Ge9lxAAAQqOwBefPEFrrrqMg477Eje+c5dKp8//viT2GabbfnRj86dEZPU0+XgMJ3qucnSUgcHyyJ22tcw7rsXaYZwFy7E3WBD3A02RCa7UV9+mdiXvoiybNmEhxpvuFV79hmvEI3au3FdF4G3miqvirTnnq39JFJi3nwTXft9iO699iB54Efo3vU9JI44DP3eeyb7UwA6w3suYOYQrJCArbbamvvvf2TM51VV5dJLm2OJ0g4mI3BQn3uO0PXXoj7xBAiw3/kuiscci7PlVgBYq9fgXHk7iXvuwR5K4Wy8McVDD8febTdUXe/49tx4tFLgYNxzN/qjj+B2z4HQqPkSw8BdsAB15QpCV19F7pvfqnqM0cmtdSfFSnClRCgC6UhA1HZQkJLo//smocsv8/6pqhUVnf7gg+gPP0T2e+dQ+OSn6nz1HuOp5wICahEUpFlGoWATCtmVsLNxBQ6OQ/Q73yZ01RWehNe/aOn/+Q/hyy4j/5nPUHrvrsTOOA1laMj7GqA9+gjmrbcgd98d5dprcUS0o9tztchmcySTCcLhUFMFDuattyJdd2wxKqMoyFAY489/Iv+VU5BdXSO+PLo9V22eyN5qa+//zLbHrJKklAghEI4N0sXZaqtxz9X43a2ELr8MKcTIfS1VRWoSUSwS/fa3sHfe2Rt+nYCgPRcwFQJRwyykHoFD5AfnEv75zzx7mPJsCXgXOctC2HblbllGYyiq6t1suy7CtqFQwNl1Vwavvra5G+bFIvqDD6CsWYMMhSjtuhtywYLmHX8UrRA4JN+/F2JoyBMbjEexiMikSV1/Y2UvqaHk1kyG7n32RgwMeG4KoxACRH8/zoKFDN5zH5hV7KCkpOvA/dGeehJZ7ev+9wjLonjEkWQu+GnN1x2o5zqfThc1BHtIs5CJHBzE2rWEfv1r767YNEdavpQ/Z9uIfB4ZiXp39P6mvOIXMGmGUB5+GP2++5pz0lJiXnM1yf0+RPzzJxP95jeInXEayY/sS/SsMxEDA815nlG0wsFBqurERqNSej9rVZ2c91wsRu7rZ4KmIQb6wRm2QrVt6OsHw4Tvfa96MQKUVSvRnnm6tmedEEjAuP1P476mmR4NEdA5BAVpllJL4GDediuiWBz3QoXjem08QBSLKIIRjhVSSm9qX0Lohuuacr7hn/4f0XO+h1i5EjeRwJ03D3fuXLBtzJtvJP6ZTyFqRHxMhWYLHOz3vAesYs2iJDJp3Pnz0bfaatLec8XDjyD7vXOQ3d2IoSHEQL/3JzWEO6+H3I9/jHbkEeNGVIhMZl1hrIUQ3u/LqPOaLdEQAZ1DsIc0S6klcFCXvYH0qkz1B7vrLjzCdby7ZCmR0rsICf/fKAL1lVemfK7qU08R+vUlSE0bGYWtKMh4HBkKoT31FKFLLyF/+hlTfr7RNFvgUDzkUIy//NkbIK3mAm9ZCNtBOe54onOSFN5YhnvtdZh/+TOhTBp34QZYhxxK8SP7QzRa+7mOOJLi/gdg3vEX1BdeACGwt90Ga7/9IRJB8yMqLKs0ZsXlzu3xco4maFUK1/VuDobtVQXtuYBWEBSkWcx4AgdpmgjKkzFjKa+GBCCFQLqy8r2VDXMBSImcxIDnaMxbb0YUi7g9PdW/QdeRmop5683kv/BFiDQ/s6mZAgf73e+hePQxmFdfjbAsT7Sg6+C6iHQaJZ9D7LorpZNOInfLbUS+doq3+pMSVBX11dfQH3yA0M9/RvqSX+NuNoF1TixG8fAjaryuLiKRENlsfsTX5Jw5WB/8EMadf/WKVbUbFP/zxaM8k+FAPRfQSoKW3SynWkRFafc91im0RiGGzbZIwNX1EYVLSu8LAu9Paa+9p3yO+oMPIDWtZutIRqMo/f2oL7805eerRlMjKoQg9/UzyZ92Ou7cuSj9fSirV6GsXYNi6HDccaR/9gsyTzxF5Mtf9AZREwlkd7f/dxIZi6G+9iqJz3wK4ascJ0O1iIrh5D//BW8fq1ilxSg9lZ2MxbBOPDFozwW0nGCFNMup5uBQev8HcDZejPrmsoqSTiC8ClNGVb1WTpWLs5QSkcuCrlM8+ugxX28UYdt17WMgJaKFLt1NdXBQFAqf/gyFo48h9PBDhDNplFiUwv9v787joqrXP4B/zuzbGcZyxRT5mV4lyyUETHNNJQX1utwyrbyakrvidvmVWaa5lHrdriVmZXrLe83S8leRhZYLIVJ5kbTtgomaaSAzw+xzfn8cZmQYGIaZgTnA8/6n5nDOme/0Ip75fs/zfZ64eJRpdfyXhPTXwBiN4HQ6789fvnwpKiyA/IP3YX5ySsBDMZkskMv5Yrm3bnlmXdrj4qHftBmaRQvBWK38rFfEF08Fw4DTamHZ9w4i7u1Wr8tzhYUF2LRpPfLyzkGlUiMxcQSmT59V43O+8eOTce3aVa/jn39+0qt9OBEeCkhNgFeLCokEhs1boJ38GBgjH1gYqRQcB3CulG+dDo6WLSEuLATENnAKBf9H024HzCb+W/WKFWC6dgWCLOZqv7sTZJcvV7uECACM2QxOLoejXXv/b+x0QnL6NKQ5ZwCbFc72UbAmPlz1c51yoWxRwTAM1K1aQPHIBNhsdpQayviEBY4Dc+MGZJ9/7plyX1n5jEb+r3eDCkgA/7kiIljI5TKvfU3WseNw6957odjzFmSHD/MJF3fcCdvEiZDMmAFlh6h6XZ4rLS3FvHlPo1279li9+mX8/vt1bNu2CWazGampy2q8fuDAIXj0Uc+WDrIQLC2TukcBqQmoKsHB3rs3rB9+BPnKF4ATJ8AZ+RkBI5HA+uCDKHt2BZyRkVC9tAryDw9DZOC/WXNiMZxRHWCaNx+KaVOhYRB0MVfL+AmQHT8GVJf553SCMZthSR7l954k8XffQpO2jE+6KP+2D6cTqrVrYHp6JszTZ1QZCEKV4FDT5lbRtWuAvTzQ+yKRQHT5csDjcLHZ7LD4SHBwdOoM44urYXxxtXtzqyJMm1sPHXoPZWVGvPTSy9Bq+U3DDocDGzeuwxNPTPXoPVaVO+64A9263VsfQyUhRgGpiXAlOFitFmzbthmHDh1Ceno6ur53ENbvzkGcnw8AsN97L5wd73ZfZ3x5A8qW/g3SrNNgzGY4ItvCnpAAiMVwlH/rdiVNBMo2aDBs8QmQnj4Np6a8woErWNjtEJWUgGt2B8wzUvy6nzgvD9qpU/hNoxrN7VmIwwHGoIfq5XVgzGaY5s2v8vpgEhz83dzKKRW3G935wnE1By0/+UpwcBFC9lxW1inExsa5gxEADB48FK+8sgbZ2VkYMSK5XsdD6g8FpCaC4zj8618HsH37Fty8eRPR0dFQq1m+T1HnP7lr11V5bYsWsCaP8jputztgNluhUinKu9UG+BVaKoVhyzZoFqdCeuokYDC4Bg2IRHC2aQPDhk1wdOnq1+1U69aUVzBo5vkMTCwGFxEBRq+H8tUdsIz5M5ztvZcAKyY4WCxWvyo41Lb2nLNDNBztoyD+5WefVRLgcMA2ZGiN7+8PV4KDSqWE2Wz1GJ+QsucKCwswcqTn7xvLsrjzzuYoLCyo8fqMjE/w4YcfQCKRoHv3npg5cx46VviSRYSLAlIT8PPPP2HDhrU4d+5bKJVKLFmyBOPGjYfDEfwaTG1aVPjC6XTQp78OyTe5kH30EUTXroJTqWF78EFYhw0HlP5VUhBfvADpmTPgVKoqEzIAgNNowBQXQ/7eAZgWplbzufxIcHA6IT19CqpDH0By9QrAsjANGoyyh0eA09RQokUshmXSZKhWrax2qZIxGACpFOZHgk8ccTGZLLhw4QLeffcdzJo1FxERWsHVntPrS6Gp4r8fy7IorWFzdL9+/RET0w2tWrXGlStFeOut3Zg1axp2795HnVkbAApITUBq6mzcvHkTgwcPxdy5C9Cly/8AAIqLbwX9h8fVooJl1b6LufqDYWDvdT/sve4P+Bbi/HzAYgY0d/p8H3AcJOe+83kvXwkOzI0b0M56GpKcM+7itBzHQXHkCGRrXoJh63bY+vruoWWe/Dgk2V9D9ukngNl8O4ja7WDKTIBEjLKFi+Do3t3vz++P/Px8HD58GGq1GitWrGhUm1sXLFji/vfu3Xuid+8ETJo0Du+8sxeLF//Nx5VECGgfUhOQmvo3bNu2EytXrkGLFq1gNFogEomgVodmg6nFYoXNZvfY6xRW/jSDYhiA8/3Ht2KCg8elNht0KU9Bmv01X9dPo4GT1YLTRsCpUoO5eRPs9GkQnzvnewzlS5WmxUvgbNUajMkERq8HY7XCERMDw8a/w/z0zJo/Sy2NGDESHTt2xP79+3H+fL7gas+xrBZGo8HruF6vh9ZHhmRVmjdvjvvu64GLF78P1fBIHaIZUhMwYMAgj9d+t6ioBWOIEhxqIr54AbLPMsDo9XC2aAlrUhKcrdu4f+7o0oVPYjCbq1/mK58W2v3IxKqc4KBQyKH64iiYs2fhVCjASSrtixGLwWlYMAY9lNu2wLBzl+83kEphmjUHpqdmQPJNLpiyMjhbtITjnnv8C6y1wH8JUUIul2H58uWYMmUKnn/+eWzfviv4zcAhFBXVwetZkcFgwM2bNxAV1SEsYyL1Qzi/haReVVXBIRgVExyYEP8hBQDmt9/ATpqIiOFDoVq/DopXX4X6hRXQ9XsA6mVL+AAEwNE1BvZe94MpK6u2uCljMAByOSzjqi63U1HFBAdXRWvujd18bb/Kwcj9Bny3VlnmFxBdveLfB5TJYI9PgG3QYDi6dQt5MOI7t2rdnVv/9Kf7MGTIUPznP+fw6af/F9L3ClZCwgPIycn26NKcmXkUIpEIcXEJtbrXjRu/49y5b9G1Fu3iSfhQQGqiampREYiyMj6VOJStHACAKS5GxCPjIfvqS77gqlIJKBX8Mxe7HYp/7gM7M8VdCsm4LI3PpisuBqwV9v84HHzNOLsdpqnT4IyOrvm9GQYikaj8nwxKSvTgLv4AiKpv6Q6Ar4Zus0FUeCmozx4sX60hZs9eAKVSiV27Xg3rGCsbPXocVCoV0tIWITs7C0eOHMb27ZsxevRYjz1I8+fPxCOPjHG//uyzT/DCC88iI+Nj5Obm4KOPPsDs2dMhEom9NsoSYaIluybMq4KDkBIcKlDufBXiX37h06Mr1mNjGEChAGezQXb0KGQf/x+syaPg6N4d+vTXoV62BOJLl4DyTb/gOHAaDczTZ8A0a06N71txc6vJZIZSWV7nTipFtaVpOQ6M2czP0Bx2sHNmwTJqNMyTJnns76prFZfnqsuea9myFVasWIVLlwrrbVz+0Gq12Lx5BzZtehlpaYugUqmRnDwGM2bM8jjP4XB4pK63adMWN278ji1bNkCv14NlWfTq1RtPPZWCyMi29f0xSACoY2wTp1BIwLJKmM2WoNK2K4qIYMGEoIIDAMBiQbP4WDB//OGzyjdjNML2wAMo3X/g9kG7HdLjxyA9mwPY7HC0bwfryGRwd3h3WK2ous2tWq0GYrEI1pSZkL+zj0/trri05nBAVFLsWbTWtSlXLIbhxVWwPDElwP8Q/hPC5lYiTELvGEsBiUCnU0IqlaCkpDQksxqJRIyICBZGoynoBAfxDxehGzqErwbuq7Cm2QzI5fjjwo8BP3+pvLnV4Ko95xqLWASdTgvzqdOQj3gY4MAvGwIAx0H0x03PYCSX87O68qrZEImg37Ub1uGJAY2vJt6bWy1UjZt4EHpAoiW7BuLEiS+Rnr4Dv/5aiJYtW+Pxx6d47Wav7OrVK5gwwfucmJhu2LnzTfdrg8EMnU4NjUYVkllNyCo4VORPkAnifWqqPQfcTnBQPtAHptlzodi6GYy+FJxSxVchrxiMxOLbvaIYhu9BZbFAuWkjv9E3hEkL/izPEdIQUEBqAL777ls888wSJCWNxvz5i3D27BmsXfsiVCoVBg16qMbrU1Jmo2fPWPdrVaWlr6paVAQrVBUcHHe1g5Nlwej1Hh1LvU90wB5T+1Rpf2vPubgqOIifWw5jixZQ7tgO0ZUrYEzlteHKM+w4udxzLAwDTiKB5HwexPn5fFp3CNDyHGlMKCA1AG+9tQsxMfdgyZL/BQD06hWLoqLLeP311/wKSHfd1a7G6seCTXBQqWD5y6NQ7toJrpr+TLDbwQAwP/Gk37etbe25itwVHKZOQ8mjEyE9eQKa2TMh+uMPPgOwuqBYXoVBVFQUdEASUu05QkKF0r4Fzmq1Ijc3xyvwPPTQMBQU/BdX/d3nUgNXiwohVnAwzUiBIzISjNkM2Gy3l+Y4DrBYwFitsCUkwJrkXxVohUKOZs0iIJNJodcbceuW/8EIqFTBQSqFbeAgcK3b8AHH1wzNNW5V4GnxIpGIOreSRosCksAVFV2G3W5H+/YdPI5HRfF7aPypfrxhw1r07x+HpKShWLduFUpLq26JbTbbYbPxFRwkEt/7bPxlNJZBLBZDoQi8WyfXujVK9x+Avdu9YOx2vsROWRm/TMYBlsSHoX/9TT6jzQeJ5PaeHIvFguLi0iqfFfnDaCyDSCSCUsm3hrAOH+6ukVctux1cRARs98dWf44PlTe3FheXwWikZ0Wk8aAlO4HT6/nqxpWrH7MsX9PLV/VjqVSGMWPGIz4+ARoNi/z8POzZsxsXLuQjPX0PJFU8kxFqgoMzKgq3jnzMFyPNyABjMMDZsgWso0bD0amzz2uDWZ6rTuUWFeaJk6B4dQcYi8X7+RF/ARgApscm+1253IWW50hTQQEpDFx1uWoS7Ga+5s2be1Q47tnzfkRHd8TSpQtw/HgmhlTRZ0fICQ5gGNjjE2CP9798jD/Zc4HyaFHRrh2M61+BJnUBH5TEYn4TL8fxGXgMA1tsb5SlLvL7/pQ9R5oaCkhhkJl5FOvWrarxvH37DrhnQpWrH7tmTrWtftynT18olUpcvPh9lQGJfy+BJjjUQm2z5wJVsUWFZfwEOJs3h/Lvm/jNuFYrwDBwNmsGy6TJKJu/0Ofm3oooe440RRSQwiA5eQySk8f4da7VaoVEIkFhYQHi4/u4j7ueHdVF9WNXggPLKqFWq0JSwcFisUKhkIdsKbA6dbE850vFBIfiYhtsAwfBNnAQxD/+ANHly+CUKth79ODbsvuBludIU0ZJDQInk8nQq1csjh373OP4F198hg4dotGmTWSt7nfy5FcwmUw1Vj8WaoKDL8FmzwWqcoIDADg6dYZt0GDYExL8CkaUPUcIzZAahCeffArz5qXglVfWYvDgh/DNN2fLKxuv8ThvwIB4JCaORFracwCArVs3QSQS4Z57ukGjYfH99+fx9ttvokuXGDz44MAa31eoCQ6V1dfyXHUqJzg4nbVbWgvH8lxhYQE2bVqPvLxzUKnUSEwcgenTZ0HqqzwT+OXXvXvfwvvv/xslJSXo1Kkz5s5NrXGfGyH+oIDUAHTv3gOrV69HevoOHDlyCK1atcayZc9i8GDPvUkOh8Pjj2F0dDQOHjyAw4cPwmw2o0WLlkhKGoVp01KqzLCrTNAJDqj/5TlfPBIc9Ea/rgnX8lxpaSnmzXsa7dq1x+rVL+P3369j27ZNMJvNSE1d5vPavXvfwu7dr+Hpp+egY8dOOHjw30hNnYM33tiHtm3vqvOxk8aNiqsSnxgGuOMONQCguPhWSDK85HIZWFYdVDHXitlzRqMppNlzgZLJpNBqNbh1ywCbzVbteZWz50wmW71mz7399hvYs2c33nvvI2i1EQCAQ4cOYuPGdXjvvY88eg5VZLFYMGrUMIwd+xekpMwGANhsNkycOBYJCX09MjqJMAm9uCo9QyI+Ca2CQyg3t4aaRwWHaghhc2tW1inExsa5gxEADB48FE6nE9nZWdVel5d3Dkaj0WNmLpVKMWDAIGRlnazTMZOmgQISqZEQEhwYhoFGo4JOx4LjOJSU6GE0mur1WZE/qkpwAHx3bq1vhYUFXpU/WJbFnXc291n5w/WzqqqG/PbbNVgs5tAOlDQ5FJCIXwwGPlEg2Lp0LhUTHJgaKnSHK3suEA6HE//9bwEmT34MublnBZk9p9eXelX+APig5Kvyh15fCplMBrnc80sEy/JfEvR6WtYnwaGARPziSnCQSCRQKHzXjPNXWRnfskGtrnqJS8jLc76YTBb88MOPWLv2JSiVMqo9R4ifKCARvxmNFjidTqhUypD0l3NVcKi8FNhQlueqc9ddbTFlypMoKirCzp3pYV2eqwrLar0qfwCAXq/3WfmDZbWwWq2wWDy7AOv1ejAMA5YV9gNzInwUkIjf6iPBoSEtz1VWcXlu1qxZaNWqFXbtSscvvxSEe2g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"}}]}}, 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"}}]}}}, 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@@ -2407,13 +2407,13 @@ <h2><span class="section-number">21.15. </span>Acknowledgement<a class="headerli
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
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-            <p class="prev-next-title"><span class="section-number">20. </span>Unsupervised learning</p>
+            <p class="prev-next-title"><span class="section-number">19. </span>Unsupervised learning</p>
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     </a>
     <a class='right-next' id="next-link" href="unsupervised-learning-pca-and-clustering.html" title="next page">
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         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">22. </span>Unsupervised learning: PCA and clustering</p>
+        <p class="prev-next-title"><span class="section-number">21. </span>Unsupervised learning: PCA and clustering</p>
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diff --git a/ml-advanced/model-selection.html b/ml-advanced/model-selection.html
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+    <link rel="next" title="23. Intro to Deep Learning" href="../deep-learning/dl-overview.html" />
+    <link rel="prev" title="21. Unsupervised learning: PCA and clustering" href="unsupervised-learning-pca-and-clustering.html" />
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-     7.3. Visualizing relationships: all about honey 🍯
+     6.3. Visualizing relationships: all about honey 🍯
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+     8.1. Introduction
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+     8.2. The “low code/no code” way
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-     9.3. Data Science in the cloud: The “Azure ML SDK” way
+     8.3. Data Science in the cloud: The “Azure ML SDK” way
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+     14.3. Yet other classifiers
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-     15.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
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+     15.1. Loss function
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+     15.2. Gradient descent
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-     17.1. Introduction to clustering
+     16.1. Introduction to clustering
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-     17.2. K-Means clustering
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+     17.1. Bagging
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+     17.2. Random forest
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+     17.3. Feature importance
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+     18.1. Gradient Boosting
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-     19.2. Gradient boosting example
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+     18.3. XGBoost
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-     19.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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+   20. Kernel method
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+   21. Unsupervised learning: PCA and clustering
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+   22. Model selection
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+     24.3.1.1. Stylenet / Neural-Style
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-     25.3.1.2. Deepdream in TensorFlow
+     24.3.1.2. Deepdream in TensorFlow
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+   30. Deep Q-learning
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+   32. Image segmentation
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+   34. Object detection
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+     40.3. First assignment
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-     41.6. Python programming introduction
+     40.6. Python programming introduction
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+     40.7. Python programming basics
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+     40.9. Analyzing text about Data Science
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+     40.10. Data Science scenarios
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-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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+     40.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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     <a class="reference internal" href="../slides/data-science/data-science-in-real-world.html">
-     42.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-fundamentals/ml-overview.html">
-     42.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-fundamentals/linear-regression.html">
-     42.12. Linear Regression
+     41.12. Linear Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-fundamentals/logistic-regression.html">
-     42.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-fundamentals/logistic-regression-condensed.html">
-     42.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-fundamentals/neural-network.html">
-     42.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-fundamentals/build-an-ml-web-app.html">
-     42.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-advanced/unsupervised-learning.html">
-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-advanced/kernel-method.html">
-     42.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-advanced/model-selection.html">
-     42.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1577,83 +1577,83 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#over-fitting-and-under-fitting">
-   23.1. Over-fitting and under-fitting
+   22.1. Over-fitting and under-fitting
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#overview">
-     23.1.1. Overview
+     22.1.1. Overview
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#underfitting">
-     23.1.2. Underfitting
+     22.1.2. Underfitting
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#how-to-avoid-underfitting">
-       23.1.2.1. How To Avoid Underfitting?
+       22.1.2.1. How To Avoid Underfitting?
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#overfitting">
-     23.1.3. Overfitting
+     22.1.3. Overfitting
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#how-to-avoid-overfitting">
-       23.1.3.1. How To Avoid Overfitting?
+       22.1.3.1. How To Avoid Overfitting?
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#good-fitting">
-     23.1.4. Good Fitting
+     22.1.4. Good Fitting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#a-simple-example-of-linear-regression">
-     23.1.5. A simple example of linear regression
+     22.1.5. A simple example of linear regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#bias-variance-tradeoff">
-   23.2. Bias variance tradeoff
+   22.2. Bias variance tradeoff
   </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#interpreting-the-learning-curves">
-   23.3. Interpreting the Learning Curves
+   22.3. Interpreting the Learning Curves
   </a>
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-   23.4. Capacity
+   22.4. Capacity
   </a>
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-   23.5. Early Stopping
+   22.5. Early Stopping
   </a>
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   <a class="reference internal nav-link" href="#adding-early-stopping">
-   23.6. Adding Early Stopping
+   22.6. Adding Early Stopping
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#l1-and-l2-regularization">
-   23.7. L1 and L2 Regularization
+   22.7. L1 and L2 Regularization
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-impact-of-the-value-of-lambda">
-     23.7.1. The impact of the value of
+     22.7.1. The impact of the value of
      <span class="math notranslate nohighlight">
       \(\lambda\)
      </span>
@@ -1663,24 +1663,24 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dropout">
-   23.8. Dropout
+   22.8. Dropout
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#prediction-after-dropout">
-     23.8.1. Prediction after dropout
+     22.8.1. Prediction after dropout
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusions">
-   23.9. Conclusions
+   22.9. Conclusions
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   23.10. Your turn! 🚀
+   22.10. Your turn! 🚀
   </a>
  </li>
 </ul>
@@ -1704,83 +1704,83 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#over-fitting-and-under-fitting">
-   23.1. Over-fitting and under-fitting
+   22.1. Over-fitting and under-fitting
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#overview">
-     23.1.1. Overview
+     22.1.1. Overview
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#underfitting">
-     23.1.2. Underfitting
+     22.1.2. Underfitting
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#how-to-avoid-underfitting">
-       23.1.2.1. How To Avoid Underfitting?
+       22.1.2.1. How To Avoid Underfitting?
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#overfitting">
-     23.1.3. Overfitting
+     22.1.3. Overfitting
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#how-to-avoid-overfitting">
-       23.1.3.1. How To Avoid Overfitting?
+       22.1.3.1. How To Avoid Overfitting?
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#good-fitting">
-     23.1.4. Good Fitting
+     22.1.4. Good Fitting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#a-simple-example-of-linear-regression">
-     23.1.5. A simple example of linear regression
+     22.1.5. A simple example of linear regression
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#bias-variance-tradeoff">
-   23.2. Bias variance tradeoff
+   22.2. Bias variance tradeoff
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#interpreting-the-learning-curves">
-   23.3. Interpreting the Learning Curves
+   22.3. Interpreting the Learning Curves
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#capacity">
-   23.4. Capacity
+   22.4. Capacity
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#early-stopping">
-   23.5. Early Stopping
+   22.5. Early Stopping
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#adding-early-stopping">
-   23.6. Adding Early Stopping
+   22.6. Adding Early Stopping
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#l1-and-l2-regularization">
-   23.7. L1 and L2 Regularization
+   22.7. L1 and L2 Regularization
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-impact-of-the-value-of-lambda">
-     23.7.1. The impact of the value of
+     22.7.1. The impact of the value of
      <span class="math notranslate nohighlight">
       \(\lambda\)
      </span>
@@ -1790,24 +1790,24 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dropout">
-   23.8. Dropout
+   22.8. Dropout
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#prediction-after-dropout">
-     23.8.1. Prediction after dropout
+     22.8.1. Prediction after dropout
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusions">
-   23.9. Conclusions
+   22.9. Conclusions
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   23.10. Your turn! 🚀
+   22.10. Your turn! 🚀
   </a>
  </li>
 </ul>
@@ -1832,22 +1832,22 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="model-selection">
-<h1><span class="section-number">23. </span>Model selection<a class="headerlink" href="#model-selection" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">22. </span>Model selection<a class="headerlink" href="#model-selection" title="Permalink to this headline">#</a></h1>
 <section id="over-fitting-and-under-fitting">
-<h2><span class="section-number">23.1. </span>Over-fitting and under-fitting<a class="headerlink" href="#over-fitting-and-under-fitting" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">22.1. </span>Over-fitting and under-fitting<a class="headerlink" href="#over-fitting-and-under-fitting" title="Permalink to this headline">#</a></h2>
 <section id="overview">
-<h3><span class="section-number">23.1.1. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">22.1.1. </span>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h3>
 <p>Remember that the main objective of any machine learning model is to generalize the learning based on training data, so that it will be able to do predictions accurately on unknown data. As you can notice the words ‘Overfitting’ and ‘Underfitting’ are kind of opposite of the term ‘Generalization’. Overfitting and underfitting models don’t generalize well and results in poor performance.</p>
 <p>These are the samples of over-fitting and under-fitting in regression:</p>
 <figure class="align-default" id="over-fitting-regression-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/under_over_justalright.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/under_over_justalright.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.1 </span><span class="caption-text">Over-fitting and under-fitting in regression</span><a class="headerlink" href="#over-fitting-regression-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.1 </span><span class="caption-text">Over-fitting and under-fitting in regression</span><a class="headerlink" href="#over-fitting-regression-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="underfitting">
-<h3><span class="section-number">23.1.2. </span>Underfitting<a class="headerlink" href="#underfitting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">22.1.2. </span>Underfitting<a class="headerlink" href="#underfitting" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Underfitting occurs when machine learning model don’t fit the training data well enough. It is usually caused by simple function that cannot capture the underlying trend in the data.</p></li>
 <li><p>Underfitting models have high error in training as well as test set. This behavior is called as ‘Low Bias’</p></li>
@@ -1855,7 +1855,7 @@ <h3><span class="section-number">23.1.2. </span>Underfitting<a class="headerlink
 <li><p>Since underfitting models don’t perform well on training set, it’s very easy to detect underfitting</p></li>
 </ul>
 <section id="how-to-avoid-underfitting">
-<h4><span class="section-number">23.1.2.1. </span>How To Avoid Underfitting?<a class="headerlink" href="#how-to-avoid-underfitting" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">22.1.2.1. </span>How To Avoid Underfitting?<a class="headerlink" href="#how-to-avoid-underfitting" title="Permalink to this headline">#</a></h4>
 <ul class="simple">
 <li><p>Increasing the model complexity. e.g. If linear function under fit then try using polynomial features</p></li>
 <li><p>Increase the number of features by performing the feature engineering</p></li>
@@ -1863,13 +1863,13 @@ <h4><span class="section-number">23.1.2.1. </span>How To Avoid Underfitting?<a c
 </section>
 </section>
 <section id="overfitting">
-<h3><span class="section-number">23.1.3. </span>Overfitting<a class="headerlink" href="#overfitting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">22.1.3. </span>Overfitting<a class="headerlink" href="#overfitting" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Overfitting occurs when machine learning model tries to fit the training data too well. It is usually caused by complicated function that creates lots of unnecessary curves and angles that are not related with data and end up capturing the noise in data.</p></li>
 <li><p>Overfitting models have low error in training set but high error in test set. This behavior is called as ‘High Variance’</p></li>
 </ul>
 <section id="how-to-avoid-overfitting">
-<h4><span class="section-number">23.1.3.1. </span>How To Avoid Overfitting?<a class="headerlink" href="#how-to-avoid-overfitting" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">22.1.3.1. </span>How To Avoid Overfitting?<a class="headerlink" href="#how-to-avoid-overfitting" title="Permalink to this headline">#</a></h4>
 <ul class="simple">
 <li><p>Since overfitting algorithm captures the noise in data, reducing the number of features will help. We can manually select only important features or can use model selection algorithm for same</p></li>
 <li><p>We can also use the ‘Regularization’ technique. It works well when we have lots of slightly useful features. Sklearn linear model(Ridge and LASSO) uses regularization parameter ‘alpha’ to control the size of the coefficients by imposing a penalty. Please refer below tutorials for more details.</p></li>
@@ -1877,7 +1877,7 @@ <h4><span class="section-number">23.1.3.1. </span>How To Avoid Overfitting?<a cl
 </section>
 </section>
 <section id="good-fitting">
-<h3><span class="section-number">23.1.4. </span>Good Fitting<a class="headerlink" href="#good-fitting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">22.1.4. </span>Good Fitting<a class="headerlink" href="#good-fitting" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>It is a sweet spot between Underfitting and Overfitting model</p></li>
 <li><p>A good fitting model generalizes the learnings from training data and provide accurate predictions on new data</p></li>
@@ -1887,74 +1887,74 @@ <h3><span class="section-number">23.1.4. </span>Good Fitting<a class="headerlink
 <figure class="align-default" id="over-fitting-classification-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/classification.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/classification.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.2 </span><span class="caption-text">Over-fitting and under-fitting in classification</span><a class="headerlink" href="#over-fitting-classification-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.2 </span><span class="caption-text">Over-fitting and under-fitting in classification</span><a class="headerlink" href="#over-fitting-classification-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="a-simple-example-of-linear-regression">
-<h3><span class="section-number">23.1.5. </span>A simple example of linear regression<a class="headerlink" href="#a-simple-example-of-linear-regression" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">22.1.5. </span>A simple example of linear regression<a class="headerlink" href="#a-simple-example-of-linear-regression" title="Permalink to this headline">#</a></h3>
 <p>This is a simple graphical representation of linear regression training.</p>
 <figure class="align-default" id="datapoints-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-datapoints.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-datapoints.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.3 </span><span class="caption-text">Training data points</span><a class="headerlink" href="#datapoints-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.3 </span><span class="caption-text">Training data points</span><a class="headerlink" href="#datapoints-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="over-fitting-train-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-overfitting.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-overfitting.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.4 </span><span class="caption-text">Over-fitting model fits very well on training data</span><a class="headerlink" href="#over-fitting-train-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.4 </span><span class="caption-text">Over-fitting model fits very well on training data</span><a class="headerlink" href="#over-fitting-train-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="over-fitting-test-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-overfitting-testdata.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-overfitting-testdata.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.5 </span><span class="caption-text">Over-fitting model fits poorly on test data</span><a class="headerlink" href="#over-fitting-test-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.5 </span><span class="caption-text">Over-fitting model fits poorly on test data</span><a class="headerlink" href="#over-fitting-test-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="under-fitting-train-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-underfitting.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-underfitting.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.6 </span><span class="caption-text">Under-fitting model fits poorly on training data</span><a class="headerlink" href="#under-fitting-train-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.6 </span><span class="caption-text">Under-fitting model fits poorly on training data</span><a class="headerlink" href="#under-fitting-train-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="under-fitting-test-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-underfitting-test-data.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-underfitting-test-data.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.7 </span><span class="caption-text">Under-fitting model fits poorly on test data</span><a class="headerlink" href="#under-fitting-test-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.7 </span><span class="caption-text">Under-fitting model fits poorly on test data</span><a class="headerlink" href="#under-fitting-test-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="perfect-fitting-train-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-perfect-fit.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-perfect-fit.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.8 </span><span class="caption-text">Perfect-fitting model fits well on training data</span><a class="headerlink" href="#perfect-fitting-train-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.8 </span><span class="caption-text">Perfect-fitting model fits well on training data</span><a class="headerlink" href="#perfect-fitting-train-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="perfect-fitting-test-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-perfect-fit-test-data.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/bias-variance-perfect-fit-test-data.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.9 </span><span class="caption-text">Perfect-fitting model fits well on test data</span><a class="headerlink" href="#perfect-fitting-test-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.9 </span><span class="caption-text">Perfect-fitting model fits well on test data</span><a class="headerlink" href="#perfect-fitting-test-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 </section>
 <section id="bias-variance-tradeoff">
-<h2><span class="section-number">23.2. </span>Bias variance tradeoff<a class="headerlink" href="#bias-variance-tradeoff" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">22.2. </span>Bias variance tradeoff<a class="headerlink" href="#bias-variance-tradeoff" title="Permalink to this headline">#</a></h2>
 <figure class="align-default" id="graphicalillustration-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/graphicalillustration.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/graphicalillustration.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.10 </span><span class="caption-text">Graphical illustration of variance and bias</span><a class="headerlink" href="#graphicalillustration-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.10 </span><span class="caption-text">Graphical illustration of variance and bias</span><a class="headerlink" href="#graphicalillustration-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="model-complexity-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/total_error.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/total_error.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.11 </span><span class="caption-text">Model complexity v.s. error</span><a class="headerlink" href="#model-complexity-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.11 </span><span class="caption-text">Model complexity v.s. error</span><a class="headerlink" href="#model-complexity-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="interpreting-the-learning-curves">
-<h2><span class="section-number">23.3. </span>Interpreting the Learning Curves<a class="headerlink" href="#interpreting-the-learning-curves" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">22.3. </span>Interpreting the Learning Curves<a class="headerlink" href="#interpreting-the-learning-curves" title="Permalink to this headline">#</a></h2>
 <p>You might think about the information in the training data as being of two kinds: <em>signal</em> and <em>noise</em>. The signal is the part that generalizes, the part that can help our model make predictions from new data. The noise is that part that is <em>only</em> true of the training data; the noise is all of the random fluctuation that comes from data in the real-world or all of the incidental, non-informative patterns that can’t actually help the model make predictions. The noise is the part might look useful but really isn’t.</p>
 <p>We train a model by choosing weights or parameters that minimize the loss on a training set. You might know, however, that to accurately assess a model’s performance, we need to evaluate it on a new set of data, the <em>validation</em> data.</p>
 <p>When we train a model we’ve been plotting the loss on the training set epoch by epoch. To this we’ll add a plot the validation data too. These plots we call the <strong>learning curves</strong>. To train deep learning models effectively, we need to be able to interpret them.</p>
@@ -1964,29 +1964,29 @@ <h2><span class="section-number">23.3. </span>Interpreting the Learning Curves<a
 <p>We’ll look at a couple ways of getting more signal out of the training data while reducing the amount of noise.</p>
 </section>
 <section id="capacity">
-<h2><span class="section-number">23.4. </span>Capacity<a class="headerlink" href="#capacity" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">22.4. </span>Capacity<a class="headerlink" href="#capacity" title="Permalink to this headline">#</a></h2>
 <p>A model’s <strong>capacity</strong> refers to the size and complexity of the patterns it is able to learn. For neural networks, this will largely be determined by how many neurons it has and how they are connected together. If it appears that your network is underfitting the data, you should try increasing its capacity.</p>
 <p>You can increase the capacity of a network either by making it <em>wider</em> (more units to existing layers) or by making it <em>deeper</em> (adding more layers). Wider networks have an easier time learning more linear relationships, while deeper networks prefer more nonlinear ones. Which is better just depends on the dataset.</p>
 <p>You’ll explore how the capacity of a network can affect its performance in the exercise.</p>
 </section>
 <section id="early-stopping">
-<h2><span class="section-number">23.5. </span>Early Stopping<a class="headerlink" href="#early-stopping" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">22.5. </span>Early Stopping<a class="headerlink" href="#early-stopping" title="Permalink to this headline">#</a></h2>
 <p>We mentioned that when a model is too eagerly learning noise, the validation loss may start to increase during training. To prevent this, we can simply stop the training whenever it seems the validation loss isn’t decreasing anymore. Interrupting the training this way is called <strong>early stopping</strong>.</p>
 <p>Once we detect that the validation loss is starting to rise again, we can reset the weights back to where the minimum occured. This ensures that the model won’t continue to learn noise and overfit the data.</p>
 <p>Training with early stopping also means we’re in less danger of stopping the training too early, before the network has finished learning signal. So besides preventing overfitting from training too long, early stopping can also prevent <em>underfitting</em> from not training long enough. Just set your training epochs to some large number (more than you’ll need), and early stopping will take care of the rest.</p>
 </section>
 <section id="adding-early-stopping">
-<h2><span class="section-number">23.6. </span>Adding Early Stopping<a class="headerlink" href="#adding-early-stopping" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">22.6. </span>Adding Early Stopping<a class="headerlink" href="#adding-early-stopping" title="Permalink to this headline">#</a></h2>
 <p>In Keras, we include early stopping in our training through a callback. A <strong>callback</strong> is just a function you want run every so often while the network trains. The early stopping callback will run after every epoch. (Keras has <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/callbacks">a variety of useful callbacks</a> pre-defined, but you can <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/LambdaCallback">define your own</a>, too.)</p>
 <figure class="align-default" id="earlystopping-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/traintestoverfitting.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/traintestoverfitting.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.12 </span><span class="caption-text">Early stopping</span><a class="headerlink" href="#earlystopping-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.12 </span><span class="caption-text">Early stopping</span><a class="headerlink" href="#earlystopping-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="l1-and-l2-regularization">
-<h2><span class="section-number">23.7. </span>L1 and L2 Regularization<a class="headerlink" href="#l1-and-l2-regularization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">22.7. </span>L1 and L2 Regularization<a class="headerlink" href="#l1-and-l2-regularization" title="Permalink to this headline">#</a></h2>
 <p>You may be familiar with Occam’s Razor principle: given two explanations for something, the explanation most likely to be correct is the ‘simplest’ one, the one that makes the least amount of assumptions. This also applies to the models learned by neural networks: given some training data and a network architecture, there are multiple sets of weights values (multiple models) that could explain the data, and simple models are less likely to overfit than complex ones.</p>
 <p>A ‘simple model’ in this context is a model where the distribution of parameter values has less entropy (or a model with fewer parmeters altogether, as we saw in the section above). Thus a common way to mitigate overfitting is to put constraints on the complexity of a network by forcing its weeights only to  take small values, which makes the distribution of weight values more ‘regular’. This is called ‘weight regularization’, and it is done by adding to the loss function of the network a cost associated with having large weights. This cost comes in two flavors:</p>
 <ul class="simple">
@@ -2001,59 +2001,59 @@ <h2><span class="section-number">23.7. </span>L1 and L2 Regularization<a class="
 <figure class="align-default" id="circlesquare-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/circlesquare.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/circlesquare.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.13 </span><span class="caption-text">L1 and L2 regularization</span><a class="headerlink" href="#circlesquare-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.13 </span><span class="caption-text">L1 and L2 regularization</span><a class="headerlink" href="#circlesquare-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="explainedairegularization-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/L1L2contour.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/L1L2contour.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.14 </span><span class="caption-text">L1 and L2 regularization</span><a class="headerlink" href="#explainedairegularization-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.14 </span><span class="caption-text">L1 and L2 regularization</span><a class="headerlink" href="#explainedairegularization-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="berkeley189s21-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/ridgelassoItayEvron.gif" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/ridgelassoItayEvron.gif" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.15 </span><span class="caption-text">Different <span class="math notranslate nohighlight">\(\beta\)</span> and ellipses</span><a class="headerlink" href="#berkeley189s21-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.15 </span><span class="caption-text">Different <span class="math notranslate nohighlight">\(\beta\)</span> and ellipses</span><a class="headerlink" href="#berkeley189s21-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="p-norm-balls">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/p-norm_balls.webp" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/p-norm_balls.webp" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.16 </span><span class="caption-text">Different p norm</span><a class="headerlink" href="#p-norm-balls" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.16 </span><span class="caption-text">Different p norm</span><a class="headerlink" href="#p-norm-balls" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="elasticnet-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/elastic_net_balls.webp" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/elastic_net_balls.webp" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.17 </span><span class="caption-text">ElasticNet</span><a class="headerlink" href="#elasticnet-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.17 </span><span class="caption-text">ElasticNet</span><a class="headerlink" href="#elasticnet-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="the-impact-of-the-value-of-lambda">
-<h3><span class="section-number">23.7.1. </span>The impact of the value of <span class="math notranslate nohighlight">\(\lambda\)</span><a class="headerlink" href="#the-impact-of-the-value-of-lambda" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">22.7.1. </span>The impact of the value of <span class="math notranslate nohighlight">\(\lambda\)</span><a class="headerlink" href="#the-impact-of-the-value-of-lambda" title="Permalink to this headline">#</a></h3>
 <figure class="align-default" id="impact-of-lambda-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/lagrange-animation.gif" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/lagrange-animation.gif" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.18 </span><span class="caption-text">The impact of the value of <span class="math notranslate nohighlight">\(\lambda\)</span></span><a class="headerlink" href="#impact-of-lambda-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.18 </span><span class="caption-text">The impact of the value of <span class="math notranslate nohighlight">\(\lambda\)</span></span><a class="headerlink" href="#impact-of-lambda-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 </section>
 <section id="dropout">
-<h2><span class="section-number">23.8. </span>Dropout<a class="headerlink" href="#dropout" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">22.8. </span>Dropout<a class="headerlink" href="#dropout" title="Permalink to this headline">#</a></h2>
 <p>Dropout is one of the most effective and most commonly used regularization techniques for neural network, developed by Hinton and his students at the University of Toronto. Dropout, applied to a layer, consists of randomly “dropping out” (i.e. set to zero) a number of output features of the layer during training. Let’s say a given layer would normally have returned a vector [0.2, 0.5, 1.3, 0.8, 1.1] for a given input sample during training; aafter applying dropout, this vector will have a few zero entries distributed at random, e.g. [0, 0.5, 1.3, 0, 1.1]. The ‘dropout rate’ is the fraction of the features that are being zeroed-out; it is usually set between 0.2 and 0.5. At test time, no units are dropped out, and instead the layer’s output values are scaled down by a factor equal to the dropout rate, so as to balance for the fact that more units are active than at training time.</p>
 <p>In tf.keras you can introduce a dropout in a network via the Dropout layer, which gets applied to the output of layer right before.</p>
 <figure class="align-default" id="dropout-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/dropoutgif.gif" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/dropoutgif.gif" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.19 </span><span class="caption-text">Dropout</span><a class="headerlink" href="#dropout-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.19 </span><span class="caption-text">Dropout</span><a class="headerlink" href="#dropout-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="prediction-after-dropout">
-<h3><span class="section-number">23.8.1. </span>Prediction after dropout<a class="headerlink" href="#prediction-after-dropout" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">22.8.1. </span>Prediction after dropout<a class="headerlink" href="#prediction-after-dropout" title="Permalink to this headline">#</a></h3>
 <figure class="align-default" id="prediction-after-dropout-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/kUc8r.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/kUc8r.jpg" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.20 </span><span class="caption-text">Prediction after dropout</span><a class="headerlink" href="#prediction-after-dropout-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.20 </span><span class="caption-text">Prediction after dropout</span><a class="headerlink" href="#prediction-after-dropout-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>During training, p neuron activations (usually, p=0.5, so 50%) are dropped. Doing this at the testing stage is not our goal (the goal is to achieve a better generalization). From the other hand, keeping all activations will lead to an input that is unexpected to the network, more precisely, too high (50% higher) input activations for the following layer</p>
@@ -2062,28 +2062,28 @@ <h3><span class="section-number">23.8.1. </span>Prediction after dropout<a class
 </section>
 </section>
 <section id="conclusions">
-<h2><span class="section-number">23.9. </span>Conclusions<a class="headerlink" href="#conclusions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">22.9. </span>Conclusions<a class="headerlink" href="#conclusions" title="Permalink to this headline">#</a></h2>
 <figure class="align-default" id="training-size-matters-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/ZahidHasan.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/ZahidHasan.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.21 </span><span class="caption-text">Training size matters</span><a class="headerlink" href="#training-size-matters-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.21 </span><span class="caption-text">Training size matters</span><a class="headerlink" href="#training-size-matters-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="steps-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/steps.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/steps.png" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.22 </span><span class="caption-text">How to choose a good model</span><a class="headerlink" href="#steps-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.22 </span><span class="caption-text">How to choose a good model</span><a class="headerlink" href="#steps-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <figure class="align-default" id="conclusion-ms">
 <img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/Bias-vs.webp" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/model-selection/Bias-vs.webp" />
 <figcaption>
-<p><span class="caption-number">Fig. 23.23 </span><span class="caption-text">Conclusion</span><a class="headerlink" href="#conclusion-ms" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 22.23 </span><span class="caption-text">Conclusion</span><a class="headerlink" href="#conclusion-ms" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">23.10. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">22.10. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Machine learning model selection and dealing with overfitting and underfitting are crucial aspects of the machine learning pipeline. In this assignment, you’ll have the opportunity to apply your understanding of these concepts and techniques. Please complete the following tasks:
 <a class="reference internal" href="../assignments/ml-advanced/model-selection/model-selection-assignment-1.html"><span class="doc std std-doc">assignment</span></a></p>
 </section>
@@ -2126,13 +2126,13 @@ <h2><span class="section-number">23.10. </span>Your turn! 🚀<a class="headerli
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">22. </span>Unsupervised learning: PCA and clustering</p>
+            <p class="prev-next-title"><span class="section-number">21. </span>Unsupervised learning: PCA and clustering</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../deep-learning/dl-overview.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">24. </span>Intro to Deep Learning</p>
+        <p class="prev-next-title"><span class="section-number">23. </span>Intro to Deep Learning</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/ml-advanced/unsupervised-learning-pca-and-clustering.html b/ml-advanced/unsupervised-learning-pca-and-clustering.html
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@@ -6,7 +6,7 @@
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>22. Unsupervised learning: PCA and clustering &#8212; Ocademy Open Machine Learning Book</title>
+    <title>21. Unsupervised learning: PCA and clustering &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -100,8 +100,8 @@
     <link rel="shortcut icon" href="../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="23. Model selection" href="model-selection.html" />
-    <link rel="prev" title="21. Kernel method" href="kernel-method.html" />
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+     16.1. Introduction to clustering
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+   21. Unsupervised learning: PCA and clustering
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+     24.3.1.1. Stylenet / Neural-Style
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-     41.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
     </a>
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     <a class="reference internal" href="../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     41.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
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   <ul>
    <li class="toctree-l2">
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-     42.1. Python programming introduction
+     41.1. Python programming introduction
     </a>
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-     42.4. Data Science introduction
+     41.4. Data Science introduction
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     <a class="reference internal" href="../slides/data-science/relational-vs-non-relational-database.html">
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     </a>
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-     42.14. Logistic Regression
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-     42.16. Build an machine learning web application
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     </a>
    </li>
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-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
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    </li>
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     <a class="reference internal" href="../slides/ml-advanced/kernel-method.html">
-     42.18. Kernel method
+     41.18. Kernel method
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     <a class="reference internal" href="../slides/ml-advanced/model-selection.html">
-     42.19. Model Selection
+     41.19. Model Selection
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../slides/deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1577,78 +1577,78 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   22.1. 1. Introduction
+   21.1. 1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#principal-component-analysis-pca">
-   22.2. 2. Principal Component Analysis (PCA)
+   21.2. 2. Principal Component Analysis (PCA)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#intuition-theories-and-application-issues">
-     22.2.1. Intuition, theories, and application issues
+     21.2.1. Intuition, theories, and application issues
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#examples">
-   22.3. Examples
+   21.3. Examples
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#fishers-iris-dataset">
-     22.3.1. Fisher’s iris dataset
+     21.3.1. Fisher’s iris dataset
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#clustering">
-   22.4. 2. Clustering
+   21.4. 2. Clustering
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#k-means">
-     22.4.1. K-means
+     21.4.1. K-means
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#choosing-the-number-of-clusters-for-k-means">
-   22.5. Choosing the number of clusters for K-means
+   21.5. Choosing the number of clusters for K-means
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#issues">
-   22.6. Issues
+   21.6. Issues
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#spectral-clustering">
-   22.7. Spectral clustering
+   21.7. Spectral clustering
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#agglomerative-clustering">
-   22.8. Agglomerative clustering
+   21.8. Agglomerative clustering
   </a>
  </li>
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   <a class="reference internal nav-link" href="#accuracy-metrics">
-   22.9. Accuracy metrics
+   21.9. Accuracy metrics
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#demo-assignment">
-   22.10. 4. Demo assignment
+   21.10. 4. Demo assignment
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#useful-resources">
-   22.11. 5. Useful resources
+   21.11. 5. Useful resources
   </a>
  </li>
 </ul>
@@ -1672,78 +1672,78 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   22.1. 1. Introduction
+   21.1. 1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#principal-component-analysis-pca">
-   22.2. 2. Principal Component Analysis (PCA)
+   21.2. 2. Principal Component Analysis (PCA)
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#intuition-theories-and-application-issues">
-     22.2.1. Intuition, theories, and application issues
+     21.2.1. Intuition, theories, and application issues
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#examples">
-   22.3. Examples
+   21.3. Examples
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#fishers-iris-dataset">
-     22.3.1. Fisher’s iris dataset
+     21.3.1. Fisher’s iris dataset
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#clustering">
-   22.4. 2. Clustering
+   21.4. 2. Clustering
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#k-means">
-     22.4.1. K-means
+     21.4.1. K-means
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#choosing-the-number-of-clusters-for-k-means">
-   22.5. Choosing the number of clusters for K-means
+   21.5. Choosing the number of clusters for K-means
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#issues">
-   22.6. Issues
+   21.6. Issues
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#spectral-clustering">
-   22.7. Spectral clustering
+   21.7. Spectral clustering
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#agglomerative-clustering">
-   22.8. Agglomerative clustering
+   21.8. Agglomerative clustering
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#accuracy-metrics">
-   22.9. Accuracy metrics
+   21.9. Accuracy metrics
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#demo-assignment">
-   22.10. 4. Demo assignment
+   21.10. 4. Demo assignment
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#useful-resources">
-   22.11. 5. Useful resources
+   21.11. 5. Useful resources
   </a>
  </li>
 </ul>
@@ -1807,17 +1807,17 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="unsupervised-learning-pca-and-clustering">
-<h1><span class="section-number">22. </span>Unsupervised learning: PCA and clustering<a class="headerlink" href="#unsupervised-learning-pca-and-clustering" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">21. </span>Unsupervised learning: PCA and clustering<a class="headerlink" href="#unsupervised-learning-pca-and-clustering" title="Permalink to this headline">#</a></h1>
 <p>In this lesson, we will work with unsupervised learning methods such as Principal Component Analysis (PCA) and clustering. You will learn why and how we can reduce the dimensionality of the original data and what the main approaches are for grouping similar data points.</p>
 <section id="introduction">
-<h2><span class="section-number">22.1. </span>1. Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">21.1. </span>1. Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>The main feature of unsupervised learning algorithms, when compared to classification and regression methods, is that input data are unlabeled (i.e. no labels or classes given) and that the algorithm learns the structure of the data without any assistance. This creates two main differences. First, it allows us to process large amounts of data because the data does not need to be manually labeled. Second, it is difficult to evaluate the quality of an unsupervised algorithm due to the absence of an explicit goodness metric as used in supervised learning.</p>
 <p>One of the most common tasks in unsupervised learning is dimensionality reduction. On one hand, dimensionality reduction may help with data visualization (e.g. t-SNA method) while, on the other hand, it may help deal with the multicollinearity of your data and prepare the data for a supervised learning method (e.g. decision trees).</p>
 </section>
 <section id="principal-component-analysis-pca">
-<h2><span class="section-number">22.2. </span>2. Principal Component Analysis (PCA)<a class="headerlink" href="#principal-component-analysis-pca" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">21.2. </span>2. Principal Component Analysis (PCA)<a class="headerlink" href="#principal-component-analysis-pca" title="Permalink to this headline">#</a></h2>
 <section id="intuition-theories-and-application-issues">
-<h3><span class="section-number">22.2.1. </span>Intuition, theories, and application issues<a class="headerlink" href="#intuition-theories-and-application-issues" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">21.2.1. </span>Intuition, theories, and application issues<a class="headerlink" href="#intuition-theories-and-application-issues" title="Permalink to this headline">#</a></h3>
 <p>Principal Component Analysis is one of the easiest, most intuitive, and most frequently used methods for dimensionality reduction, projecting data onto its orthogonal feature subspace.</p>
 <img align="right" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/ml-advanced/introduction-to-gradient-boosting/iq0d24li.bmp" width="400"><div class="cell tag_hide-input docutils container">
 <div class="cell_input docutils container">
@@ -1861,9 +1861,9 @@ <h3><span class="section-number">22.2.1. </span>Intuition, theories, and applica
 </section>
 </section>
 <section id="examples">
-<h2><span class="section-number">22.3. </span>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">21.3. </span>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">#</a></h2>
 <section id="fishers-iris-dataset">
-<h3><span class="section-number">22.3.1. </span>Fisher’s iris dataset<a class="headerlink" href="#fishers-iris-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">21.3.1. </span>Fisher’s iris dataset<a class="headerlink" href="#fishers-iris-dataset" title="Permalink to this headline">#</a></h3>
 <p>Let’s start by uploading all of the essential modules and try out the iris example from the <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> documentation.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2122,7 +2122,7 @@ <h3><span class="section-number">22.3.1. </span>Fisher’s iris dataset<a class=
 </section>
 </section>
 <section id="clustering">
-<h2><span class="section-number">22.4. </span>2. Clustering<a class="headerlink" href="#clustering" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">21.4. </span>2. Clustering<a class="headerlink" href="#clustering" title="Permalink to this headline">#</a></h2>
 <p>The main idea behind clustering is pretty straightforward. Basically, we say to ourselves, “I have these points here, and I can see that they organize into groups. It would be nice to describe these things more concretely, and, when a new point comes in, assign it to the correct group.” This general idea encourages exploration and opens up a variety of algorithms for clustering.</p>
 <div class="cell tag_hide-input docutils container">
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@@ -2151,7 +2151,7 @@ <h2><span class="section-number">22.4. </span>2. Clustering<a class="headerlink"
 </div>
 <p>The algorithms listed below do not cover all the clustering methods out there, but they are the most commonly used ones.</p>
 <section id="k-means">
-<h3><span class="section-number">22.4.1. </span>K-means<a class="headerlink" href="#k-means" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">21.4.1. </span>K-means<a class="headerlink" href="#k-means" title="Permalink to this headline">#</a></h3>
 <p>K-means algorithm is the most popular and yet simplest of all the clustering algorithms. Here is how it works:</p>
 <ol class="simple">
 <li><p>Select the number of clusters <span class="math notranslate nohighlight">\(k\)</span> that you think is the optimal number.</p></li>
@@ -2246,7 +2246,7 @@ <h3><span class="section-number">22.4.1. </span>K-means<a class="headerlink" hre
 </section>
 </section>
 <section id="choosing-the-number-of-clusters-for-k-means">
-<h2><span class="section-number">22.5. </span>Choosing the number of clusters for K-means<a class="headerlink" href="#choosing-the-number-of-clusters-for-k-means" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">21.5. </span>Choosing the number of clusters for K-means<a class="headerlink" href="#choosing-the-number-of-clusters-for-k-means" title="Permalink to this headline">#</a></h2>
 <p>In contrast to the supervised learning tasks such as classification and regression, clustering requires more effort to choose the optimization criterion. Usually, when working with k-means, we optimize the sum of squared distances between the observations and their centroids.</p>
 <div class="math notranslate nohighlight">
 \[\Large J(C) = \sum_{k=1}^K\sum_{i~\in~C_k} ||x_i - \mu_k|| \rightarrow \min\limits_C,\]</div>
@@ -2281,18 +2281,18 @@ <h2><span class="section-number">22.5. </span>Choosing the number of clusters fo
 <p>We see that <span class="math notranslate nohighlight">\(J(C_k)\)</span> decreases significantly until the number of clusters is 3 and then does not change as much anymore. This means that the optimal number of clusters is 3.</p>
 </section>
 <section id="issues">
-<h2><span class="section-number">22.6. </span>Issues<a class="headerlink" href="#issues" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">21.6. </span>Issues<a class="headerlink" href="#issues" title="Permalink to this headline">#</a></h2>
 <p>Inherently, K-means is NP-hard. For <span class="math notranslate nohighlight">\(d\)</span> dimensions, <span class="math notranslate nohighlight">\(k\)</span> clusters, and <span class="math notranslate nohighlight">\(n\)</span> observations, we will find a solution in <span class="math notranslate nohighlight">\(O(n^{d k+1})\)</span> in time. There are some heuristics to deal with this; an example is MiniBatch K-means, which takes portions (batches) of data instead of fitting the whole dataset and then moves centroids by taking the average of the previous steps. Compare the implementation of K-means and MiniBatch K-means in the <a class="reference external" href="http://scikit-learn.org/stable/auto_examples/cluster/plot_mini_batch_kmeans.html">sckit-learn documentation</a>.</p>
 <p>The <a class="reference external" href="http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html">implemetation</a> of the algorithm using <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> has its benefits such as the possibility to state the number of initializations with the <code class="docutils literal notranslate"><span class="pre">n_init</span></code> function parameter, which enables us to identify more robust centroids. Moreover, these runs can be done in parallel to decrease the computation time.</p>
 </section>
 <section id="spectral-clustering">
-<h2><span class="section-number">22.7. </span>Spectral clustering<a class="headerlink" href="#spectral-clustering" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">21.7. </span>Spectral clustering<a class="headerlink" href="#spectral-clustering" title="Permalink to this headline">#</a></h2>
 <p>Spectral clustering combines some of the approaches described above to create a stronger clustering method.</p>
 <p>First of all, this algorithm requires us to define the similarity matrix for observations called the adjacency matrix. This can be done in a similar fashion as in the Affinity Propagation algorithm: <span class="math notranslate nohighlight">\(A_{i, j} = - ||x_i - x_j||^{2}\)</span>. This matrix describes a full graph with the observations as vertices and the estimated similarity value between a pair of observations as edge weights for that pair of vertices. For the metric defined above and two-dimensional observations, this is pretty intuitive - two observations are similar if the edge between them is shorter.
 We’d like to split up the graph into two subgraphs in such a way that each observation in each subgraph would be similar to another observation in that subgraph. Formally, this is a Normalized cuts problem; for more details, we recommend reading <a class="reference external" href="http://people.eecs.berkeley.edu/~malik/papers/SM-ncut.pdf">this paper</a>.</p>
 </section>
 <section id="agglomerative-clustering">
-<h2><span class="section-number">22.8. </span>Agglomerative clustering<a class="headerlink" href="#agglomerative-clustering" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">21.8. </span>Agglomerative clustering<a class="headerlink" href="#agglomerative-clustering" title="Permalink to this headline">#</a></h2>
 <p>The following algorithm is the simplest and easiest to understand among all the the clustering algorithms without a fixed number of clusters.</p>
 <p>The algorithm is fairly simple:</p>
 <ol class="simple">
@@ -2344,7 +2344,7 @@ <h2><span class="section-number">22.8. </span>Agglomerative clustering<a class="
 </div>
 </section>
 <section id="accuracy-metrics">
-<h2><span class="section-number">22.9. </span>Accuracy metrics<a class="headerlink" href="#accuracy-metrics" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">21.9. </span>Accuracy metrics<a class="headerlink" href="#accuracy-metrics" title="Permalink to this headline">#</a></h2>
 <p>As opposed to classfication, it is difficult to assess the quality of results from clustering. Here, a metric cannot depend on the labels but only on the goodness of split. Secondly, we do not usually have true labels of the observations when we use clustering.</p>
 <p>There are <em>internal</em> and <em>external</em> goodness metrics. External metrics use the information about the known true split while internal metrics do not use any external information and assess the goodness of clusters based only on the initial data. The optimal number of clusters is usually defined with respect to some internal metrics.</p>
 <p>All the metrics described below are implemented in <code class="docutils literal notranslate"><span class="pre">sklearn.metrics</span></code>.</p>
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 </div>
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-<h2><span class="section-number">22.10. </span>4. Demo assignment<a class="headerlink" href="#demo-assignment" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">21.10. </span>4. Demo assignment<a class="headerlink" href="#demo-assignment" title="Permalink to this headline">#</a></h2>
 <p>To practice with PCA and clustering, you can complete <a class="reference external" href="https://www.kaggle.com/kashnitsky/a7-demo-unsupervised-learning">this assignment</a> where you’ll be analyzing data from accelerometers and gyros of Samsung Galaxy S3 mobile phones. The assignment is just for you to practice, and goes with <a class="reference external" href="https://www.kaggle.com/kashnitsky/a7-demo-unsupervised-learning-solution">solution</a>.</p>
 </section>
 <section id="useful-resources">
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 <li><p>Medium <a class="reference external" href="https://medium.com/open-machine-learning-course/open-machine-learning-course-topic-7-unsupervised-learning-pca-and-clustering-db7879568417">“story”</a> based on this notebook</p></li>
 <li><p>Main course <a class="reference external" href="https://mlcourse.ai">site</a>, <a class="reference external" href="https://github.com/Yorko/mlcourse.ai">course repo</a>, and YouTube <a class="reference external" href="https://www.youtube.com/watch?v=QKTuw4PNOsU&amp;list=PLVlY_7IJCMJeRfZ68eVfEcu-UcN9BbwiX">channel</a></p></li>
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-     41.85. Case Study: Debugging in Regression
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-     41.89. Parameter play
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     <a class="reference internal" href="../assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
-     41.90. How to choose cnn architecture mnist
+     40.90. How to choose cnn architecture mnist
     </a>
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-     41.92. Sign Language Digits Classification with CNN
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     </a>
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-     41.94. Object Recognition in Images using CNN
+     40.94. Object Recognition in Images using CNN
     </a>
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-     41.95. Intro to TensorFlow for Deep Learning
+     40.95. Intro to TensorFlow for Deep Learning
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-     41.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
     </a>
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-     41.99. Google Stock Price Prediction RNN
+     40.99. Google Stock Price Prediction RNN
     </a>
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-     41.101. Intro to Autoencoders
+     40.101. Intro to Autoencoders
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-     41.102. Base/Denoising Autoencoder &amp; Dimension Reduction
+     40.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
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-     41.103. Fun with Variational Autoencoders
+     40.103. Fun with Variational Autoencoders
     </a>
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-     41.104. Time Series Forecasting Assignment
+     40.104. Time Series Forecasting Assignment
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-     41.106. Neural Networks for Classification with TensorFlow
+     40.106. Neural Networks for Classification with TensorFlow
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-     41.107. NN Classify 15 Fruits Assignment
+     40.107. NN Classify 15 Fruits Assignment
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-     41.112. DQN On Foreign Exchange Market
+     40.112. DQN On Foreign Exchange Market
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-     41.113. Art by gan
+     40.113. Art by gan
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     <a class="reference internal" href="../assignments/deep-learning/gan/gan-introduction.html">
-     41.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
     </a>
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     <a class="reference internal" href="../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     41.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
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   </ul>
  </li>
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+   41. Slides
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@@ -1211,107 +1211,107 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/python-programming/python-programming-introduction.html">
-     42.1. Python programming introduction
+     41.1. Python programming introduction
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../slides/python-programming/python-programming-basics.html">
-     42.2. Python programming basics
+     41.2. Python programming basics
     </a>
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     <a class="reference internal" href="../slides/python-programming/python-programming-advanced.html">
-     42.3. Python programming advanced
+     41.3. Python programming advanced
     </a>
    </li>
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     <a class="reference internal" href="../slides/data-science/data-science-introduction.html">
-     42.4. Data Science introduction
+     41.4. Data Science introduction
     </a>
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     <a class="reference internal" href="../slides/data-science/relational-vs-non-relational-database.html">
-     42.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
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     <a class="reference internal" href="../slides/data-science/numpy-and-pandas.html">
-     42.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../slides/data-science/data-visualization.html">
-     42.7. Data visualization
+     41.7. Data visualization
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     <a class="reference internal" href="../slides/data-science/data-science-lifecycle.html">
-     42.8. Data Science lifecycle
+     41.8. Data Science lifecycle
     </a>
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     <a class="reference internal" href="../slides/data-science/data-science-in-the-cloud.html">
-     42.9. Data Science in the cloud
+     41.9. Data Science in the cloud
     </a>
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     <a class="reference internal" href="../slides/data-science/data-science-in-real-world.html">
-     42.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
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     <a class="reference internal" href="../slides/ml-fundamentals/ml-overview.html">
-     42.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
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-     42.12. Linear Regression
+     41.12. Linear Regression
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-     42.13. Logistic Regression
+     41.13. Logistic Regression
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     <a class="reference internal" href="../slides/ml-fundamentals/logistic-regression-condensed.html">
-     42.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
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     <a class="reference internal" href="../slides/ml-fundamentals/neural-network.html">
-     42.15. Neural Network
+     41.15. Neural Network
     </a>
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     <a class="reference internal" href="../slides/ml-fundamentals/build-an-ml-web-app.html">
-     42.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-advanced/unsupervised-learning.html">
-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-advanced/kernel-method.html">
-     42.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-advanced/model-selection.html">
-     42.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1577,101 +1577,101 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#k-means">
-   20.1. K-Means
+   19.1. K-Means
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#decision-boundaries">
-     20.1.1. Decision Boundaries
+     19.1.1. Decision Boundaries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-k-means-algorithm">
-     20.1.2. The K-Means Algorithm
+     19.1.2. The K-Means Algorithm
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#k-means-variability">
-     20.1.3. K-Means Variability
+     19.1.3. K-Means Variability
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#inertia">
-     20.1.4. Inertia
+     19.1.4. Inertia
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#multiple-initializations">
-     20.1.5. Multiple Initializations
+     19.1.5. Multiple Initializations
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#centroid-initialization-methods">
-     20.1.6. Centroid initialization methods
+     19.1.6. Centroid initialization methods
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#accelerated-k-means">
-     20.1.7. Accelerated K-Means
+     19.1.7. Accelerated K-Means
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#mini-batch-k-means">
-     20.1.8. Mini-Batch K-Means
+     19.1.8. Mini-Batch K-Means
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#pca">
-   20.2. PCA
+   19.2. PCA
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#finding-the-optimal-number-of-clusters">
-     20.2.1. Finding the optimal number of clusters
+     19.2.1. Finding the optimal number of clusters
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#limits-of-k-means">
-     20.2.2. Limits of K-Means
+     19.2.2. Limits of K-Means
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#using-clustering-for-image-segmentation">
-     20.2.3. Using Clustering for Image Segmentation
+     19.2.3. Using Clustering for Image Segmentation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#using-clustering-for-preprocessing">
-     20.2.4. Using Clustering for Preprocessing
+     19.2.4. Using Clustering for Preprocessing
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#lda">
-   20.3. LDA
+   19.3. LDA
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dbscan">
-   20.4. DBSCAN
+   19.4. DBSCAN
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#hierarchical-clustering">
-   20.5. Hierarchical Clustering
+   19.5. Hierarchical Clustering
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#spectral-clustering">
-     20.5.1. Spectral Clustering
+     19.5.1. Spectral Clustering
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#agglomerative-clustering">
-     20.5.2. Agglomerative Clustering
+     19.5.2. Agglomerative Clustering
     </a>
    </li>
   </ul>
@@ -1697,101 +1697,101 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#k-means">
-   20.1. K-Means
+   19.1. K-Means
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#decision-boundaries">
-     20.1.1. Decision Boundaries
+     19.1.1. Decision Boundaries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-k-means-algorithm">
-     20.1.2. The K-Means Algorithm
+     19.1.2. The K-Means Algorithm
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#k-means-variability">
-     20.1.3. K-Means Variability
+     19.1.3. K-Means Variability
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#inertia">
-     20.1.4. Inertia
+     19.1.4. Inertia
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#multiple-initializations">
-     20.1.5. Multiple Initializations
+     19.1.5. Multiple Initializations
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#centroid-initialization-methods">
-     20.1.6. Centroid initialization methods
+     19.1.6. Centroid initialization methods
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#accelerated-k-means">
-     20.1.7. Accelerated K-Means
+     19.1.7. Accelerated K-Means
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#mini-batch-k-means">
-     20.1.8. Mini-Batch K-Means
+     19.1.8. Mini-Batch K-Means
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#pca">
-   20.2. PCA
+   19.2. PCA
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#finding-the-optimal-number-of-clusters">
-     20.2.1. Finding the optimal number of clusters
+     19.2.1. Finding the optimal number of clusters
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#limits-of-k-means">
-     20.2.2. Limits of K-Means
+     19.2.2. Limits of K-Means
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#using-clustering-for-image-segmentation">
-     20.2.3. Using Clustering for Image Segmentation
+     19.2.3. Using Clustering for Image Segmentation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#using-clustering-for-preprocessing">
-     20.2.4. Using Clustering for Preprocessing
+     19.2.4. Using Clustering for Preprocessing
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#lda">
-   20.3. LDA
+   19.3. LDA
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dbscan">
-   20.4. DBSCAN
+   19.4. DBSCAN
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#hierarchical-clustering">
-   20.5. Hierarchical Clustering
+   19.5. Hierarchical Clustering
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#spectral-clustering">
-     20.5.1. Spectral Clustering
+     19.5.1. Spectral Clustering
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#agglomerative-clustering">
-     20.5.2. Agglomerative Clustering
+     19.5.2. Agglomerative Clustering
     </a>
    </li>
   </ul>
@@ -1807,7 +1807,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="unsupervised-learning">
-<h1><span class="section-number">20. </span>Unsupervised learning<a class="headerlink" href="#unsupervised-learning" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">19. </span>Unsupervised learning<a class="headerlink" href="#unsupervised-learning" title="Permalink to this headline">#</a></h1>
 <p>First, let’s import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20.</p>
 <div class="cell tag_hide-input docutils container">
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@@ -1866,7 +1866,7 @@ <h1><span class="section-number">20. </span>Unsupervised learning<a class="heade
 </div>
 </div>
 <section id="k-means">
-<h2><span class="section-number">20.1. </span>K-Means<a class="headerlink" href="#k-means" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">19.1. </span>K-Means<a class="headerlink" href="#k-means" title="Permalink to this headline">#</a></h2>
 <div class="cell tag_hide-input docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
@@ -2034,7 +2034,7 @@ <h2><span class="section-number">20.1. </span>K-Means<a class="headerlink" href=
 </div>
 </div>
 <section id="decision-boundaries">
-<h3><span class="section-number">20.1.1. </span>Decision Boundaries<a class="headerlink" href="#decision-boundaries" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.1.1. </span>Decision Boundaries<a class="headerlink" href="#decision-boundaries" title="Permalink to this headline">#</a></h3>
 <p>Let’s plot the model’s decision boundaries. This gives us a <em>Voronoi diagram</em>:</p>
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@@ -2130,7 +2130,7 @@ <h3><span class="section-number">20.1.1. </span>Decision Boundaries<a class="hea
 </div>
 </section>
 <section id="the-k-means-algorithm">
-<h3><span class="section-number">20.1.2. </span>The K-Means Algorithm<a class="headerlink" href="#the-k-means-algorithm" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.1.2. </span>The K-Means Algorithm<a class="headerlink" href="#the-k-means-algorithm" title="Permalink to this headline">#</a></h3>
 <p>The KMeans class applies an optimized algorithm by default. To get the original K-Means algorithm (for educational purposes only), you must set init=“random”, n_init=1 and algorithm=“full”. These hyperparameters will be explained below.</p>
 <p>Let’s run the K-Means algorithm for 1, 2 and 3 iterations, to see how the centroids move around:</p>
 <div class="cell docutils container">
@@ -2196,7 +2196,7 @@ <h3><span class="section-number">20.1.2. </span>The K-Means Algorithm<a class="h
 </div>
 </section>
 <section id="k-means-variability">
-<h3><span class="section-number">20.1.3. </span>K-Means Variability<a class="headerlink" href="#k-means-variability" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.1.3. </span>K-Means Variability<a class="headerlink" href="#k-means-variability" title="Permalink to this headline">#</a></h3>
 <p>In the original K-Means algorithm, the centroids are just initialized randomly, and the algorithm simply runs a single iteration to gradually improve the centroids, as we saw above.</p>
 <p>However, one major problem with this approach is that if you run K-Means multiple times (or with different random seeds), it can converge to very different solutions, as you can see below:</p>
 <div class="cell docutils container">
@@ -2244,7 +2244,7 @@ <h3><span class="section-number">20.1.3. </span>K-Means Variability<a class="hea
 </div>
 </section>
 <section id="inertia">
-<h3><span class="section-number">20.1.4. </span>Inertia<a class="headerlink" href="#inertia" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.1.4. </span>Inertia<a class="headerlink" href="#inertia" title="Permalink to this headline">#</a></h3>
 <p>To select the best model, we will need a way to evaluate a K-Mean model’s performance. Unfortunately, clustering is an unsupervised task, so we do not have the targets. But at least we can measure the distance between each instance and its centroid. This is the idea behind the <em>inertia</em> metric:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2287,7 +2287,7 @@ <h3><span class="section-number">20.1.4. </span>Inertia<a class="headerlink" hre
 </div>
 </section>
 <section id="multiple-initializations">
-<h3><span class="section-number">20.1.5. </span>Multiple Initializations<a class="headerlink" href="#multiple-initializations" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.1.5. </span>Multiple Initializations<a class="headerlink" href="#multiple-initializations" title="Permalink to this headline">#</a></h3>
 <p>So one approach to solve the variability issue is to simply run the K-Means algorithm multiple times with different random initializations, and select the solution that minimizes the inertia. For example, here are the inertias of the two “bad” models shown in the previous figure:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2341,7 +2341,7 @@ <h3><span class="section-number">20.1.5. </span>Multiple Initializations<a class
 </div>
 </section>
 <section id="centroid-initialization-methods">
-<h3><span class="section-number">20.1.6. </span>Centroid initialization methods<a class="headerlink" href="#centroid-initialization-methods" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.1.6. </span>Centroid initialization methods<a class="headerlink" href="#centroid-initialization-methods" title="Permalink to this headline">#</a></h3>
 <p>Instead of initializing the centroids entirely randomly, it is preferable to initialize them using the following algorithm, proposed in a <a class="reference external" href="https://goo.gl/eNUPw6">2006 paper</a> by David Arthur and Sergei Vassilvitskii:</p>
 <ul class="simple">
 <li><p>Take one centroid <span class="math notranslate nohighlight">\(c_1\)</span>, chosen uniformly at random from the dataset.</p></li>
@@ -2376,7 +2376,7 @@ <h3><span class="section-number">20.1.6. </span>Centroid initialization methods<
 </div>
 </section>
 <section id="accelerated-k-means">
-<h3><span class="section-number">20.1.7. </span>Accelerated K-Means<a class="headerlink" href="#accelerated-k-means" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.1.7. </span>Accelerated K-Means<a class="headerlink" href="#accelerated-k-means" title="Permalink to this headline">#</a></h3>
 <p>The K-Means algorithm can be significantly accelerated by avoiding many unnecessary distance calculations: this is achieved by exploiting the triangle inequality (given three points A, B and C, the distance AC is always such that AC ≤ AB + BC) and by keeping track of lower and upper bounds for distances between instances and centroids.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2406,7 +2406,7 @@ <h3><span class="section-number">20.1.7. </span>Accelerated K-Means<a class="hea
 <p>There’s no big difference in this case, as the dataset is fairly small.</p>
 </section>
 <section id="mini-batch-k-means">
-<h3><span class="section-number">20.1.8. </span>Mini-Batch K-Means<a class="headerlink" href="#mini-batch-k-means" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.1.8. </span>Mini-Batch K-Means<a class="headerlink" href="#mini-batch-k-means" title="Permalink to this headline">#</a></h3>
 <p>Scikit-Learn also implements a variant of the K-Means algorithm that supports mini-batches.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2440,7 +2440,7 @@ <h3><span class="section-number">20.1.8. </span>Mini-Batch K-Means<a class="head
 </section>
 </section>
 <section id="pca">
-<h2><span class="section-number">20.2. </span>PCA<a class="headerlink" href="#pca" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">19.2. </span>PCA<a class="headerlink" href="#pca" title="Permalink to this headline">#</a></h2>
 <p>If the dataset does not fit in memory, the simplest option is to use the memmap class, just like we did for incremental PCA in the previous chapter. First let’s load MNIST:</p>
 <p>Warning: since Scikit-Learn 0.24, fetch_openml() returns a Pandas DataFrame by default. To avoid this and keep the same code as in the book, we use as_frame=False.</p>
 <div class="cell docutils container">
@@ -2628,7 +2628,7 @@ <h2><span class="section-number">20.2. </span>PCA<a class="headerlink" href="#pc
 </div>
 </div>
 <section id="finding-the-optimal-number-of-clusters">
-<h3><span class="section-number">20.2.1. </span>Finding the optimal number of clusters<a class="headerlink" href="#finding-the-optimal-number-of-clusters" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.2.1. </span>Finding the optimal number of clusters<a class="headerlink" href="#finding-the-optimal-number-of-clusters" title="Permalink to this headline">#</a></h3>
 <p>What if the number of clusters was set to a lower or greater value than 5?</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2826,7 +2826,7 @@ <h3><span class="section-number">20.2.1. </span>Finding the optimal number of cl
 <p>As you can see, <span class="math notranslate nohighlight">\(k=5\)</span> looks like the best option here, as all clusters are roughly the same size, and they all cross the dashed line, which represents the mean silhouette score.</p>
 </section>
 <section id="limits-of-k-means">
-<h3><span class="section-number">20.2.2. </span>Limits of K-Means<a class="headerlink" href="#limits-of-k-means" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.2.2. </span>Limits of K-Means<a class="headerlink" href="#limits-of-k-means" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X1</span><span class="p">,</span> <span class="n">y1</span> <span class="o">=</span> <span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="o">-</span><span class="mi">4</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
@@ -2887,7 +2887,7 @@ <h3><span class="section-number">20.2.2. </span>Limits of K-Means<a class="heade
 </div>
 </section>
 <section id="using-clustering-for-image-segmentation">
-<h3><span class="section-number">20.2.3. </span>Using Clustering for Image Segmentation<a class="headerlink" href="#using-clustering-for-image-segmentation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.2.3. </span>Using Clustering for Image Segmentation<a class="headerlink" href="#using-clustering-for-image-segmentation" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Download the ladybug image</span>
@@ -2977,7 +2977,7 @@ <h3><span class="section-number">20.2.3. </span>Using Clustering for Image Segme
 </div>
 </section>
 <section id="using-clustering-for-preprocessing">
-<h3><span class="section-number">20.2.4. </span>Using Clustering for Preprocessing<a class="headerlink" href="#using-clustering-for-preprocessing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.2.4. </span>Using Clustering for Preprocessing<a class="headerlink" href="#using-clustering-for-preprocessing" title="Permalink to this headline">#</a></h3>
 <p>Let’s tackle the <em>digits dataset</em> which is a simple MNIST-like dataset containing 1,797 grayscale 8×8 images representing digits 0 to 9.</p>
 <div class="cell docutils container">
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@@ -3011,7 +3011,7 @@ <h3><span class="section-number">20.2.4. </span>Using Clustering for Preprocessi
 </section>
 </section>
 <section id="lda">
-<h2><span class="section-number">20.3. </span>LDA<a class="headerlink" href="#lda" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">19.3. </span>LDA<a class="headerlink" href="#lda" title="Permalink to this headline">#</a></h2>
 <div class="cell tag_hide-input docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
@@ -3682,7 +3682,7 @@ <h2><span class="section-number">20.3. </span>LDA<a class="headerlink" href="#ld
 </ol>
 </section>
 <section id="dbscan">
-<h2><span class="section-number">20.4. </span>DBSCAN<a class="headerlink" href="#dbscan" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">19.4. </span>DBSCAN<a class="headerlink" href="#dbscan" title="Permalink to this headline">#</a></h2>
 <div class="cell tag_hide-input docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
@@ -3949,9 +3949,9 @@ <h2><span class="section-number">20.4. </span>DBSCAN<a class="headerlink" href="
 </div>
 </section>
 <section id="hierarchical-clustering">
-<h2><span class="section-number">20.5. </span>Hierarchical Clustering<a class="headerlink" href="#hierarchical-clustering" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">19.5. </span>Hierarchical Clustering<a class="headerlink" href="#hierarchical-clustering" title="Permalink to this headline">#</a></h2>
 <section id="spectral-clustering">
-<h3><span class="section-number">20.5.1. </span>Spectral Clustering<a class="headerlink" href="#spectral-clustering" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.5.1. </span>Spectral Clustering<a class="headerlink" href="#spectral-clustering" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">SpectralClustering</span>
@@ -4035,7 +4035,7 @@ <h3><span class="section-number">20.5.1. </span>Spectral Clustering<a class="hea
 </div>
 </section>
 <section id="agglomerative-clustering">
-<h3><span class="section-number">20.5.2. </span>Agglomerative Clustering<a class="headerlink" href="#agglomerative-clustering" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">19.5.2. </span>Agglomerative Clustering<a class="headerlink" href="#agglomerative-clustering" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">AgglomerativeClustering</span>
@@ -4132,13 +4132,13 @@ <h3><span class="section-number">20.5.2. </span>Agglomerative Clustering<a class
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">19.4. </span>XGBoost + k-fold CV + Feature Importance</p>
+            <p class="prev-next-title"><span class="section-number">18.4. </span>XGBoost + k-fold CV + Feature Importance</p>
         </div>
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-        <p class="prev-next-title"><span class="section-number">21. </span>Kernel method</p>
+        <p class="prev-next-title"><span class="section-number">20. </span>Kernel method</p>
     </div>
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diff --git a/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html b/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html
index cbe293927..ee1424a85 100644
--- a/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html
+++ b/ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>14. Build a web app to use a Machine Learning model &#8212; Ocademy Open Machine Learning Book</title>
+    <title>13. Build a web app to use a Machine Learning model &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="15. Getting started with classification" href="classification/getting-started-with-classification.html" />
-    <link rel="prev" title="13.4. Logistic regression" href="regression/logistic-regression.html" />
+    <link rel="next" title="14. Getting started with classification" href="classification/getting-started-with-classification.html" />
+    <link rel="prev" title="12.4. Logistic regression" href="regression/logistic-regression.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -159,17 +159,17 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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   <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
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+   1. Python programming introduction
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-   3. Python programming basics
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-     5.1. Defining data science
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-     5.2. Data Science ethics
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     <a class="reference internal" href="../data-science/introduction/defining-data.html">
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     <a class="reference internal" href="../data-science/introduction/introduction-to-statistics-and-probability.html">
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     <a class="reference internal" href="../data-science/data-visualization/visualization-relationships.html">
-     7.3. Visualizing relationships: all about honey 🍯
+     6.3. Visualizing relationships: all about honey 🍯
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-     7.4. Making meaningful visualizations
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+     8.1. Introduction
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     <a class="reference internal" href="../data-science/data-science-in-the-cloud/the-low-code-no-code-way.html">
-     9.2. The “low code/no code” way
+     8.2. The “low code/no code” way
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-     9.3. Data Science in the cloud: The “Azure ML SDK” way
+     8.3. Data Science in the cloud: The “Azure ML SDK” way
     </a>
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+   9. Data Science in the real world
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+   10. Machine Learning overview
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-   12. Summary of machine learning fundamentals
+   11. Summary of machine learning fundamentals
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-     13.1. Tools of the trade
+     12.1. Tools of the trade
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-     13.2. Managing data
+     12.2. Managing data
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-     13.3. Linear and polynomial regression
+     12.3. Linear and polynomial regression
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+     12.4. Logistic regression
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+   13. Build a web app to use a Machine Learning model
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+   14. Getting started with classification
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+     14.1. Introduction to classification
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-     15.2. More classifiers
+     14.2. More classifiers
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+     14.3. Yet other classifiers
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-     15.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
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+     15.1. Loss function
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-     16.2. Gradient descent
+     15.2. Gradient descent
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+     16.1. Introduction to clustering
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-     17.2. K-Means clustering
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+     17.1. Bagging
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+     17.3. Feature importance
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+     18.2. Gradient boosting example
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-     19.3. XGBoost
+     18.3. XGBoost
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-     19.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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+   21. Unsupervised learning: PCA and clustering
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-     25.3.1.1. Stylenet / Neural-Style
+     24.3.1.1. Stylenet / Neural-Style
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-     25.3.1.2. Deepdream in TensorFlow
+     24.3.1.2. Deepdream in TensorFlow
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+   30. Deep Q-learning
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+   32. Image segmentation
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+   34. Object detection
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+   38. Model training &amp; evaluation
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+     40.3. First assignment
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+     40.4. Second assignment
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+     40.5. Project Plan​ Template
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-     41.6. Python programming introduction
+     40.6. Python programming introduction
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+     40.7. Python programming basics
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+     40.8. Python programming advanced
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-     41.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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+     40.10. Data Science scenarios
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-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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-     41.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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+     40.15. Dive into the beehive
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
+     40.17. Classifying datasets
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-     41.18. Small diabetes study
+     40.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
+     40.20. Displaying airport data
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-     41.21. Soda profits
+     40.21. Soda profits
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+     40.24. Data processing in Python
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+     40.28. NYC taxi data in winter and summer
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-     41.35. Tell a story
+     40.35. Tell a story
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-     41.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
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+     40.38. Market research
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-     41.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
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-     41.40. Data Science project using Azure ML SDK
+     40.40. Data Science project using Azure ML SDK
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-     41.41. Data Science in the cloud: The “Azure ML SDK” way
+     40.41. Data Science in the cloud: The “Azure ML SDK” way
     </a>
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+   13.6. Exercise - build a Flask app
   </a>
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-   14.7. Your turn! 🚀
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 <section class="tex2jax_ignore mathjax_ignore" id="build-a-web-app-to-use-a-machine-learning-model">
-<h1><span class="section-number">14. </span>Build a web app to use a Machine Learning model<a class="headerlink" href="#build-a-web-app-to-use-a-machine-learning-model" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">13. </span>Build a web app to use a Machine Learning model<a class="headerlink" href="#build-a-web-app-to-use-a-machine-learning-model" title="Permalink to this headline">#</a></h1>
 <p>In this section, you will train an ML model on a data set that’s out of this world: <em>UFO sightings over the past century</em>, sourced from NUFORC’s database.</p>
 <p>You will learn:</p>
 <ul class="simple">
@@ -1744,10 +1744,10 @@ <h1><span class="section-number">14. </span>Build a web app to use a Machine Lea
 <p>We will continue our use of notebooks to clean data and train our model, but you can take the process one step further by exploring using a model ‘in the wild’, so to speak: in a web app.</p>
 <p>To do this, you need to build a web app using Flask.</p>
 <section id="building-an-app">
-<h2><span class="section-number">14.1. </span>Building an app<a class="headerlink" href="#building-an-app" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">13.1. </span>Building an app<a class="headerlink" href="#building-an-app" title="Permalink to this headline">#</a></h2>
 <p>There are several ways to build web apps to consume machine learning models. Your web architecture may influence the way your model is trained. Imagine that you are working in a business where the data science group has trained a model that they want you to use in an app.</p>
 <section id="considerations">
-<h3><span class="section-number">14.1.1. </span>Considerations<a class="headerlink" href="#considerations" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">13.1.1. </span>Considerations<a class="headerlink" href="#considerations" title="Permalink to this headline">#</a></h3>
 <p>There are many questions you need to ask:</p>
 <ul class="simple">
 <li><p><strong>Is it a web app or a mobile app?</strong> If you are building a mobile app or need to use the model in an IoT context, you could use <a class="reference external" href="https://www.tensorflow.org/lite/">TensorFlow Lite</a> and use the model in an Android or iOS app.</p></li>
@@ -1766,13 +1766,13 @@ <h3><span class="section-number">14.1.1. </span>Considerations<a class="headerli
 </section>
 </section>
 <section id="tool">
-<h2><span class="section-number">14.2. </span>Tool<a class="headerlink" href="#tool" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">13.2. </span>Tool<a class="headerlink" href="#tool" title="Permalink to this headline">#</a></h2>
 <p>For this task, you need two tools: Flask and Pickle, both of which run on Python.</p>
 <p>✅ What’s <a class="reference external" href="https://palletsprojects.com/p/flask/">Flask</a>? Defined as a ‘micro-framework’ by its creators, Flask provides the basic features of web frameworks using Python and a templating engine to build web pages. Take a look at <a class="reference external" href="https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-77952-leestott">this Learn module</a> to practice building with Flask.</p>
 <p>✅ What’s <a class="reference external" href="https://docs.python.org/3/library/pickle.html">Pickle</a>? Pickle 🥒 is a Python module that serializes and de-serializes a Python object structure. When you ‘pickle’ a model, you serialize or flatten its structure for use on the web. Be careful: pickle is not intrinsically secure, so be careful if prompted to ‘un-pickle’ a file. A pickled file has the suffix <code class="docutils literal notranslate"><span class="pre">.pkl</span></code>.</p>
 </section>
 <section id="exercise-clean-your-data">
-<h2><span class="section-number">14.3. </span>Exercise - clean your data<a class="headerlink" href="#exercise-clean-your-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">13.3. </span>Exercise - clean your data<a class="headerlink" href="#exercise-clean-your-data" title="Permalink to this headline">#</a></h2>
 <p>In this section, you’ll use data from 80,000 UFO sightings, gathered by <a class="reference external" href="https://nuforc.org">NUFORC</a> (The National UFO Reporting Center). This data has some interesting descriptions of UFO sightings, for example:</p>
 <ul class="simple">
 <li><p><strong>Long example description.</strong> “A man emerges from a beam of light that shines on a grassy field at night and he runs towards the Texas Instruments parking lot”.</p></li>
@@ -2022,7 +2022,7 @@ <h2><span class="section-number">14.3. </span>Exercise - clean your data<a class
 </div>
 </section>
 <section id="exercise-build-your-model">
-<h2><span class="section-number">14.4. </span>Exercise - build your model<a class="headerlink" href="#exercise-build-your-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">13.4. </span>Exercise - build your model<a class="headerlink" href="#exercise-build-your-model" title="Permalink to this headline">#</a></h2>
 <p>Now you can get ready to train a model by dividing the data into the training and testing group.</p>
 <p>Select the three features you want to train on as your X vector, and the y vector will be the <code class="docutils literal notranslate"><span class="pre">Country</span></code>. You want to be able to input <code class="docutils literal notranslate"><span class="pre">Seconds</span></code>, <code class="docutils literal notranslate"><span class="pre">Latitude</span></code> and <code class="docutils literal notranslate"><span class="pre">Longitude</span></code> and get a country id to return.</p>
 <div class="cell docutils container">
@@ -2073,7 +2073,7 @@ <h2><span class="section-number">14.4. </span>Exercise - build your model<a clas
 <p>The model you created isn’t very revolutionary as you should be able to infer a <code class="docutils literal notranslate"><span class="pre">Country</span></code> from its <code class="docutils literal notranslate"><span class="pre">Latitude</span></code> and <code class="docutils literal notranslate"><span class="pre">Longitude</span></code>, but it’s a good exercise to try to train from raw data that you cleaned, exported, and then use this model in a web app.</p>
 </section>
 <section id="exercise-pickle-your-model">
-<h2><span class="section-number">14.5. </span>Exercise - ‘pickle’ your model<a class="headerlink" href="#exercise-pickle-your-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">13.5. </span>Exercise - ‘pickle’ your model<a class="headerlink" href="#exercise-pickle-your-model" title="Permalink to this headline">#</a></h2>
 <p>Now, it’s time to <em>pickle</em> your model! You can do that in a few lines of code. Once it’s <em>pickled</em>, load your pickled model and test it against a sample data array containing values for seconds, latitude and longitude.</p>
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@@ -2100,7 +2100,7 @@ <h2><span class="section-number">14.5. </span>Exercise - ‘pickle’ your model
 <p>The model returns <strong>‘3’</strong>, which is the country code for the UK. Wild! 👽</p>
 </section>
 <section id="exercise-build-a-flask-app">
-<h2><span class="section-number">14.6. </span>Exercise - build a Flask app<a class="headerlink" href="#exercise-build-a-flask-app" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">13.6. </span>Exercise - build a Flask app<a class="headerlink" href="#exercise-build-a-flask-app" title="Permalink to this headline">#</a></h2>
 <p>Now you can build a Flask app to call your model and return similar results but in a more visually pleasing way.</p>
 <p>Start by creating a folder called <code class="docutils literal notranslate"><span class="pre">/web-app</span></code> next to the <code class="docutils literal notranslate"><span class="pre">notebook.ipynb</span></code> file where your <code class="docutils literal notranslate"><span class="pre">ufo-model.pkl</span></code> file resides.</p>
 <p>In that folder create three more folders: <code class="docutils literal notranslate"><span class="pre">/static</span></code>, with a folder <code class="docutils literal notranslate"><span class="pre">/css</span></code> inside it, and <code class="docutils literal notranslate"><span class="pre">/templates</span></code>. You should now have the following files and directories:</p>
@@ -2255,15 +2255,15 @@ <h2><span class="section-number">14.6. </span>Exercise - build a Flask app<a cla
 <p>Using a model this way, with Flask and a pickled model, is relatively straightforward. The hardest thing is to understand what shape the data is that must be sent to the model to get a prediction. That all depends on how the model was trained. This one has three data points to be input in order to get a prediction.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">14.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">13.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Instead of working in a notebook and importing the model to the Flask app, you could train the model right within the Flask app! Try converting your Python code in the notebook, perhaps after your data is cleaned, to train the model from within the app on a route called <code class="docutils literal notranslate"><span class="pre">/train</span></code>. What are the pros and cons of pursuing this method? Practice it by following this <a class="reference internal" href="../assignments/ml-fundamentals/try-a-different-model.html"><span class="doc std std-doc">assignment</span></a></p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">14.8. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">13.8. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>There are many ways to build a web app to consume ML models. Make a list of the ways you could use JavaScript or Python to build a web app to leverage machine learning. Consider architecture: should the model stay in the app or live in the cloud? If the latter, how would you access it? Draw out an architectural model for an applied ML web solution.</p>
 </section>
 <section id="acknowledgements">
-<h2><span class="section-number">14.9. </span>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">13.9. </span>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2305,13 +2305,13 @@ <h2><span class="section-number">14.9. </span>Acknowledgements<a class="headerli
         <i class="fas fa-angle-left"></i>
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+            <p class="prev-next-title"><span class="section-number">12.4. </span>Logistic regression</p>
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-        <p class="prev-next-title"><span class="section-number">15. </span>Getting started with classification</p>
+        <p class="prev-next-title"><span class="section-number">14. </span>Getting started with classification</p>
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-    <title>15.4. Applied Machine Learning : build a web app &#8212; Ocademy Open Machine Learning Book</title>
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-    <link rel="prev" title="15.3. Yet other classifiers" href="yet-other-classifiers.html" />
+    <link rel="next" title="15. parameter-optimization" href="../parameter-optimization/parameter-optimization.html" />
+    <link rel="prev" title="14.3. Yet other classifiers" href="yet-other-classifiers.html" />
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-     41.9. Analyzing text about Data Science
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-     41.14. Try it in Excel
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-     41.16. Build your own custom vis
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-     41.21. Soda profits
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+<h1><span class="section-number">14.4. </span>Applied Machine Learning : build a web app<a class="headerlink" href="#applied-machine-learning-build-a-web-app" title="Permalink to this headline">#</a></h1>
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+<h1><span class="section-number">14. </span>Getting started with classification<a class="headerlink" href="#getting-started-with-classification" title="Permalink to this headline">#</a></h1>
 <p>In Asia and India, food traditions are extremely diverse, and very delicious! Let’s look at data about regional cuisines to try to understand their ingredients.</p>
 <p><img alt="Thai food seller" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/thai-food.jpg" /></p>
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-<li class="toctree-l1"><a class="reference internal" href="applied-ml-build-a-web-app.html">15.4. Applied Machine Learning : build a web app</a></li>
+<li class="toctree-l1"><a class="reference internal" href="introduction-to-classification.html">14.1. Introduction to classification</a></li>
+<li class="toctree-l1"><a class="reference internal" href="more-classifiers.html">14.2. More classifiers</a></li>
+<li class="toctree-l1"><a class="reference internal" href="yet-other-classifiers.html">14.3. Yet other classifiers</a></li>
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+            <p class="prev-next-title"><span class="section-number">13. </span>Build a web app to use a Machine Learning model</p>
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diff --git a/ml-fundamentals/classification/introduction-to-classification.html b/ml-fundamentals/classification/introduction-to-classification.html
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-<h1><span class="section-number">15.1. </span>Introduction to classification<a class="headerlink" href="#introduction-to-classification" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">14.1. </span>Introduction to classification<a class="headerlink" href="#introduction-to-classification" title="Permalink to this headline">#</a></h1>
 <p>In these four sections, you will explore a fundamental focus of classic machine learning <em>classification</em>. We will walk through using various classification algorithms with a dataset about all the brilliant cuisines of Asia and India. Hope you’re hungry!</p>
 <figure class="align-default" id="celebrate-pan-asian-cuisines-in-these-lessons">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/pinch.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/pinch.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/pinch.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 15.1 </span><span class="caption-text">Image by <a class="reference external" href="https://twitter.com/jenlooper">Jen Looper</a></span><a class="headerlink" href="#celebrate-pan-asian-cuisines-in-these-lessons" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 14.1 </span><span class="caption-text">Image by <a class="reference external" href="https://twitter.com/jenlooper">Jen Looper</a></span><a class="headerlink" href="#celebrate-pan-asian-cuisines-in-these-lessons" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Classification is a form of <a class="reference external" href="https://wikipedia.org/wiki/Supervised_learning">supervised learning</a> that bears a lot in common with regression techniques. If machine learning is all about predicting values or names to things by using datasets, then classification generally falls into two groups: <em>binary classification</em> and <em>multiclass classification</em>.</p>
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+<h2><span class="section-number">14.1.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>Classification is one of the fundamental activities of the machine learning researcher and data scientist. From basic classification of a binary value (“is this email spam or not?”), to complex image classification and segmentation using computer vision, it’s always useful to be able to sort data into classes and ask questions of it.</p>
 <p>To state the process in a more scientific way, your classification method creates a predictive model that enables you to map the relationship between input variables to output variables.</p>
 <figure class="align-default" id="binary-vs-multiclass-classification">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/binary-multiclass.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/binary-multiclass.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/binary-multiclass.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 15.2 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/jenlooper">Jen Looper</a></span><a class="headerlink" href="#binary-vs-multiclass-classification" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 14.2 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/jenlooper">Jen Looper</a></span><a class="headerlink" href="#binary-vs-multiclass-classification" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Before starting the process of cleaning our data, visualizing it, and prepping it for our ML tasks, let’s learn a bit about the various ways machine learning can be leveraged to classify data.</p>
@@ -1814,12 +1814,12 @@ <h2><span class="section-number">15.1.1. </span>Introduction<a class="headerlink
 <p>Click the video above. The whole premise of the show ‘Chopped’ is the ‘mystery basket’ where chefs have to make some dish out of a random choice of ingredients. Surely a ML model would have helped!</p>
 </section>
 <section id="hello-classifier">
-<h2><span class="section-number">15.1.2. </span>Hello ‘classifier’<a class="headerlink" href="#hello-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.1.2. </span>Hello ‘classifier’<a class="headerlink" href="#hello-classifier" title="Permalink to this headline">#</a></h2>
 <p>The question we want to ask of this cuisine dataset is actually a <strong>multiclass question</strong>, as we have several potential national cuisines to work with. Given a batch of ingredients, which of these many classes will the data fit?</p>
 <p>Scikit-learn offers several different algorithms to use to classify data, depending on the kind of problem you want to solve. In the next two sections, you’ll learn about several of these algorithms.</p>
 </section>
 <section id="exercise-clean-and-balance-your-data">
-<h2><span class="section-number">15.1.3. </span>Exercise - clean and balance your data<a class="headerlink" href="#exercise-clean-and-balance-your-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.1.3. </span>Exercise - clean and balance your data<a class="headerlink" href="#exercise-clean-and-balance-your-data" title="Permalink to this headline">#</a></h2>
 <p>The first task at hand, before starting this project, is to clean and <strong>balance</strong> your data to get better results. Start with the blank <a class="reference internal" href="../../assignments/ml-fundamentals/delicious-asian-and-indian-cuisines.html"><span class="doc std std-doc">delicious-asian-and-indian-cuisines.ipynb</span></a> file.</p>
 <p>The first thing to install is <a class="reference external" href="https://imbalanced-learn.org/stable/">imblearn</a>. This is a Scikit-learn package that will allow you to better balance the data (you will learn more about this task in a minute).</p>
 <p>1. Import the packages you need to import your data and visualize it, also import <code class="docutils literal notranslate"><span class="pre">SMOTE</span></code> from <code class="docutils literal notranslate"><span class="pre">imblearn</span></code>.</p>
@@ -2039,7 +2039,7 @@ <h2><span class="section-number">15.1.3. </span>Exercise - clean and balance you
 </div>
 </section>
 <section id="exercise-learning-about-cuisines">
-<h2><span class="section-number">15.1.4. </span>Exercise - learning about cuisines<a class="headerlink" href="#exercise-learning-about-cuisines" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.1.4. </span>Exercise - learning about cuisines<a class="headerlink" href="#exercise-learning-about-cuisines" title="Permalink to this headline">#</a></h2>
 <p>Now the work starts to become more interesting. Let’s discover the distribution of data, per cuisine</p>
 <p>1. Plot the data as bars by calling <code class="docutils literal notranslate"><span class="pre">barh()</span></code>:</p>
 <div class="cell docutils container">
@@ -2085,7 +2085,7 @@ <h2><span class="section-number">15.1.4. </span>Exercise - learning about cuisin
 </div>
 </section>
 <section id="discovering-ingredients">
-<h2><span class="section-number">15.1.5. </span>Discovering ingredients<a class="headerlink" href="#discovering-ingredients" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.1.5. </span>Discovering ingredients<a class="headerlink" href="#discovering-ingredients" title="Permalink to this headline">#</a></h2>
 <p>Now you can dig deeper into the data and learn what are the typical ingredients per cuisine. You should clean out recurrent data that creates confusion between cuisines, so let’s learn about this problem.</p>
 <p>1. Create a function <code class="docutils literal notranslate"><span class="pre">create_ingredient()</span></code> in Python to create an ingredient dataframe. This function will start by dropping an unhelpful column and sort through ingredients by their count:</p>
 <div class="cell docutils container">
@@ -2356,7 +2356,7 @@ <h2><span class="section-number">15.1.5. </span>Discovering ingredients<a class=
 </div>
 </section>
 <section id="balance-the-dataset">
-<h2><span class="section-number">15.1.6. </span>Balance the dataset<a class="headerlink" href="#balance-the-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.1.6. </span>Balance the dataset<a class="headerlink" href="#balance-the-dataset" title="Permalink to this headline">#</a></h2>
 <p>Now that you have cleaned the data, use <a class="reference external" href="https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html">SMOTE</a> - “Synthetic Minority Over-sampling Technique” - to balance it.</p>
 <p>1. Call <code class="docutils literal notranslate"><span class="pre">fit_resample()</span></code>, this strategy generates new samples by interpolation.</p>
 <div class="cell docutils container">
@@ -2424,16 +2424,16 @@ <h2><span class="section-number">15.1.6. </span>Balance the dataset<a class="hea
 </section>
 <hr class="docutils" />
 <section id="self-study">
-<h2><span class="section-number">15.1.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.1.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>This curriculum contains several interesting datasets. Dig through the <a class="reference external" href="https://github.com/YinYi000/machine-learning/tree/main/open-machine-learning-jupyter-book/assets/data">/data/classification</a> folders and see if any contain datasets that would be appropriate for binary or multi-class classification? What questions would you ask of this dataset?</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">15.1.8. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.1.8. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Explore SMOTE’s API. What use cases is it best used for? What problems does it solve?</p>
 <p>Assignment - <a class="reference internal" href="../../assignments/ml-fundamentals/explore-classification-methods.html"><span class="doc std std-doc">Explore classification methods</span></a></p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">15.1.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.1.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2475,13 +2475,13 @@ <h2><span class="section-number">15.1.9. </span>Acknowledgments<a class="headerl
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-            <p class="prev-next-title"><span class="section-number">15. </span>Getting started with classification</p>
+            <p class="prev-next-title"><span class="section-number">14. </span>Getting started with classification</p>
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-        <p class="prev-next-title"><span class="section-number">15.2. </span>More classifiers</p>
+        <p class="prev-next-title"><span class="section-number">14.2. </span>More classifiers</p>
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diff --git a/ml-fundamentals/classification/more-classifiers.html b/ml-fundamentals/classification/more-classifiers.html
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     <link rel="index" title="Index" href="../../genindex.html" />
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-    <link rel="next" title="15.3. Yet other classifiers" href="yet-other-classifiers.html" />
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+   14.2.4. Exercise - apply logistic regression
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 <section class="tex2jax_ignore mathjax_ignore" id="more-classifiers">
-<h1><span class="section-number">15.2. </span>More classifiers<a class="headerlink" href="#more-classifiers" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">14.2. </span>More classifiers<a class="headerlink" href="#more-classifiers" title="Permalink to this headline">#</a></h1>
 <p>In this section, you will use the dataset you saved from the last section full of balanced, clean data all about cuisines.</p>
 <p>You will use this dataset with a variety of classifiers to <em>predict a given national cuisine based on a group of ingredients</em>. While doing so, you’ll learn more about some of the ways that algorithms can be leveraged for classification tasks.</p>
 <section id="exercise-predict-a-national-cuisine">
-<h2><span class="section-number">15.2.1. </span>Exercise - predict a national cuisine<a class="headerlink" href="#exercise-predict-a-national-cuisine" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.2.1. </span>Exercise - predict a national cuisine<a class="headerlink" href="#exercise-predict-a-national-cuisine" title="Permalink to this headline">#</a></h2>
 <p>1. Working in this section’s <a class="reference external" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/assignments/ml-fundamentals/build-classification-models.ipynb">build-classification-models</a> file, import that file along with the Pandas library:</p>
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 <p>Now you are ready to train your model!</p>
 </section>
 <section id="choosing-your-classifier">
-<h2><span class="section-number">15.2.2. </span>Choosing your classifier<a class="headerlink" href="#choosing-your-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.2.2. </span>Choosing your classifier<a class="headerlink" href="#choosing-your-classifier" title="Permalink to this headline">#</a></h2>
 <p>Now that your data is clean and ready for training, you have to decide which algorithm to use for the job.</p>
 <p>Scikit-learn groups classification under Supervised Learning, and in that category you will find many ways to classify. <a class="reference external" href="https://scikit-learn.org/stable/supervised_learning.html">The variety</a> is quite bewildering at first sight. The following methods all include classification techniques:</p>
 <ul class="simple">
@@ -2128,12 +2128,12 @@ <h2><span class="section-number">15.2.2. </span>Choosing your classifier<a class
 <p>You can also use <a class="reference external" href="https://scikit-learn.org/stable/modules/neural_networks_supervised.html#classification">neural networks to classify data</a>, but that is outside the scope of this section.</p>
 </div>
 <section id="what-classifier-to-go-with">
-<h3><span class="section-number">15.2.2.1. </span>What classifier to go with?<a class="headerlink" href="#what-classifier-to-go-with" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">14.2.2.1. </span>What classifier to go with?<a class="headerlink" href="#what-classifier-to-go-with" title="Permalink to this headline">#</a></h3>
 <p>So, which classifier should you choose? Often, running through several and looking for a good result is a way to test. Scikit-learn offers a <a class="reference external" href="https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html">side-by-side comparison</a> on a created dataset, comparing KNeighbors, SVC two ways, GaussianProcessClassifier, DecisionTreeClassifier, RandomForestClassifier, MLPClassifier, AdaBoostClassifier, GaussianNB and QuadraticDiscrinationAnalysis, showing the results visualized:</p>
 <figure class="align-default" id="comparison-of-classifiers">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/comparison.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/comparison.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/comparison.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 15.3 </span><span class="caption-text">Comparison of classifiers <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/2-Classifiers-1/images/comparison.png">🔗source</a></span><a class="headerlink" href="#comparison-of-classifiers" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 14.3 </span><span class="caption-text">Comparison of classifiers <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/2-Classifiers-1/images/comparison.png">🔗source</a></span><a class="headerlink" href="#comparison-of-classifiers" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="admonition seealso">
@@ -2143,12 +2143,12 @@ <h3><span class="section-number">15.2.2.1. </span>What classifier to go with?<a
 </div>
 </section>
 <section id="a-better-approach">
-<h3><span class="section-number">15.2.2.2. </span>A better approach<a class="headerlink" href="#a-better-approach" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">14.2.2.2. </span>A better approach<a class="headerlink" href="#a-better-approach" title="Permalink to this headline">#</a></h3>
 <p>A better way than wildly guessing, however, is to follow the ideas on this downloadable <a class="reference external" href="https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott">ML Cheat sheet</a>. Here, we discover that, for our multiclass problem, we have some choices:</p>
 <figure class="align-default" id="cheatsheet-for-multiclass-problems">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/cheatsheet.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/cheatsheet.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/cheatsheet.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 15.4 </span><span class="caption-text">Cheatsheet for multiclass problems <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/2-Classifiers-1/images/cheatsheet.png">🔗source</a></span><a class="headerlink" href="#cheatsheet-for-multiclass-problems" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 14.4 </span><span class="caption-text">Cheatsheet for multiclass problems <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/2-Classifiers-1/images/cheatsheet.png">🔗source</a></span><a class="headerlink" href="#cheatsheet-for-multiclass-problems" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="admonition note">
@@ -2161,7 +2161,7 @@ <h3><span class="section-number">15.2.2.2. </span>A better approach<a class="hea
 </div>
 </section>
 <section id="reasoning">
-<h3><span class="section-number">15.2.2.3. </span>Reasoning<a class="headerlink" href="#reasoning" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">14.2.2.3. </span>Reasoning<a class="headerlink" href="#reasoning" title="Permalink to this headline">#</a></h3>
 <p>Let’s see if we can reason our way through different approaches given the constraints we have:</p>
 <ul class="simple">
 <li><p><strong>Neural networks are too heavy</strong>. Given our clean, but minimal dataset, and the fact that we are running training locally via notebooks, neural networks are too heavyweight for this task.</p></li>
@@ -2171,7 +2171,7 @@ <h3><span class="section-number">15.2.2.3. </span>Reasoning<a class="headerlink"
 </ul>
 </section>
 <section id="using-scikit-learn">
-<h3><span class="section-number">15.2.2.4. </span>Using Scikit-learn<a class="headerlink" href="#using-scikit-learn" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">14.2.2.4. </span>Using Scikit-learn<a class="headerlink" href="#using-scikit-learn" title="Permalink to this headline">#</a></h3>
 <p>We will be using Scikit-learn to analyze our data. However, there are many ways to use logistic regression in Scikit-learn. Take a look at the <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression">parameters to pass</a>.</p>
 <p>Essentially there are two important parameters - <code class="docutils literal notranslate"><span class="pre">multi_class</span></code> and <code class="docutils literal notranslate"><span class="pre">solver</span></code> - that we need to specify, when we ask Scikit-learn to perform a logistic regression. The <code class="docutils literal notranslate"><span class="pre">multi_class</span></code> value applies a certain behavior. The value of the solver is what algorithm to use. Not all solvers can be paired with all <code class="docutils literal notranslate"><span class="pre">multi_class</span></code> values.</p>
 <p>According to the docs, in the multiclass case, the training algorithm:</p>
@@ -2188,13 +2188,13 @@ <h3><span class="section-number">15.2.2.4. </span>Using Scikit-learn<a class="he
 <figure class="align-default" id="solvers">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/solvers.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/solvers.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/solvers.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 15.5 </span><span class="caption-text">Solvers <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/2-Classifiers-1/images/solvers.png">🔗source</a></span><a class="headerlink" href="#solvers" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 14.5 </span><span class="caption-text">Solvers <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/2-Classifiers-1/images/solvers.png">🔗source</a></span><a class="headerlink" href="#solvers" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 </section>
 <section id="exercise-split-the-data">
-<h2><span class="section-number">15.2.3. </span>Exercise - split the data<a class="headerlink" href="#exercise-split-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.2.3. </span>Exercise - split the data<a class="headerlink" href="#exercise-split-the-data" title="Permalink to this headline">#</a></h2>
 <p>We can focus on logistic regression for our first training trial since you recently learned about the latter in a previous section.
 Split your data into training and testing groups by calling <code class="docutils literal notranslate"><span class="pre">train_test_split()</span></code>:</p>
 <div class="cell docutils container">
@@ -2206,7 +2206,7 @@ <h2><span class="section-number">15.2.3. </span>Exercise - split the data<a clas
 </div>
 </section>
 <section id="exercise-apply-logistic-regression">
-<h2><span class="section-number">15.2.4. </span>Exercise - apply logistic regression<a class="headerlink" href="#exercise-apply-logistic-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.2.4. </span>Exercise - apply logistic regression<a class="headerlink" href="#exercise-apply-logistic-regression" title="Permalink to this headline">#</a></h2>
 <p>Since you are using the multiclass case, you need to choose what <em>scheme</em> to use and what <em>solver</em> to set. Use LogisticRegression with a multiclass setting and the <strong>liblinear</strong> solver to train.</p>
 <p>1. Create a logistic regression with multi_class set to <code class="docutils literal notranslate"><span class="pre">ovr</span></code> and the solver set to <code class="docutils literal notranslate"><span class="pre">liblinear</span></code>:</p>
 <div class="cell docutils container">
@@ -2220,7 +2220,7 @@ <h2><span class="section-number">15.2.4. </span>Exercise - apply logistic regres
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy is 0.786488740617181
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy is 0.8065054211843202
 </pre></div>
 </div>
 </div>
@@ -2243,10 +2243,10 @@ <h2><span class="section-number">15.2.4. </span>Exercise - apply logistic regres
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>ingredients: Index([&#39;cabbage&#39;, &#39;cilantro&#39;, &#39;egg&#39;, &#39;mushroom&#39;, &#39;nut&#39;, &#39;pepper&#39;, &#39;pork&#39;,
-       &#39;scallion&#39;, &#39;sesame_oil&#39;, &#39;soy_sauce&#39;, &#39;starch&#39;, &#39;wine&#39;],
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>ingredients: Index([&#39;barley&#39;, &#39;bean&#39;, &#39;buckwheat&#39;, &#39;carrot&#39;, &#39;katsuobushi&#39;, &#39;scallion&#39;,
+       &#39;seaweed&#39;, &#39;soy_sauce&#39;, &#39;soybean&#39;],
       dtype=&#39;object&#39;)
-cuisine: chinese
+cuisine: japanese
 </pre></div>
 </div>
 </div>
@@ -2296,24 +2296,24 @@ <h2><span class="section-number">15.2.4. </span>Exercise - apply logistic regres
   </thead>
   <tbody>
     <tr>
-      <th>chinese</th>
-      <td>0.996021</td>
+      <th>japanese</th>
+      <td>0.991627</td>
     </tr>
     <tr>
       <th>korean</th>
-      <td>0.002319</td>
+      <td>0.004940</td>
     </tr>
     <tr>
-      <th>thai</th>
-      <td>0.001016</td>
+      <th>chinese</th>
+      <td>0.002909</td>
     </tr>
     <tr>
-      <th>japanese</th>
-      <td>0.000642</td>
+      <th>thai</th>
+      <td>0.000457</td>
     </tr>
     <tr>
       <th>indian</th>
-      <td>0.000001</td>
+      <td>0.000067</td>
     </tr>
   </tbody>
 </table>
@@ -2334,27 +2334,27 @@ <h2><span class="section-number">15.2.4. </span>Exercise - apply logistic regres
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>              precision    recall  f1-score   support
 
-     chinese       0.71      0.66      0.69       228
-      indian       0.89      0.88      0.89       246
-    japanese       0.74      0.74      0.74       236
-      korean       0.82      0.79      0.80       229
-        thai       0.77      0.85      0.80       260
+     chinese       0.76      0.75      0.76       236
+      indian       0.88      0.87      0.87       233
+    japanese       0.74      0.74      0.74       227
+      korean       0.84      0.83      0.83       259
+        thai       0.80      0.84      0.82       244
 
-    accuracy                           0.79      1199
-   macro avg       0.79      0.78      0.78      1199
-weighted avg       0.79      0.79      0.79      1199
+    accuracy                           0.81      1199
+   macro avg       0.81      0.81      0.81      1199
+weighted avg       0.81      0.81      0.81      1199
 </pre></div>
 </div>
 </div>
 </div>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">15.2.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.2.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>In this section, you used your cleaned data to build a machine learning model that can predict a national cuisine based on a series of ingredients. Take some time to read through the many options Scikit-learn provides to classify data. Dig deeper into the concept of ‘solver’ to understand what goes on behind the scenes.</p>
 <p>Assignment - <a class="reference internal" href="../../assignments/ml-fundamentals/study-the-solvers.html"><span class="doc std std-doc">Study the solvers</span></a>.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">15.2.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.2.6. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2396,13 +2396,13 @@ <h2><span class="section-number">15.2.6. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">15.1. </span>Introduction to classification</p>
+            <p class="prev-next-title"><span class="section-number">14.1. </span>Introduction to classification</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="yet-other-classifiers.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">15.3. </span>Yet other classifiers</p>
+        <p class="prev-next-title"><span class="section-number">14.3. </span>Yet other classifiers</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/ml-fundamentals/classification/yet-other-classifiers.html b/ml-fundamentals/classification/yet-other-classifiers.html
index 3a774253d..5d4f35d01 100644
--- a/ml-fundamentals/classification/yet-other-classifiers.html
+++ b/ml-fundamentals/classification/yet-other-classifiers.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>15.3. Yet other classifiers &#8212; Ocademy Open Machine Learning Book</title>
+    <title>14.3. Yet other classifiers &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -98,8 +98,8 @@
     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="15.4. Applied Machine Learning : build a web app" href="applied-ml-build-a-web-app.html" />
-    <link rel="prev" title="15.2. More classifiers" href="more-classifiers.html" />
+    <link rel="next" title="14.4. Applied Machine Learning : build a web app" href="applied-ml-build-a-web-app.html" />
+    <link rel="prev" title="14.2. More classifiers" href="more-classifiers.html" />
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@@ -159,17 +159,17 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
-   1. Python programming basics
+   1. Python programming introduction
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../prerequisites/python-programming-basics.html">
-   3. Python programming basics
+   2. Python programming basics
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   <a class="reference internal" href="../../prerequisites/python-programming-advanced.html">
-   4. Python programming advanced
+   3. Python programming advanced
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 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../data-science/introduction/introduction.html">
-   5. Introduction
+   4. Introduction
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/introduction/defining-data-science.html">
-     5.1. Defining data science
+     4.1. Defining data science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/introduction/data-science-ethics.html">
-     5.2. Data Science ethics
+     4.2. Data Science ethics
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     <a class="reference internal" href="../../data-science/introduction/defining-data.html">
-     5.3. Defining data
+     4.3. Defining data
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     <a class="reference internal" href="../../data-science/introduction/introduction-to-statistics-and-probability.html">
-     5.4. Introduction to statistics and probability
+     4.4. Introduction to statistics and probability
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../data-science/working-with-data/working-with-data.html">
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+   5. Working with data
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/relational-databases.html">
-     6.1. Relational databases
+     5.1. Relational databases
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/non-relational-data.html">
-     6.2. Non-relational data
+     5.2. Non-relational data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/numpy.html">
-     6.3. NumPy
+     5.3. NumPy
     </a>
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    <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas/pandas.html">
-     6.4. Pandas
+     5.4. Pandas
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      <li class="toctree-l3">
       <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
-       6.4.1. Introduction and Data Structures
+       5.4.1. Introduction and Data Structures
       </a>
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      <li class="toctree-l3">
       <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
-       6.4.2. Data Selection
+       5.4.2. Data Selection
       </a>
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-       6.4.3. Advanced Pandas Techniques
+       5.4.3. Advanced Pandas Techniques
       </a>
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     </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
-     6.5. Data preparation
+     5.5. Data preparation
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+     8.3. Data Science in the cloud: The “Azure ML SDK” way
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+   11. Summary of machine learning fundamentals
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+     40.13. Apply your skills
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+     40.14. Try it in Excel
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-     41.21. Soda profits
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-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1575,80 +1575,80 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#preparation">
-   15.3.1. Preparation
+   14.3.1. Preparation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#a-classification-map">
-   15.3.2. A classification map
+   14.3.2. A classification map
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-plan">
-     15.3.2.1. The plan
+     14.3.2.1. The plan
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-split-the-data">
-   15.3.3. Exercise - split the data
+   14.3.3. Exercise - split the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#linear-svc-classifier">
-   15.3.4. Linear SVC classifier
+   14.3.4. Linear SVC classifier
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exercise-apply-a-linear-svc">
-     15.3.4.1. Exercise - apply a linear SVC
+     14.3.4.1. Exercise - apply a linear SVC
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#k-neighbors-classifier">
-   15.3.5. K-Neighbors classifier
+   14.3.5. K-Neighbors classifier
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exercise-apply-the-k-neighbors-classifier">
-     15.3.5.1. Exercise - apply the K-Neighbors classifier
+     14.3.5.1. Exercise - apply the K-Neighbors classifier
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#support-vector-classifier">
-   15.3.6. Support Vector Classifier
+   14.3.6. Support Vector Classifier
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exercise-apply-a-support-vector-classifier">
-     15.3.6.1. Exercise - apply a Support Vector Classifier
+     14.3.6.1. Exercise - apply a Support Vector Classifier
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#ensemble-classifiers">
-   15.3.7. Ensemble Classifiers
+   14.3.7. Ensemble Classifiers
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   15.3.8. Self Study
+   14.3.8. Self Study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   15.3.9. Your turn! 🚀
+   14.3.9. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   15.3.10. Acknowledgments
+   14.3.10. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1672,80 +1672,80 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#preparation">
-   15.3.1. Preparation
+   14.3.1. Preparation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#a-classification-map">
-   15.3.2. A classification map
+   14.3.2. A classification map
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-plan">
-     15.3.2.1. The plan
+     14.3.2.1. The plan
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-split-the-data">
-   15.3.3. Exercise - split the data
+   14.3.3. Exercise - split the data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#linear-svc-classifier">
-   15.3.4. Linear SVC classifier
+   14.3.4. Linear SVC classifier
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exercise-apply-a-linear-svc">
-     15.3.4.1. Exercise - apply a linear SVC
+     14.3.4.1. Exercise - apply a linear SVC
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#k-neighbors-classifier">
-   15.3.5. K-Neighbors classifier
+   14.3.5. K-Neighbors classifier
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exercise-apply-the-k-neighbors-classifier">
-     15.3.5.1. Exercise - apply the K-Neighbors classifier
+     14.3.5.1. Exercise - apply the K-Neighbors classifier
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#support-vector-classifier">
-   15.3.6. Support Vector Classifier
+   14.3.6. Support Vector Classifier
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exercise-apply-a-support-vector-classifier">
-     15.3.6.1. Exercise - apply a Support Vector Classifier
+     14.3.6.1. Exercise - apply a Support Vector Classifier
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#ensemble-classifiers">
-   15.3.7. Ensemble Classifiers
+   14.3.7. Ensemble Classifiers
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   15.3.8. Self Study
+   14.3.8. Self Study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   15.3.9. Your turn! 🚀
+   14.3.9. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   15.3.10. Acknowledgments
+   14.3.10. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1770,23 +1770,23 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="yet-other-classifiers">
-<h1><span class="section-number">15.3. </span>Yet other classifiers<a class="headerlink" href="#yet-other-classifiers" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">14.3. </span>Yet other classifiers<a class="headerlink" href="#yet-other-classifiers" title="Permalink to this headline">#</a></h1>
 <p>In this second classification section, you will explore more ways to classify numeric data. You will also learn about the ramifications for choosing one classifier over the other.</p>
 <section id="preparation">
-<h2><span class="section-number">15.3.1. </span>Preparation<a class="headerlink" href="#preparation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.3.1. </span>Preparation<a class="headerlink" href="#preparation" title="Permalink to this headline">#</a></h2>
 <p>We have loaded your <a class="reference external" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/assignments/ml-fundamentals/build-classification-model.ipynb">build-classification-model.ipynb</a> file with the cleaned dataset and have divided it into x and y dataframes, ready for the model building process.</p>
 </section>
 <section id="a-classification-map">
-<h2><span class="section-number">15.3.2. </span>A classification map<a class="headerlink" href="#a-classification-map" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.3.2. </span>A classification map<a class="headerlink" href="#a-classification-map" title="Permalink to this headline">#</a></h2>
 <p>Previously, you learned about the various options you have when classifying data using Microsoft’s cheat sheet. Scikit-learn offers a similar, but more granular cheat sheet that can further help narrow down your estimators (another term for classifiers):</p>
 <figure class="align-default" id="ml-map-from-scikit-learn">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/map.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/map.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-classification/map.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 15.6 </span><span class="caption-text">ML Map from Scikit-learn. <a class="reference external" href="https://scikit-learn.org/stable/tutorial/machine_learning_map/">Ref</a></span><a class="headerlink" href="#ml-map-from-scikit-learn" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 14.6 </span><span class="caption-text">ML Map from Scikit-learn. <a class="reference external" href="https://scikit-learn.org/stable/tutorial/machine_learning_map/">Ref</a></span><a class="headerlink" href="#ml-map-from-scikit-learn" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <section id="the-plan">
-<h3><span class="section-number">15.3.2.1. </span>The plan<a class="headerlink" href="#the-plan" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">14.3.2.1. </span>The plan<a class="headerlink" href="#the-plan" title="Permalink to this headline">#</a></h3>
 <p>This map is very helpful once you have a clear grasp of your data, as you can ‘walk’ along its paths to a decision:</p>
 <ul class="simple">
 <li><p>We have &gt;50 samples</p></li>
@@ -1808,7 +1808,7 @@ <h3><span class="section-number">15.3.2.1. </span>The plan<a class="headerlink"
 </section>
 </section>
 <section id="exercise-split-the-data">
-<h2><span class="section-number">15.3.3. </span>Exercise - split the data<a class="headerlink" href="#exercise-split-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.3.3. </span>Exercise - split the data<a class="headerlink" href="#exercise-split-the-data" title="Permalink to this headline">#</a></h2>
 <p>Following this path, we should start by importing some libraries to use.</p>
 <p>1. Import the needed libraries:</p>
 <div class="cell docutils container">
@@ -1839,10 +1839,10 @@ <h2><span class="section-number">15.3.3. </span>Exercise - split the data<a clas
 </div>
 </section>
 <section id="linear-svc-classifier">
-<h2><span class="section-number">15.3.4. </span>Linear SVC classifier<a class="headerlink" href="#linear-svc-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.3.4. </span>Linear SVC classifier<a class="headerlink" href="#linear-svc-classifier" title="Permalink to this headline">#</a></h2>
 <p>Support-Vector clustering (SVC) is a child of the Support-Vector machines family of ML techniques (learn more about these below). In this method, you can choose a ‘kernel’ to decide how to cluster the labels. The ‘C’ parameter refers to ‘regularization’ which regulates the influence of parameters. The kernel can be one of <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC">several</a>; here we set it to ‘linear’ to ensure that we leverage linear SVC. Probability defaults to ‘false’; here we set it to ‘true’ to gather probability estimates. We set the random state to ‘0’ to shuffle the data to get probabilities.</p>
 <section id="exercise-apply-a-linear-svc">
-<h3><span class="section-number">15.3.4.1. </span>Exercise - apply a linear SVC<a class="headerlink" href="#exercise-apply-a-linear-svc" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">14.3.4.1. </span>Exercise - apply a linear SVC<a class="headerlink" href="#exercise-apply-a-linear-svc" title="Permalink to this headline">#</a></h3>
 <p>Start by creating an array of classifiers. You will add progressively to this array as we test.</p>
 <p>1. Start with a Linear SVC:</p>
 <div class="cell docutils container">
@@ -1875,18 +1875,18 @@ <h3><span class="section-number">15.3.4.1. </span>Exercise - apply a linear SVC<
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 78.0% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 79.9% 
               precision    recall  f1-score   support
 
-     chinese       0.68      0.69      0.68       233
-      indian       0.86      0.89      0.88       217
-    japanese       0.78      0.75      0.76       261
-      korean       0.82      0.73      0.77       243
-        thai       0.77      0.85      0.81       245
+     chinese       0.69      0.75      0.72       230
+      indian       0.91      0.91      0.91       252
+    japanese       0.77      0.77      0.77       222
+      korean       0.84      0.74      0.78       244
+        thai       0.79      0.82      0.81       251
 
-    accuracy                           0.78      1199
-   macro avg       0.78      0.78      0.78      1199
-weighted avg       0.78      0.78      0.78      1199
+    accuracy                           0.80      1199
+   macro avg       0.80      0.80      0.80      1199
+weighted avg       0.80      0.80      0.80      1199
 </pre></div>
 </div>
 </div>
@@ -1895,10 +1895,10 @@ <h3><span class="section-number">15.3.4.1. </span>Exercise - apply a linear SVC<
 </section>
 </section>
 <section id="k-neighbors-classifier">
-<h2><span class="section-number">15.3.5. </span>K-Neighbors classifier<a class="headerlink" href="#k-neighbors-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.3.5. </span>K-Neighbors classifier<a class="headerlink" href="#k-neighbors-classifier" title="Permalink to this headline">#</a></h2>
 <p>K-Neighbors is part of the “neighbors” family of ML methods, which can be used for both supervised and unsupervised learning. In this method, a predefined number of points is created and data are gathered around these points such that generalized labels can be predicted for the data.</p>
 <section id="exercise-apply-the-k-neighbors-classifier">
-<h3><span class="section-number">15.3.5.1. </span>Exercise - apply the K-Neighbors classifier<a class="headerlink" href="#exercise-apply-the-k-neighbors-classifier" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">14.3.5.1. </span>Exercise - apply the K-Neighbors classifier<a class="headerlink" href="#exercise-apply-the-k-neighbors-classifier" title="Permalink to this headline">#</a></h3>
 <p>The previous classifier was good, and worked well with the data, but maybe we can get better accuracy. Try a K-Neighbors classifier.</p>
 <p>1. Add a line to your classifier array (add a comma after the Linear SVC item):</p>
 <div class="cell docutils container">
@@ -1910,31 +1910,31 @@ <h3><span class="section-number">15.3.5.1. </span>Exercise - apply the K-Neighbo
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 78.0% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 79.9% 
               precision    recall  f1-score   support
 
-     chinese       0.68      0.69      0.68       233
-      indian       0.86      0.89      0.88       217
-    japanese       0.78      0.75      0.76       261
-      korean       0.82      0.73      0.77       243
-        thai       0.77      0.85      0.81       245
+     chinese       0.69      0.75      0.72       230
+      indian       0.91      0.91      0.91       252
+    japanese       0.77      0.77      0.77       222
+      korean       0.84      0.74      0.78       244
+        thai       0.79      0.82      0.81       251
 
-    accuracy                           0.78      1199
-   macro avg       0.78      0.78      0.78      1199
-weighted avg       0.78      0.78      0.78      1199
+    accuracy                           0.80      1199
+   macro avg       0.80      0.80      0.80      1199
+weighted avg       0.80      0.80      0.80      1199
 
-Accuracy (train) for KNN classifier: 71.5% 
+Accuracy (train) for KNN classifier: 72.6% 
               precision    recall  f1-score   support
 
-     chinese       0.63      0.66      0.65       233
-      indian       0.80      0.81      0.81       217
-    japanese       0.67      0.80      0.73       261
-      korean       0.90      0.54      0.67       243
-        thai       0.67      0.76      0.71       245
+     chinese       0.63      0.71      0.67       230
+      indian       0.87      0.79      0.83       252
+    japanese       0.63      0.86      0.73       222
+      korean       0.92      0.54      0.68       244
+        thai       0.70      0.73      0.72       251
 
-    accuracy                           0.71      1199
-   macro avg       0.73      0.71      0.71      1199
-weighted avg       0.73      0.71      0.71      1199
+    accuracy                           0.73      1199
+   macro avg       0.75      0.73      0.72      1199
+weighted avg       0.75      0.73      0.73      1199
 </pre></div>
 </div>
 </div>
@@ -1947,10 +1947,10 @@ <h3><span class="section-number">15.3.5.1. </span>Exercise - apply the K-Neighbo
 </section>
 </section>
 <section id="support-vector-classifier">
-<h2><span class="section-number">15.3.6. </span>Support Vector Classifier<a class="headerlink" href="#support-vector-classifier" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.3.6. </span>Support Vector Classifier<a class="headerlink" href="#support-vector-classifier" title="Permalink to this headline">#</a></h2>
 <p>Support-Vector classifiers are part of the <a class="reference external" href="https://wikipedia.org/wiki/Support-vector_machine">Support-Vector Machine</a> family of ML methods that are used for classification and regression tasks. SVMs “map training examples to points in space” to maximize the distance between two categories. Subsequent data is mapped into this space so their category can be predicted.</p>
 <section id="exercise-apply-a-support-vector-classifier">
-<h3><span class="section-number">15.3.6.1. </span>Exercise - apply a Support Vector Classifier<a class="headerlink" href="#exercise-apply-a-support-vector-classifier" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">14.3.6.1. </span>Exercise - apply a Support Vector Classifier<a class="headerlink" href="#exercise-apply-a-support-vector-classifier" title="Permalink to this headline">#</a></h3>
 <p>Let’s try for a little better accuracy with a Support Vector Classifier.</p>
 <p>1. Add a comma after the K-Neighbors item, and then add this line:</p>
 <div class="cell docutils container">
@@ -1962,45 +1962,45 @@ <h3><span class="section-number">15.3.6.1. </span>Exercise - apply a Support Vec
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 78.0% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 79.9% 
               precision    recall  f1-score   support
 
-     chinese       0.68      0.69      0.68       233
-      indian       0.86      0.89      0.88       217
-    japanese       0.78      0.75      0.76       261
-      korean       0.82      0.73      0.77       243
-        thai       0.77      0.85      0.81       245
+     chinese       0.69      0.75      0.72       230
+      indian       0.91      0.91      0.91       252
+    japanese       0.77      0.77      0.77       222
+      korean       0.84      0.74      0.78       244
+        thai       0.79      0.82      0.81       251
 
-    accuracy                           0.78      1199
-   macro avg       0.78      0.78      0.78      1199
-weighted avg       0.78      0.78      0.78      1199
+    accuracy                           0.80      1199
+   macro avg       0.80      0.80      0.80      1199
+weighted avg       0.80      0.80      0.80      1199
 
-Accuracy (train) for KNN classifier: 71.5% 
+Accuracy (train) for KNN classifier: 72.6% 
               precision    recall  f1-score   support
 
-     chinese       0.63      0.66      0.65       233
-      indian       0.80      0.81      0.81       217
-    japanese       0.67      0.80      0.73       261
-      korean       0.90      0.54      0.67       243
-        thai       0.67      0.76      0.71       245
+     chinese       0.63      0.71      0.67       230
+      indian       0.87      0.79      0.83       252
+    japanese       0.63      0.86      0.73       222
+      korean       0.92      0.54      0.68       244
+        thai       0.70      0.73      0.72       251
 
-    accuracy                           0.71      1199
-   macro avg       0.73      0.71      0.71      1199
-weighted avg       0.73      0.71      0.71      1199
+    accuracy                           0.73      1199
+   macro avg       0.75      0.73      0.72      1199
+weighted avg       0.75      0.73      0.73      1199
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for SVC: 80.7% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for SVC: 83.9% 
               precision    recall  f1-score   support
 
-     chinese       0.73      0.73      0.73       233
-      indian       0.86      0.91      0.89       217
-    japanese       0.82      0.79      0.80       261
-      korean       0.87      0.76      0.81       243
-        thai       0.76      0.86      0.81       245
+     chinese       0.76      0.79      0.78       230
+      indian       0.91      0.91      0.91       252
+    japanese       0.83      0.82      0.83       222
+      korean       0.91      0.80      0.85       244
+        thai       0.79      0.87      0.83       251
 
-    accuracy                           0.81      1199
-   macro avg       0.81      0.81      0.81      1199
-weighted avg       0.81      0.81      0.81      1199
+    accuracy                           0.84      1199
+   macro avg       0.84      0.84      0.84      1199
+weighted avg       0.84      0.84      0.84      1199
 </pre></div>
 </div>
 </div>
@@ -2013,7 +2013,7 @@ <h3><span class="section-number">15.3.6.1. </span>Exercise - apply a Support Vec
 </section>
 </section>
 <section id="ensemble-classifiers">
-<h2><span class="section-number">15.3.7. </span>Ensemble Classifiers<a class="headerlink" href="#ensemble-classifiers" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.3.7. </span>Ensemble Classifiers<a class="headerlink" href="#ensemble-classifiers" title="Permalink to this headline">#</a></h2>
 <p>Let’s follow the path to the very end, even though the previous test was quite good. Let’s try some ‘Ensemble Classifiers, specifically Random Forest and AdaBoost:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2025,73 +2025,73 @@ <h2><span class="section-number">15.3.7. </span>Ensemble Classifiers<a class="he
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 78.0% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for Linear SVC: 79.9% 
               precision    recall  f1-score   support
 
-     chinese       0.68      0.69      0.68       233
-      indian       0.86      0.89      0.88       217
-    japanese       0.78      0.75      0.76       261
-      korean       0.82      0.73      0.77       243
-        thai       0.77      0.85      0.81       245
+     chinese       0.69      0.75      0.72       230
+      indian       0.91      0.91      0.91       252
+    japanese       0.77      0.77      0.77       222
+      korean       0.84      0.74      0.78       244
+        thai       0.79      0.82      0.81       251
 
-    accuracy                           0.78      1199
-   macro avg       0.78      0.78      0.78      1199
-weighted avg       0.78      0.78      0.78      1199
+    accuracy                           0.80      1199
+   macro avg       0.80      0.80      0.80      1199
+weighted avg       0.80      0.80      0.80      1199
 
-Accuracy (train) for KNN classifier: 71.5% 
+Accuracy (train) for KNN classifier: 72.6% 
               precision    recall  f1-score   support
 
-     chinese       0.63      0.66      0.65       233
-      indian       0.80      0.81      0.81       217
-    japanese       0.67      0.80      0.73       261
-      korean       0.90      0.54      0.67       243
-        thai       0.67      0.76      0.71       245
+     chinese       0.63      0.71      0.67       230
+      indian       0.87      0.79      0.83       252
+    japanese       0.63      0.86      0.73       222
+      korean       0.92      0.54      0.68       244
+        thai       0.70      0.73      0.72       251
 
-    accuracy                           0.71      1199
-   macro avg       0.73      0.71      0.71      1199
-weighted avg       0.73      0.71      0.71      1199
+    accuracy                           0.73      1199
+   macro avg       0.75      0.73      0.72      1199
+weighted avg       0.75      0.73      0.73      1199
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for SVC: 80.7% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for SVC: 83.9% 
               precision    recall  f1-score   support
 
-     chinese       0.73      0.73      0.73       233
-      indian       0.86      0.91      0.89       217
-    japanese       0.82      0.79      0.80       261
-      korean       0.87      0.76      0.81       243
-        thai       0.76      0.86      0.81       245
+     chinese       0.76      0.79      0.78       230
+      indian       0.91      0.91      0.91       252
+    japanese       0.83      0.82      0.83       222
+      korean       0.91      0.80      0.85       244
+        thai       0.79      0.87      0.83       251
 
-    accuracy                           0.81      1199
-   macro avg       0.81      0.81      0.81      1199
-weighted avg       0.81      0.81      0.81      1199
+    accuracy                           0.84      1199
+   macro avg       0.84      0.84      0.84      1199
+weighted avg       0.84      0.84      0.84      1199
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for RFST: 81.2% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for RFST: 85.2% 
               precision    recall  f1-score   support
 
-     chinese       0.77      0.73      0.75       233
-      indian       0.87      0.92      0.89       217
-    japanese       0.84      0.76      0.80       261
-      korean       0.82      0.77      0.80       243
-        thai       0.78      0.89      0.83       245
+     chinese       0.80      0.82      0.81       230
+      indian       0.93      0.90      0.92       252
+    japanese       0.85      0.83      0.84       222
+      korean       0.87      0.81      0.84       244
+        thai       0.81      0.89      0.85       251
 
-    accuracy                           0.81      1199
-   macro avg       0.81      0.81      0.81      1199
-weighted avg       0.81      0.81      0.81      1199
+    accuracy                           0.85      1199
+   macro avg       0.85      0.85      0.85      1199
+weighted avg       0.85      0.85      0.85      1199
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for ADA: 68.6% 
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Accuracy (train) for ADA: 71.5% 
               precision    recall  f1-score   support
 
-     chinese       0.69      0.47      0.56       233
-      indian       0.82      0.82      0.82       217
-    japanese       0.65      0.65      0.65       261
-      korean       0.66      0.73      0.69       243
-        thai       0.65      0.77      0.70       245
+     chinese       0.67      0.49      0.56       230
+      indian       0.89      0.81      0.85       252
+    japanese       0.64      0.68      0.66       222
+      korean       0.66      0.79      0.72       244
+        thai       0.72      0.78      0.75       251
 
-    accuracy                           0.69      1199
-   macro avg       0.69      0.69      0.68      1199
-weighted avg       0.69      0.69      0.68      1199
+    accuracy                           0.71      1199
+   macro avg       0.72      0.71      0.71      1199
+weighted avg       0.72      0.71      0.71      1199
 </pre></div>
 </div>
 </div>
@@ -2109,16 +2109,16 @@ <h2><span class="section-number">15.3.7. </span>Ensemble Classifiers<a class="he
 </section>
 <hr class="docutils" />
 <section id="self-study">
-<h2><span class="section-number">15.3.8. </span>Self Study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.3.8. </span>Self Study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>There’s a lot of jargon in these sections, so take a minute to review <a class="reference external" href="https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-77952-leestott">this list</a> of useful terminology!</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">15.3.9. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.3.9. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Each of these techniques has a large number of parameters that you can tweak. Research each one’s default parameters and think about what tweaking these parameters would mean for the model’s quality.</p>
 <p>Assignment - <a class="reference internal" href="../../assignments/ml-fundamentals/parameter-play.html"><span class="doc std std-doc">Parameter play</span></a></p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">15.3.10. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">14.3.10. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2160,13 +2160,13 @@ <h2><span class="section-number">15.3.10. </span>Acknowledgments<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">15.2. </span>More classifiers</p>
+            <p class="prev-next-title"><span class="section-number">14.2. </span>More classifiers</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="applied-ml-build-a-web-app.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">15.4. </span>Applied Machine Learning : build a web app</p>
+        <p class="prev-next-title"><span class="section-number">14.4. </span>Applied Machine Learning : build a web app</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/ml-fundamentals/ml-overview.html b/ml-fundamentals/ml-overview.html
index fee32f74d..c2f84adf6 100644
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+++ b/ml-fundamentals/ml-overview.html
@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>11. Machine Learning overview &#8212; Ocademy Open Machine Learning Book</title>
+    <title>10. Machine Learning overview &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -100,8 +100,8 @@
     <link rel="shortcut icon" href="../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="12. Summary of machine learning fundamentals" href="ml-summary.html" />
-    <link rel="prev" title="10. Data Science in the real world" href="../data-science/data-science-in-the-wild.html" />
+    <link rel="next" title="11. Summary of machine learning fundamentals" href="ml-summary.html" />
+    <link rel="prev" title="9. Data Science in the real world" href="../data-science/data-science-in-the-wild.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -161,17 +161,17 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../prerequisites/python-programming-introduction.html">
-   1. Python programming basics
+   1. Python programming introduction
   </a>
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  <li class="toctree-l1">
   <a class="reference internal" href="../prerequisites/python-programming-basics.html">
-   3. Python programming basics
+   2. Python programming basics
   </a>
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   <a class="reference internal" href="../prerequisites/python-programming-advanced.html">
-   4. Python programming advanced
+   3. Python programming advanced
   </a>
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 </ul>
@@ -183,7 +183,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../data-science/introduction/introduction.html">
-   5. Introduction
+   4. Introduction
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/introduction/defining-data-science.html">
-     5.1. Defining data science
+     4.1. Defining data science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/introduction/data-science-ethics.html">
-     5.2. Data Science ethics
+     4.2. Data Science ethics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/introduction/defining-data.html">
-     5.3. Defining data
+     4.3. Defining data
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/introduction/introduction-to-statistics-and-probability.html">
-     5.4. Introduction to statistics and probability
+     4.4. Introduction to statistics and probability
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   <a class="reference internal" href="../data-science/working-with-data/working-with-data.html">
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+   5. Working with data
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/working-with-data/relational-databases.html">
-     6.1. Relational databases
+     5.1. Relational databases
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/working-with-data/non-relational-data.html">
-     6.2. Non-relational data
+     5.2. Non-relational data
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/working-with-data/numpy.html">
-     6.3. NumPy
+     5.3. NumPy
     </a>
    </li>
    <li class="toctree-l2 has-children">
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-     6.4. Pandas
+     5.4. Pandas
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     <ul>
      <li class="toctree-l3">
       <a class="reference internal" href="../data-science/working-with-data/pandas/introduction-and-data-structures.html">
-       6.4.1. Introduction and Data Structures
+       5.4.1. Introduction and Data Structures
       </a>
      </li>
      <li class="toctree-l3">
       <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
-       6.4.2. Data Selection
+       5.4.2. Data Selection
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      </li>
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       <a class="reference internal" href="../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
-       6.4.3. Advanced Pandas Techniques
+       5.4.3. Advanced Pandas Techniques
       </a>
      </li>
     </ul>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/working-with-data/data-preparation.html">
-     6.5. Data preparation
+     5.5. Data preparation
     </a>
    </li>
   </ul>
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-   7. Data visualization
+   6. Data visualization
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-visualization/visualization-distributions.html">
-     7.1. Visualizing distributions
+     6.1. Visualizing distributions
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-visualization/visualization-proportions.html">
-     7.2. Visualizing proportions
+     6.2. Visualizing proportions
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-visualization/visualization-relationships.html">
-     7.3. Visualizing relationships: all about honey 🍯
+     6.3. Visualizing relationships: all about honey 🍯
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-visualization/meaningful-visualizations.html">
-     7.4. Making meaningful visualizations
+     6.4. Making meaningful visualizations
     </a>
    </li>
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+     7.1. Introduction to the Data Science lifecycle
     </a>
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+     7.2. Analyzing
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+     8.2. The “low code/no code” way
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+     8.3. Data Science in the cloud: The “Azure ML SDK” way
     </a>
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@@ -372,17 +372,17 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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+   10. Machine Learning overview
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-   12. Summary of machine learning fundamentals
+   11. Summary of machine learning fundamentals
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+     12.1. Tools of the trade
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-     13.2. Managing data
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+     12.3. Linear and polynomial regression
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-   15. Getting started with classification
+   14. Getting started with classification
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+     14.4. Applied Machine Learning : build a web app
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+     18.4. XGBoost + k-fold CV + Feature Importance
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+     40.3. First assignment
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+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.36. Explore a planetary computer dataset
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+     40.39. Low code/no code Data Science project on Azure ML
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+     40.40. Data Science project using Azure ML SDK
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+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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+     40.42. Machine Learning overview - assignment 1
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+     40.43. Machine Learning overview - assignment 2
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-     41.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
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-     41.47. Loss Function
+     40.47. Loss Function
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+     40.49. Linear Regression Implementation from Scratch
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+     40.50. ML logistic regression - assignment 1
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+     40.52. ML neural network - Assignment 1
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+     40.53. Regression tools
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+     40.54. Managing data
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+     40.56. Try a different model
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-     41.116. Basic classification: Classify images of clothing
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-     42.4. Data Science introduction
+     41.4. Data Science introduction
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+     41.11. Machine Learning overview
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+     41.17. Unsupervised learning
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+   10.1. What is Machine Learning?
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+   10.2. Ordinary Programming vs Machine Learning
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+     10.3.2. Features
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     <a class="reference internal nav-link" href="#examples">
-     11.3.3. Examples
+     10.3.3. Examples
     </a>
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    <li class="toc-h3 nav-item toc-entry">
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-     11.3.4. Models
+     10.3.4. Models
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     <a class="reference internal nav-link" href="#regression-vs-classification">
-     11.3.5. Regression vs classification
+     10.3.5. Regression vs classification
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+     10.6.3. Establishing a Baseline
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     <a class="reference internal nav-link" href="#exploratory-data-analysis-eda">
-     11.6.4. Exploratory data analysis(EDA)
+     10.6.4. Exploratory data analysis(EDA)
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     <a class="reference internal nav-link" href="#data-preprocessing">
-     11.6.5. Data Preprocessing
+     10.6.5. Data Preprocessing
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     <a class="reference internal nav-link" href="#selecting-and-training-a-model">
-     11.6.6. Selecting and Training a Model
+     10.6.6. Selecting and Training a Model
     </a>
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-     11.6.7. Performing Error Analysis
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+   10.7. Your turn! 🚀
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+   10.1. What is Machine Learning?
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  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#ordinary-programming-vs-machine-learning">
-   11.2. Ordinary Programming vs Machine Learning
+   10.2. Ordinary Programming vs Machine Learning
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#key-terminology">
-   11.3. Key terminology
+   10.3. Key terminology
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#labels">
-     11.3.1. Labels
+     10.3.1. Labels
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#features">
-     11.3.2. Features
+     10.3.2. Features
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#examples">
-     11.3.3. Examples
+     10.3.3. Examples
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#models">
-     11.3.4. Models
+     10.3.4. Models
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#regression-vs-classification">
-     11.3.5. Regression vs classification
+     10.3.5. Regression vs classification
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#when-and-when-not-to-use-machine-learning">
-   11.4. When And When Not to Use Machine Learning
+   10.4. When And When Not to Use Machine Learning
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#types-of-machine-learning-systems">
-   11.5. Types of Machine Learning Systems
+   10.5. Types of Machine Learning Systems
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#supervised-learning">
-     11.5.1. Supervised Learning
+     10.5.1. Supervised Learning
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#reinforcement-learning">
-     11.5.2. Reinforcement Learning
+     10.5.2. Reinforcement Learning
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#typical-machine-learning-workflow">
-   11.6. Typical Machine Learning Workflow
+   10.6. Typical Machine Learning Workflow
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#defining-a-problem">
-     11.6.1. Defining a Problem
+     10.6.1. Defining a Problem
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#collecting-data">
-     11.6.2. Collecting Data
+     10.6.2. Collecting Data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#establishing-a-baseline">
-     11.6.3. Establishing a Baseline
+     10.6.3. Establishing a Baseline
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploratory-data-analysis-eda">
-     11.6.4. Exploratory data analysis(EDA)
+     10.6.4. Exploratory data analysis(EDA)
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-preprocessing">
-     11.6.5. Data Preprocessing
+     10.6.5. Data Preprocessing
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#selecting-and-training-a-model">
-     11.6.6. Selecting and Training a Model
+     10.6.6. Selecting and Training a Model
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#performing-error-analysis">
-     11.6.7. Performing Error Analysis
+     10.6.7. Performing Error Analysis
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#deploying-a-model">
-     11.6.8. Deploying a Model
+     10.6.8. Deploying a Model
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   11.7. Your turn! 🚀
+   10.7. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   11.8. Acknowledgments
+   10.8. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1782,7 +1782,7 @@ <h2> Contents </h2>
               <div>
                 
   <section class="tex2jax_ignore mathjax_ignore" id="machine-learning-overview">
-<h1><span class="section-number">11. </span>Machine Learning overview<a class="headerlink" href="#machine-learning-overview" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">10. </span>Machine Learning overview<a class="headerlink" href="#machine-learning-overview" title="Permalink to this headline">#</a></h1>
 <p>We are living in the age of the intelligent machines. In the last decade, Machine Learning powered systems went from beating the world-class chess players to now influencing most of our daily decisions like what we watch on streaming services like Netflix and YouTube. What is Machine Learning? And why is it important to be studying it now?</p>
 <p>We hear that Machine Learning is transforming many industries, but what exactly can we use it for? And what are the set of problems that this technology is or not suited for?</p>
 <p>This introduction will give you the basics of Machine Learning or these things that you will always need to know. More concretely, it will cover the high level foundational knowledge such as:</p>
@@ -1796,7 +1796,7 @@ <h1><span class="section-number">11. </span>Machine Learning overview<a class="h
 <li><p>A typical Machine Learning project workflow</p></li>
 </ul>
 <section id="what-is-machine-learning">
-<h2><span class="section-number">11.1. </span>What is Machine Learning?<a class="headerlink" href="#what-is-machine-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">10.1. </span>What is Machine Learning?<a class="headerlink" href="#what-is-machine-learning" title="Permalink to this headline">#</a></h2>
 <p>Machine Learning is a new programming paradigm in which instead of explicitly programming computers to perform some tasks, we let them learn from data in order to find the underlying patterns in the data. In few words, Machine Learning is the science of giving the machine the ability to reason about the data.</p>
 <p>The term <a class="reference external" href="https://en.wikipedia.org/wiki/Machine_learning">Machine Learning</a> was coined by Arthur Samuel in 1959. At that time, Arthur defined Machine Learning as a:</p>
 <blockquote>
@@ -1815,12 +1815,12 @@ <h2><span class="section-number">11.1. </span>What is Machine Learning?<a class=
 <figure class="align-default" id="ai-ml-dl">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/ai-ml-dl.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/ai-ml-dl.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/ai-ml-dl.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 11.1 </span><span class="caption-text">The difference between AI, Machine Learning and deep learning</span><a class="headerlink" href="#ai-ml-dl" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 10.1 </span><span class="caption-text">The difference between AI, Machine Learning and deep learning</span><a class="headerlink" href="#ai-ml-dl" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="ordinary-programming-vs-machine-learning">
-<h2><span class="section-number">11.2. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#ordinary-programming-vs-machine-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">10.2. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#ordinary-programming-vs-machine-learning" title="Permalink to this headline">#</a></h2>
 <p>In ordinary programming, the job of the programmer is to clearly write every single rule that makes up the task he/she is trying to accomplish. In order to get the results, she/he must write all rules that acts up on the data.</p>
 <p>Machine Learning flips that. Instead of having to write the rules that makes up a particular application, we can feed data and results(or labels) to the Machine Learning model, and its job can be to determine the set of rules that map the data and labels.</p>
 <p>Let’s take a real world example. If you wanted to build an application that given a picture of person can determine if he/she is wearing or not wearing a facemask, you can just feed a bunch of images of people with and without facemasks to the Machine Learning model, and the model can learn the rules or patterns that map the images to whether they have a facemask or not.</p>
@@ -1828,24 +1828,24 @@ <h2><span class="section-number">11.2. </span>Ordinary Programming vs Machine Le
 <figure class="align-default" id="traditional-ml-png">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/traditional-ml.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/traditional-ml.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/traditional-ml.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 11.2 </span><span class="caption-text">Traditional Programming vs Deep Learning</span><a class="headerlink" href="#traditional-ml-png" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 10.2 </span><span class="caption-text">Traditional Programming vs Deep Learning</span><a class="headerlink" href="#traditional-ml-png" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Approaching a facemask recognition problem with rule based programming would really be a hard problem. You would have to write lots of code that would later turn out to not work typically because your program will be tested on different kinds of facemasks and people in various scenarios and it’s almost impossible to express that in rules. Whereas with Machine Learning, all you need is a bunch of images of people with and without facemasks, and there you are few steps away from getting an effective facemask recognizer.</p>
 </section>
 <section id="key-terminology">
-<h2><span class="section-number">11.3. </span>Key terminology<a class="headerlink" href="#key-terminology" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">10.3. </span>Key terminology<a class="headerlink" href="#key-terminology" title="Permalink to this headline">#</a></h2>
 <p>What is Machine Learning? Concisely put, it is the following:</p>
 <ul class="simple">
 <li><p>ML systems learn how to combine input to produce useful predictions on never-before-seen data.</p></li>
 </ul>
 <p>Let’s explore fundamental Machine Learning terminology.</p>
 <section id="labels">
-<h3><span class="section-number">11.3.1. </span>Labels<a class="headerlink" href="#labels" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.3.1. </span>Labels<a class="headerlink" href="#labels" title="Permalink to this headline">#</a></h3>
 <p>A <code class="docutils literal notranslate"><span class="pre">label</span></code> is the thing we’re predicting—the <code class="docutils literal notranslate"><span class="pre">y</span></code> variable in simple linear regression. The label could be the future price of wheat, the kind of animal shown in a picture, the meaning of an audio clip, or just about anything.</p>
 </section>
 <section id="features">
-<h3><span class="section-number">11.3.2. </span>Features<a class="headerlink" href="#features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.3.2. </span>Features<a class="headerlink" href="#features" title="Permalink to this headline">#</a></h3>
 <p>A <code class="docutils literal notranslate"><span class="pre">feature</span></code> is an input variable—the <code class="docutils literal notranslate"><span class="pre">x</span></code> variable in simple linear regression. A simple Machine Learning project might use a single feature, while a more sophisticated Machine Learning project could use millions of features, specified as:</p>
 <div class="math notranslate nohighlight">
 \[x_1, x_2, ..., x_N\]</div>
@@ -1858,7 +1858,7 @@ <h3><span class="section-number">11.3.2. </span>Features<a class="headerlink" hr
 </ul>
 </section>
 <section id="examples">
-<h3><span class="section-number">11.3.3. </span>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.3.3. </span>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">#</a></h3>
 <p>An <code class="docutils literal notranslate"><span class="pre">example</span></code> is a particular instance of data, <strong><code class="docutils literal notranslate"><span class="pre">x</span></code></strong>. (We put <strong><code class="docutils literal notranslate"><span class="pre">x</span></code></strong> in boldface to indicate that it is a vector.) We break examples into two categories:</p>
 <ul class="simple">
 <li><p>labeled examples</p></li>
@@ -1876,7 +1876,7 @@ <h3><span class="section-number">11.3.3. </span>Examples<a class="headerlink" hr
 <p>Once we’ve trained our model with labeled examples, we use that model to predict the label on unlabeled examples. In the spam detector, unlabeled examples are new emails that humans haven’t yet labeled.</p>
 </section>
 <section id="models">
-<h3><span class="section-number">11.3.4. </span>Models<a class="headerlink" href="#models" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.3.4. </span>Models<a class="headerlink" href="#models" title="Permalink to this headline">#</a></h3>
 <p>A model defines the relationship between features and label. For example, a spam detection model might associate certain features strongly with “spam”. Let’s highlight two phases of a model’s life:</p>
 <ul class="simple">
 <li><p><strong>Training</strong> means creating or <strong>learning</strong> the model. That is, you show the model labeled examples and enable the model to gradually learn the relationships between features and label.</p></li>
@@ -1884,7 +1884,7 @@ <h3><span class="section-number">11.3.4. </span>Models<a class="headerlink" href
 </ul>
 </section>
 <section id="regression-vs-classification">
-<h3><span class="section-number">11.3.5. </span>Regression vs classification<a class="headerlink" href="#regression-vs-classification" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.3.5. </span>Regression vs classification<a class="headerlink" href="#regression-vs-classification" title="Permalink to this headline">#</a></h3>
 <p>A <strong>regression</strong> model predicts continuous values. For example, regression models make predictions that answer questions like the following:</p>
 <ul class="simple">
 <li><p>What is the value of a house in California?</p></li>
@@ -1898,7 +1898,7 @@ <h3><span class="section-number">11.3.5. </span>Regression vs classification<a c
 </section>
 </section>
 <section id="when-and-when-not-to-use-machine-learning">
-<h2><span class="section-number">11.4. </span>When And When Not to Use Machine Learning<a class="headerlink" href="#when-and-when-not-to-use-machine-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">10.4. </span>When And When Not to Use Machine Learning<a class="headerlink" href="#when-and-when-not-to-use-machine-learning" title="Permalink to this headline">#</a></h2>
 <p>Machine Learning is an incredible technology and it has shown a lot of successes in solving various real world problems. However, like any other technology, Machine Learning is not suitable for solving all kinds of problems. It is thus equally important to know when and when not to use Machine Learning.</p>
 <p>When to use Machine Learning? Machine Learning is preferred when approaching:</p>
 <ul class="simple">
@@ -1918,7 +1918,7 @@ <h2><span class="section-number">11.4. </span>When And When Not to Use Machine L
 <p>We talked about when you should use Machine Learning and when you should not, but also, there are other areas where Machine Learning is being heavily used but with extra care and human in the loop. Example of such critical areas include medicine, self driving cars, etc…In some of those areas like self-driving cars(or driver assistants), Machine Learning is surely a big factor but also because the cost of error made by the model can be very high, human assistance becomes important.</p>
 </section>
 <section id="types-of-machine-learning-systems">
-<h2><span class="section-number">11.5. </span>Types of Machine Learning Systems<a class="headerlink" href="#types-of-machine-learning-systems" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">10.5. </span>Types of Machine Learning Systems<a class="headerlink" href="#types-of-machine-learning-systems" title="Permalink to this headline">#</a></h2>
 <p>In broad, there are 3 main types of Machine Learning systems that are:</p>
 <ul class="simple">
 <li><p>Supervised learning</p></li>
@@ -1927,7 +1927,7 @@ <h2><span class="section-number">11.5. </span>Types of Machine Learning Systems<
 </ul>
 <p>Let’s review all of these types to get a high level understanding of what’s they really mean.</p>
 <section id="supervised-learning">
-<h3><span class="section-number">11.5.1. </span>Supervised Learning<a class="headerlink" href="#supervised-learning" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.5.1. </span>Supervised Learning<a class="headerlink" href="#supervised-learning" title="Permalink to this headline">#</a></h3>
 <p>Most Machine Learning tasks fall into supervised learning type. As the name implies, a supervised learning model is trained with input data along with some form of guidance that we can call labels. In other words, a supervised learning model maps the input data (or X in many textbooks) to output labels (y). Labels are also known as targets and they act as a description of the input data.</p>
 <p>The example of facemask recognition that we used in above sections is a good example of supervised learning. In broad, there are 2 main kinds of supervised learning problems that are:</p>
 <ul class="simple">
@@ -1937,7 +1937,7 @@ <h3><span class="section-number">11.5.1. </span>Supervised Learning<a class="hea
 <figure class="align-default" id="class-reg-png">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/class-reg.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/class-reg.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/class-reg.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 11.3 </span><span class="caption-text">Graphical representation of classification and regression problems</span><a class="headerlink" href="#class-reg-png" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 10.3 </span><span class="caption-text">Graphical representation of classification and regression problems</span><a class="headerlink" href="#class-reg-png" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Supervised learning algorithms includes shallow algorithms such as linear and logistic regression, decision trees, random forests, K-Nearest Neighbors (KNN), and support vector machines (SVM). Neural networks can be both supervised(like using them for image classification) and unsupervised.</p>
@@ -1950,7 +1950,7 @@ <h3><span class="section-number">11.5.1. </span>Supervised Learning<a class="hea
 <figure class="align-default" id="vision-png">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/vision.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/vision.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/vision.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 11.4 </span><span class="caption-text">Advanced Tasks: Object detection, image segmentation, image captioning. You can test these advanced algorithms on your images using <a class="reference external" href="https://vision-explorer.allenai.org">vision-explorer</a></span><a class="headerlink" href="#vision-png" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 10.4 </span><span class="caption-text">Advanced Tasks: Object detection, image segmentation, image captioning. You can test these advanced algorithms on your images using <a class="reference external" href="https://vision-explorer.allenai.org">vision-explorer</a></span><a class="headerlink" href="#vision-png" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Some of those tasks can involve both classification and regression. Take an example for object detection, a task of recognizing and localizing an object in an image: it involves classification (recognizing the object among many other objects) and regression (predicting the coordinates of the objects in an image to make a bounding box).</p>
@@ -1958,7 +1958,7 @@ <h3><span class="section-number">11.5.1. </span>Supervised Learning<a class="hea
 <p><a class="reference external" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/class-reg.png">https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-fundamentals/ml-overview/class-reg.png</a></p>
 </section>
 <section id="reinforcement-learning">
-<h3><span class="section-number">11.5.2. </span>Reinforcement Learning<a class="headerlink" href="#reinforcement-learning" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.5.2. </span>Reinforcement Learning<a class="headerlink" href="#reinforcement-learning" title="Permalink to this headline">#</a></h3>
 <p>Reinforcement learning is a special type of Machine Learning that is most applicable in robotics and games.</p>
 <p>In reinforcement learning, a learning system called <em>an agent</em> can perceives the environment, performs some actions, and gets <em>rewarded</em> or <em>penalized</em> depending on how it is performing. The main goal of the agent is to accumulate as much as rewards as possible.</p>
 <p>The <em>agent</em> learns the best strategy(<em>policy</em>) necessary for getting the most reward itself.</p>
@@ -1968,7 +1968,7 @@ <h3><span class="section-number">11.5.2. </span>Reinforcement Learning<a class="
 </section>
 </section>
 <section id="typical-machine-learning-workflow">
-<h2><span class="section-number">11.6. </span>Typical Machine Learning Workflow<a class="headerlink" href="#typical-machine-learning-workflow" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">10.6. </span>Typical Machine Learning Workflow<a class="headerlink" href="#typical-machine-learning-workflow" title="Permalink to this headline">#</a></h2>
 <p>Although every Machine Learning problem is unique, they all follow a similar workflow. In this section, we will learn how to approach Machine Learning problems systematically.</p>
 <p>Overall, a typical Machine Learning project workflow consists of:</p>
 <ul class="simple">
@@ -1983,7 +1983,7 @@ <h2><span class="section-number">11.6. </span>Typical Machine Learning Workflow<
 </ul>
 <p>Let’s talk about each step in brief.</p>
 <section id="defining-a-problem">
-<h3><span class="section-number">11.6.1. </span>Defining a Problem<a class="headerlink" href="#defining-a-problem" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.6.1. </span>Defining a Problem<a class="headerlink" href="#defining-a-problem" title="Permalink to this headline">#</a></h3>
 <p>Everything starts here. Problem definition is the important and the initial step in any Machine Learning project. This is where you make sure you understand the problem really well. Understanding the problem will give you proper intuitions about the next steps to follow such as right learning algorithms, etc. But wait, what does it mean to understand the problem?</p>
 <p>Understanding the problem is all about diving deep into the details of the problem at hand and asking the right questions. First, it’s important to narrow down the problem until you can have a simple and a well-defined goal. Here are examples of simple goals: To <em>classify</em> products into different categories, to <em>predict</em> the price of a used car given its features (such as brand, age, etc…), to <em>recognize</em> if a person is wearing a facemask, to <em>divide</em> customers into different groups that share similar behaviors, etc…As you can see, the goal can tell whether the problem is classification, regression, or clustering…</p>
 <p>At this stage, avoid vague words. The simpler you can formulate the problem, the better things will be down the hall. It’s also worth questioning if the project should be pursued. Most problems do not require Machine Learning.</p>
@@ -1991,7 +1991,7 @@ <h3><span class="section-number">11.6.1. </span>Defining a Problem<a class="head
 <p>After you understand the problem and have an idea of the data you want, the next step is to collect it.</p>
 </section>
 <section id="collecting-data">
-<h3><span class="section-number">11.6.2. </span>Collecting Data<a class="headerlink" href="#collecting-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.6.2. </span>Collecting Data<a class="headerlink" href="#collecting-data" title="Permalink to this headline">#</a></h3>
 <p>This is usually the next stage after formulating a problem. Before we talk about collecting data, let’s even understand the meaning of data. According to <a class="reference external" href="https://en.wikipedia.org/wiki/Data">wikipedia</a>, “data are a set of values of qualitative or quantitative variables about one or more persons or objects.”</p>
 <p>There are 2 main types of data that are:</p>
 <ul class="simple">
@@ -2003,12 +2003,12 @@ <h3><span class="section-number">11.6.2. </span>Collecting Data<a class="headerl
 <p>Also, when collecting the data, quality is better than quantity. There are times when small data but good data can outwork big poor data. The amount of data you need is going to depend on the problem you’re solving and its scope, but whatever the problem is, aiming to collect good data is the way to go.</p>
 </section>
 <section id="establishing-a-baseline">
-<h3><span class="section-number">11.6.3. </span>Establishing a Baseline<a class="headerlink" href="#establishing-a-baseline" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.6.3. </span>Establishing a Baseline<a class="headerlink" href="#establishing-a-baseline" title="Permalink to this headline">#</a></h3>
 <p>Without a benchmark, you won’t know how to evaluate your results properly. A baseline is the simplest model that can solve your problem with minimal requirements. It does not have to be a model. It can be an existing open-source application, a statistical analysis or intuitions you get from data at a quick glance.</p>
 <p>The single most purpose of a baseline is to act as a reference point when comparing the actual model with the baseline. The ultimate goal is to beat a baseline, and sometime, if you can’t beat it, it might mean the project is not worth pursuing, or the baseline can be all you need.</p>
 </section>
 <section id="exploratory-data-analysis-eda">
-<h3><span class="section-number">11.6.4. </span>Exploratory data analysis(EDA)<a class="headerlink" href="#exploratory-data-analysis-eda" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.6.4. </span>Exploratory data analysis(EDA)<a class="headerlink" href="#exploratory-data-analysis-eda" title="Permalink to this headline">#</a></h3>
 <p>Before manipulating the data, it is quite important to learn about the dataset. This can be overlooked, but doing it well will help you know the effective strategies to be applied while cleaning the data.</p>
 <p>Go through some values, plot some features, and try to understand the correlation between them. If the work is vision-related, visualize some images, and spot what’s missing in the images. Are they diverse enough? What types of image scenarios can you add? Or what are images that can mislead the model?</p>
 <p>Here are other things that you would want to inspect in the data:</p>
@@ -2022,7 +2022,7 @@ <h3><span class="section-number">11.6.4. </span>Exploratory data analysis(EDA)<a
 <p>Also, to avoid data leakage, do not touch the test set in EDA or anywhere before the model training.</p>
 </section>
 <section id="data-preprocessing">
-<h3><span class="section-number">11.6.5. </span>Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.6.5. </span>Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h3>
 <p>Data processing is perhaps the biggest part of any Machine Learning project. There is a notion that Data Scientists and Machine Learning Engineers spend more than 80% of their time preparing the data and this makes sense because the real-world data are messy.</p>
 <p>In this step, it is where you convert the raw data to go in a format that can be accepted by the Machine Learning algorithms.</p>
 <p>Data preprocessing is hard because there are different types of data and the way you process one is different from the other. For example, in structured data, the way you process numerical features is going to be different from categorical features. Also in unstructured data, the way you manipulate images is going to be different from how you manipulate texts or sounds.</p>
@@ -2035,7 +2035,7 @@ <h3><span class="section-number">11.6.5. </span>Data Preprocessing<a class="head
 <p>In many textbooks and courses, data preprocessing is also referred to as data cleaning or data preparation. Feature engineering is also a part of data preprocessing. Feature engineering is a creative task and requires some extra knowledge about the data and the problem as it involves creating new features from existing features.</p>
 </section>
 <section id="selecting-and-training-a-model">
-<h3><span class="section-number">11.6.6. </span>Selecting and Training a Model<a class="headerlink" href="#selecting-and-training-a-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.6.6. </span>Selecting and Training a Model<a class="headerlink" href="#selecting-and-training-a-model" title="Permalink to this headline">#</a></h3>
 <p>Selecting, creating, and training a Machine Learning model is the tiniest part of any typical Machine Learning workflow. There are different types of models but in broad, most of them fall into these categories: linear models such as linear and logistic regression, tree-based models such as decision trees, ensemble models such as random forests, and finally neural networks.</p>
 <p>Depending on your problem, you can choose any relevant model from these categories or tries many of them. But, it is worth mentioning that getting a model that can ultimately solve a given problem is no <em>free lunch scenario</em>. You have to experiment with different models to get one that works for your problem and dataset.</p>
 <p>To reduce your modeling curve, here are a few things that you can consider while choosing a Machine Learning model:</p>
@@ -2048,7 +2048,7 @@ <h3><span class="section-number">11.6.6. </span>Selecting and Training a Model<a
 <p>As you can see, there is a trade-off during model selection. You want explainability, choose models that can provide that for you, most models don’t. You have a small dataset or you care about the training time, same thing, a right model for you.</p>
 </section>
 <section id="performing-error-analysis">
-<h3><span class="section-number">11.6.7. </span>Performing Error Analysis<a class="headerlink" href="#performing-error-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.6.7. </span>Performing Error Analysis<a class="headerlink" href="#performing-error-analysis" title="Permalink to this headline">#</a></h3>
 <p>Performing error analysis will guide you throughout the process of improving the results of the model. The improvement can either be from the data or the model.</p>
 <p>One of the best ways to do error analysis is to plot the learning curve and to try noticing where the model is failing and what might be the reason and the right actions that you can take to reduce the errors.</p>
 <p>To improve the model part, you can try different model configuration values or hyperparameters. You can also try different models to see one that works well. But also, a good model comes from good data, so it’s important to spend time examining the results of the model with respect to the input data.</p>
@@ -2062,16 +2062,16 @@ <h3><span class="section-number">11.6.7. </span>Performing Error Analysis<a clas
 <p>When improving the data, you can create artificial data (a.k.a data augmentation). This will work well most of the time. The whole error analysis is an iterative process, keep doing it and always aim to improve the data than the model.</p>
 </section>
 <section id="deploying-a-model">
-<h3><span class="section-number">11.6.8. </span>Deploying a Model<a class="headerlink" href="#deploying-a-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">10.6.8. </span>Deploying a Model<a class="headerlink" href="#deploying-a-model" title="Permalink to this headline">#</a></h3>
 <p>Model deployment is the last part of this workflow. When all the previous steps have gone right, and you are happy about the results of the model on the test set, the next step will be to deploy the model so that the users can start to make requests and get predictions or enhanced services.</p>
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">11.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">10.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">11.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">10.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/Nyandwi">Nyandwi</a> for creating the open-source course <a class="reference external" href="https://github.com/Nyandwi/machine_learning_complete">Complete Machine Learning Package</a>. It inspires the majority of the content in this chapter. Some contents come from <a class="reference external" href="https://developers.google.com/machine-learning/crash-course/framing/ml-terminology">Google Machine Learning Crash Course</a>.</p>
 </section>
 </section>
@@ -2113,13 +2113,13 @@ <h2><span class="section-number">11.8. </span>Acknowledgments<a class="headerlin
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+     40.13. Apply your skills
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+     40.39. Low code/no code Data Science project on Azure ML
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+     40.40. Data Science project using Azure ML SDK
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+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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+     40.42. Machine Learning overview - assignment 1
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-     41.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
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-     41.47. Loss Function
+     40.47. Loss Function
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+     40.57. Create a regression model
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-     42.16. Build an machine learning web application
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+   11.1. Machine Learning Landscape : Discriminative Models
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-   12.2. How to conceive a “new” Machine Learning algorithm : Discriminative Models
+   11.2. How to conceive a “new” Machine Learning algorithm : Discriminative Models
   </a>
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+   11.3. Machine Learning Landscape : Generative Models
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+   11.6. Semi-supervised learning
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-   12.7. Your turn! 🚀
+   11.7. Your turn! 🚀
   </a>
  </li>
 </ul>
@@ -1566,37 +1566,37 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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+   11.1. Machine Learning Landscape : Discriminative Models
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-   12.2. How to conceive a “new” Machine Learning algorithm : Discriminative Models
+   11.2. How to conceive a “new” Machine Learning algorithm : Discriminative Models
   </a>
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+   11.3. Machine Learning Landscape : Generative Models
   </a>
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+   11.4. LDA
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+   11.5. Unsupervised learning
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-   12.6. Semi-supervised learning
+   11.6. Semi-supervised learning
   </a>
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+   11.7. Your turn! 🚀
   </a>
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 </ul>
@@ -1610,9 +1610,9 @@ <h2> Contents </h2>
               <div>
                 
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-<h1><span class="section-number">12. </span>Summary of machine learning fundamentals<a class="headerlink" href="#summary-of-machine-learning-fundamentals" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">11. </span>Summary of machine learning fundamentals<a class="headerlink" href="#summary-of-machine-learning-fundamentals" title="Permalink to this headline">#</a></h1>
 <section id="machine-learning-landscape-discriminative-models">
-<h2><span class="section-number">12.1. </span>Machine Learning Landscape : Discriminative Models<a class="headerlink" href="#machine-learning-landscape-discriminative-models" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">11.1. </span>Machine Learning Landscape : Discriminative Models<a class="headerlink" href="#machine-learning-landscape-discriminative-models" title="Permalink to this headline">#</a></h2>
 <p>Most of supervised machine learning can be looked at using the following framework:
 You have a set of training points <span class="math notranslate nohighlight">\((x_i, y_i)\)</span>, and you want to find a function f that “fits the data well”,
 that is, <span class="math notranslate nohighlight">\(yi \approx f(x_i)\)</span> for most <span class="math notranslate nohighlight">\(i\)</span>.</p>
@@ -1639,7 +1639,7 @@ <h2><span class="section-number">12.1. </span>Machine Learning Landscape : Discr
 <p>and so on.</p>
 </section>
 <section id="how-to-conceive-a-new-machine-learning-algorithm-discriminative-models">
-<h2><span class="section-number">12.2. </span>How to conceive a “new” Machine Learning algorithm : Discriminative Models<a class="headerlink" href="#how-to-conceive-a-new-machine-learning-algorithm-discriminative-models" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">11.2. </span>How to conceive a “new” Machine Learning algorithm : Discriminative Models<a class="headerlink" href="#how-to-conceive-a-new-machine-learning-algorithm-discriminative-models" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="https://math.stackexchange.com/questions/1530298/variant-of-linear-regression-using-perpendicular-distance-instead-of-vertical">How about perpendicular distance instead of vertical distance for Linear Regression?</a></p></li>
 <li><p><a class="reference external" href="https://www.quora.com/How-can-one-use-kernels-utilizing-the-kernel-trick-in-logistic-regression">How about Logistic Regression with Kernel Trick?</a></p></li>
@@ -1651,20 +1651,20 @@ <h2><span class="section-number">12.2. </span>How to conceive a “new” Machin
 </ul>
 </section>
 <section id="machine-learning-landscape-generative-models">
-<h2><span class="section-number">12.3. </span>Machine Learning Landscape : Generative Models<a class="headerlink" href="#machine-learning-landscape-generative-models" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">11.3. </span>Machine Learning Landscape : Generative Models<a class="headerlink" href="#machine-learning-landscape-generative-models" title="Permalink to this headline">#</a></h2>
 </section>
 <section id="lda">
-<h2><span class="section-number">12.4. </span>LDA<a class="headerlink" href="#lda" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">11.4. </span>LDA<a class="headerlink" href="#lda" title="Permalink to this headline">#</a></h2>
 </section>
 <section id="unsupervised-learning">
-<h2><span class="section-number">12.5. </span>Unsupervised learning<a class="headerlink" href="#unsupervised-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">11.5. </span>Unsupervised learning<a class="headerlink" href="#unsupervised-learning" title="Permalink to this headline">#</a></h2>
 </section>
 <section id="semi-supervised-learning">
-<h2><span class="section-number">12.6. </span>Semi-supervised learning<a class="headerlink" href="#semi-supervised-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">11.6. </span>Semi-supervised learning<a class="headerlink" href="#semi-supervised-learning" title="Permalink to this headline">#</a></h2>
 <p><a class="reference external" href="https://www.baeldung.com/cs/svm-vs-neural-network">Semi-supervised learning</a></p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">12.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">11.7. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>TBD</p>
 <div hidden>
     https://www.baeldung.com/cs/svm-vs-neural-network
@@ -1708,13 +1708,13 @@ <h2><span class="section-number">12.7. </span>Your turn! 🚀<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">11. </span>Machine Learning overview</p>
+            <p class="prev-next-title"><span class="section-number">10. </span>Machine Learning overview</p>
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-        <p class="prev-next-title"><span class="section-number">13. </span>Regression models for Machine Learning</p>
+        <p class="prev-next-title"><span class="section-number">12. </span>Regression models for Machine Learning</p>
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diff --git a/ml-fundamentals/parameter-optimization/gradient-descent.html b/ml-fundamentals/parameter-optimization/gradient-descent.html
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-    <title>16.2. Gradient descent &#8212; Ocademy Open Machine Learning Book</title>
+    <title>15.2. Gradient descent &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="17. Clustering models for Machine Learning" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html" />
-    <link rel="prev" title="16.1. Loss function" href="loss-function.html" />
+    <link rel="next" title="16. Clustering models for Machine Learning" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html" />
+    <link rel="prev" title="15.1. Loss function" href="loss-function.html" />
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+   1. Python programming introduction
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   </a>
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+   15.2.4. Some mathematics … to gain more insight
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+     15.2.4.3. Partial derivative with respect to
      <span class="math notranslate nohighlight">
       \(m\)
      </span>
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    </li>
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      <span class="math notranslate nohighlight">
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    </li>
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-     16.2.4.5. Final function
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+   15.2.5. Time to code!
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#linear-regression-with-gradient-descent">
-     16.2.5.1. Linear regression With gradient descent
+     15.2.5.1. Linear regression With gradient descent
     </a>
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+   15.2.6. Your turn 🚀
   </a>
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   <a class="reference internal nav-link" href="#optional-at-the-frontier-of-machine-learning-research">
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+   15.2.7. [optional] At the frontier of Machine Learning Research
   </a>
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 </div>
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 <section class="tex2jax_ignore mathjax_ignore" id="gradient-descent">
-<h1><span class="section-number">16.2. </span>Gradient descent<a class="headerlink" href="#gradient-descent" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">15.2. </span>Gradient descent<a class="headerlink" href="#gradient-descent" title="Permalink to this headline">#</a></h1>
 <section id="objective-of-this-session">
-<h2><span class="section-number">16.2.1. </span>Objective of this session<a class="headerlink" href="#objective-of-this-session" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">15.2.1. </span>Objective of this session<a class="headerlink" href="#objective-of-this-session" title="Permalink to this headline">#</a></h2>
 <p>We have already learnt how to use Linear Regression and Logistic Regression models.</p>
 <p>The code might seem quite easy and intuitive for you. And you might naturally ask:</p>
 <ul class="simple">
@@ -1819,7 +1819,7 @@ <h2><span class="section-number">16.2.1. </span>Objective of this session<a clas
 i.e. tuning of parameters for ML models. And the technique behind is called “Gradient Descent”.</p>
 </section>
 <section id="video">
-<h2><span class="section-number">16.2.2. </span>Video<a class="headerlink" href="#video" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">15.2.2. </span>Video<a class="headerlink" href="#video" title="Permalink to this headline">#</a></h2>
 <p>The corresponding video (in Chinese) for this notebook is <a class="reference external" href="https://www.bilibili.com/video/BV1SY4y1G7o9/">👉 available here on Bilibili</a>.
 You can (and should) watch the video before diving into the details of gradient descent:</p>
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-<h2><span class="section-number">16.2.3. </span>Let’s be playful … to gain some intuition<a class="headerlink" href="#lets-be-playful-to-gain-some-intuition" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">15.2.3. </span>Let’s be playful … to gain some intuition<a class="headerlink" href="#lets-be-playful-to-gain-some-intuition" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="https://playground.tensorflow.org/#activation=sigmoid&amp;batchSize=10&amp;dataset=circle%C2%AEDataset=reg-plane&amp;learningRate=0.00001%C2%AEularizationRate=0&amp;noise=0&amp;networkShape=&amp;seed=0.71864&amp;showTestData=false&amp;discretize=false&amp;percTrainData=50&amp;x=true&amp;y=true&amp;xTimesY=true&amp;xSquared=true&amp;ySquared=true&amp;cosX=false&amp;sinX=false&amp;cosY=false&amp;sinY=false&amp;collectStats=false&amp;problem=classification&amp;initZero=false&amp;hideText=false">Tensorflow Playground</a></p></li>
 <li><p><a class="reference external" href="https://github.com/lilipads/gradient_descent_viz">Gradient Descent Visualization</a></p></li>
@@ -1857,9 +1857,9 @@ <h2><span class="section-number">16.2.3. </span>Let’s be playful … to gain s
 </ul>
 </section>
 <section id="some-mathematics-to-gain-more-insight">
-<h2><span class="section-number">16.2.4. </span>Some mathematics … to gain more insight<a class="headerlink" href="#some-mathematics-to-gain-more-insight" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">15.2.4. </span>Some mathematics … to gain more insight<a class="headerlink" href="#some-mathematics-to-gain-more-insight" title="Permalink to this headline">#</a></h2>
 <section id="abstract">
-<h3><span class="section-number">16.2.4.1. </span>Abstract<a class="headerlink" href="#abstract" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">15.2.4.1. </span>Abstract<a class="headerlink" href="#abstract" title="Permalink to this headline">#</a></h3>
 <p>The idea behind gradient descent is simple - by gradually tuning parameters, such as slope (<span class="math notranslate nohighlight">\(m\)</span>) and the intercept (<span class="math notranslate nohighlight">\(b\)</span>) in our regression function <span class="math notranslate nohighlight">\(y = mx + b\)</span>, we minimize cost.
 By cost, we usually mean some kind of a function that tells us how far off our model predicted result. For regression problems we often use <code class="docutils literal notranslate"><span class="pre">mean</span> <span class="pre">squared</span> <span class="pre">error</span></code> (MSE) cost function. If we use gradient descent for the classification problem, we will have a different set of parameters to tune.</p>
 <div class="math notranslate nohighlight">
@@ -1867,14 +1867,14 @@ <h3><span class="section-number">16.2.4.1. </span>Abstract<a class="headerlink"
 <p>Now we have to figure out how to tweak parameters <span class="math notranslate nohighlight">\(m\)</span> and <span class="math notranslate nohighlight">\(b\)</span> to reduce MSE.</p>
 </section>
 <section id="partial-derivatives">
-<h3><span class="section-number">16.2.4.2. </span>Partial derivatives<a class="headerlink" href="#partial-derivatives" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">15.2.4.2. </span>Partial derivatives<a class="headerlink" href="#partial-derivatives" title="Permalink to this headline">#</a></h3>
 <p>We use partial derivatives to find how each individual parameter affects MSE, so that’s where word <em>partial</em> comes from. In simple words, we take the derivative with respect to <span class="math notranslate nohighlight">\(m\)</span> and <span class="math notranslate nohighlight">\(b\)</span> <strong>separately</strong>. Take a look at the formula below. It looks almost exactly the same as MSE, but this time we added f(m, b) to it. It essentially changes nothing, except now we can plug <span class="math notranslate nohighlight">\(m\)</span> and <span class="math notranslate nohighlight">\(b\)</span> numbers into it and calculate the result.</p>
 <div class="math notranslate nohighlight">
 \[𝑓(𝑚,𝑏)= \frac{1}{n}\sum_{i=1}^{n}(y_i - (mx_i+b))^2\]</div>
 <p>This formula (or better say function) is better representation for further calculations of partial derivatives. We can ignore sum for now and what comes before that and focus only on <span class="math notranslate nohighlight">\(y - (mx + b)^2\)</span>.</p>
 </section>
 <section id="partial-derivative-with-respect-to-m">
-<h3><span class="section-number">16.2.4.3. </span>Partial derivative with respect to <span class="math notranslate nohighlight">\(m\)</span><a class="headerlink" href="#partial-derivative-with-respect-to-m" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">15.2.4.3. </span>Partial derivative with respect to <span class="math notranslate nohighlight">\(m\)</span><a class="headerlink" href="#partial-derivative-with-respect-to-m" title="Permalink to this headline">#</a></h3>
 <p>With respect to <span class="math notranslate nohighlight">\(m\)</span> means we derive parameter <span class="math notranslate nohighlight">\(m\)</span> and basically ignore what is going on with <span class="math notranslate nohighlight">\(b\)</span>, or we can say its 0. To derive with respect to <span class="math notranslate nohighlight">\(m\)</span> we will use chain rule.</p>
 <div class="math notranslate nohighlight">
 \[ [f(g(x))]' = f'(g(x)) * g(x)' \: - \textrm{chain rule}\]</div>
@@ -1893,7 +1893,7 @@ <h3><span class="section-number">16.2.4.3. </span>Partial derivative with respec
 <p>Here, <span class="math notranslate nohighlight">\(\frac{df}{dm}\)</span> means we find partial derivative of function f (we mentioned it earlier) with respect to m. We plug our derivative to the summation and we’re done.</p>
 </section>
 <section id="partial-derivative-with-respect-to-b">
-<h3><span class="section-number">16.2.4.4. </span>Partial derivative with respect to <span class="math notranslate nohighlight">\(b\)</span><a class="headerlink" href="#partial-derivative-with-respect-to-b" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">15.2.4.4. </span>Partial derivative with respect to <span class="math notranslate nohighlight">\(b\)</span><a class="headerlink" href="#partial-derivative-with-respect-to-b" title="Permalink to this headline">#</a></h3>
 <p>Same rules apply to the derivative with respect to b.</p>
 <ol class="simple">
 <li><p><span class="math notranslate nohighlight">\(()^2\)</span> becomes <span class="math notranslate nohighlight">\(2()\)</span>, same as <span class="math notranslate nohighlight">\(x^2\)</span> becomes <span class="math notranslate nohighlight">\(2x\)</span></p></li>
@@ -1905,7 +1905,7 @@ <h3><span class="section-number">16.2.4.4. </span>Partial derivative with respec
 \[\frac{\partial f}{\partial b} = \frac{1}{n}\sum_{i=1}^{n}-2(y_i - (mx_i+b))\]</div>
 </section>
 <section id="final-function">
-<h3><span class="section-number">16.2.4.5. </span>Final function<a class="headerlink" href="#final-function" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">15.2.4.5. </span>Final function<a class="headerlink" href="#final-function" title="Permalink to this headline">#</a></h3>
 <p>Few details we should discuss before jumping into code:</p>
 <ol class="simple">
 <li><p>Gradient descent is an iterative process and with each iteration (<span class="math notranslate nohighlight">\(epoch\)</span>) we slightly minimizing MSE, so each time we use our derived functions to update parameters <span class="math notranslate nohighlight">\(m\)</span> and <span class="math notranslate nohighlight">\(b\)</span>.</p></li>
@@ -1916,9 +1916,9 @@ <h3><span class="section-number">16.2.4.5. </span>Final function<a class="header
 </section>
 </section>
 <section id="time-to-code">
-<h2><span class="section-number">16.2.5. </span>Time to code!<a class="headerlink" href="#time-to-code" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">15.2.5. </span>Time to code!<a class="headerlink" href="#time-to-code" title="Permalink to this headline">#</a></h2>
 <section id="linear-regression-with-gradient-descent">
-<h3><span class="section-number">16.2.5.1. </span>Linear regression With gradient descent<a class="headerlink" href="#linear-regression-with-gradient-descent" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">15.2.5.1. </span>Linear regression With gradient descent<a class="headerlink" href="#linear-regression-with-gradient-descent" title="Permalink to this headline">#</a></h3>
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 </div>
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-        1.37121698e-01,  1.36604503e-01,  1.18709452e-01, -4.49976117e-03]), &#39;bias&#39;: 0.021707132617479422}
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+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;lr&#39;: 0.0003, &#39;n_iters&#39;: 5000, &#39;weights&#39;: array([ 0.45351552,  0.21147914,  0.01485353,  0.08785205,  0.14878446,
+        0.12828472,  0.12230661, -0.00201684]), &#39;bias&#39;: 0.02192744125483242}
+0    -1.952323
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+4    -2.072147
         ...   
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@@ -2086,7 +2086,7 @@ <h3><span class="section-number">16.2.5.1. </span>Linear regression With gradien
 </div>
 </section>
 <section id="logistic-regression-with-gradient-descent">
-<h3><span class="section-number">16.2.5.2. </span>Logistic regression with gradient descent<a class="headerlink" href="#logistic-regression-with-gradient-descent" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">15.2.5.2. </span>Logistic regression with gradient descent<a class="headerlink" href="#logistic-regression-with-gradient-descent" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">LogisticRegression</span><span class="p">:</span>
@@ -2168,11 +2168,11 @@ <h3><span class="section-number">16.2.5.2. </span>Logistic regression with gradi
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">16.2.6. </span>Your turn 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">15.2.6. </span>Your turn 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Modify <code class="docutils literal notranslate"><span class="pre">LogisticRegression</span></code> so that the training will use SGD instead of GD.</p>
 </section>
 <section id="optional-at-the-frontier-of-machine-learning-research">
-<h2><span class="section-number">16.2.7. </span>[optional] At the frontier of Machine Learning Research<a class="headerlink" href="#optional-at-the-frontier-of-machine-learning-research" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">15.2.7. </span>[optional] At the frontier of Machine Learning Research<a class="headerlink" href="#optional-at-the-frontier-of-machine-learning-research" title="Permalink to this headline">#</a></h2>
 <div class="cell tag_hide-input docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
@@ -2200,7 +2200,7 @@ <h2><span class="section-number">16.2.7. </span>[optional] At the frontier of Ma
 </div>
 </section>
 <section id="bibliography">
-<h2><span class="section-number">16.2.8. </span>Bibliography<a class="headerlink" href="#bibliography" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">15.2.8. </span>Bibliography<a class="headerlink" href="#bibliography" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="https://www.youtube.com/watch?v=sDv4f4s2SB8">Gradient Descent, Step-by-Step - StatQuest</a></p></li>
 <li><p><a class="reference external" href="https://www.youtube.com/watch?v=vMh0zPT0tLI">Stochastic Gradient Descent, Clearly Explained!!! - StatQuest</a></p></li>
@@ -2247,13 +2247,13 @@ <h2><span class="section-number">16.2.8. </span>Bibliography<a class="headerlink
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">16.1. </span>Loss function</p>
+            <p class="prev-next-title"><span class="section-number">15.1. </span>Loss function</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../../ml-advanced/clustering/clustering-models-for-machine-learning.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">17. </span>Clustering models for Machine Learning</p>
+        <p class="prev-next-title"><span class="section-number">16. </span>Clustering models for Machine Learning</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/ml-fundamentals/parameter-optimization/loss-function.html b/ml-fundamentals/parameter-optimization/loss-function.html
index 857e99732..056453197 100644
--- a/ml-fundamentals/parameter-optimization/loss-function.html
+++ b/ml-fundamentals/parameter-optimization/loss-function.html
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>16.1. Loss function &#8212; Ocademy Open Machine Learning Book</title>
+    <title>15.1. Loss function &#8212; Ocademy Open Machine Learning Book</title>
     
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   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="16.2. Gradient descent" href="gradient-descent.html" />
-    <link rel="prev" title="16. parameter-optimization" href="parameter-optimization.html" />
+    <link rel="next" title="15.2. Gradient descent" href="gradient-descent.html" />
+    <link rel="prev" title="15. parameter-optimization" href="parameter-optimization.html" />
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 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
-   1. Python programming basics
+   1. Python programming introduction
   </a>
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   <a class="reference internal" href="../../prerequisites/python-programming-basics.html">
-   3. Python programming basics
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   <a class="reference internal" href="../../prerequisites/python-programming-advanced.html">
-   4. Python programming advanced
+   3. Python programming advanced
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 <ul class="nav bd-sidenav">
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   <ul>
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     <a class="reference internal" href="../../data-science/introduction/defining-data-science.html">
-     5.1. Defining data science
+     4.1. Defining data science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/introduction/data-science-ethics.html">
-     5.2. Data Science ethics
+     4.2. Data Science ethics
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     <a class="reference internal" href="../../data-science/introduction/defining-data.html">
-     5.3. Defining data
+     4.3. Defining data
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     <a class="reference internal" href="../../data-science/introduction/introduction-to-statistics-and-probability.html">
-     5.4. Introduction to statistics and probability
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     <a class="reference internal" href="../../data-science/working-with-data/relational-databases.html">
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     <a class="reference internal" href="../../data-science/working-with-data/non-relational-data.html">
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     <a class="reference internal" href="../../data-science/working-with-data/numpy.html">
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+     5.3. NumPy
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     <a class="reference internal" href="../../data-science/working-with-data/pandas/pandas.html">
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+       5.4.1. Introduction and Data Structures
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       <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
-       6.4.2. Data Selection
+       5.4.2. Data Selection
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-       6.4.3. Advanced Pandas Techniques
+       5.4.3. Advanced Pandas Techniques
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-     6.5. Data preparation
+     5.5. Data preparation
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-visualization/visualization-distributions.html">
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    </li>
    <li class="toctree-l2">
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-     7.2. Visualizing proportions
+     6.2. Visualizing proportions
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-visualization/visualization-relationships.html">
-     7.3. Visualizing relationships: all about honey 🍯
+     6.3. Visualizing relationships: all about honey 🍯
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-visualization/meaningful-visualizations.html">
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+     6.4. Making meaningful visualizations
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     <a class="reference internal" href="../../data-science/data-science-in-the-cloud/introduction.html">
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+     15.2. Gradient descent
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-     41.45. Linear regression - California Housing
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+     15.1.3.1. Squared Error Loss
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     <a class="reference internal nav-link" href="#absolute-error-loss">
-     16.1.3.2. Absolute Error Loss
+     15.1.3.2. Absolute Error Loss
     </a>
    </li>
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  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#classification-loss-functions">
-   16.1.4. Classification Loss Functions
+   15.1.4. Classification Loss Functions
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#binary-cross-entropy-loss">
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+     15.1.4.1. Binary Cross Entropy Loss
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#multi-class-cross-entropy-loss">
-     16.1.4.2. Multi-Class Cross-Entropy Loss
+     15.1.4.2. Multi-Class Cross-Entropy Loss
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-   16.1.5. [optional] At the frontier of Machine Learning Research
+   15.1.5. [optional] At the frontier of Machine Learning Research
   </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#bibliography">
-   16.1.6. Bibliography
+   15.1.6. Bibliography
   </a>
  </li>
 </ul>
@@ -1724,22 +1724,22 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="loss-function">
-<h1><span class="section-number">16.1. </span>Loss function<a class="headerlink" href="#loss-function" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">15.1. </span>Loss function<a class="headerlink" href="#loss-function" title="Permalink to this headline">#</a></h1>
 <p>The function we want to minimize or maximize is called the objective function or criterion. When we are minimizing it, we may also call it the cost function, loss function, or error function.</p>
 <p>— Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville</p>
 <section id="objective-of-this-section">
-<h2><span class="section-number">16.1.1. </span>Objective of this section<a class="headerlink" href="#objective-of-this-section" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">15.1.1. </span>Objective of this section<a class="headerlink" href="#objective-of-this-section" title="Permalink to this headline">#</a></h2>
 <p>We have already learned math and code for “Gradient Descent”, as well as other optimization techniques.</p>
 <p>In this section, we will learn more about loss functions for Linear Regression and Logistic Regression.</p>
 </section>
 <section id="whats-the-difference-between-a-loss-function-and-a-cost-function">
-<h2><span class="section-number">16.1.2. </span>What’s the Difference between a Loss Function and a Cost Function?<a class="headerlink" href="#whats-the-difference-between-a-loss-function-and-a-cost-function" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">15.1.2. </span>What’s the Difference between a Loss Function and a Cost Function?<a class="headerlink" href="#whats-the-difference-between-a-loss-function-and-a-cost-function" title="Permalink to this headline">#</a></h2>
 <p>A loss function is for a single training example. It is also sometimes called an error function. A cost function, on the other hand, is the average loss over the entire training dataset. The optimization strategies aim at minimizing the cost function.</p>
 </section>
 <section id="regression-loss-functions">
-<h2><span class="section-number">16.1.3. </span>Regression Loss Functions<a class="headerlink" href="#regression-loss-functions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">15.1.3. </span>Regression Loss Functions<a class="headerlink" href="#regression-loss-functions" title="Permalink to this headline">#</a></h2>
 <section id="squared-error-loss">
-<h3><span class="section-number">16.1.3.1. </span>Squared Error Loss<a class="headerlink" href="#squared-error-loss" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">15.1.3.1. </span>Squared Error Loss<a class="headerlink" href="#squared-error-loss" title="Permalink to this headline">#</a></h3>
 <p>Squared Error loss for each training example,
 also known as <strong>L2 Loss</strong>, is the square of the
 difference between the actual and the predicted values:</p>
@@ -1749,7 +1749,7 @@ <h3><span class="section-number">16.1.3.1. </span>Squared Error Loss<a class="he
 Mean of these Squared Errors (MSE).</p>
 </section>
 <section id="absolute-error-loss">
-<h3><span class="section-number">16.1.3.2. </span>Absolute Error Loss<a class="headerlink" href="#absolute-error-loss" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">15.1.3.2. </span>Absolute Error Loss<a class="headerlink" href="#absolute-error-loss" title="Permalink to this headline">#</a></h3>
 <p>Absolute Error for each training example
 is the distance between the predicted and the actual values,
 irrespective of the sign. Absolute Error is also
@@ -1760,9 +1760,9 @@ <h3><span class="section-number">16.1.3.2. </span>Absolute Error Loss<a class="h
 </section>
 </section>
 <section id="classification-loss-functions">
-<h2><span class="section-number">16.1.4. </span>Classification Loss Functions<a class="headerlink" href="#classification-loss-functions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">15.1.4. </span>Classification Loss Functions<a class="headerlink" href="#classification-loss-functions" title="Permalink to this headline">#</a></h2>
 <section id="binary-cross-entropy-loss">
-<h3><span class="section-number">16.1.4.1. </span>Binary Cross Entropy Loss<a class="headerlink" href="#binary-cross-entropy-loss" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">15.1.4.1. </span>Binary Cross Entropy Loss<a class="headerlink" href="#binary-cross-entropy-loss" title="Permalink to this headline">#</a></h3>
 <p>Cross-entropy is the default loss function to use for binary classification problems.</p>
 <p>It is intended for use with binary classification where the target values are in the set {0, 1}.</p>
 <p>Mathematically, it is the preferred loss function
@@ -1802,7 +1802,7 @@ <h3><span class="section-number">16.1.4.1. </span>Binary Cross Entropy Loss<a cl
 </div>
 </section>
 <section id="multi-class-cross-entropy-loss">
-<h3><span class="section-number">16.1.4.2. </span>Multi-Class Cross-Entropy Loss<a class="headerlink" href="#multi-class-cross-entropy-loss" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">15.1.4.2. </span>Multi-Class Cross-Entropy Loss<a class="headerlink" href="#multi-class-cross-entropy-loss" title="Permalink to this headline">#</a></h3>
 <p>Cross-entropy is the default loss function to
 use for multi-class classification problems.</p>
 <p>In this case, it is intended for use with
@@ -1821,7 +1821,7 @@ <h3><span class="section-number">16.1.4.2. </span>Multi-Class Cross-Entropy Loss
 </section>
 </section>
 <section id="optional-at-the-frontier-of-machine-learning-research">
-<h2><span class="section-number">16.1.5. </span>[optional] At the frontier of Machine Learning Research<a class="headerlink" href="#optional-at-the-frontier-of-machine-learning-research" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">15.1.5. </span>[optional] At the frontier of Machine Learning Research<a class="headerlink" href="#optional-at-the-frontier-of-machine-learning-research" title="Permalink to this headline">#</a></h2>
 <div class="cell tag_hide-input docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
@@ -1850,7 +1850,7 @@ <h2><span class="section-number">16.1.5. </span>[optional] At the frontier of Ma
 <p>With its corresponding paper: <a class="reference external" href="https://arxiv.org/abs/1701.03077">A General and Adaptive Robust Loss Function</a></p>
 </section>
 <section id="bibliography">
-<h2><span class="section-number">16.1.6. </span>Bibliography<a class="headerlink" href="#bibliography" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">15.1.6. </span>Bibliography<a class="headerlink" href="#bibliography" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="https://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html">ML cheat sheet for loss functions</a></p></li>
 <li><p><a class="reference external" href="https://www.youtube.com/watch?v=ErfnhcEV1O8">A Short Introduction to Entropy, Cross-Entropy and KL-Divergence</a></p></li>
@@ -1895,13 +1895,13 @@ <h2><span class="section-number">16.1.6. </span>Bibliography<a class="headerlink
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">16. </span>parameter-optimization</p>
+            <p class="prev-next-title"><span class="section-number">15. </span>parameter-optimization</p>
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-        <p class="prev-next-title"><span class="section-number">16.2. </span>Gradient descent</p>
+        <p class="prev-next-title"><span class="section-number">15.2. </span>Gradient descent</p>
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diff --git a/ml-fundamentals/parameter-optimization/parameter-optimization.html b/ml-fundamentals/parameter-optimization/parameter-optimization.html
index 429a04c9f..88b9a0f27 100644
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="16.1. Loss function" href="loss-function.html" />
-    <link rel="prev" title="15.4. Applied Machine Learning : build a web app" href="../classification/applied-ml-build-a-web-app.html" />
+    <link rel="next" title="15.1. Loss function" href="loss-function.html" />
+    <link rel="prev" title="14.4. Applied Machine Learning : build a web app" href="../classification/applied-ml-build-a-web-app.html" />
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   <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
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-     42.10. Data Science in real world
+     41.10. Data Science in real world
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-     42.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
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-     42.12. Linear Regression
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-     42.15. Neural Network
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-     42.16. Build an machine learning web application
+     41.16. Build an machine learning web application
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-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
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-     42.18. Kernel method
+     41.18. Kernel method
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-     42.19. Model Selection
+     41.19. Model Selection
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     <a class="reference internal" href="../../slides/deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../slides/deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
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   </ul>
@@ -1577,74 +1577,74 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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-   13.3.1. Build a regression model using Scikit-learn: regression four ways
+   12.3.1. Build a regression model using Scikit-learn: regression four ways
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
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-     13.3.1.1. Introduction
+     12.3.1.1. Introduction
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#prerequisite">
-     13.3.1.2. Prerequisite
+     12.3.1.2. Prerequisite
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#preparation">
-     13.3.1.3. Preparation
+     12.3.1.3. Preparation
     </a>
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  </li>
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-   13.3.2. A linear regression line
+   12.3.2. A linear regression line
   </a>
  </li>
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-   13.3.3. Correlation
+   12.3.3. Correlation
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-   13.3.4. Looking for Correlation
+   12.3.4. Looking for Correlation
   </a>
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   <a class="reference internal nav-link" href="#simple-linear-regression">
-   13.3.5. Simple Linear Regression
+   12.3.5. Simple Linear Regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#polynomial-regression">
-   13.3.6. Polynomial Regression
+   12.3.6. Polynomial Regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#categorical-features">
-   13.3.7. Categorical Features
+   12.3.7. Categorical Features
   </a>
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   <a class="reference internal nav-link" href="#putting-it-all-together">
-   13.3.8. Putting it all together
+   12.3.8. Putting it all together
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   13.3.9. Self study
+   12.3.9. Self study
   </a>
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   <a class="reference internal nav-link" href="#your-turn">
-   13.3.10. Your turn! 🚀
+   12.3.10. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   13.3.11. Acknowledgments
+   12.3.11. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1668,74 +1668,74 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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-   13.3.1. Build a regression model using Scikit-learn: regression four ways
+   12.3.1. Build a regression model using Scikit-learn: regression four ways
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#introduction">
-     13.3.1.1. Introduction
+     12.3.1.1. Introduction
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#prerequisite">
-     13.3.1.2. Prerequisite
+     12.3.1.2. Prerequisite
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#preparation">
-     13.3.1.3. Preparation
+     12.3.1.3. Preparation
     </a>
    </li>
   </ul>
  </li>
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   <a class="reference internal nav-link" href="#a-linear-regression-line">
-   13.3.2. A linear regression line
+   12.3.2. A linear regression line
   </a>
  </li>
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-   13.3.3. Correlation
+   12.3.3. Correlation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
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-   13.3.4. Looking for Correlation
+   12.3.4. Looking for Correlation
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#simple-linear-regression">
-   13.3.5. Simple Linear Regression
+   12.3.5. Simple Linear Regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#polynomial-regression">
-   13.3.6. Polynomial Regression
+   12.3.6. Polynomial Regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
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-   13.3.7. Categorical Features
+   12.3.7. Categorical Features
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#putting-it-all-together">
-   13.3.8. Putting it all together
+   12.3.8. Putting it all together
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   13.3.9. Self study
+   12.3.9. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   13.3.10. Your turn! 🚀
+   12.3.10. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   13.3.11. Acknowledgments
+   12.3.11. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1760,11 +1760,11 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="linear-and-polynomial-regression">
-<h1><span class="section-number">13.3. </span>Linear and polynomial regression<a class="headerlink" href="#linear-and-polynomial-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">12.3. </span>Linear and polynomial regression<a class="headerlink" href="#linear-and-polynomial-regression" title="Permalink to this headline">#</a></h1>
 <figure class="align-default" id="linear-vs-polynomial-regression-infographic">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/linear-polynomial.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/linear-polynomial.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/linear-polynomial.png" style="width: 100%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 13.5 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/dasani_decoded">Dasani Madipalli</a></span><a class="headerlink" href="#linear-vs-polynomial-regression-infographic" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.5 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/dasani_decoded">Dasani Madipalli</a></span><a class="headerlink" href="#linear-vs-polynomial-regression-infographic" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <div class="admonition seealso">
@@ -1772,7 +1772,7 @@ <h1><span class="section-number">13.3. </span>Linear and polynomial regression<a
 <p>Run the notebook accompanying this lesson and look at the Month to Price scatterplot. Does the data associating Month to Price for pumpkin sales seem to have high or low correlation, according to our visual interpretation of the scatterplot? Does that change if we use more fine-grained measure instead of <code class="docutils literal notranslate"><span class="pre">Month</span></code>, eg. <em>day of the year</em> (i.e. number of days since the beginning of the year)?</p>
 </div>
 <section id="build-a-regression-model-using-scikit-learn-regression-four-ways">
-<h2><span class="section-number">13.3.1. </span>Build a regression model using Scikit-learn: regression four ways<a class="headerlink" href="#build-a-regression-model-using-scikit-learn-regression-four-ways" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.3.1. </span>Build a regression model using Scikit-learn: regression four ways<a class="headerlink" href="#build-a-regression-model-using-scikit-learn-regression-four-ways" title="Permalink to this headline">#</a></h2>
 <div class="cell tag_hide-input docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
@@ -1863,7 +1863,7 @@ <h2><span class="section-number">13.3.1. </span>Build a regression model using S
 </div></div>
 </div>
 <section id="introduction">
-<h3><span class="section-number">13.3.1.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.3.1.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h3>
 <p>So far we have explored what regression is with sample data gathered from the pumpkin pricing dataset that we will use throughout this lesson. We have also visualized it using Matplotlib.</p>
 <p>Now we are ready to dive deeper into regression for Machine Learning. While visualization allows we to make sense of data, the real power of Machine Learning comes from <em>training models</em>. Models are trained on historical data to automatically capture data dependencies, and they allow we to predict outcomes for new data, which the model has not seen before.</p>
 <p>In this lesson, we will learn more about two types of regression: <em>basic linear regression</em> and <em>polynomial regression</em>, along with some of the math underlying these techniques. Those models will allow us to predict pumpkin prices depending on different input data.</p>
@@ -1873,11 +1873,11 @@ <h3><span class="section-number">13.3.1.1. </span>Introduction<a class="headerli
 </div>
 </section>
 <section id="prerequisite">
-<h3><span class="section-number">13.3.1.2. </span>Prerequisite<a class="headerlink" href="#prerequisite" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.3.1.2. </span>Prerequisite<a class="headerlink" href="#prerequisite" title="Permalink to this headline">#</a></h3>
 <p>We should be familiar by now with the structure of the pumpkin data that we are examining. We can find it preloaded and pre-cleaned in this section’s <a class="reference internal" href="../../assignments/ml-fundamentals/linear-and-polynomial-regression.html"><span class="doc std std-doc">linear and polynomial regression.ipynb</span></a> file. In the file, the pumpkin price is displayed per bushel in a new data frame.  Make sure we can run these notebooks in kernels in Visual Studio Code.</p>
 </section>
 <section id="preparation">
-<h3><span class="section-number">13.3.1.3. </span>Preparation<a class="headerlink" href="#preparation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.3.1.3. </span>Preparation<a class="headerlink" href="#preparation" title="Permalink to this headline">#</a></h3>
 <p>As a reminder, we are loading this data so as to ask questions about it.</p>
 <ul class="simple">
 <li><p>When is the best time to buy pumpkins?</p></li>
@@ -1890,7 +1890,7 @@ <h3><span class="section-number">13.3.1.3. </span>Preparation<a class="headerlin
 </section>
 </section>
 <section id="a-linear-regression-line">
-<h2><span class="section-number">13.3.2. </span>A linear regression line<a class="headerlink" href="#a-linear-regression-line" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.3.2. </span>A linear regression line<a class="headerlink" href="#a-linear-regression-line" title="Permalink to this headline">#</a></h2>
 <p>As we learned in section 1, the goal of a linear regression exercise is to be able to plot a line to:</p>
 <ul class="simple">
 <li><p><strong>Show variable relationships</strong>. Show the relationship between variables</p></li>
@@ -1912,7 +1912,7 @@ <h2><span class="section-number">13.3.2. </span>A linear regression line<a class
 <div><figure class="align-default" id="calculate-the-slope">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/slope.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/slope.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/slope.png" style="width: 60%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 13.6 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/jenlooper">Jen Looper</a></span><a class="headerlink" href="#calculate-the-slope" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.6 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/jenlooper">Jen Looper</a></span><a class="headerlink" href="#calculate-the-slope" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </div></blockquote>
@@ -1922,7 +1922,7 @@ <h2><span class="section-number">13.3.2. </span>A linear regression line<a class
 <div><figure class="align-default" id="complete-the-equation">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/calculation.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/calculation.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/calculation.png" style="width: 60%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 13.7 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/jenlooper">Jen Looper</a></span><a class="headerlink" href="#complete-the-equation" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.7 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/jenlooper">Jen Looper</a></span><a class="headerlink" href="#complete-the-equation" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </div></blockquote>
@@ -1932,7 +1932,7 @@ <h2><span class="section-number">13.3.2. </span>A linear regression line<a class
 </div>
 </section>
 <section id="correlation">
-<h2><span class="section-number">13.3.3. </span>Correlation<a class="headerlink" href="#correlation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.3.3. </span>Correlation<a class="headerlink" href="#correlation" title="Permalink to this headline">#</a></h2>
 <p>One more term to understand is the <strong>Correlation Coefficient</strong> between given X and Y variables. Using a scatterplot, we can quickly visualize this coefficient. A plot with data points scattered in a neat line has a high correlation, but a plot with data points scattered everywhere between X and Y has a low correlation.</p>
 <p>A good linear regression model will be one that has a high (nearer to 1 than 0) Correlation Coefficient using the Least-Squares Regression method with a line of regression.</p>
 <div class="admonition seealso">
@@ -2262,7 +2262,7 @@ <h2><span class="section-number">13.3.3. </span>Correlation<a class="headerlink"
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.collections.PathCollection at 0x7f1ce8915be0&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.collections.PathCollection at 0x7f740b095b80&gt;
 </pre></div>
 </div>
 <img alt="../../_images/linear-and-polynomial-regression_12_1.png" src="../../_images/linear-and-polynomial-regression_12_1.png" />
@@ -2284,19 +2284,19 @@ <h2><span class="section-number">13.3.3. </span>Correlation<a class="headerlink"
 <p>Now that we have an understanding of the math behind linear regression, let’s create a Regression model to see if we can predict which package of pumpkins will have the best pumpkin prices. Someone buying pumpkins for a holiday pumpkin patch might want this information to be able to optimize their purchases of pumpkin packages for the patch.</p>
 </section>
 <section id="looking-for-correlation">
-<h2><span class="section-number">13.3.4. </span>Looking for Correlation<a class="headerlink" href="#looking-for-correlation" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.3.4. </span>Looking for Correlation<a class="headerlink" href="#looking-for-correlation" title="Permalink to this headline">#</a></h2>
 <p>From the previous section, we have probably seen that the average price for different months looks like this:</p>
 <figure class="align-default" id="average-price-by-month">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/barchart.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/barchart.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/barchart.png" style="width: 70%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 13.8 </span><span class="caption-text">Average price by month<span id="id1">[<a class="reference internal" href="#id65" title="Jen Looper. Average price by month. URL: https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/2-Data/images/barchart.png (visited on 2022).">Looa</a>]</span></span><a class="headerlink" href="#average-price-by-month" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.8 </span><span class="caption-text">Average price by month<span id="id1">[<a class="reference internal" href="#id65" title="Jen Looper. Average price by month. URL: https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/2-Data/images/barchart.png (visited on 2022).">Looa</a>]</span></span><a class="headerlink" href="#average-price-by-month" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>This suggests that there should be some correlation, and we can try training a linear regression model to predict the relationship between <code class="docutils literal notranslate"><span class="pre">Month</span></code> and <code class="docutils literal notranslate"><span class="pre">Price</span></code>, or between <code class="docutils literal notranslate"><span class="pre">DayOfYear</span></code> and <code class="docutils literal notranslate"><span class="pre">Price</span></code>. Here is the scatter plot that shows the latter relationship:</p>
 <figure class="align-default" id="scatter-plot-of-price-vs-day-of-year">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/scatter-dayofyear.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/scatter-dayofyear.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/scatter-dayofyear.png" style="width: 70%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 13.9 </span><span class="caption-text">Scatter plot of Price vs. Day of Year<span id="id2">[<a class="reference internal" href="#id66" title="Jen Looper. Scatter plot of price vs. day of year. URL: https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/images/scatter-dayofyear.png (visited on 2022).">Loob</a>]</span></span><a class="headerlink" href="#scatter-plot-of-price-vs-day-of-year" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.9 </span><span class="caption-text">Scatter plot of Price vs. Day of Year<span id="id2">[<a class="reference internal" href="#id66" title="Jen Looper. Scatter plot of price vs. day of year. URL: https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/images/scatter-dayofyear.png (visited on 2022).">Loob</a>]</span></span><a class="headerlink" href="#scatter-plot-of-price-vs-day-of-year" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>It looks like there are different clusters of prices corresponding to different pumpkin varieties. To confirm this hypothesis, let’s plot each pumpkin category using a different color. By passing an <code class="docutils literal notranslate"><span class="pre">ax</span></code> parameter to the <code class="docutils literal notranslate"><span class="pre">scatter</span></code> plotting function we can plot all points on the same graph:</p>
@@ -2364,7 +2364,7 @@ <h2><span class="section-number">13.3.4. </span>Looking for Correlation<a class=
 <p>Another approach would be to fill those empty values with mean values from the corresponding column.</p>
 </section>
 <section id="simple-linear-regression">
-<h2><span class="section-number">13.3.5. </span>Simple Linear Regression<a class="headerlink" href="#simple-linear-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.3.5. </span>Simple Linear Regression<a class="headerlink" href="#simple-linear-regression" title="Permalink to this headline">#</a></h2>
 <p>To train our Linear Regression model, we will use the <strong>Scikit-learn</strong> library.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2448,7 +2448,7 @@ <h2><span class="section-number">13.3.5. </span>Simple Linear Regression<a class
 </div>
 </div>
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-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x7f1cd7c27e50&gt;]
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x7f73fa378c70&gt;]
 </pre></div>
 </div>
 <img alt="../../_images/linear-and-polynomial-regression_34_1.png" src="../../_images/linear-and-polynomial-regression_34_1.png" />
@@ -2456,7 +2456,7 @@ <h2><span class="section-number">13.3.5. </span>Simple Linear Regression<a class
 </div>
 </section>
 <section id="polynomial-regression">
-<h2><span class="section-number">13.3.6. </span>Polynomial Regression<a class="headerlink" href="#polynomial-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.3.6. </span>Polynomial Regression<a class="headerlink" href="#polynomial-regression" title="Permalink to this headline">#</a></h2>
 <p style="text-align: center;">
 <iframe src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/html/polynomial-regression-demo/simulation.html" width="105%" height="900px;"
 style="border:none;" scrolling="auto"></iframe>
@@ -2494,7 +2494,7 @@ <h2><span class="section-number">13.3.6. </span>Polynomial Regression<a class="h
 <figure class="align-default" id="id4">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/poly-results.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/poly-results.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/poly-results.png" style="width: 70%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 13.10 </span><span class="caption-text">Polynomial regression<span id="id3">[<a class="reference internal" href="#id67" title="Dmitri Soshnikov. Polynomial regression. URL: https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/images/poly-results.png (visited on 2022).">Sosb</a>]</span></span><a class="headerlink" href="#id4" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.10 </span><span class="caption-text">Polynomial regression<span id="id3">[<a class="reference internal" href="#id67" title="Dmitri Soshnikov. Polynomial regression. URL: https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/images/poly-results.png (visited on 2022).">Sosb</a>]</span></span><a class="headerlink" href="#id4" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>Using Polynomial Regression, we can get slightly lower MSE and higher determination, but not significantly. We need to take into account other features!</p>
@@ -2505,13 +2505,13 @@ <h2><span class="section-number">13.3.6. </span>Polynomial Regression<a class="h
 <p>🎃 Congratulations, we just created a model that can help predict the price of pie pumpkins. We can probably repeat the same procedure for all pumpkin types, but that would be tedious. Let’s learn now how to take pumpkin variety into account in our model!</p>
 </section>
 <section id="categorical-features">
-<h2><span class="section-number">13.3.7. </span>Categorical Features<a class="headerlink" href="#categorical-features" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.3.7. </span>Categorical Features<a class="headerlink" href="#categorical-features" title="Permalink to this headline">#</a></h2>
 <p>In the ideal world, we want to be able to predict prices for different pumpkin varieties using the same model. However, the <code class="docutils literal notranslate"><span class="pre">Variety</span></code> column is somewhat different from columns like <code class="docutils literal notranslate"><span class="pre">Month</span></code>, because it contains non-numeric values. Such columns are called <strong>categorical</strong>.</p>
 <p>Here we can see how the average price depends on variety:</p>
 <figure class="align-default" id="average-price-by-variety">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/price-by-variety.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/price-by-variety.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/price-by-variety.png" style="width: 70%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 13.11 </span><span class="caption-text">Average price by variety<span id="id5">[<a class="reference internal" href="#id68" title="Dmitri Soshnikov. Average price by variety. URL: https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/images/price-by-variety.png (visited on 2022).">Sosa</a>]</span></span><a class="headerlink" href="#average-price-by-variety" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.11 </span><span class="caption-text">Average price by variety<span id="id5">[<a class="reference internal" href="#id68" title="Dmitri Soshnikov. Average price by variety. URL: https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/images/price-by-variety.png (visited on 2022).">Sosa</a>]</span></span><a class="headerlink" href="#average-price-by-variety" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>To take variety into account, we first need to convert it to numeric form or <strong>encode</strong>** it. There are several ways we can do it:</p>
@@ -2660,7 +2660,7 @@ <h2><span class="section-number">13.3.7. </span>Categorical Features<a class="he
 <p>Here we also take into account <code class="docutils literal notranslate"><span class="pre">City</span></code> and <code class="docutils literal notranslate"><span class="pre">Package</span></code> type, which gives us MSE 2.84 (10%), and determination 0.94!</p>
 </section>
 <section id="putting-it-all-together">
-<h2><span class="section-number">13.3.8. </span>Putting it all together<a class="headerlink" href="#putting-it-all-together" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.3.8. </span>Putting it all together<a class="headerlink" href="#putting-it-all-together" title="Permalink to this headline">#</a></h2>
 <p>To make the best model, we can use combined (one-hot encoded categorical + numeric) data from the above example together with Polynomial Regression. Here is the complete code for our convenience:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2694,7 +2694,12 @@ <h2><span class="section-number">13.3.8. </span>Putting it all together<a class=
 </div>
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Mean error: 2.23 (8.28%)
-Model determination:  0.9652659492293556
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Model determination:  0.9652659492293556
 </pre></div>
 </div>
 </div>
@@ -2733,16 +2738,16 @@ <h2><span class="section-number">13.3.8. </span>Putting it all together<a class=
 <p>🏆 Well done! We created four Regression models in one section and improved the model quality to 97%. In the final section on Regression, we will learn about Logistic Regression to determine categories.</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">13.3.9. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.3.9. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>In this section, we learned about Linear Regression. There are other important types of Regression. Read about Stepwise, Ridge, Lasso and Elasticnet techniques. A good course to study to learn more is the <a class="reference external" href="https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning">Stanford Statistical Learning course</a></p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">13.3.10. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.3.10. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Test several different variables in this notebook to see how correlation corresponds to model accuracy.</p>
 <p>Assignment - <a class="reference internal" href="../../assignments/ml-fundamentals/create-a-regression-model.html"><span class="doc std std-doc">Create a regression model</span></a></p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">13.3.11. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.3.11. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 <hr class="docutils" />
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         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">13.2. </span>Managing data</p>
+            <p class="prev-next-title"><span class="section-number">12.2. </span>Managing data</p>
         </div>
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-        <p class="prev-next-title"><span class="section-number">13.4. </span>Logistic regression</p>
+        <p class="prev-next-title"><span class="section-number">12.4. </span>Logistic regression</p>
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diff --git a/ml-fundamentals/regression/logistic-regression.html b/ml-fundamentals/regression/logistic-regression.html
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     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>13.4. Logistic regression &#8212; Ocademy Open Machine Learning Book</title>
+    <title>12.4. Logistic regression &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="14. Build a web app to use a Machine Learning model" href="../build-a-web-app-to-use-a-machine-learning-model.html" />
-    <link rel="prev" title="13.3. Linear and polynomial regression" href="linear-and-polynomial-regression.html" />
+    <link rel="next" title="13. Build a web app to use a Machine Learning model" href="../build-a-web-app-to-use-a-machine-learning-model.html" />
+    <link rel="prev" title="12.3. Linear and polynomial regression" href="linear-and-polynomial-regression.html" />
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-     5.2. Data Science ethics
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.45. Linear regression - California Housing
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+   12.4.7. Better comprehension via a confusion matrix
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualize-the-roc-curve-of-this-model">
-   13.4.8. Visualize the ROC curve of this model
+   12.4.8. Visualize the ROC curve of this model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   13.4.9. Your turn! 🚀
+   12.4.9. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   13.4.10. Self study
+   12.4.10. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   13.4.11. Acknowledgments
+   12.4.11. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1695,101 +1695,101 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   13.4.1. Introduction
+   12.4.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#prerequisite">
-   13.4.2. Prerequisite
+   12.4.2. Prerequisite
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#define-the-question">
-   13.4.3. Define the question
+   12.4.3. Define the question
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#about-logistic-regression">
-   13.4.4. About logistic regression
+   12.4.4. About logistic regression
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#binary-classification">
-     13.4.4.1. Binary classification
+     12.4.4.1. Binary classification
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#other-classifications">
-     13.4.4.2. Other classifications
+     12.4.4.2. Other classifications
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#its-still-linear">
-     13.4.4.3. It’s still linear
+     12.4.4.3. It’s still linear
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#variables-do-not-have-to-correlate">
-     13.4.4.4. Variables DO NOT have to correlate
+     12.4.4.4. Variables DO NOT have to correlate
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#you-need-a-lot-of-clean-data">
-     13.4.4.5. You need a lot of clean data
+     12.4.4.5. You need a lot of clean data
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-tidy-the-data">
-   13.4.5. Exercise - tidy the data
+   12.4.5. Exercise - tidy the data
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#visualization-side-by-side-grid">
-     13.4.5.1. Visualization - side-by-side grid
+     12.4.5.1. Visualization - side-by-side grid
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#use-a-swarm-plot">
-     13.4.5.2. Use a swarm plot
+     12.4.5.2. Use a swarm plot
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#violin-plot">
-     13.4.5.3. Violin plot
+     12.4.5.3. Violin plot
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#build-your-model">
-   13.4.6. Build your model
+   12.4.6. Build your model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#better-comprehension-via-a-confusion-matrix">
-   13.4.7. Better comprehension via a confusion matrix
+   12.4.7. Better comprehension via a confusion matrix
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualize-the-roc-curve-of-this-model">
-   13.4.8. Visualize the ROC curve of this model
+   12.4.8. Visualize the ROC curve of this model
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   13.4.9. Your turn! 🚀
+   12.4.9. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   13.4.10. Self study
+   12.4.10. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   13.4.11. Acknowledgments
+   12.4.11. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1814,11 +1814,11 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="logistic-regression">
-<h1><span class="section-number">13.4. </span>Logistic regression<a class="headerlink" href="#logistic-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">12.4. </span>Logistic regression<a class="headerlink" href="#logistic-regression" title="Permalink to this headline">#</a></h1>
 <figure class="align-default" id="logistic-vs-linear-regression-infographic">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/logistic-linear.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/logistic-linear.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/logistic-linear.png" style="width: 100%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 13.12 </span><span class="caption-text">Logistic regression to predict categories. Infographic by <a class="reference external" href="https://twitter.com/dasani_decoded">Dasani Madipalli</a></span><a class="headerlink" href="#logistic-vs-linear-regression-infographic" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.12 </span><span class="caption-text">Logistic regression to predict categories. Infographic by <a class="reference external" href="https://twitter.com/dasani_decoded">Dasani Madipalli</a></span><a class="headerlink" href="#logistic-vs-linear-regression-infographic" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p style="text-align: center;">
@@ -1826,7 +1826,7 @@ <h1><span class="section-number">13.4. </span>Logistic regression<a class="heade
 style="border:none;" scrolling="auto"></iframe>
 A demo of logistic-regression. <a href="https://mlu-explain.github.io/logistic-regression/"> [source]</a >
 </p ><section id="introduction">
-<h2><span class="section-number">13.4.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.4.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>In this final section on Regression, one of the basic <em>classic</em> Machine Learning techniques, we will take a look at Logistic Regression. You would use this technique to discover patterns to predict binary categories. Is this candy chocolate or not? Is this disease contagious or not? Will this customer choose this product or not?</p>
 <p>In this section, you will learn:</p>
 <ul class="simple">
@@ -1839,7 +1839,7 @@ <h2><span class="section-number">13.4.1. </span>Introduction<a class="headerlink
 </div>
 </section>
 <section id="prerequisite">
-<h2><span class="section-number">13.4.2. </span>Prerequisite<a class="headerlink" href="#prerequisite" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.4.2. </span>Prerequisite<a class="headerlink" href="#prerequisite" title="Permalink to this headline">#</a></h2>
 <p>Having worked with the pumpkin data, we are now familiar enough with it to realize that there’s one binary category that we can work with: <code class="docutils literal notranslate"><span class="pre">Color</span></code>.</p>
 <p>Let’s build a logistic regression model to predict that, given some variables, <em>what color a given pumpkin is likely to be</em> (orange 🎃 or white 👻).</p>
 <div class="admonition note">
@@ -1848,7 +1848,7 @@ <h2><span class="section-number">13.4.2. </span>Prerequisite<a class="headerlink
 </div>
 </section>
 <section id="define-the-question">
-<h2><span class="section-number">13.4.3. </span>Define the question<a class="headerlink" href="#define-the-question" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.4.3. </span>Define the question<a class="headerlink" href="#define-the-question" title="Permalink to this headline">#</a></h2>
 <p>For our purposes, we will express this as a binary: ‘Orange’ or ‘Not Orange’. There is also a ‘striped’ category in our dataset but there are few instances of it, so we will not use it. It disappears once we remove null values from the dataset, anyway.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -1856,20 +1856,20 @@ <h2><span class="section-number">13.4.3. </span>Define the question<a class="hea
 </div>
 </section>
 <section id="about-logistic-regression">
-<h2><span class="section-number">13.4.4. </span>About logistic regression<a class="headerlink" href="#about-logistic-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.4.4. </span>About logistic regression<a class="headerlink" href="#about-logistic-regression" title="Permalink to this headline">#</a></h2>
 <p>Logistic regression differs from linear regression, which you learned about previously, in a few important ways.</p>
 <section id="binary-classification">
-<h3><span class="section-number">13.4.4.1. </span>Binary classification<a class="headerlink" href="#binary-classification" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.4.4.1. </span>Binary classification<a class="headerlink" href="#binary-classification" title="Permalink to this headline">#</a></h3>
 <p>Logistic regression does not offer the same features as linear regression. The former offers a prediction about a binary category (“orange or not orange”) whereas the latter is capable of predicting continual values, for example given the origin of a pumpkin and the time of harvest, <em>how much its price will rise</em>.</p>
 <figure class="align-default" id="pumpkin-classification-model">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/pumpkin-classifier.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/pumpkin-classifier.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/pumpkin-classifier.png" style="width: 100%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 13.13 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/dasani_decoded">Dasani Madipalli</a></span><a class="headerlink" href="#pumpkin-classification-model" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.13 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/dasani_decoded">Dasani Madipalli</a></span><a class="headerlink" href="#pumpkin-classification-model" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="other-classifications">
-<h3><span class="section-number">13.4.4.2. </span>Other classifications<a class="headerlink" href="#other-classifications" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.4.4.2. </span>Other classifications<a class="headerlink" href="#other-classifications" title="Permalink to this headline">#</a></h3>
 <p>There are other types of logistic regression, including multinomial and ordinal:</p>
 <ul class="simple">
 <li><p><strong>Multinomial</strong>, which involves having more than one category - “Orange, White, and Striped”.</p></li>
@@ -1878,20 +1878,20 @@ <h3><span class="section-number">13.4.4.2. </span>Other classifications<a class=
 <figure class="align-default" id="multinomial-vs-ordinal-regression">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/multinomial-ordinal.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/multinomial-ordinal.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/multinomial-ordinal.png" style="width: 100%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 13.14 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/dasani_decoded">Dasani Madipalli</a></span><a class="headerlink" href="#multinomial-vs-ordinal-regression" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.14 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/dasani_decoded">Dasani Madipalli</a></span><a class="headerlink" href="#multinomial-vs-ordinal-regression" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="its-still-linear">
-<h3><span class="section-number">13.4.4.3. </span>It’s still linear<a class="headerlink" href="#its-still-linear" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.4.4.3. </span>It’s still linear<a class="headerlink" href="#its-still-linear" title="Permalink to this headline">#</a></h3>
 <p>Even though this type of Regression is all about ‘category predictions’, it still works best when there is a clear linear relationship between the dependent variable (color) and the other independent variables (the rest of the dataset, like city name and size). It’s good to get an idea of whether there is any linearity dividing these variables or not.</p>
 </section>
 <section id="variables-do-not-have-to-correlate">
-<h3><span class="section-number">13.4.4.4. </span>Variables DO NOT have to correlate<a class="headerlink" href="#variables-do-not-have-to-correlate" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.4.4.4. </span>Variables DO NOT have to correlate<a class="headerlink" href="#variables-do-not-have-to-correlate" title="Permalink to this headline">#</a></h3>
 <p>Remember how linear regression worked better with more correlated variables? Logistic regression is the opposite - the variables don’t have to align. That works for this data which has somewhat weak correlations.</p>
 </section>
 <section id="you-need-a-lot-of-clean-data">
-<h3><span class="section-number">13.4.4.5. </span>You need a lot of clean data<a class="headerlink" href="#you-need-a-lot-of-clean-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.4.4.5. </span>You need a lot of clean data<a class="headerlink" href="#you-need-a-lot-of-clean-data" title="Permalink to this headline">#</a></h3>
 <p>Logistic regression will give more accurate results if you use more data; our small dataset is not optimal for this task, so keep that in mind.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1900,7 +1900,7 @@ <h3><span class="section-number">13.4.4.5. </span>You need a lot of clean data<a
 </section>
 </section>
 <section id="exercise-tidy-the-data">
-<h2><span class="section-number">13.4.5. </span>Exercise - tidy the data<a class="headerlink" href="#exercise-tidy-the-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.4.5. </span>Exercise - tidy the data<a class="headerlink" href="#exercise-tidy-the-data" title="Permalink to this headline">#</a></h2>
 <p>First, clean the data a bit, dropping null values and selecting only some of the columns:</p>
 <ol class="simple">
 <li><p>Add the following code:</p></li>
@@ -2125,7 +2125,7 @@ <h2><span class="section-number">13.4.5. </span>Exercise - tidy the data<a class
 </div>
 </div>
 <section id="visualization-side-by-side-grid">
-<h3><span class="section-number">13.4.5.1. </span>Visualization - side-by-side grid<a class="headerlink" href="#visualization-side-by-side-grid" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.4.5.1. </span>Visualization - side-by-side grid<a class="headerlink" href="#visualization-side-by-side-grid" title="Permalink to this headline">#</a></h3>
 <p>By now you have loaded up the <a class="reference internal" href="../../assignments/ml-fundamentals/pumpkin-varieties-and-color.html"><span class="doc std std-doc">starter notebook</span></a> with pumpkin data once again and cleaned it so as to preserve a dataset containing a few variables, including <code class="docutils literal notranslate"><span class="pre">Color</span></code>. Let’s visualize the dataframe in the notebook using a different library: <a class="reference external" href="https://seaborn.pydata.org/index.html">Seaborn</a>, which is built on Matplotlib which we used earlier.</p>
 <p>Seaborn offers some neat ways to visualize your data. For example, you can compare distributions of the data for each point in a side-by-side grid.</p>
 <ol class="simple">
@@ -2141,7 +2141,7 @@ <h3><span class="section-number">13.4.5.1. </span>Visualization - side-by-side g
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;seaborn.axisgrid.PairGrid at 0x7fa7947935e0&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;seaborn.axisgrid.PairGrid at 0x7f93dee0aac0&gt;
 </pre></div>
 </div>
 <img alt="../../_images/logistic-regression_19_1.png" src="../../_images/logistic-regression_19_1.png" />
@@ -2154,7 +2154,7 @@ <h3><span class="section-number">13.4.5.1. </span>Visualization - side-by-side g
 </div>
 </section>
 <section id="use-a-swarm-plot">
-<h3><span class="section-number">13.4.5.2. </span>Use a swarm plot<a class="headerlink" href="#use-a-swarm-plot" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.4.5.2. </span>Use a swarm plot<a class="headerlink" href="#use-a-swarm-plot" title="Permalink to this headline">#</a></h3>
 <p>Since Color is a binary category (Orange or Not), it’s called ‘categorical data’ and needs ‘a more <a class="reference external" href="https://seaborn.pydata.org/tutorial/categorical.html?highlight=bar">specialized approach</a> to visualization’. There are other ways to visualize the relationship of this category with other variables.</p>
 <p>You can visualize variables side-by-side with Seaborn plots.</p>
 <ol class="simple">
@@ -2187,7 +2187,7 @@ <h3><span class="section-number">13.4.5.2. </span>Use a swarm plot<a class="head
 </div>
 </section>
 <section id="violin-plot">
-<h3><span class="section-number">13.4.5.3. </span>Violin plot<a class="headerlink" href="#violin-plot" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.4.5.3. </span>Violin plot<a class="headerlink" href="#violin-plot" title="Permalink to this headline">#</a></h3>
 <p>A ‘violin’ type plot is useful as you can easily visualize the way that data in the two categories is distributed. Violin plots don’t work so well with smaller datasets as the distribution is displayed more ‘smoothly’.</p>
 <ol class="simple">
 <li><p>As parameters <code class="docutils literal notranslate"><span class="pre">x=Color</span></code>, <code class="docutils literal notranslate"><span class="pre">kind=&quot;violin&quot;</span></code> and call <code class="docutils literal notranslate"><span class="pre">catplot()</span></code>:</p></li>
@@ -2200,7 +2200,7 @@ <h3><span class="section-number">13.4.5.3. </span>Violin plot<a class="headerlin
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;seaborn.axisgrid.FacetGrid at 0x7fa7947503d0&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;seaborn.axisgrid.FacetGrid at 0x7f93cbc52d30&gt;
 </pre></div>
 </div>
 <img alt="../../_images/logistic-regression_24_1.png" src="../../_images/logistic-regression_24_1.png" />
@@ -2218,7 +2218,7 @@ <h3><span class="section-number">13.4.5.3. </span>Violin plot<a class="headerlin
 <figure class="align-default" id="logistic-function">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/sigmoid.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/sigmoid.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/sigmoid.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 13.15 </span><span class="caption-text">Logistic function<span id="id1">[<a class="reference internal" href="#id65" title="Jen Looper. Logistic function. URL: https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/4-Logistic/images/sigmoid.png (visited on 2022).">Loo</a>]</span></span><a class="headerlink" href="#logistic-function" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.15 </span><span class="caption-text">Logistic function<span id="id1">[<a class="reference internal" href="#id65" title="Jen Looper. Logistic function. URL: https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/4-Logistic/images/sigmoid.png (visited on 2022).">Loo</a>]</span></span><a class="headerlink" href="#logistic-function" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </div>
@@ -2229,7 +2229,7 @@ <h3><span class="section-number">13.4.5.3. </span>Violin plot<a class="headerlin
 </section>
 </section>
 <section id="build-your-model">
-<h2><span class="section-number">13.4.6. </span>Build your model<a class="headerlink" href="#build-your-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.4.6. </span>Build your model<a class="headerlink" href="#build-your-model" title="Permalink to this headline">#</a></h2>
 <p>Building a model to find these binary classification is surprisingly straightforward in Scikit-learn.</p>
 <p>1 . Select the variables you want to use in your classification model and split the training and test sets calling <code class="docutils literal notranslate"><span class="pre">train_test_split()</span></code>:</p>
 <div class="cell docutils container">
@@ -2290,7 +2290,7 @@ <h2><span class="section-number">13.4.6. </span>Build your model<a class="header
 </div>
 </section>
 <section id="better-comprehension-via-a-confusion-matrix">
-<h2><span class="section-number">13.4.7. </span>Better comprehension via a confusion matrix<a class="headerlink" href="#better-comprehension-via-a-confusion-matrix" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.4.7. </span>Better comprehension via a confusion matrix<a class="headerlink" href="#better-comprehension-via-a-confusion-matrix" title="Permalink to this headline">#</a></h2>
 <p>While you can get a scoreboard report <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html?highlight=classification_report#sklearn.metrics.classification_report">terms</a> by printing out the items above, you might be able to understand your model more easily by using a <a class="reference external" href="https://scikit-learn.org/stable/modules/model_evaluation.html#confusion-matrix">confusion matrix</a> to help us understand how the model is performing.</p>
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 </div>
 </section>
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-<h2><span class="section-number">13.4.8. </span>Visualize the ROC curve of this model<a class="headerlink" href="#visualize-the-roc-curve-of-this-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.4.8. </span>Visualize the ROC curve of this model<a class="headerlink" href="#visualize-the-roc-curve-of-this-model" title="Permalink to this headline">#</a></h2>
 <p>This is not a bad model; its accuracy is in the 80% range so ideally you could use it to predict the color of a pumpkin given a set of variables.</p>
 <p>Let’s do one more visualization to see the so-called ‘ROC’ score:</p>
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@@ -2391,16 +2391,16 @@ <h2><span class="section-number">13.4.8. </span>Visualize the ROC curve of this
 <p>In future lessons on classifications, you will learn how to iterate to improve your model’s scores. But for now, congratulations! You’ve completed these regression sections!</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">13.4.9. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.4.9. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>There’s a lot more to unpack regarding logistic regression! But the best way to learn is to experiment. Find a dataset that lends itself to this type of analysis and build a model with it. What do you learn? tip: try <a class="reference external" href="https://www.kaggle.com/search?q=logistic+regression+datasets">Kaggle</a> for interesting datasets.</p>
 <p>Assignment - <a class="reference internal" href="../../assignments/ml-fundamentals/retrying-some-regression.html"><span class="doc std std-doc">Retrying some regression</span></a></p>
 </section>
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-<h2><span class="section-number">13.4.10. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.4.10. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>Read the first few pages of <a class="reference external" href="https://web.stanford.edu/~jurafsky/slp3/5.pdf">this paper from Stanford</a> on some practical uses for logistic regression. Think about tasks that are better suited for one or the other type of regression tasks that we have studied up to this point. What would work best?</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">13.4.11. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.4.11. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 <hr class="docutils" />
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-     42.20. Convolutional Neural Network
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+   12.2.1. Build a regression model using Scikit-learn: prepare and visualize data
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+   12.2.2. Introduction
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+   12.2.3. Asking the right question of your data
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+   12.2.4. Case study: ‘the pumpkin market’
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+     12.2.4.1. Preparing data
     </a>
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-     13.2.4.2. The pumpkin data - early conclusions
+     12.2.4.2. The pumpkin data - early conclusions
     </a>
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+   12.2.5. Exercise - analyze the pumpkin data
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+     12.2.5.1. First, check for missing dates
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+     12.2.5.2. Second, determine average price of pumpkin
     </a>
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+     12.2.5.3. But wait! There’s something odd here
     </a>
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+     12.2.5.4. But wait! There’s one more thing to do
     </a>
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+   12.2.6. Visualization strategies
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+   12.2.7. Exercise - experiment with Matplotlib
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+     12.2.7.1. Make it useful
     </a>
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+   12.2.8. Self study
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-   13.2.9. Your turn! 🚀
+   12.2.9. Your turn! 🚀
   </a>
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 </ul>
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                             <ul class="visible nav section-nav flex-column">
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+   12.2.1. Build a regression model using Scikit-learn: prepare and visualize data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
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+   12.2.2. Introduction
   </a>
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-   13.2.3. Asking the right question of your data
+   12.2.3. Asking the right question of your data
   </a>
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   <a class="reference internal nav-link" href="#case-study-the-pumpkin-market">
-   13.2.4. Case study: ‘the pumpkin market’
+   12.2.4. Case study: ‘the pumpkin market’
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#preparing-data">
-     13.2.4.1. Preparing data
+     12.2.4.1. Preparing data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-pumpkin-data-early-conclusions">
-     13.2.4.2. The pumpkin data - early conclusions
+     12.2.4.2. The pumpkin data - early conclusions
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-analyze-the-pumpkin-data">
-   13.2.5. Exercise - analyze the pumpkin data
+   12.2.5. Exercise - analyze the pumpkin data
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#first-check-for-missing-dates">
-     13.2.5.1. First, check for missing dates
+     12.2.5.1. First, check for missing dates
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#second-determine-average-price-of-pumpkin">
-     13.2.5.2. Second, determine average price of pumpkin
+     12.2.5.2. Second, determine average price of pumpkin
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#but-wait-theres-something-odd-here">
-     13.2.5.3. But wait! There’s something odd here
+     12.2.5.3. But wait! There’s something odd here
     </a>
    </li>
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     <a class="reference internal nav-link" href="#but-wait-theres-one-more-thing-to-do">
-     13.2.5.4. But wait! There’s one more thing to do
+     12.2.5.4. But wait! There’s one more thing to do
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualization-strategies">
-   13.2.6. Visualization strategies
+   12.2.6. Visualization strategies
   </a>
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   <a class="reference internal nav-link" href="#exercise-experiment-with-matplotlib">
-   13.2.7. Exercise - experiment with Matplotlib
+   12.2.7. Exercise - experiment with Matplotlib
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#make-it-useful">
-     13.2.7.1. Make it useful
+     12.2.7.1. Make it useful
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   13.2.8. Self study
+   12.2.8. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   13.2.9. Your turn! 🚀
+   12.2.9. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   13.2.10. Acknowledgments
+   12.2.10. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1795,18 +1795,18 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="managing-data">
-<h1><span class="section-number">13.2. </span>Managing data<a class="headerlink" href="#managing-data" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">12.2. </span>Managing data<a class="headerlink" href="#managing-data" title="Permalink to this headline">#</a></h1>
 <section id="build-a-regression-model-using-scikit-learn-prepare-and-visualize-data">
-<h2><span class="section-number">13.2.1. </span>Build a regression model using Scikit-learn: prepare and visualize data<a class="headerlink" href="#build-a-regression-model-using-scikit-learn-prepare-and-visualize-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.2.1. </span>Build a regression model using Scikit-learn: prepare and visualize data<a class="headerlink" href="#build-a-regression-model-using-scikit-learn-prepare-and-visualize-data" title="Permalink to this headline">#</a></h2>
 <figure class="align-default" id="data-visualization-infographic">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/data-visualization.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/data-visualization.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/data-visualization.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 13.4 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/dasani_decoded">Dasani Madipalli</a></span><a class="headerlink" href="#data-visualization-infographic" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.4 </span><span class="caption-text">Infographic by <a class="reference external" href="https://twitter.com/dasani_decoded">Dasani Madipalli</a></span><a class="headerlink" href="#data-visualization-infographic" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="introduction">
-<h2><span class="section-number">13.2.2. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.2.2. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>Now that you are set up with the tools you need to start tackling machine learning model building with Scikit-learn, you are ready to start asking questions of your data. As you work with data and apply ML solutions, it’s very important to understand how to ask the right question to properly unlock the potentials of your dataset.</p>
 <p>In this section, you will learn:</p>
 <ul class="simple">
@@ -1837,29 +1837,29 @@ <h2><span class="section-number">13.2.2. </span>Introduction<a class="headerlink
 </div>
 </section>
 <section id="asking-the-right-question-of-your-data">
-<h2><span class="section-number">13.2.3. </span>Asking the right question of your data<a class="headerlink" href="#asking-the-right-question-of-your-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.2.3. </span>Asking the right question of your data<a class="headerlink" href="#asking-the-right-question-of-your-data" title="Permalink to this headline">#</a></h2>
 <p>The question you need answered will determine what type of Machine Learning algorithms you will leverage. And the quality of the answer you get back will be heavily dependent on the nature of your data.</p>
 <p>Take a look at the <a class="reference external" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/us-pumpkins.csv">data</a> provided for this section. You can open this .csv file in VS Code. A quick skim immediately shows that there are blanks and a mix of strings and numeric data. There’s also a strange column called ‘Package’ where the data is a mix between ‘sacks’, ‘bins’ and other values. The data, in fact, is a bit of a mess.</p>
 <p>In fact, it is not very common to be gifted a dataset that is completely ready to use to create a Machine Learning model out of the box. In this section, you will learn how to prepare a raw dataset using standard Python libraries. You will also learn various techniques to visualize the data.</p>
 </section>
 <section id="case-study-the-pumpkin-market">
-<h2><span class="section-number">13.2.4. </span>Case study: ‘the pumpkin market’<a class="headerlink" href="#case-study-the-pumpkin-market" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.2.4. </span>Case study: ‘the pumpkin market’<a class="headerlink" href="#case-study-the-pumpkin-market" title="Permalink to this headline">#</a></h2>
 <p>You will find a .csv file in the root <code class="docutils literal notranslate"><span class="pre">/assets/data/</span></code> folder called <a href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/us-pumpkins.csv">us-pumpkins.csv</a> which includes 1757 lines of data about the market for pumpkins, sorted into groupings by city. This is raw data extracted from the <a class="reference external" href="https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice">Specialty Crops Terminal Markets Standard Reports</a> distributed by the United States Department of Agriculture.</p>
 <section id="preparing-data">
-<h3><span class="section-number">13.2.4.1. </span>Preparing data<a class="headerlink" href="#preparing-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.2.4.1. </span>Preparing data<a class="headerlink" href="#preparing-data" title="Permalink to this headline">#</a></h3>
 <p>This data is in the public domain. It can be downloaded in many separate files, per city, from the USDA website. To avoid too many separate files, we have concatenated all the city data into one spreadsheet, thus we have already <em>prepared</em> the data a bit. Next, let’s take a closer look at the data.</p>
 </section>
 <section id="the-pumpkin-data-early-conclusions">
-<h3><span class="section-number">13.2.4.2. </span>The pumpkin data - early conclusions<a class="headerlink" href="#the-pumpkin-data-early-conclusions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.2.4.2. </span>The pumpkin data - early conclusions<a class="headerlink" href="#the-pumpkin-data-early-conclusions" title="Permalink to this headline">#</a></h3>
 <p>What do you notice about this data? You already saw that there is a mix of strings, numbers, blanks and strange values that you need to make sense of.</p>
 <p>What question can you ask of this data, using a Regression technique? What about “Predict the price of a pumpkin for sale during a given month”. Looking again at the data, there are some changes you need to make to create the data structure necessary for the task.</p>
 </section>
 </section>
 <section id="exercise-analyze-the-pumpkin-data">
-<h2><span class="section-number">13.2.5. </span>Exercise - analyze the pumpkin data<a class="headerlink" href="#exercise-analyze-the-pumpkin-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.2.5. </span>Exercise - analyze the pumpkin data<a class="headerlink" href="#exercise-analyze-the-pumpkin-data" title="Permalink to this headline">#</a></h2>
 <p>Let’s use <a class="reference external" href="https://pandas.pydata.org/">Pandas</a>, a very useful tool for shaping data, to analyze and prepare this pumpkin data.</p>
 <section id="first-check-for-missing-dates">
-<h3><span class="section-number">13.2.5.1. </span>First, check for missing dates<a class="headerlink" href="#first-check-for-missing-dates" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.2.5.1. </span>First, check for missing dates<a class="headerlink" href="#first-check-for-missing-dates" title="Permalink to this headline">#</a></h3>
 <p>You will first need to take steps to check for missing dates:</p>
 <ol class="simple">
 <li><p>Convert the dates to a month format (these are US dates, so the format is <code class="docutils literal notranslate"><span class="pre">MM/DD/YYYY</span></code>).</p></li>
@@ -2098,7 +2098,7 @@ <h3><span class="section-number">13.2.5.1. </span>First, check for missing dates
 </div>
 </section>
 <section id="second-determine-average-price-of-pumpkin">
-<h3><span class="section-number">13.2.5.2. </span>Second, determine average price of pumpkin<a class="headerlink" href="#second-determine-average-price-of-pumpkin" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.2.5.2. </span>Second, determine average price of pumpkin<a class="headerlink" href="#second-determine-average-price-of-pumpkin" title="Permalink to this headline">#</a></h3>
 <p>Think about how to determine the average price of a pumpkin in a given month. What columns would you pick for this task? Hint: you’ll need 3 columns.</p>
 <p>Solution: take the average of the <code class="docutils literal notranslate"><span class="pre">Low</span> <span class="pre">Price</span></code> and <code class="docutils literal notranslate"><span class="pre">High</span> <span class="pre">Price</span></code> columns to populate the new Price column, and convert the Date column to only show the month. Fortunately, according to the check above, there is no missing data for dates or prices.</p>
 <p>1 . To calculate the average, add the following code:</p>
@@ -2125,7 +2125,7 @@ <h3><span class="section-number">13.2.5.2. </span>Second, determine average pric
 <p>Printing out your dataframe will show you a clean, tidy dataset on which you can build your new regression model.</p>
 </section>
 <section id="but-wait-theres-something-odd-here">
-<h3><span class="section-number">13.2.5.3. </span>But wait! There’s something odd here<a class="headerlink" href="#but-wait-theres-something-odd-here" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.2.5.3. </span>But wait! There’s something odd here<a class="headerlink" href="#but-wait-theres-something-odd-here" title="Permalink to this headline">#</a></h3>
 <p>If you look at the <code class="docutils literal notranslate"><span class="pre">Package</span></code> column, pumpkins are sold in many different configurations. Some are sold in ‘1 1/9 bushel’ measures, and some in ‘1/2 bushel’ measures, some per pumpkin, some per pound, and some in big boxes with varying widths.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -2145,7 +2145,7 @@ <h3><span class="section-number">13.2.5.3. </span>But wait! There’s something
 <p>If you print the data now, you can see that you are only getting the 415 or so rows of data containing pumpkins by the bushel.</p>
 </section>
 <section id="but-wait-theres-one-more-thing-to-do">
-<h3><span class="section-number">13.2.5.4. </span>But wait! There’s one more thing to do<a class="headerlink" href="#but-wait-theres-one-more-thing-to-do" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.2.5.4. </span>But wait! There’s one more thing to do<a class="headerlink" href="#but-wait-theres-one-more-thing-to-do" title="Permalink to this headline">#</a></h3>
 <p>Did you notice that the bushel amount varies per row? You need to normalize the pricing so that you show the pricing per bushel, so do some math to standardize it.</p>
 <ol class="simple">
 <li><p>Add these lines after the block creating the new_pumpkins dataframe:</p></li>
@@ -2171,7 +2171,7 @@ <h3><span class="section-number">13.2.5.4. </span>But wait! There’s one more t
 </section>
 </section>
 <section id="visualization-strategies">
-<h2><span class="section-number">13.2.6. </span>Visualization strategies<a class="headerlink" href="#visualization-strategies" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.2.6. </span>Visualization strategies<a class="headerlink" href="#visualization-strategies" title="Permalink to this headline">#</a></h2>
 <p>Part of the data scientist’s role is to demonstrate the quality and nature of the data they are working with. To do this, they often create interesting visualizations, or plots, graphs, and charts, showing different aspects of data. In this way, they are able to visually show relationships and gaps that are otherwise hard to uncover.</p>
 <p>Visualizations can also help determine the machine learning technique most appropriate for the data. A scatterplot that seems to follow a line, for example, indicates that the data is a good candidate for a linear regression exercise.</p>
 <p>One data visualization library that works well in Jupyter notebooks is <a class="reference external" href="https://matplotlib.org/">Matplotlib</a> (which you also saw in the previous lesson).</p>
@@ -2181,7 +2181,7 @@ <h2><span class="section-number">13.2.6. </span>Visualization strategies<a class
 </div>
 </section>
 <section id="exercise-experiment-with-matplotlib">
-<h2><span class="section-number">13.2.7. </span>Exercise - experiment with Matplotlib<a class="headerlink" href="#exercise-experiment-with-matplotlib" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.2.7. </span>Exercise - experiment with Matplotlib<a class="headerlink" href="#exercise-experiment-with-matplotlib" title="Permalink to this headline">#</a></h2>
 <p>Try to create some basic plots to display the new dataframe you just created. What would a basic line plot show?</p>
 <p>1 . Import Matplotlib at the top of the file, under the Pandas import:</p>
 <div class="cell tag_hide-input tag_output-scoll docutils container">
@@ -2213,7 +2213,7 @@ <h2><span class="section-number">13.2.7. </span>Exercise - experiment with Matpl
 <p>Is this a useful plot? Does anything about it surprise you?</p>
 <p>It’s not particularly useful as all it does is display in your data as a spread of points in a given month.</p>
 <section id="make-it-useful">
-<h3><span class="section-number">13.2.7.1. </span>Make it useful<a class="headerlink" href="#make-it-useful" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.2.7.1. </span>Make it useful<a class="headerlink" href="#make-it-useful" title="Permalink to this headline">#</a></h3>
 <p>To get charts to display useful data, you usually need to group the data somehow. Let’s try creating a plot where the y axis shows the months and the data demonstrates the distribution of data.</p>
 <ol class="simple">
 <li><p>Add a cell to create a grouped bar chart:</p></li>
@@ -2240,16 +2240,16 @@ <h3><span class="section-number">13.2.7.1. </span>Make it useful<a class="header
 </section>
 </section>
 <section id="self-study">
-<h2><span class="section-number">13.2.8. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.2.8. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>Take a look at the many ways to visualize data. Make a list of the various libraries available and note which are best for given types of tasks, for example 2D visualizations vs. 3D visualizations. What do you discover?</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">13.2.9. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.2.9. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Explore the different types of visualization that Matplotlib offers. Which types are most appropriate for regression problems?</p>
 <p>Assignment - <a class="reference external" href="https://github.com/ocademy-ai/machine-learning/open-machine-learning-jupyter-book/assignments/ml-fundamentals/exploring-visualizations.md">Exploring visualization</a></p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">13.2.10. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.2.10. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 </section>
 </section>
@@ -2291,13 +2291,13 @@ <h2><span class="section-number">13.2.10. </span>Acknowledgments<a class="header
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-            <p class="prev-next-title"><span class="section-number">13.1. </span>Tools of the trade</p>
+            <p class="prev-next-title"><span class="section-number">12.1. </span>Tools of the trade</p>
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-        <p class="prev-next-title"><span class="section-number">13.3. </span>Linear and polynomial regression</p>
+        <p class="prev-next-title"><span class="section-number">12.3. </span>Linear and polynomial regression</p>
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+    <title>12. Regression models for Machine Learning &#8212; Ocademy Open Machine Learning Book</title>
     
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+     8.3. Data Science in the cloud: The “Azure ML SDK” way
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+   11. Summary of machine learning fundamentals
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+     14.4. Applied Machine Learning : build a web app
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-     41.6. Python programming introduction
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-     41.9. Analyzing text about Data Science
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-     41.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.16. Build your own custom vis
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-     41.17. Classifying datasets
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-     41.18. Small diabetes study
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-     41.20. Displaying airport data
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-     41.21. Soda profits
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-     41.24. Data processing in Python
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-     41.35. Tell a story
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-     41.36. Explore a planetary computer dataset
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-<h1><span class="section-number">13. </span>Regression models for Machine Learning<a class="headerlink" href="#regression-models-for-machine-learning" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">12. </span>Regression models for Machine Learning<a class="headerlink" href="#regression-models-for-machine-learning" title="Permalink to this headline">#</a></h1>
 <p>In North America, pumpkins are often carved into scary faces for Halloween. Let’s discover more about these fascinating vegetables!</p>
 <figure class="align-default" id="jack-o-lanterns">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/jack-o-lanterns.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/jack-o-lanterns.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/jack-o-lanterns.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 13.1 </span><span class="caption-text">Photo by <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> on <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a></span><a class="headerlink" href="#jack-o-lanterns" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.1 </span><span class="caption-text">Photo by <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> on <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a></span><a class="headerlink" href="#jack-o-lanterns" title="Permalink to this image">#</a></p>
 </figcaption>
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-<li class="toctree-l1"><a class="reference internal" href="logistic-regression.html">13.4. Logistic regression</a></li>
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-     41.94. Object Recognition in Images using CNN
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-     41.106. Neural Networks for Classification with TensorFlow
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-     41.112. DQN On Foreign Exchange Market
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-     41.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
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-     41.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
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   <ul>
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+     41.1. Python programming introduction
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-     42.2. Python programming basics
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-     42.3. Python programming advanced
+     41.3. Python programming advanced
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-     42.4. Data Science introduction
+     41.4. Data Science introduction
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-     42.6. NumPy and Pandas
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-     42.8. Data Science lifecycle
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-     42.9. Data Science in the cloud
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-     42.10. Data Science in real world
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-     42.11. Machine Learning overview
+     41.11. Machine Learning overview
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-     42.13. Logistic Regression
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-     42.14. Logistic Regression
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-     42.15. Neural Network
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-     42.16. Build an machine learning web application
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-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
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-     42.18. Kernel method
+     41.18. Kernel method
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-     42.19. Model Selection
+     41.19. Model Selection
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-     42.20. Convolutional Neural Network
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-     42.21. Generative Adversarial Network
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@@ -1575,66 +1575,66 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
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-   13.1.1. Get started with Python and Scikit-learn for regression models
+   12.1.1. Get started with Python and Scikit-learn for regression models
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   13.1.2. Introduction
+   12.1.2. Introduction
   </a>
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   <a class="reference internal nav-link" href="#installations-and-configurations">
-   13.1.3. Installations and configurations
+   12.1.3. Installations and configurations
   </a>
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-   13.1.4. Your Machine Learning authoring environment
+   12.1.4. Your Machine Learning authoring environment
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exercise-work-with-a-notebook">
-     13.1.4.1. Exercise - work with a notebook
+     12.1.4.1. Exercise - work with a notebook
     </a>
    </li>
   </ul>
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   <a class="reference internal nav-link" href="#up-and-running-with-scikit-learn">
-   13.1.5. Up and running with Scikit-learn
+   12.1.5. Up and running with Scikit-learn
   </a>
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   <a class="reference internal nav-link" href="#exercise-your-first-scikit-learn-notebook">
-   13.1.6. Exercise - your first Scikit-learn notebook
+   12.1.6. Exercise - your first Scikit-learn notebook
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#import-libraries">
-     13.1.6.1. Import libraries
+     12.1.6.1. Import libraries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-diabetes-dataset">
-     13.1.6.2. The diabetes dataset
+     12.1.6.2. The diabetes dataset
     </a>
    </li>
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  </li>
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   <a class="reference internal nav-link" href="#self-study">
-   13.1.7. Self study
+   12.1.7. Self study
   </a>
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   <a class="reference internal nav-link" href="#your-turn">
-   13.1.8. Your turn! 🚀
+   12.1.8. Your turn! 🚀
   </a>
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   <a class="reference internal nav-link" href="#acknowledgments">
-   13.1.9. Acknowledgments
+   12.1.9. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1658,66 +1658,66 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#get-started-with-python-and-scikit-learn-for-regression-models">
-   13.1.1. Get started with Python and Scikit-learn for regression models
+   12.1.1. Get started with Python and Scikit-learn for regression models
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   13.1.2. Introduction
+   12.1.2. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#installations-and-configurations">
-   13.1.3. Installations and configurations
+   12.1.3. Installations and configurations
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-machine-learning-authoring-environment">
-   13.1.4. Your Machine Learning authoring environment
+   12.1.4. Your Machine Learning authoring environment
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exercise-work-with-a-notebook">
-     13.1.4.1. Exercise - work with a notebook
+     12.1.4.1. Exercise - work with a notebook
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#up-and-running-with-scikit-learn">
-   13.1.5. Up and running with Scikit-learn
+   12.1.5. Up and running with Scikit-learn
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#exercise-your-first-scikit-learn-notebook">
-   13.1.6. Exercise - your first Scikit-learn notebook
+   12.1.6. Exercise - your first Scikit-learn notebook
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#import-libraries">
-     13.1.6.1. Import libraries
+     12.1.6.1. Import libraries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-diabetes-dataset">
-     13.1.6.2. The diabetes dataset
+     12.1.6.2. The diabetes dataset
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   13.1.7. Self study
+   12.1.7. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   13.1.8. Your turn! 🚀
+   12.1.8. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   13.1.9. Acknowledgments
+   12.1.9. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1742,18 +1742,18 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="tools-of-the-trade">
-<h1><span class="section-number">13.1. </span>Tools of the trade<a class="headerlink" href="#tools-of-the-trade" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">12.1. </span>Tools of the trade<a class="headerlink" href="#tools-of-the-trade" title="Permalink to this headline">#</a></h1>
 <section id="get-started-with-python-and-scikit-learn-for-regression-models">
-<h2><span class="section-number">13.1.1. </span>Get started with Python and Scikit-learn for regression models<a class="headerlink" href="#get-started-with-python-and-scikit-learn-for-regression-models" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.1.1. </span>Get started with Python and Scikit-learn for regression models<a class="headerlink" href="#get-started-with-python-and-scikit-learn-for-regression-models" title="Permalink to this headline">#</a></h2>
 <figure class="align-default" id="summary-of-regressions-in-a-sketchnote">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/ml-regression.png"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/ml-regression.png" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/ml-regression.png" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 13.2 </span><span class="caption-text">Sketchnote by <a class="reference external" href="https://www.twitter.com/girlie_mac">Tomomi Imura</a></span><a class="headerlink" href="#summary-of-regressions-in-a-sketchnote" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.2 </span><span class="caption-text">Sketchnote by <a class="reference external" href="https://www.twitter.com/girlie_mac">Tomomi Imura</a></span><a class="headerlink" href="#summary-of-regressions-in-a-sketchnote" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>
 <section id="introduction">
-<h2><span class="section-number">13.1.2. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.1.2. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <p>In these four sections, you will discover how to build regression models. We will discuss what these are for shortly. But before you do anything, make sure you have the right tools in place to start the process!</p>
 <p>In this section, you will learn how to:</p>
 <ul class="simple">
@@ -1764,7 +1764,7 @@ <h2><span class="section-number">13.1.2. </span>Introduction<a class="headerlink
 </ul>
 </section>
 <section id="installations-and-configurations">
-<h2><span class="section-number">13.1.3. </span>Installations and configurations<a class="headerlink" href="#installations-and-configurations" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.1.3. </span>Installations and configurations<a class="headerlink" href="#installations-and-configurations" title="Permalink to this headline">#</a></h2>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
 <p>Click here for a video : <a class="reference external" href="https://youtu.be/yyQM70vi7V8">Setup Python with Visual Studio Code</a>, by author <a class="reference external" href="https://www.youtube.com/&#64;alfredodeza1">Alfredo Deza</a>.</p>
@@ -1780,11 +1780,11 @@ <h2><span class="section-number">13.1.3. </span>Installations and configurations
 <p>4 . <strong>Install Jupyter Notebook</strong>. You will need to <a class="reference external" href="https://pypi.org/project/jupyter/">install the Jupyter package</a>.</p>
 </section>
 <section id="your-machine-learning-authoring-environment">
-<h2><span class="section-number">13.1.4. </span>Your Machine Learning authoring environment<a class="headerlink" href="#your-machine-learning-authoring-environment" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.1.4. </span>Your Machine Learning authoring environment<a class="headerlink" href="#your-machine-learning-authoring-environment" title="Permalink to this headline">#</a></h2>
 <p>You are going to use <strong>notebooks</strong> to develop your Python code and create machine learning models. This type of file is a common tool for data scientists, and they can be identified by their suffix or extension <code class="docutils literal notranslate"><span class="pre">.ipynb</span></code>.</p>
 <p>Notebooks are an interactive environment that allow the developer to both code and add notes and write documentation around the code which is quite helpful for experimental or research-oriented projects.</p>
 <section id="exercise-work-with-a-notebook">
-<h3><span class="section-number">13.1.4.1. </span>Exercise - work with a notebook<a class="headerlink" href="#exercise-work-with-a-notebook" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.1.4.1. </span>Exercise - work with a notebook<a class="headerlink" href="#exercise-work-with-a-notebook" title="Permalink to this headline">#</a></h3>
 <p>In this section, you will find the file <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html"><span class="doc std std-doc">regression-tools.ipynb</span></a>.</p>
 <ol>
 <li><p>Open <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html"><span class="doc std std-doc">regression-tools.ipynb</span></a> in Visual Studio Code.</p>
@@ -1804,7 +1804,7 @@ <h3><span class="section-number">13.1.4.1. </span>Exercise - work with a noteboo
 <figure class="align-default" id="vs-code-with-a-notebook-open">
 <a class="reference internal image-reference" href="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/notebook.jpg"><img alt="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/notebook.jpg" src="https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/ml-regression/notebook.jpg" style="width: 90%;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 13.3 </span><span class="caption-text">VS Code with a notebook open{cite}“VS_Code_with_a_notebook_open”</span><a class="headerlink" href="#vs-code-with-a-notebook-open" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 12.3 </span><span class="caption-text">VS Code with a notebook open{cite}“VS_Code_with_a_notebook_open”</span><a class="headerlink" href="#vs-code-with-a-notebook-open" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 <p>You can interleaf your code with comments to self-document the notebook.</p>
@@ -1815,14 +1815,14 @@ <h3><span class="section-number">13.1.4.1. </span>Exercise - work with a noteboo
 </section>
 </section>
 <section id="up-and-running-with-scikit-learn">
-<h2><span class="section-number">13.1.5. </span>Up and running with Scikit-learn<a class="headerlink" href="#up-and-running-with-scikit-learn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.1.5. </span>Up and running with Scikit-learn<a class="headerlink" href="#up-and-running-with-scikit-learn" title="Permalink to this headline">#</a></h2>
 <p>Now that Python is set up in your local environment, and you are comfortable with Jupyter notebooks, let’s get equally comfortable with Scikit-learn (pronounce it <code class="docutils literal notranslate"><span class="pre">sci</span></code> as in <code class="docutils literal notranslate"><span class="pre">science</span></code>). Scikit-learn provides an <a class="reference external" href="https://scikit-learn.org/stable/modules/classes.html#api-ref">extensive API</a> to help you perform ML tasks.</p>
 <p>According to their <a class="reference external" href="https://scikit-learn.org/stable/getting_started.html">website</a>, “Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities.”</p>
 <p>In this course, you will use Scikit-learn and other tools to build machine learning models to perform what we call ‘traditional machine learning’ tasks. We have deliberately avoided neural networks and deep learning, as they are better covered in our forthcoming ‘AI for Beginners’ curriculum.</p>
 <p>Scikit-learn makes it straightforward to build models and evaluate them for use. It is primarily focused on using numeric data and contains several ready-made datasets for use as learning tools. It also includes pre-built models for students to try. Let’s explore the process of loading prepackaged data and using a built in estimator  first ML model with Scikit-learn with some basic data.</p>
 </section>
 <section id="exercise-your-first-scikit-learn-notebook">
-<h2><span class="section-number">13.1.6. </span>Exercise - your first Scikit-learn notebook<a class="headerlink" href="#exercise-your-first-scikit-learn-notebook" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.1.6. </span>Exercise - your first Scikit-learn notebook<a class="headerlink" href="#exercise-your-first-scikit-learn-notebook" title="Permalink to this headline">#</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>This tutorial was inspired by the <a class="reference external" href="https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py">linear regression example</a> on Scikit-learn’s web site.</p>
@@ -1835,7 +1835,7 @@ <h2><span class="section-number">13.1.6. </span>Exercise - your first Scikit-lea
 </div>
 <p>Let’s get started on this task.</p>
 <section id="import-libraries">
-<h3><span class="section-number">13.1.6.1. </span>Import libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.1.6.1. </span>Import libraries<a class="headerlink" href="#import-libraries" title="Permalink to this headline">#</a></h3>
 <p>For this task we will import some libraries:</p>
 <ul class="simple">
 <li><p><strong>matplotlib</strong>. It’s a useful <a class="reference external" href="https://matplotlib.org/">graphing tool</a> and we will use it to create a line plot.</p></li>
@@ -1856,7 +1856,7 @@ <h3><span class="section-number">13.1.6.1. </span>Import libraries<a class="head
 <p>Above you are importing <code class="docutils literal notranslate"><span class="pre">matplotlib</span></code>, <code class="docutils literal notranslate"><span class="pre">numpy</span></code> and you are importing <code class="docutils literal notranslate"><span class="pre">datasets</span></code>, <code class="docutils literal notranslate"><span class="pre">linear_model</span></code> and <code class="docutils literal notranslate"><span class="pre">model_selection</span></code> from <code class="docutils literal notranslate"><span class="pre">sklearn</span></code>. <code class="docutils literal notranslate"><span class="pre">model_selection</span></code> is used for splitting data into training and test sets.</p>
 </section>
 <section id="the-diabetes-dataset">
-<h3><span class="section-number">13.1.6.2. </span>The diabetes dataset<a class="headerlink" href="#the-diabetes-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">12.1.6.2. </span>The diabetes dataset<a class="headerlink" href="#the-diabetes-dataset" title="Permalink to this headline">#</a></h3>
 <p>The built-in <a class="reference external" href="https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset">diabetes dataset</a> includes 442 samples of data around diabetes, with 10 feature variables, some of which include:</p>
 <ul class="simple">
 <li><p>age: age in years</p></li>
@@ -1964,17 +1964,17 @@ <h3><span class="section-number">13.1.6.2. </span>The diabetes dataset<a class="
 </section>
 </section>
 <section id="self-study">
-<h2><span class="section-number">13.1.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.1.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>In this tutorial, you worked with simple linear regression, rather than univariate or multiple linear regression. Read a little about the differences between these methods, or take a look at <a class="reference external" href="https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef">this video</a></p>
 <p>Read more about the concept of regression and think about what kinds of questions can be answered by this technique. Take this <a class="reference external" href="https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-77952-leestott">tutorial</a> to deepen your understanding.</p>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">13.1.8. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.1.8. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Plot a different variable from this dataset. Hint: edit this line: <code class="docutils literal notranslate"><span class="pre">X</span> <span class="pre">=</span> <span class="pre">X[:,</span> <span class="pre">np.newaxis,</span> <span class="pre">2]</span></code>. Given this dataset’s target, what are you able to discover about the progression of diabetes as a disease?</p>
 <p>Assignment - <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html"><span class="doc std std-doc">Regression with scikit-learn</span></a></p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">13.1.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">12.1.9. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to Microsoft for creating the open-source course <a class="reference external" href="https://github.com/microsoft/ML-For-Beginners">ML-For-Beginners</a>. It inspires the majority of the content in this chapter.</p>
 <hr class="docutils" />
 <span class="target" id="id1"></span></section>
@@ -2017,13 +2017,13 @@ <h2><span class="section-number">13.1.9. </span>Acknowledgments<a class="headerl
         <i class="fas fa-angle-left"></i>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">13. </span>Regression models for Machine Learning</p>
+            <p class="prev-next-title"><span class="section-number">12. </span>Regression models for Machine Learning</p>
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-        <p class="prev-next-title"><span class="section-number">13.2. </span>Managing data</p>
+        <p class="prev-next-title"><span class="section-number">12.2. </span>Managing data</p>
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-    <title>4. Python programming advanced &#8212; Ocademy Open Machine Learning Book</title>
+    <title>3. Python programming advanced &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="next" title="5. Introduction" href="../data-science/introduction/introduction.html" />
-    <link rel="prev" title="3. Python programming basics" href="python-programming-basics.html" />
+    <link rel="next" title="4. Introduction" href="../data-science/introduction/introduction.html" />
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+     40.3. First assignment
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+     40.39. Low code/no code Data Science project on Azure ML
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+     40.40. Data Science project using Azure ML SDK
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+     40.41. Data Science in the cloud: The “Azure ML SDK” way
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+     40.42. Machine Learning overview - assignment 1
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-     41.45. Linear regression - California Housing
+     40.45. Linear regression - California Housing
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-     41.116. Basic classification: Classify images of clothing
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+     3.1.2. The for statement (and range() function)
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     <a class="reference internal nav-link" href="#packages">
-     4.4.2. Packages
+     3.4.2. Packages
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#errors-and-exceptions">
-   4.5. Errors and exceptions
+   3.5. Errors and exceptions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#handling-exceptions-try-statement">
-     4.5.1. Handling exceptions (try statement)
+     3.5.1. Handling exceptions (try statement)
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#raising-exceptions-raise-statement">
-     4.5.2. Raising exceptions (raise statement)
+     3.5.2. Raising exceptions (raise statement)
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   4.6. Your turn! 🚀
+   3.6. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   4.7. Self study
+   3.7. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   4.8. Acknowledgments
+   3.8. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1779,187 +1779,187 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#control-flow">
-   4.1. Control flow
+   3.1. Control flow
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-if-statement">
-     4.1.1. The if statement
+     3.1.1. The if statement
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-for-statement-and-range-function">
-     4.1.2. The for statement (and range() function)
+     3.1.2. The for statement (and range() function)
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-while-statement">
-     4.1.3. The while statement
+     3.1.3. The while statement
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-try-statement">
-     4.1.4. The try statement
+     3.1.4. The try statement
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-break-statement">
-     4.1.5. The break statement
+     3.1.5. The break statement
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-continue-statement">
-     4.1.6. The continue statement
+     3.1.6. The continue statement
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#functions">
-   4.2. Functions
+   3.2. Functions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#function-definition-def-and-return-statements">
-     4.2.1. Function definition (def and return statements)
+     3.2.1. Function definition (def and return statements)
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#scopes-of-variables-inside-functions-global-and-nonlocal-statements">
-     4.2.2. Scopes of variables inside functions (global and nonlocal statements)
+     3.2.2. Scopes of variables inside functions (global and nonlocal statements)
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#default-argument-values">
-     4.2.3. Default argument values
+     3.2.3. Default argument values
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#keyword-arguments">
-     4.2.4. Keyword arguments
+     3.2.4. Keyword arguments
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#arbitrary-argument-lists">
-     4.2.5. Arbitrary argument lists
+     3.2.5. Arbitrary argument lists
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#unpacking-argument-lists-and-statements">
-     4.2.6. Unpacking argument lists (* and ** statements)
+     3.2.6. Unpacking argument lists (* and ** statements)
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#lambda-expressions-lambda-statement">
-     4.2.7. Lambda expressions (lambda statement)
+     3.2.7. Lambda expressions (lambda statement)
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#documentation-strings">
-     4.2.8. Documentation strings
+     3.2.8. Documentation strings
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#function-annotations">
-     4.2.9. Function annotations
+     3.2.9. Function annotations
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#function-decorators">
-     4.2.10. Function decorators
+     3.2.10. Function decorators
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#classes">
-   4.3. Classes
+   3.3. Classes
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#class-definition-class-statement">
-     4.3.1. Class definition (class statement)
+     3.3.1. Class definition (class statement)
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#class-objects">
-     4.3.2. Class objects
+     3.3.2. Class objects
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#instance-objects">
-     4.3.3. Instance objects
+     3.3.3. Instance objects
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#methods-objects">
-     4.3.4. Methods objects
+     3.3.4. Methods objects
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#class-and-instance-variables">
-     4.3.5. Class and instance variables
+     3.3.5. Class and instance variables
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#inheritance">
-     4.3.6. Inheritance
+     3.3.6. Inheritance
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#multiple-inheritance">
-     4.3.7. Multiple inheritance
+     3.3.7. Multiple inheritance
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#modules">
-   4.4. Modules
+   3.4. Modules
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#modules-import-statement">
-     4.4.1. Modules (import statement)
+     3.4.1. Modules (import statement)
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#packages">
-     4.4.2. Packages
+     3.4.2. Packages
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#errors-and-exceptions">
-   4.5. Errors and exceptions
+   3.5. Errors and exceptions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#handling-exceptions-try-statement">
-     4.5.1. Handling exceptions (try statement)
+     3.5.1. Handling exceptions (try statement)
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#raising-exceptions-raise-statement">
-     4.5.2. Raising exceptions (raise statement)
+     3.5.2. Raising exceptions (raise statement)
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   4.6. Your turn! 🚀
+   3.6. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   4.7. Self study
+   3.7. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   4.8. Acknowledgments
+   3.8. Acknowledgments
   </a>
  </li>
 </ul>
@@ -2003,11 +2003,11 @@ <h2> Contents </h2>
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.</p>
 </details><section class="tex2jax_ignore mathjax_ignore" id="python-programming-advanced">
-<h1><span class="section-number">4. </span>Python programming advanced<a class="headerlink" href="#python-programming-advanced" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">3. </span>Python programming advanced<a class="headerlink" href="#python-programming-advanced" title="Permalink to this headline">#</a></h1>
 <section id="control-flow">
-<h2><span class="section-number">4.1. </span>Control flow<a class="headerlink" href="#control-flow" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">3.1. </span>Control flow<a class="headerlink" href="#control-flow" title="Permalink to this headline">#</a></h2>
 <section id="the-if-statement">
-<h3><span class="section-number">4.1.1. </span>The if statement<a class="headerlink" href="#the-if-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.1.1. </span>The if statement<a class="headerlink" href="#the-if-statement" title="Permalink to this headline">#</a></h3>
 <p>There can be zero or more <code class="docutils literal notranslate"><span class="pre">elif</span></code> parts, and the else part is optional. The keyword <code class="docutils literal notranslate"><span class="pre">elif</span></code> is short for ‘else if’, and is useful to avoid excessive indentation.</p>
 <p>An <code class="docutils literal notranslate"><span class="pre">if</span> <span class="pre">…</span> <span class="pre">elif</span> <span class="pre">…</span> <span class="pre">elif</span> <span class="pre">…</span></code> sequence is a substitute for the switch or case statements found in other languages.</p>
 <div class="admonition seealso">
@@ -2056,7 +2056,7 @@ <h3><span class="section-number">4.1.1. </span>The if statement<a class="headerl
 </div>
 </section>
 <section id="the-for-statement-and-range-function">
-<h3><span class="section-number">4.1.2. </span>The for statement (and range() function)<a class="headerlink" href="#the-for-statement-and-range-function" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.1.2. </span>The for statement (and range() function)<a class="headerlink" href="#the-for-statement-and-range-function" title="Permalink to this headline">#</a></h3>
 <p>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement in Python differs a bit from what you may be used to in C or Pascal. Rather than always iterating over an arithmetic progression of numbers (like in Pascal), or giving the user the ability to define both the iteration step and halting condition (as C), Python’s for statement iterates over the items of any sequence (a list or a string), in the order that they appear in the sequence. For example (no pun intended):</p>
 <p>Measure some strings:</p>
 <ul class="simple">
@@ -2221,7 +2221,7 @@ <h3><span class="section-number">4.1.2. </span>The for statement (and range() fu
 </div>
 </section>
 <section id="the-while-statement">
-<h3><span class="section-number">4.1.3. </span>The while statement<a class="headerlink" href="#the-while-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.1.3. </span>The while statement<a class="headerlink" href="#the-while-statement" title="Permalink to this headline">#</a></h3>
 <p>The while loop executes as long as the condition remains true. In Python, like in C, any non-zero integer value is true; zero is false. The condition may also be a string or list value, in fact, any sequence; anything with a non-zero length is true, and empty sequences are false.</p>
 <p>The test used in the example is a simple comparison. The standard comparison operators are written the same as in C: <code class="docutils literal notranslate"><span class="pre">&lt;</span></code> (less than), <code class="docutils literal notranslate"><span class="pre">&gt;</span></code> (greater than), <code class="docutils literal notranslate"><span class="pre">==</span></code> (equal to), <code class="docutils literal notranslate"><span class="pre">&lt;=</span></code> (less than or equal to), <code class="docutils literal notranslate"><span class="pre">&gt;=</span></code> (greater than or equal to) and <code class="docutils literal notranslate"><span class="pre">!=</span></code> (not equal to).</p>
 <div class="admonition seealso">
@@ -2250,7 +2250,7 @@ <h3><span class="section-number">4.1.3. </span>The while statement<a class="head
 </div>
 </section>
 <section id="the-try-statement">
-<h3><span class="section-number">4.1.4. </span>The try statement<a class="headerlink" href="#the-try-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.1.4. </span>The try statement<a class="headerlink" href="#the-try-statement" title="Permalink to this headline">#</a></h3>
 <p>The try statement is used for exception handling.</p>
 <p>When an error occurs, or an exception as we call it, Python will normally stop and generate an error message. These exceptions can be handled using the try statement:</p>
 <ul class="simple">
@@ -2333,7 +2333,7 @@ <h3><span class="section-number">4.1.4. </span>The try statement<a class="header
 </div>
 </section>
 <section id="the-break-statement">
-<h3><span class="section-number">4.1.5. </span>The break statement<a class="headerlink" href="#the-break-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.1.5. </span>The break statement<a class="headerlink" href="#the-break-statement" title="Permalink to this headline">#</a></h3>
 <p>The break statement, like in C, breaks out of the innermost enclosing “for” or “while” loop.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -2363,7 +2363,7 @@ <h3><span class="section-number">4.1.5. </span>The break statement<a class="head
 </div>
 </section>
 <section id="the-continue-statement">
-<h3><span class="section-number">4.1.6. </span>The continue statement<a class="headerlink" href="#the-continue-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.1.6. </span>The continue statement<a class="headerlink" href="#the-continue-statement" title="Permalink to this headline">#</a></h3>
 <p>The continue statement is borrowed from C, and continues with the next iteration of the loop.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -2396,9 +2396,9 @@ <h3><span class="section-number">4.1.6. </span>The continue statement<a class="h
 </section>
 </section>
 <section id="functions">
-<h2><span class="section-number">4.2. </span>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">3.2. </span>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">#</a></h2>
 <section id="function-definition-def-and-return-statements">
-<h3><span class="section-number">4.2.1. </span>Function definition (def and return statements)<a class="headerlink" href="#function-definition-def-and-return-statements" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.2.1. </span>Function definition (def and return statements)<a class="headerlink" href="#function-definition-def-and-return-statements" title="Permalink to this headline">#</a></h3>
 <p>The keyword def introduces a function definition. It must be followed by the function name and the parenthesized list of formal parameters. The statements that form the body of the function start at the next line and must be indented.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -2541,7 +2541,7 @@ <h3><span class="section-number">4.2.1. </span>Function definition (def and retu
 </div>
 </section>
 <section id="scopes-of-variables-inside-functions-global-and-nonlocal-statements">
-<h3><span class="section-number">4.2.2. </span>Scopes of variables inside functions (global and nonlocal statements)<a class="headerlink" href="#scopes-of-variables-inside-functions-global-and-nonlocal-statements" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.2.2. </span>Scopes of variables inside functions (global and nonlocal statements)<a class="headerlink" href="#scopes-of-variables-inside-functions-global-and-nonlocal-statements" title="Permalink to this headline">#</a></h3>
 <p>A NAMESPACE is a mapping from names to objects. Most namespaces are currently implemented as Python dictionaries, but that’s normally not noticeable in any way (except for performance), and it may change in the future. Examples of namespaces are the set of built-in names (containing functions such as <code class="docutils literal notranslate"><span class="pre">abs()</span></code>, and built-in exception names); the global names in a module; and the local names in a function invocation. In a sense, the set of attributes of an object also forms a namespace.</p>
 <p>The important thing to know about namespaces is that there is absolutely no relation between names in different namespaces; for instance, two different modules may both define a function <code class="docutils literal notranslate"><span class="pre">maximize</span></code> without confusion — users of the modules must prefix it with the module name.</p>
 <p>By the way, we use the word attribute for any name following a dot — for example, in the expression <code class="docutils literal notranslate"><span class="pre">z.real</span></code>, <code class="docutils literal notranslate"><span class="pre">real</span></code> is an attribute of the object <code class="docutils literal notranslate"><span class="pre">z</span></code>. Strictly speaking, references to names in modules are attribute references: in the expression <code class="docutils literal notranslate"><span class="pre">modname.func_name</span></code>, <code class="docutils literal notranslate"><span class="pre">modname</span></code> is a module object and <code class="docutils literal notranslate"><span class="pre">func_name</span></code> is an attribute of it. In this case, there happens to be a straightforward mapping between the module’s attributes and the global names defined in the module: they share the same namespace!</p>
@@ -2637,7 +2637,7 @@ <h3><span class="section-number">4.2.2. </span>Scopes of variables inside functi
 <p>On this example you may see how accessing and changing global variables from within inner functions might make debugging more difficult and code to be less predictable. Since you might have expected that <code class="docutils literal notranslate"><span class="pre">test_variable</span></code> should still be equal to ‘initial global value’ but it was changed by “someone” you need to know about the CONTEXT of who had changed that. So once again access global and non-local scope only if you know what you’re doing otherwise it might be considered a bad practice.</p>
 </section>
 <section id="default-argument-values">
-<h3><span class="section-number">4.2.3. </span>Default argument values<a class="headerlink" href="#default-argument-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.2.3. </span>Default argument values<a class="headerlink" href="#default-argument-values" title="Permalink to this headline">#</a></h3>
 <p>The most useful form is to specify a default value for one or more arguments. This creates a function that can be called with fewer arguments than it is defined to allow.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -2675,7 +2675,7 @@ <h3><span class="section-number">4.2.3. </span>Default argument values<a class="
 </div>
 </section>
 <section id="keyword-arguments">
-<h3><span class="section-number">4.2.4. </span>Keyword arguments<a class="headerlink" href="#keyword-arguments" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.2.4. </span>Keyword arguments<a class="headerlink" href="#keyword-arguments" title="Permalink to this headline">#</a></h3>
 <p>Functions can be called using keyword arguments of the form <code class="docutils literal notranslate"><span class="pre">kwarg=value</span></code>.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -2778,7 +2778,7 @@ <h3><span class="section-number">4.2.4. </span>Keyword arguments<a class="header
 <p>In a function call, keyword arguments must follow positional arguments. All the keyword arguments passed must match one of the arguments accepted by the function (e.g. <code class="docutils literal notranslate"><span class="pre">actor</span></code> is not a valid argument for the <code class="docutils literal notranslate"><span class="pre">parrot</span></code> function), and their order is not important. This also includes non-optional arguments (e.g. <code class="docutils literal notranslate"><span class="pre">parrot(voltage=1000)</span></code> is valid too).</p>
 </section>
 <section id="arbitrary-argument-lists">
-<h3><span class="section-number">4.2.5. </span>Arbitrary argument lists<a class="headerlink" href="#arbitrary-argument-lists" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.2.5. </span>Arbitrary argument lists<a class="headerlink" href="#arbitrary-argument-lists" title="Permalink to this headline">#</a></h3>
 <p>Function can be called with an arbitrary number of arguments. These arguments will be wrapped up in a tuple. Before the variable number of arguments, zero or more normal arguments may occur.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -2813,7 +2813,7 @@ <h3><span class="section-number">4.2.5. </span>Arbitrary argument lists<a class=
 </div>
 </section>
 <section id="unpacking-argument-lists-and-statements">
-<h3><span class="section-number">4.2.6. </span>Unpacking argument lists (* and ** statements)<a class="headerlink" href="#unpacking-argument-lists-and-statements" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.2.6. </span>Unpacking argument lists (* and ** statements)<a class="headerlink" href="#unpacking-argument-lists-and-statements" title="Permalink to this headline">#</a></h3>
 <p>Unpacking arguments may be executed via <code class="docutils literal notranslate"><span class="pre">*</span></code> and <code class="docutils literal notranslate"><span class="pre">**</span></code> operators. See below for further details.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -2843,7 +2843,7 @@ <h3><span class="section-number">4.2.6. </span>Unpacking argument lists (* and *
 </div>
 </section>
 <section id="lambda-expressions-lambda-statement">
-<h3><span class="section-number">4.2.7. </span>Lambda expressions (lambda statement)<a class="headerlink" href="#lambda-expressions-lambda-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.2.7. </span>Lambda expressions (lambda statement)<a class="headerlink" href="#lambda-expressions-lambda-statement" title="Permalink to this headline">#</a></h3>
 <p>Small anonymous functions can be created with the <code class="docutils literal notranslate"><span class="pre">lambda</span></code> keyword. Lambda functions can be used wherever function objects are required. They are syntactically restricted to a single expression. Semantically, they are just syntactic sugar for a normal function definition. Like nested function definitions, lambda functions can reference variables from the containing scope.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -2880,7 +2880,7 @@ <h3><span class="section-number">4.2.7. </span>Lambda expressions (lambda statem
 </div>
 </section>
 <section id="documentation-strings">
-<h3><span class="section-number">4.2.8. </span>Documentation strings<a class="headerlink" href="#documentation-strings" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.2.8. </span>Documentation strings<a class="headerlink" href="#documentation-strings" title="Permalink to this headline">#</a></h3>
 <p>Here are some conventions about the content and formatting of documentation strings.</p>
 <p>The first line should always be a short, concise summary of the object’s purpose. For brevity, it should not explicitly state the object’s name or type, since these are available by other means (except if the name happens to be a verb describing a function’s operation). This line should begin with a capital letter and end with a period.</p>
 <p>If there are more lines in the documentation string, the second line should be blank, visually separating the summary from the rest of the description. The following lines should be one or more paragraphs describing the object’s calling conventions, its side effects, etc.</p>
@@ -2913,7 +2913,7 @@ <h3><span class="section-number">4.2.8. </span>Documentation strings<a class="he
 </div>
 </section>
 <section id="function-annotations">
-<h3><span class="section-number">4.2.9. </span>Function annotations<a class="headerlink" href="#function-annotations" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.2.9. </span>Function annotations<a class="headerlink" href="#function-annotations" title="Permalink to this headline">#</a></h3>
 <p>Function annotations are completely optional metadata information about the types used by user-defined functions.</p>
 <p>Annotations are stored in the <code class="docutils literal notranslate"><span class="pre">__annotations__</span></code> attribute of the function as a dictionary and have no effect on any other part of the function. Parameter annotations are defined by a colon after the parameter name, followed by an expression evaluating the value of the annotation. Return annotations are defined by a literal <code class="docutils literal notranslate"><span class="pre">-&gt;</span></code>, followed by an expression, between the parameter list and the colon denoting the end of the <code class="docutils literal notranslate"><span class="pre">def</span></code> statement.</p>
 <div class="admonition seealso">
@@ -2935,7 +2935,7 @@ <h3><span class="section-number">4.2.9. </span>Function annotations<a class="hea
 </div>
 </section>
 <section id="function-decorators">
-<h3><span class="section-number">4.2.10. </span>Function decorators<a class="headerlink" href="#function-decorators" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.2.10. </span>Function decorators<a class="headerlink" href="#function-decorators" title="Permalink to this headline">#</a></h3>
 <p>Function decorators are simply wrappers to existing functions. In the context of design patterns, decorators dynamically alter the functionality of a function, method or class without having to directly use subclasses. This is ideal when you need to extend the functionality of functions that you don’t want to modify. We can implement the decorator pattern anywhere, but Python facilitates the implementation by providing much more expressive features and syntax for that.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -3044,9 +3044,9 @@ <h3><span class="section-number">4.2.10. </span>Function decorators<a class="hea
 </section>
 </section>
 <section id="classes">
-<h2><span class="section-number">4.3. </span>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">3.3. </span>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">#</a></h2>
 <section id="class-definition-class-statement">
-<h3><span class="section-number">4.3.1. </span>Class definition (class statement)<a class="headerlink" href="#class-definition-class-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.3.1. </span>Class definition (class statement)<a class="headerlink" href="#class-definition-class-statement" title="Permalink to this headline">#</a></h3>
 <p>Python is an object-oriented programming language. Almost everything in Python is an object, with its properties and methods. A Class is like an object constructor or a “blueprint” for creating objects.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -3092,7 +3092,7 @@ <h3><span class="section-number">4.3.1. </span>Class definition (class statement
 </div>
 </section>
 <section id="class-objects">
-<h3><span class="section-number">4.3.2. </span>Class objects<a class="headerlink" href="#class-objects" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.3.2. </span>Class objects<a class="headerlink" href="#class-objects" title="Permalink to this headline">#</a></h3>
 <p>After defining the class attributes to a class, the class object can be created by assigning the object to a variable. The created object would have instance attributes associated with it.</p>
 <p>Class objects support two kinds of operations:</p>
 <ul class="simple">
@@ -3182,7 +3182,7 @@ <h3><span class="section-number">4.3.2. </span>Class objects<a class="headerlink
 </div>
 </section>
 <section id="instance-objects">
-<h3><span class="section-number">4.3.3. </span>Instance objects<a class="headerlink" href="#instance-objects" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.3.3. </span>Instance objects<a class="headerlink" href="#instance-objects" title="Permalink to this headline">#</a></h3>
 <p>Now, what can we do with instance objects? The only operations understood by instance objects are attribute references. There are two kinds of valid attribute names:</p>
 <ul class="simple">
 <li><p>data attributes</p></li>
@@ -3214,7 +3214,7 @@ <h3><span class="section-number">4.3.3. </span>Instance objects<a class="headerl
 </div>
 </section>
 <section id="methods-objects">
-<h3><span class="section-number">4.3.4. </span>Methods objects<a class="headerlink" href="#methods-objects" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.3.4. </span>Methods objects<a class="headerlink" href="#methods-objects" title="Permalink to this headline">#</a></h3>
 <p>Classes can have two types of attribute references: data or methods. Class methods are called by <code class="docutils literal notranslate"><span class="pre">variable_name.method_name(parameters)</span></code> as opposed to class data which lacks the <code class="docutils literal notranslate"><span class="pre">()</span></code>.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -3272,7 +3272,7 @@ <h3><span class="section-number">4.3.4. </span>Methods objects<a class="headerli
 </div>
 </section>
 <section id="class-and-instance-variables">
-<h3><span class="section-number">4.3.5. </span>Class and instance variables<a class="headerlink" href="#class-and-instance-variables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.3.5. </span>Class and instance variables<a class="headerlink" href="#class-and-instance-variables" title="Permalink to this headline">#</a></h3>
 <p>Generally speaking, instance variables are for data unique to each instance and class variables are for attributes and methods shared by all instances of the class.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -3369,7 +3369,7 @@ <h3><span class="section-number">4.3.5. </span>Class and instance variables<a cl
 </div>
 </section>
 <section id="inheritance">
-<h3><span class="section-number">4.3.6. </span>Inheritance<a class="headerlink" href="#inheritance" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.3.6. </span>Inheritance<a class="headerlink" href="#inheritance" title="Permalink to this headline">#</a></h3>
 <p>Inheritance is one of the principles of object-oriented programming. Since classes may share a lot of the same code, inheritance allows a derived class to reuse the same code and modify it accordingly.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -3447,7 +3447,7 @@ <h3><span class="section-number">4.3.6. </span>Inheritance<a class="headerlink"
 </div>
 </section>
 <section id="multiple-inheritance">
-<h3><span class="section-number">4.3.7. </span>Multiple inheritance<a class="headerlink" href="#multiple-inheritance" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.3.7. </span>Multiple inheritance<a class="headerlink" href="#multiple-inheritance" title="Permalink to this headline">#</a></h3>
 <p>Some classes may derive from multiple classes. This means that the derived class would have its attributes, along with the attributes of all the classes that it was derived from.</p>
 <div class="admonition seealso">
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@@ -3506,7 +3506,7 @@ <h3><span class="section-number">4.3.7. </span>Multiple inheritance<a class="hea
 </section>
 </section>
 <section id="modules">
-<h2><span class="section-number">4.4. </span>Modules<a class="headerlink" href="#modules" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">3.4. </span>Modules<a class="headerlink" href="#modules" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Download the required module and package for this chapter</span>
@@ -3563,7 +3563,7 @@ <h2><span class="section-number">4.4. </span>Modules<a class="headerlink" href="
 </div>
 </div>
 <section id="modules-import-statement">
-<h3><span class="section-number">4.4.1. </span>Modules (import statement)<a class="headerlink" href="#modules-import-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.4.1. </span>Modules (import statement)<a class="headerlink" href="#modules-import-statement" title="Permalink to this headline">#</a></h3>
 <p>As your program gets longer, you may want to split it into several files for easier maintenance. You may also want to use a handy function that you’ve written in several programs without copying its definition into each program.</p>
 <p>To support this, Python has a way to put definitions in a file and use them in a script or in an interactive instance of the interpreter. Such a file is called a module; definitions from a module can be imported into other modules or into the main module (the collection of variables that you have access to in a script executed at the top level and in calculator mode).</p>
 <p>A module is a file containing Python definitions and statements. The file name is the module name with the suffix .py appended. Within a module, the module’s name (as a string) is available as the value of the global variable <code class="docutils literal notranslate"><span class="pre">__name__</span></code>.</p>
@@ -3685,7 +3685,7 @@ <h3><span class="section-number">4.4.1. </span>Modules (import statement)<a clas
 </div>
 </section>
 <section id="packages">
-<h3><span class="section-number">4.4.2. </span>Packages<a class="headerlink" href="#packages" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.4.2. </span>Packages<a class="headerlink" href="#packages" title="Permalink to this headline">#</a></h3>
 <p>Packages are a way of structuring Python’s module namespace by using “dotted module names”. For example, the module name <code class="docutils literal notranslate"><span class="pre">A.B</span></code> designates a submodule named <code class="docutils literal notranslate"><span class="pre">B</span></code> in a package named <code class="docutils literal notranslate"><span class="pre">A</span></code>. Just like the use of modules saves the authors of different modules from having to worry about each other’s global variable names, the use of dotted module names saves the authors of multi-module packages like NumPy or Pillow from having to worry about each other’s module names.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">__init__.py</span></code> files are required to make Python treat the directories as containing packages; this is done to prevent directories with a common name, such as string, from unintentionally hiding valid modules that occur later on the module search path. In the simplest case, <code class="docutils literal notranslate"><span class="pre">__init__.py</span></code> can just be an empty file, but it can also execute the initialization code for the package or set the <code class="docutils literal notranslate"><span class="pre">__all__</span></code> variable, described later.</p>
 <p>Users of the package can import individual modules from the package, for example:</p>
@@ -3728,9 +3728,9 @@ <h3><span class="section-number">4.4.2. </span>Packages<a class="headerlink" hre
 </section>
 </section>
 <section id="errors-and-exceptions">
-<h2><span class="section-number">4.5. </span>Errors and exceptions<a class="headerlink" href="#errors-and-exceptions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">3.5. </span>Errors and exceptions<a class="headerlink" href="#errors-and-exceptions" title="Permalink to this headline">#</a></h2>
 <section id="handling-exceptions-try-statement">
-<h3><span class="section-number">4.5.1. </span>Handling exceptions (try statement)<a class="headerlink" href="#handling-exceptions-try-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.5.1. </span>Handling exceptions (try statement)<a class="headerlink" href="#handling-exceptions-try-statement" title="Permalink to this headline">#</a></h3>
 <p>Even if a statement or expression is syntactically correct, it may cause an error when an attempt
 is made to execute it. Errors detected during execution are called exceptions and are not
 unconditionally fatal.</p>
@@ -3842,7 +3842,7 @@ <h3><span class="section-number">4.5.1. </span>Handling exceptions (try statemen
 </div>
 </section>
 <section id="raising-exceptions-raise-statement">
-<h3><span class="section-number">4.5.2. </span>Raising exceptions (raise statement)<a class="headerlink" href="#raising-exceptions-raise-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">3.5.2. </span>Raising exceptions (raise statement)<a class="headerlink" href="#raising-exceptions-raise-statement" title="Permalink to this headline">#</a></h3>
 <p>The <code class="docutils literal notranslate"><span class="pre">raise</span></code> statement allows the programmer to force a specified exception to occur.</p>
 <div class="admonition seealso">
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@@ -3892,15 +3892,15 @@ <h3><span class="section-number">4.5.2. </span>Raising exceptions (raise stateme
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">4.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">3.6. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Practice the Python programming basics by following this <a class="reference internal" href="../assignments/prerequisites/python-programming-advanced.html"><span class="doc std std-doc">assignment</span></a>.</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">4.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">3.7. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>Here is a list of free/open-source learning resources for advanced <a class="reference external" href="https://github.com/ocademy-ai/open-learning-resources/blob/main/README.md#python">Python programming</a>.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">4.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">3.8. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/trekhleb">Oleksii Trekhleb</a> who helped create this awesome open source project <a class="reference external" href="https://github.com/trekhleb/learn-python">learn-python</a> for Python learning. It contributes the majority of the content in this chapter.</p>
 <p>Thanks to <a class="reference external" href="https://pythontutor.com/">pythontutor</a> for providing the ability to visualize the code execution. It also contributes some code to this chapter.</p>
 </section>
@@ -3943,13 +3943,13 @@ <h2><span class="section-number">4.8. </span>Acknowledgments<a class="headerlink
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">3. </span>Python programming basics</p>
+            <p class="prev-next-title"><span class="section-number">2. </span>Python programming basics</p>
         </div>
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     <div class="prev-next-info">
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-        <p class="prev-next-title"><span class="section-number">5. </span>Introduction</p>
+        <p class="prev-next-title"><span class="section-number">4. </span>Introduction</p>
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diff --git a/prerequisites/python-programming-basics.html b/prerequisites/python-programming-basics.html
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-    <title>3. Python programming basics &#8212; Ocademy Open Machine Learning Book</title>
+    <title>2. Python programming basics &#8212; Ocademy Open Machine Learning Book</title>
     
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     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="4. Python programming advanced" href="python-programming-advanced.html" />
-    <link rel="prev" title="1. Python programming basics" href="python-programming-introduction.html" />
+    <link rel="next" title="3. Python programming advanced" href="python-programming-advanced.html" />
+    <link rel="prev" title="1. Python programming introduction" href="python-programming-introduction.html" />
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-   1. Python programming basics
+   1. Python programming introduction
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-   3. Python programming basics
+   2. Python programming basics
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-   5. Introduction
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-     5.1. Defining data science
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-     5.2. Data Science ethics
+     4.2. Data Science ethics
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-     5.3. Defining data
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       <a class="reference internal" href="../data-science/working-with-data/pandas/data-selection.html">
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-     7.3. Visualizing relationships: all about honey 🍯
+     6.3. Visualizing relationships: all about honey 🍯
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+     8.2. The “low code/no code” way
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-     9.3. Data Science in the cloud: The “Azure ML SDK” way
+     8.3. Data Science in the cloud: The “Azure ML SDK” way
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+     12.3. Linear and polynomial regression
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+   13. Build a web app to use a Machine Learning model
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+     14.3. Yet other classifiers
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-     15.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
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+     15.1. Loss function
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+     15.2. Gradient descent
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+     16.1. Introduction to clustering
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+     17.1. Bagging
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+     17.2. Random forest
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+     17.3. Feature importance
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+     18.1. Gradient Boosting
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-     19.2. Gradient boosting example
+     18.2. Gradient boosting example
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-     19.3. XGBoost
+     18.3. XGBoost
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-     19.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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+   21. Unsupervised learning: PCA and clustering
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+   22. Model selection
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-     25.3.1.1. Stylenet / Neural-Style
+     24.3.1.1. Stylenet / Neural-Style
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-     25.3.1.2. Deepdream in TensorFlow
+     24.3.1.2. Deepdream in TensorFlow
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+   25. Generative adversarial networks
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+   27. Autoencoder
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+   30. Deep Q-learning
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+   32. Image segmentation
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+   34. Object detection
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+   38. Model training &amp; evaluation
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+   39. Model deployment
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+     40.3. First assignment
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+     40.4. Second assignment
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+     40.5. Project Plan​ Template
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+     40.6. Python programming introduction
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+     40.7. Python programming basics
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+     40.8. Python programming advanced
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+     40.9. Analyzing text about Data Science
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+     40.10. Data Science scenarios
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-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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-     41.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
+     40.17. Classifying datasets
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+     40.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
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+     40.20. Displaying airport data
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-     41.21. Soda profits
+     40.21. Soda profits
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+     40.23. Estimation of COVID-19 pandemic
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-     41.24. Data processing in Python
+     40.24. Data processing in Python
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     <a class="reference internal" href="../slides/ml-advanced/model-selection.html">
-     42.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1575,187 +1575,187 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#python-syntax">
-   3.1. Python syntax
+   2.1. Python syntax
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#python-indentations">
-     3.1.1. Python indentations
+     2.1.1. Python indentations
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#comments">
-     3.1.2. Comments
+     2.1.2. Comments
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#docstrings">
-     3.1.3. Docstrings
+     2.1.3. Docstrings
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#variables">
-   3.2. Variables
+   2.2. Variables
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#operators">
-   3.3. Operators
+   2.3. Operators
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#arithmetic-operators">
-     3.3.1. Arithmetic operators
+     2.3.1. Arithmetic operators
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#comparison-operators">
-     3.3.2. Comparison operators
+     2.3.2. Comparison operators
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#lets-visualize-it">
-     3.3.3. Let’s visualize it
+     2.3.3. Let’s visualize it
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-types">
-   3.4. Data types
+   2.4. Data types
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#numbers-including-booleans">
-     3.4.1. Numbers (including booleans)
+     2.4.1. Numbers (including booleans)
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#integers">
-       3.4.1.1. Integers
+       2.4.1.1. Integers
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#booleans">
-       3.4.1.2. Booleans
+       2.4.1.2. Booleans
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#floats">
-       3.4.1.3. Floats
+       2.4.1.3. Floats
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#complexes">
-       3.4.1.4. Complexes
+       2.4.1.4. Complexes
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#number-operation">
-       3.4.1.5. Number operation
+       2.4.1.5. Number operation
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#strings-and-their-methods">
-     3.4.2. Strings and their methods
+     2.4.2. Strings and their methods
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string-type">
-       3.4.2.1. String type
+       2.4.2.1. String type
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string-operations">
-       3.4.2.2. String operations
+       2.4.2.2. String operations
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string-methods">
-       3.4.2.3. String methods
+       2.4.2.3. String methods
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string-formatting">
-       3.4.2.4. String formatting
+       2.4.2.4. String formatting
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#formatted-string-literals">
-         3.4.2.4.1. Formatted string literals
+         2.4.2.4.1. Formatted string literals
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#the-string-format-method">
-         3.4.2.4.2. The string format() method
+         2.4.2.4.2. The string format() method
         </a>
        </li>
       </ul>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       3.4.2.5. Let’s visualize it
+       2.4.2.5. Let’s visualize it
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#lists-and-their-methods-including-list-comprehensions">
-     3.4.3. Lists and their methods (including list comprehensions)
+     2.4.3. Lists and their methods (including list comprehensions)
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#list-type">
-       3.4.3.1. List type
+       2.4.3.1. List type
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#list-methods">
-       3.4.3.2. List methods
+       2.4.3.2. List methods
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#the-del-statement">
-       3.4.3.3. The del statement
+       2.4.3.3. The del statement
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#list-comprehensions">
-       3.4.3.4. List comprehensions
+       2.4.3.4. List comprehensions
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#nested-list-comprehensions">
-       3.4.3.5. Nested list comprehensions
+       2.4.3.5. Nested list comprehensions
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id2">
-       3.4.3.6. Let’s visualize it
+       2.4.3.6. Let’s visualize it
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#basic-reverse-by-hand">
-         3.4.3.6.1. Basic reverse by hand
+         2.4.3.6.1. Basic reverse by hand
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#insert">
-         3.4.3.6.2. Insert
+         2.4.3.6.2. Insert
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#recursion">
-         3.4.3.6.3. Recursion
+         2.4.3.6.3. Recursion
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#list-comprehension">
-         3.4.3.6.4. List comprehension
+         2.4.3.6.4. List comprehension
         </a>
        </li>
       </ul>
@@ -1764,47 +1764,47 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#tuples">
-     3.4.4. Tuples
+     2.4.4. Tuples
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#sets-and-their-methods">
-     3.4.5. Sets and their methods
+     2.4.5. Sets and their methods
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#set-type">
-       3.4.5.1. Set type
+       2.4.5.1. Set type
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#set-methods">
-       3.4.5.2. Set methods
+       2.4.5.2. Set methods
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id3">
-       3.4.5.3. Let’s visualize it
+       2.4.5.3. Let’s visualize it
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#remove-duplicates-from-a-list">
-         3.4.5.3.1. Remove Duplicates from a List
+         2.4.5.3.1. Remove Duplicates from a List
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#check-if-two-strings-are-anagrams">
-         3.4.5.3.2. Check if two strings are Anagrams:
+         2.4.5.3.2. Check if two strings are Anagrams:
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#find-common-elements-in-multiple-lists">
-         3.4.5.3.3. Find common elements in multiple lists:
+         2.4.5.3.3. Find common elements in multiple lists:
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#count-the-number-of-vowels-in-a-string">
-         3.4.5.3.4. Count the number of Vowels in a string:
+         2.4.5.3.4. Count the number of Vowels in a string:
         </a>
        </li>
       </ul>
@@ -1813,27 +1813,27 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dictionaries">
-     3.4.6. Dictionaries
+     2.4.6. Dictionaries
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id4">
-       3.4.6.1. Let’s visualize it
+       2.4.6.1. Let’s visualize it
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#turning-keys-into-values-and-vice-versa">
-         3.4.6.1.1. Turning keys into values and vice versa
+         2.4.6.1.1. Turning keys into values and vice versa
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#filtering-items">
-         3.4.6.1.2. Filtering Items
+         2.4.6.1.2. Filtering Items
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#doing-some-calculations">
-         3.4.6.1.3. Doing Some Calculations
+         2.4.6.1.3. Doing Some Calculations
         </a>
        </li>
       </ul>
@@ -1842,24 +1842,24 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#type-casting">
-     3.4.7. Type casting
+     2.4.7. Type casting
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   3.5. Your turn! 🚀
+   2.5. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   3.6. Self study
+   2.6. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   3.7. Acknowledgments
+   2.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1883,187 +1883,187 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#python-syntax">
-   3.1. Python syntax
+   2.1. Python syntax
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#python-indentations">
-     3.1.1. Python indentations
+     2.1.1. Python indentations
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#comments">
-     3.1.2. Comments
+     2.1.2. Comments
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#docstrings">
-     3.1.3. Docstrings
+     2.1.3. Docstrings
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#variables">
-   3.2. Variables
+   2.2. Variables
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#operators">
-   3.3. Operators
+   2.3. Operators
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#arithmetic-operators">
-     3.3.1. Arithmetic operators
+     2.3.1. Arithmetic operators
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#comparison-operators">
-     3.3.2. Comparison operators
+     2.3.2. Comparison operators
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#lets-visualize-it">
-     3.3.3. Let’s visualize it
+     2.3.3. Let’s visualize it
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-types">
-   3.4. Data types
+   2.4. Data types
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#numbers-including-booleans">
-     3.4.1. Numbers (including booleans)
+     2.4.1. Numbers (including booleans)
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#integers">
-       3.4.1.1. Integers
+       2.4.1.1. Integers
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#booleans">
-       3.4.1.2. Booleans
+       2.4.1.2. Booleans
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#floats">
-       3.4.1.3. Floats
+       2.4.1.3. Floats
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#complexes">
-       3.4.1.4. Complexes
+       2.4.1.4. Complexes
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#number-operation">
-       3.4.1.5. Number operation
+       2.4.1.5. Number operation
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#strings-and-their-methods">
-     3.4.2. Strings and their methods
+     2.4.2. Strings and their methods
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string-type">
-       3.4.2.1. String type
+       2.4.2.1. String type
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string-operations">
-       3.4.2.2. String operations
+       2.4.2.2. String operations
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string-methods">
-       3.4.2.3. String methods
+       2.4.2.3. String methods
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string-formatting">
-       3.4.2.4. String formatting
+       2.4.2.4. String formatting
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#formatted-string-literals">
-         3.4.2.4.1. Formatted string literals
+         2.4.2.4.1. Formatted string literals
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#the-string-format-method">
-         3.4.2.4.2. The string format() method
+         2.4.2.4.2. The string format() method
         </a>
        </li>
       </ul>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id1">
-       3.4.2.5. Let’s visualize it
+       2.4.2.5. Let’s visualize it
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#lists-and-their-methods-including-list-comprehensions">
-     3.4.3. Lists and their methods (including list comprehensions)
+     2.4.3. Lists and their methods (including list comprehensions)
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#list-type">
-       3.4.3.1. List type
+       2.4.3.1. List type
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#list-methods">
-       3.4.3.2. List methods
+       2.4.3.2. List methods
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#the-del-statement">
-       3.4.3.3. The del statement
+       2.4.3.3. The del statement
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#list-comprehensions">
-       3.4.3.4. List comprehensions
+       2.4.3.4. List comprehensions
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#nested-list-comprehensions">
-       3.4.3.5. Nested list comprehensions
+       2.4.3.5. Nested list comprehensions
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id2">
-       3.4.3.6. Let’s visualize it
+       2.4.3.6. Let’s visualize it
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#basic-reverse-by-hand">
-         3.4.3.6.1. Basic reverse by hand
+         2.4.3.6.1. Basic reverse by hand
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#insert">
-         3.4.3.6.2. Insert
+         2.4.3.6.2. Insert
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#recursion">
-         3.4.3.6.3. Recursion
+         2.4.3.6.3. Recursion
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#list-comprehension">
-         3.4.3.6.4. List comprehension
+         2.4.3.6.4. List comprehension
         </a>
        </li>
       </ul>
@@ -2072,47 +2072,47 @@ <h2> Contents </h2>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#tuples">
-     3.4.4. Tuples
+     2.4.4. Tuples
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#sets-and-their-methods">
-     3.4.5. Sets and their methods
+     2.4.5. Sets and their methods
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#set-type">
-       3.4.5.1. Set type
+       2.4.5.1. Set type
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#set-methods">
-       3.4.5.2. Set methods
+       2.4.5.2. Set methods
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id3">
-       3.4.5.3. Let’s visualize it
+       2.4.5.3. Let’s visualize it
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#remove-duplicates-from-a-list">
-         3.4.5.3.1. Remove Duplicates from a List
+         2.4.5.3.1. Remove Duplicates from a List
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#check-if-two-strings-are-anagrams">
-         3.4.5.3.2. Check if two strings are Anagrams:
+         2.4.5.3.2. Check if two strings are Anagrams:
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#find-common-elements-in-multiple-lists">
-         3.4.5.3.3. Find common elements in multiple lists:
+         2.4.5.3.3. Find common elements in multiple lists:
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#count-the-number-of-vowels-in-a-string">
-         3.4.5.3.4. Count the number of Vowels in a string:
+         2.4.5.3.4. Count the number of Vowels in a string:
         </a>
        </li>
       </ul>
@@ -2121,27 +2121,27 @@ <h2> Contents </h2>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dictionaries">
-     3.4.6. Dictionaries
+     2.4.6. Dictionaries
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#id4">
-       3.4.6.1. Let’s visualize it
+       2.4.6.1. Let’s visualize it
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#turning-keys-into-values-and-vice-versa">
-         3.4.6.1.1. Turning keys into values and vice versa
+         2.4.6.1.1. Turning keys into values and vice versa
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#filtering-items">
-         3.4.6.1.2. Filtering Items
+         2.4.6.1.2. Filtering Items
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#doing-some-calculations">
-         3.4.6.1.3. Doing Some Calculations
+         2.4.6.1.3. Doing Some Calculations
         </a>
        </li>
       </ul>
@@ -2150,24 +2150,24 @@ <h2> Contents </h2>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#type-casting">
-     3.4.7. Type casting
+     2.4.7. Type casting
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   3.5. Your turn! 🚀
+   2.5. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#self-study">
-   3.6. Self study
+   2.6. Self study
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   3.7. Acknowledgments
+   2.7. Acknowledgments
   </a>
  </li>
 </ul>
@@ -2180,13 +2180,13 @@ <h2> Contents </h2>
                 
               <div>
                 
-  <div class="cell docutils container">
+  <div class="cell tag_hide-input docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Install the necessary dependencies</span>
 
 <span class="kn">import</span> <span class="nn">sys</span>
 <span class="kn">import</span> <span class="nn">os</span>
-<span class="o">!{</span>sys.executable<span class="o">}</span><span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>--quiet<span class="w"> </span>jupyterlab_myst<span class="w"> </span>ipython
+<span class="o">!{</span>sys.executable<span class="o">}</span><span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>--quiet<span class="w"> </span>jupyterlab_myst<span class="w"> </span>ipython<span class="w"> </span>pytest
 </pre></div>
 </div>
 </div>
@@ -2211,11 +2211,10 @@ <h2> Contents </h2>
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.</p>
-</details>
-<section class="tex2jax_ignore mathjax_ignore" id="python-programming-basics">
-<h1><span class="section-number">3. </span>Python programming basics<a class="headerlink" href="#python-programming-basics" title="Permalink to this headline">#</a></h1>
+</details><section class="tex2jax_ignore mathjax_ignore" id="python-programming-basics">
+<h1><span class="section-number">2. </span>Python programming basics<a class="headerlink" href="#python-programming-basics" title="Permalink to this headline">#</a></h1>
 <section id="python-syntax">
-<h2><span class="section-number">3.1. </span>Python syntax<a class="headerlink" href="#python-syntax" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">2.1. </span>Python syntax<a class="headerlink" href="#python-syntax" title="Permalink to this headline">#</a></h2>
 <p><strong>Python syntax compared to other programming languages</strong></p>
 <ul class="simple">
 <li><p>Python was designed for readability and has some similarities to the English language with influence from mathematics.</p></li>
@@ -2223,7 +2222,7 @@ <h2><span class="section-number">3.1. </span>Python syntax<a class="headerlink"
 <li><p>Python relies on indentation, using whitespace, to define scope; such as the scope of loops, functions and classes. Other programming languages often use curly brackets for this purpose.</p></li>
 </ul>
 <section id="python-indentations">
-<h3><span class="section-number">3.1.1. </span>Python indentations<a class="headerlink" href="#python-indentations" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">2.1.1. </span>Python indentations<a class="headerlink" href="#python-indentations" title="Permalink to this headline">#</a></h3>
 <p>While in other programming languages the indentation in code is for readability only, in Python the indentation is very important.</p>
 <p>Python uses indentation to indicate a block of code.</p>
 <div class="cell docutils container">
@@ -2242,7 +2241,7 @@ <h3><span class="section-number">3.1.1. </span>Python indentations<a class="head
 <p>Python will give you an error if you skip the indentation.</p>
 </section>
 <section id="comments">
-<h3><span class="section-number">3.1.2. </span>Comments<a class="headerlink" href="#comments" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">2.1.2. </span>Comments<a class="headerlink" href="#comments" title="Permalink to this headline">#</a></h3>
 <p>Python has the commenting capability for the purpose of in-code documentation. Comments start with a <code class="docutils literal notranslate"><span class="pre">#</span></code>, and Python will render the rest of the line as a comment:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2259,7 +2258,7 @@ <h3><span class="section-number">3.1.2. </span>Comments<a class="headerlink" hre
 </div>
 </section>
 <section id="docstrings">
-<h3><span class="section-number">3.1.3. </span>Docstrings<a class="headerlink" href="#docstrings" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">2.1.3. </span>Docstrings<a class="headerlink" href="#docstrings" title="Permalink to this headline">#</a></h3>
 <p>Python also has extended documentation capability, called docstrings. Docstrings can be one line, or multiline. Docstrings are also comments. Python uses <code class="docutils literal notranslate"><span class="pre">&quot;&quot;&quot;</span></code> at the beginning and end of the docstring:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2278,7 +2277,7 @@ <h3><span class="section-number">3.1.3. </span>Docstrings<a class="headerlink" h
 </section>
 </section>
 <section id="variables">
-<h2><span class="section-number">3.2. </span>Variables<a class="headerlink" href="#variables" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">2.2. </span>Variables<a class="headerlink" href="#variables" title="Permalink to this headline">#</a></h2>
 <p>Python is completely object-oriented, and not “statically typed”. You do not need to declare variables before using them or declare their type. Every variable in Python is an object. Unlike other programming languages, Python has no command for declaring a variable. A variable is created the moment you first assign a value to it. A variable can have a short name (like x and y) or a more descriptive name (age, carname, total_volume). Rules for Python variables:</p>
 <ul class="simple">
 <li><p>A variable name must start with a letter or the underscore character.</p></li>
@@ -2307,7 +2306,7 @@ <h2><span class="section-number">3.2. </span>Variables<a class="headerlink" href
 </div>
 </section>
 <section id="operators">
-<h2><span class="section-number">3.3. </span>Operators<a class="headerlink" href="#operators" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">2.3. </span>Operators<a class="headerlink" href="#operators" title="Permalink to this headline">#</a></h2>
 <p>Operators are used to performing operations on variables and values. Python divides the operators into the following groups:</p>
 <ul class="simple">
 <li><p>Arithmetic operators</p></li>
@@ -2325,7 +2324,7 @@ <h2><span class="section-number">3.3. </span>Operators<a class="headerlink" href
 </ul>
 </div>
 <section id="arithmetic-operators">
-<h3><span class="section-number">3.3.1. </span>Arithmetic operators<a class="headerlink" href="#arithmetic-operators" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">2.3.1. </span>Arithmetic operators<a class="headerlink" href="#arithmetic-operators" title="Permalink to this headline">#</a></h3>
 <p>Arithmetic operators are used with numeric values to perform common mathematical operations.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2368,7 +2367,7 @@ <h3><span class="section-number">3.3.1. </span>Arithmetic operators<a class="hea
 </div>
 </section>
 <section id="comparison-operators">
-<h3><span class="section-number">3.3.2. </span>Comparison operators<a class="headerlink" href="#comparison-operators" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">2.3.2. </span>Comparison operators<a class="headerlink" href="#comparison-operators" title="Permalink to this headline">#</a></h3>
 <p>Comparison operators are used to compare two values.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2403,7 +2402,7 @@ <h3><span class="section-number">3.3.2. </span>Comparison operators<a class="hea
 </div>
 </section>
 <section id="lets-visualize-it">
-<h3><span class="section-number">3.3.3. </span>Let’s visualize it<a class="headerlink" href="#lets-visualize-it" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">2.3.3. </span>Let’s visualize it<a class="headerlink" href="#lets-visualize-it" title="Permalink to this headline">#</a></h3>
 <p>Let’s write a Python program to calculate the discriminant value. It calculates the roots of a quadratic equation of the form ax^2 + bx + c = 0, where a, b, and c are arbitrary numeric values.</p>
 <p>The discriminant value is calculated by using the formula (b^2 - 4ac). The discriminant value is used to determine the number of roots of the quadratic equation.</p>
 <p>If the discriminant is positive, there are two real solutions to the equation. The program prints the discriminant value and the message “Two Solutions.” If the discriminant is zero, there is one real solution to the equation. The program prints the discriminant value and the message “One Solution.”</p>
@@ -2440,7 +2439,7 @@ <h3><span class="section-number">3.3.3. </span>Let’s visualize it<a class="hea
 </section>
 </section>
 <section id="data-types">
-<h2><span class="section-number">3.4. </span>Data types<a class="headerlink" href="#data-types" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">2.4. </span>Data types<a class="headerlink" href="#data-types" title="Permalink to this headline">#</a></h2>
 <p>In programming, data type is an important concept. Variables can store data of different types, and different types can do different things. Python has the following data types built in by default, in these categories:</p>
 <table class="colwidths-auto table">
 <thead>
@@ -2483,7 +2482,7 @@ <h2><span class="section-number">3.4. </span>Data types<a class="headerlink" hre
 </ul>
 </div>
 <section id="numbers-including-booleans">
-<h3><span class="section-number">3.4.1. </span>Numbers (including booleans)<a class="headerlink" href="#numbers-including-booleans" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">2.4.1. </span>Numbers (including booleans)<a class="headerlink" href="#numbers-including-booleans" title="Permalink to this headline">#</a></h3>
 <p>There are three numeric types in Python:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">int</span></code></p></li>
@@ -2498,7 +2497,7 @@ <h3><span class="section-number">3.4.1. </span>Numbers (including booleans)<a cl
 </ul>
 </div>
 <section id="integers">
-<h4><span class="section-number">3.4.1.1. </span>Integers<a class="headerlink" href="#integers" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">2.4.1.1. </span>Integers<a class="headerlink" href="#integers" title="Permalink to this headline">#</a></h4>
 <p>Int, or integer, is a whole number, positive or negative, without decimals, of unlimited length.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2515,7 +2514,7 @@ <h4><span class="section-number">3.4.1.1. </span>Integers<a class="headerlink" h
 </div>
 </section>
 <section id="booleans">
-<h4><span class="section-number">3.4.1.2. </span>Booleans<a class="headerlink" href="#booleans" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">2.4.1.2. </span>Booleans<a class="headerlink" href="#booleans" title="Permalink to this headline">#</a></h4>
 <p>Booleans represent the truth values <code class="docutils literal notranslate"><span class="pre">False</span></code> and <code class="docutils literal notranslate"><span class="pre">True</span></code>. The two objects representing the values <code class="docutils literal notranslate"><span class="pre">False</span></code> and <code class="docutils literal notranslate"><span class="pre">True</span></code> are the only Boolean objects. The Boolean type is a subtype of the integer type, and Boolean values behave like the values 0 and 1, respectively, in almost all contexts, the exception being that when converted to a string, the strings <code class="docutils literal notranslate"><span class="pre">False</span></code> or <code class="docutils literal notranslate"><span class="pre">True</span></code> are returned, respectively.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2537,7 +2536,7 @@ <h4><span class="section-number">3.4.1.2. </span>Booleans<a class="headerlink" h
 </div>
 </section>
 <section id="floats">
-<h4><span class="section-number">3.4.1.3. </span>Floats<a class="headerlink" href="#floats" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">2.4.1.3. </span>Floats<a class="headerlink" href="#floats" title="Permalink to this headline">#</a></h4>
 <p>Float, or “floating point number” is a number, positive or negative, containing one or more decimals.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2570,7 +2569,7 @@ <h4><span class="section-number">3.4.1.3. </span>Floats<a class="headerlink" hre
 </div>
 </section>
 <section id="complexes">
-<h4><span class="section-number">3.4.1.4. </span>Complexes<a class="headerlink" href="#complexes" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">2.4.1.4. </span>Complexes<a class="headerlink" href="#complexes" title="Permalink to this headline">#</a></h4>
 <p>A complex number has two parts, a real part and an imaginary part. Complex numbers are represented as A+Bi or A+Bj, where A is the real part and B is the imaginary part.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2586,7 +2585,7 @@ <h4><span class="section-number">3.4.1.4. </span>Complexes<a class="headerlink"
 </div>
 </section>
 <section id="number-operation">
-<h4><span class="section-number">3.4.1.5. </span>Number operation<a class="headerlink" href="#number-operation" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">2.4.1.5. </span>Number operation<a class="headerlink" href="#number-operation" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Addition.</span>
@@ -2620,7 +2619,7 @@ <h4><span class="section-number">3.4.1.5. </span>Number operation<a class="heade
 </section>
 </section>
 <section id="strings-and-their-methods">
-<h3><span class="section-number">3.4.2. </span>Strings and their methods<a class="headerlink" href="#strings-and-their-methods" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">2.4.2. </span>Strings and their methods<a class="headerlink" href="#strings-and-their-methods" title="Permalink to this headline">#</a></h3>
 <p>Besides numbers, Python can also manipulate strings, which can be expressed in several ways. They can be enclosed in single quotes <code class="docutils literal notranslate"><span class="pre">''</span></code> or double quotes <code class="docutils literal notranslate"><span class="pre">&quot;&quot;</span></code> with the same result.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -2646,7 +2645,7 @@ <h3><span class="section-number">3.4.2. </span>Strings and their methods<a class
 </div>
 </div>
 <section id="string-type">
-<h4><span class="section-number">3.4.2.1. </span>String type<a class="headerlink" href="#string-type" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">2.4.2.1. </span>String type<a class="headerlink" href="#string-type" title="Permalink to this headline">#</a></h4>
 <p><code class="docutils literal notranslate"><span class="pre">\</span></code> can be used to escape quotes. Use <code class="docutils literal notranslate"><span class="pre">\'</span></code> to escape the single quote or use double quotes instead.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2832,7 +2831,7 @@ <h4><span class="section-number">3.4.2.1. </span>String type<a class="headerlink
 </div>
 </section>
 <section id="string-operations">
-<h4><span class="section-number">3.4.2.2. </span>String operations<a class="headerlink" href="#string-operations" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">2.4.2.2. </span>String operations<a class="headerlink" href="#string-operations" title="Permalink to this headline">#</a></h4>
 <p>Strings can be concatenated (glued together) with the <code class="docutils literal notranslate"><span class="pre">+</span></code> operator, and repeated with <code class="docutils literal notranslate"><span class="pre">*</span></code>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2867,7 +2866,7 @@ <h4><span class="section-number">3.4.2.2. </span>String operations<a class="head
 </div>
 </section>
 <section id="string-methods">
-<h4><span class="section-number">3.4.2.3. </span>String methods<a class="headerlink" href="#string-methods" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">2.4.2.3. </span>String methods<a class="headerlink" href="#string-methods" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">hello_world_string</span> <span class="o">=</span> <span class="s2">&quot;Hello, World!&quot;</span>
@@ -3002,10 +3001,10 @@ <h4><span class="section-number">3.4.2.3. </span>String methods<a class="headerl
 </div>
 </section>
 <section id="string-formatting">
-<h4><span class="section-number">3.4.2.4. </span>String formatting<a class="headerlink" href="#string-formatting" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">2.4.2.4. </span>String formatting<a class="headerlink" href="#string-formatting" title="Permalink to this headline">#</a></h4>
 <p>Often you’ll want more control over the formatting of your output than simply printing space-separated values. There are several ways to format output.</p>
 <section id="formatted-string-literals">
-<h5><span class="section-number">3.4.2.4.1. </span>Formatted string literals<a class="headerlink" href="#formatted-string-literals" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">2.4.2.4.1. </span>Formatted string literals<a class="headerlink" href="#formatted-string-literals" title="Permalink to this headline">#</a></h5>
 <p>Formatted string literals (also called f-strings for short) let you include the value of Python expressions inside a string by prefixing the string with <code class="docutils literal notranslate"><span class="pre">f</span></code> or <code class="docutils literal notranslate"><span class="pre">F</span></code> and writing expressions as <code class="docutils literal notranslate"><span class="pre">{expression}</span></code>.</p>
 <p>An optional format specifier can follow the expression. This allows greater control over how the value is formatted. The following example rounds pi to three places after the decimal.</p>
 <div class="cell docutils container">
@@ -3034,7 +3033,7 @@ <h5><span class="section-number">3.4.2.4.1. </span>Formatted string literals<a c
 </div>
 </section>
 <section id="the-string-format-method">
-<h5><span class="section-number">3.4.2.4.2. </span>The string format() method<a class="headerlink" href="#the-string-format-method" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">2.4.2.4.2. </span>The string format() method<a class="headerlink" href="#the-string-format-method" title="Permalink to this headline">#</a></h5>
 <p>Basic usage of the <code class="docutils literal notranslate"><span class="pre">str.format()</span></code> method looks like this:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -3103,7 +3102,7 @@ <h5><span class="section-number">3.4.2.4.2. </span>The string format() method<a
 </section>
 </section>
 <section id="id1">
-<h4><span class="section-number">3.4.2.5. </span>Let’s visualize it<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">2.4.2.5. </span>Let’s visualize it<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h4>
 <p>In this example, every augmented assignment adds a new syllable to the final word using the += operator. The concatenation technique is used in a for loop to construct a string from several strings in a list or any other iterable:</p>
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@@ -3169,7 +3168,7 @@ <h4><span class="section-number">3.4.2.5. </span>Let’s visualize it<a class="h
 </section>
 </section>
 <section id="lists-and-their-methods-including-list-comprehensions">
-<h3><span class="section-number">3.4.3. </span>Lists and their methods (including list comprehensions)<a class="headerlink" href="#lists-and-their-methods-including-list-comprehensions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">2.4.3. </span>Lists and their methods (including list comprehensions)<a class="headerlink" href="#lists-and-their-methods-including-list-comprehensions" title="Permalink to this headline">#</a></h3>
 <p>Python knows several compound data types, used to group together other values. The most versatile is the list, which can be written as a list of comma-separated values (items) between square brackets. Lists might contain items of different types, but usually, the items all have the same type.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
@@ -3180,7 +3179,7 @@ <h3><span class="section-number">3.4.3. </span>Lists and their methods (includin
 </ul>
 </div>
 <section id="list-type">
-<h4><span class="section-number">3.4.3.1. </span>List type<a class="headerlink" href="#list-type" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">2.4.3.1. </span>List type<a class="headerlink" href="#list-type" title="Permalink to this headline">#</a></h4>
 <p>Lists are very similar to arrays. They can contain any type of variable, and they can contain as many variables as you wish. Lists can also be iterated over in a very simple manner.</p>
 <p>Here is an example of how to build a list.</p>
 <div class="cell docutils container">
@@ -3284,7 +3283,7 @@ <h4><span class="section-number">3.4.3.1. </span>List type<a class="headerlink"
 </div>
 </section>
 <section id="list-methods">
-<h4><span class="section-number">3.4.3.2. </span>List methods<a class="headerlink" href="#list-methods" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">2.4.3.2. </span>List methods<a class="headerlink" href="#list-methods" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pytest</span>
@@ -3404,7 +3403,7 @@ <h4><span class="section-number">3.4.3.2. </span>List methods<a class="headerlin
 </div>
 </section>
 <section id="the-del-statement">
-<h4><span class="section-number">3.4.3.3. </span>The del statement<a class="headerlink" href="#the-del-statement" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">2.4.3.3. </span>The del statement<a class="headerlink" href="#the-del-statement" title="Permalink to this headline">#</a></h4>
 <p>There is a way to remove an item from a list given its index instead of its value: the <code class="docutils literal notranslate"><span class="pre">del</span></code> statement. This differs from the <code class="docutils literal notranslate"><span class="pre">pop()</span></code> method which returns a value. The <code class="docutils literal notranslate"><span class="pre">del</span></code> statement can also be used to remove slices from a list or clear the entire list (which we did earlier by assignment of an empty list to the slice).</p>
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@@ -3433,7 +3432,7 @@ <h4><span class="section-number">3.4.3.3. </span>The del statement<a class="head
 </div>
 </section>
 <section id="list-comprehensions">
-<h4><span class="section-number">3.4.3.4. </span>List comprehensions<a class="headerlink" href="#list-comprehensions" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">2.4.3.4. </span>List comprehensions<a class="headerlink" href="#list-comprehensions" title="Permalink to this headline">#</a></h4>
 <p>List comprehensions provide a concise way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence or iterable or to create a subsequence of those elements that satisfy a certain condition. A list comprehension consists of brackets containing an expression followed by a for clause, then zero or more <code class="docutils literal notranslate"><span class="pre">for</span></code> or <code class="docutils literal notranslate"><span class="pre">if</span></code> clauses. The result will be a new list resulting from evaluating the expression in the context of the <code class="docutils literal notranslate"><span class="pre">for</span></code> and <code class="docutils literal notranslate"><span class="pre">if</span></code> clauses that follow it.</p>
 <p>For example, assume we want to create a list of squares, like:</p>
 <div class="cell docutils container">
@@ -3555,7 +3554,7 @@ <h4><span class="section-number">3.4.3.4. </span>List comprehensions<a class="he
 </div>
 </section>
 <section id="nested-list-comprehensions">
-<h4><span class="section-number">3.4.3.5. </span>Nested list comprehensions<a class="headerlink" href="#nested-list-comprehensions" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">2.4.3.5. </span>Nested list comprehensions<a class="headerlink" href="#nested-list-comprehensions" title="Permalink to this headline">#</a></h4>
 <p>The initial expression in a list comprehension can be any arbitrary expression, including another list comprehension.</p>
 <p>Consider the following example of a 3x4 matrix implemented as a list of 3 lists of length 4:</p>
 <div class="cell docutils container">
@@ -3636,7 +3635,7 @@ <h4><span class="section-number">3.4.3.5. </span>Nested list comprehensions<a cl
 </div>
 </section>
 <section id="id2">
-<h4><span class="section-number">3.4.3.6. </span>Let’s visualize it<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">2.4.3.6. </span>Let’s visualize it<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
 <p>So far, you’ve seen a few tools and techniques to either reverse lists in place or create reversed copies of existing lists. Most of the time, these tools and techniques are the way to go when it comes to reversing lists in Python. For example, Your first approach to iterating over a list in reverse order might be to use reversed().</p>
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@@ -3690,7 +3689,7 @@ <h4><span class="section-number">3.4.3.6. </span>Let’s visualize it<a class="h
 </div>
 <iframe width="800" height="500" frameborder="0" src="https://pythontutor.com/iframe-embed.html#code=digits%20%3D%20%5B0,%201,%202,%203,%204,%205,%206,%207,%208,%209%5D%0A%0Afor%20digit%20in%20digits%5B%3A%3A-1%5D%3A%0A%20%20%20%20print%28digit%29%0A&codeDivHeight=400&codeDivWidth=350&cumulative=false&curInstr=0&heapPrimitives=nevernest&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false"> </iframe>
 <section id="basic-reverse-by-hand">
-<h5><span class="section-number">3.4.3.6.1. </span>Basic reverse by hand<a class="headerlink" href="#basic-reverse-by-hand" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">2.4.3.6.1. </span>Basic reverse by hand<a class="headerlink" href="#basic-reverse-by-hand" title="Permalink to this headline">#</a></h5>
 <p>However, if you ever need to reverse lists by hand, then it’d be beneficial for you to understand the logic behind the process.</p>
 <div class="cell docutils container">
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@@ -3716,7 +3715,7 @@ <h5><span class="section-number">3.4.3.6.1. </span>Basic reverse by hand<a class
 <p>Every iteration of the for loop takes a subsequent item from a_list and creates a new list that results from concatenating [item] and result, which initially holds an empty list. The newly created list is reassigned to result. This function doesn’t modify a_list.</p>
 </section>
 <section id="insert">
-<h5><span class="section-number">3.4.3.6.2. </span>Insert<a class="headerlink" href="#insert" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">2.4.3.6.2. </span>Insert<a class="headerlink" href="#insert" title="Permalink to this headline">#</a></h5>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
 <p>Note: The example above uses a wasteful technique because it creates several lists only to throw them away in the next iteration.</p>
@@ -3746,7 +3745,7 @@ <h5><span class="section-number">3.4.3.6.2. </span>Insert<a class="headerlink" h
 <p>Using .insert() like in the above example has a significant drawback. Insert operations at the left end of Python lists are known to be inefficient regarding execution time. That’s because Python needs to move all the items one step back to insert the new item at the first position.</p>
 </section>
 <section id="recursion">
-<h5><span class="section-number">3.4.3.6.3. </span>Recursion<a class="headerlink" href="#recursion" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">2.4.3.6.3. </span>Recursion<a class="headerlink" href="#recursion" title="Permalink to this headline">#</a></h5>
 <p>You can also use recursion to reverse your lists. Recursion is when you define a function that calls itself. This creates a loop that can become infinite if you don’t provide a base case that produces a result without calling the function again.</p>
 <p>You need the base case to end the recursive loop. When it comes to reversing lists, the base case would be reached when the recursive calls get to the end of the input list. You also need to define the recursive case, which reduces all successive cases toward the base case and, therefore, to the loop’s end.</p>
 <p>Here’s how you can define a recursive function to return a reversed copy of a given list:</p>
@@ -3776,7 +3775,7 @@ <h5><span class="section-number">3.4.3.6.3. </span>Recursion<a class="headerlink
 <p>Inside reversed_list(), you first check the base case, in which the input list is empty and makes the function return. The else clause provides the recursive case, which is a call to reversed_list() itself but with a slice of the original list, a_list[1:]. This slice contains all the items in a_list except for the first item, which is then added as a single-item list (a_list[:1]) to the result of the recursive call.</p>
 </section>
 <section id="list-comprehension">
-<h5><span class="section-number">3.4.3.6.4. </span>List comprehension<a class="headerlink" href="#list-comprehension" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">2.4.3.6.4. </span>List comprehension<a class="headerlink" href="#list-comprehension" title="Permalink to this headline">#</a></h5>
 <p>If you’re working with lists in Python, then you probably want to consider using a list comprehension. This tool is quite popular in the Python space because it represents the Pythonic way to process lists.</p>
 <p>Here’s an example of how to use a list comprehension to create a reversed list:</p>
 <div class="cell docutils container">
@@ -3800,7 +3799,7 @@ <h5><span class="section-number">3.4.3.6.4. </span>List comprehension<a class="h
 </section>
 </section>
 <section id="tuples">
-<h3><span class="section-number">3.4.4. </span>Tuples<a class="headerlink" href="#tuples" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">2.4.4. </span>Tuples<a class="headerlink" href="#tuples" title="Permalink to this headline">#</a></h3>
 <p>A tuple is a collection that is ordered and unchangeable. In Python, tuples are written with
 round brackets.</p>
 <p>The Tuples have the following properties:</p>
@@ -3921,7 +3920,7 @@ <h3><span class="section-number">3.4.4. </span>Tuples<a class="headerlink" href=
 </div>
 </section>
 <section id="sets-and-their-methods">
-<h3><span class="section-number">3.4.5. </span>Sets and their methods<a class="headerlink" href="#sets-and-their-methods" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">2.4.5. </span>Sets and their methods<a class="headerlink" href="#sets-and-their-methods" title="Permalink to this headline">#</a></h3>
 <p>A set is a collection that is unordered and unindexed.</p>
 <p>In Python, sets are written with <code class="docutils literal notranslate"><span class="pre">{}</span></code>.</p>
 <p>Set objects also support mathematical operations like union, intersection, difference, and symmetric difference.</p>
@@ -3933,7 +3932,7 @@ <h3><span class="section-number">3.4.5. </span>Sets and their methods<a class="h
 </ul>
 </div>
 <section id="set-type">
-<h4><span class="section-number">3.4.5.1. </span>Set type<a class="headerlink" href="#set-type" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">2.4.5.1. </span>Set type<a class="headerlink" href="#set-type" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fruits_set</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;apple&quot;</span><span class="p">,</span> <span class="s2">&quot;banana&quot;</span><span class="p">,</span> <span class="s2">&quot;cherry&quot;</span><span class="p">}</span>
@@ -3955,7 +3954,7 @@ <h4><span class="section-number">3.4.5.1. </span>Set type<a class="headerlink" h
 </div>
 </section>
 <section id="set-methods">
-<h4><span class="section-number">3.4.5.2. </span>Set methods<a class="headerlink" href="#set-methods" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">2.4.5.2. </span>Set methods<a class="headerlink" href="#set-methods" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fruits_set</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;apple&quot;</span><span class="p">,</span> <span class="s2">&quot;banana&quot;</span><span class="p">,</span> <span class="s2">&quot;cherry&quot;</span><span class="p">}</span>
@@ -4055,10 +4054,10 @@ <h4><span class="section-number">3.4.5.2. </span>Set methods<a class="headerlink
 </div>
 </section>
 <section id="id3">
-<h4><span class="section-number">3.4.5.3. </span>Let’s visualize it<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">2.4.5.3. </span>Let’s visualize it<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h4>
 <p>Now you’ve seen some of the tools and techniques of <code class="docutils literal notranslate"><span class="pre">set</span></code>, so when you come across a bunch of data, you can use <code class="docutils literal notranslate"><span class="pre">set</span></code> to process them. Let’s look at some real-world examples.</p>
 <section id="remove-duplicates-from-a-list">
-<h5><span class="section-number">3.4.5.3.1. </span>Remove Duplicates from a List<a class="headerlink" href="#remove-duplicates-from-a-list" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">2.4.5.3.1. </span>Remove Duplicates from a List<a class="headerlink" href="#remove-duplicates-from-a-list" title="Permalink to this headline">#</a></h5>
 <p>Suppose you have a list, but there are some duplicate elements in it, and you want to get rid of these duplicates, you can use set to implement it.</p>
 <div class="cell docutils container">
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@@ -4081,7 +4080,7 @@ <h5><span class="section-number">3.4.5.3.1. </span>Remove Duplicates from a List
 <p>We can do it by converting a list into a set and then into a list, taking advantage of the fact that there are no duplicate elements in a set</p>
 </section>
 <section id="check-if-two-strings-are-anagrams">
-<h5><span class="section-number">3.4.5.3.2. </span>Check if two strings are Anagrams:<a class="headerlink" href="#check-if-two-strings-are-anagrams" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">2.4.5.3.2. </span>Check if two strings are Anagrams:<a class="headerlink" href="#check-if-two-strings-are-anagrams" title="Permalink to this headline">#</a></h5>
 <p>When using set on an object of type string, we find that string is split into unordered letters, so we can use it to do some things.</p>
 <div class="cell docutils container">
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@@ -4107,7 +4106,7 @@ <h5><span class="section-number">3.4.5.3.2. </span>Check if two strings are Anag
 <p>In this example, we use this feature of set to compare two words to see if they are Anagrams.</p>
 </section>
 <section id="find-common-elements-in-multiple-lists">
-<h5><span class="section-number">3.4.5.3.3. </span>Find common elements in multiple lists:<a class="headerlink" href="#find-common-elements-in-multiple-lists" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">2.4.5.3.3. </span>Find common elements in multiple lists:<a class="headerlink" href="#find-common-elements-in-multiple-lists" title="Permalink to this headline">#</a></h5>
 <p>When you want to ask for the intersection of some lists to find their common ground, you need set to help you.</p>
 <div class="cell docutils container">
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@@ -4133,7 +4132,7 @@ <h5><span class="section-number">3.4.5.3.3. </span>Find common elements in multi
 <p>The function of the intersection method is to find the intersection of the object and the argument, which has the same effect as using the &amp; operation. But regardless of which method is used to find the intersection, the object must be set.</p>
 </section>
 <section id="count-the-number-of-vowels-in-a-string">
-<h5><span class="section-number">3.4.5.3.4. </span>Count the number of Vowels in a string:<a class="headerlink" href="#count-the-number-of-vowels-in-a-string" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">2.4.5.3.4. </span>Count the number of Vowels in a string:<a class="headerlink" href="#count-the-number-of-vowels-in-a-string" title="Permalink to this headline">#</a></h5>
 <p>Similarly, we can put keywords in set to count the number of times they appear in the input.</p>
 <div class="cell docutils container">
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@@ -4163,7 +4162,7 @@ <h5><span class="section-number">3.4.5.3.4. </span>Count the number of Vowels in
 </section>
 </section>
 <section id="dictionaries">
-<h3><span class="section-number">3.4.6. </span>Dictionaries<a class="headerlink" href="#dictionaries" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">2.4.6. </span>Dictionaries<a class="headerlink" href="#dictionaries" title="Permalink to this headline">#</a></h3>
 <p>A dictionary is a collection that is unordered, changeable and indexed. In Python, dictionaries are written with curly brackets, and they have keys and values.</p>
 <p>Dictionaries are sometimes found in other languages as “associative memories” or “associative arrays”. Unlike sequences, which are indexed by a range of numbers, dictionaries are indexed by keys, which can be any immutable type; strings and numbers can always be keys. Tuples can be used as keys if they contain only strings, numbers, or tuples; if a tuple contains any mutable object either directly or indirectly, it cannot be used as a key. You can’t use lists as keys, since lists can be modified in place using index assignments, slice assignments, or methods like <code class="docutils literal notranslate"><span class="pre">append()</span></code> and <code class="docutils literal notranslate"><span class="pre">extend()</span></code>.</p>
 <p>It is best to think of a dictionary as a set of key: value pairs, with the requirement that the keys are unique (within one dictionary). A pair of braces creates an empty dictionary: <code class="docutils literal notranslate"><span class="pre">{}</span></code>. Placing a comma-separated list of key-value pairs within the braces adds initial key-value pairs to the dictionary; this is also the way dictionaries are written on output.</p>
@@ -4276,14 +4275,14 @@ <h3><span class="section-number">3.4.6. </span>Dictionaries<a class="headerlink"
 </div>
 </div>
 <section id="id4">
-<h4><span class="section-number">3.4.6.1. </span>Let’s visualize it<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">2.4.6.1. </span>Let’s visualize it<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h4>
 <p>So far, you’ve seen the more basic ways of accessing through a dictionary in Python. Now it’s time to see how you can perform some actions with the items of a dictionary during iteration. Let’s look at some real-world examples.</p>
 <div class="admonition seealso">
 <p class="admonition-title">See also</p>
 <p>Note: Later on in this article, you’ll see another way of solving these very same problems by using other Python tools.</p>
 </div>
 <section id="turning-keys-into-values-and-vice-versa">
-<h5><span class="section-number">3.4.6.1.1. </span>Turning keys into values and vice versa<a class="headerlink" href="#turning-keys-into-values-and-vice-versa" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">2.4.6.1.1. </span>Turning keys into values and vice versa<a class="headerlink" href="#turning-keys-into-values-and-vice-versa" title="Permalink to this headline">#</a></h5>
 <p>Suppose you have a dictionary and for some reason need to turn keys into values and vice versa. In this situation, you can use a for loop to iterate through the dictionary and build the new dictionary by using the keys as values and vice versa:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -4306,7 +4305,7 @@ <h5><span class="section-number">3.4.6.1.1. </span>Turning keys into values and
 <p>The expression new_dict[value] = key did all the work for you by turning the keys into values and using the values as keys. For this code to work, the data stored in the original values must be of a hashable data type.</p>
 </section>
 <section id="filtering-items">
-<h5><span class="section-number">3.4.6.1.2. </span>Filtering Items<a class="headerlink" href="#filtering-items" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">2.4.6.1.2. </span>Filtering Items<a class="headerlink" href="#filtering-items" title="Permalink to this headline">#</a></h5>
 <p>Sometimes you’ll be in situations where you have a dictionary and you want to create a new one to store only the data that satisfies a given condition. You can do this with an if statement inside a for loop as follows:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -4331,7 +4330,7 @@ <h5><span class="section-number">3.4.6.1.2. </span>Filtering Items<a class="head
 <p>In this example, you’ve filtered out the items with a value greater than 2. Now new_dict only contains the items that satisfy the condition value &lt;= 2. This is one possible solution for this kind of problem. Later on, you’ll see a more Pythonic and readable way to get the same result.</p>
 </section>
 <section id="doing-some-calculations">
-<h5><span class="section-number">3.4.6.1.3. </span>Doing Some Calculations<a class="headerlink" href="#doing-some-calculations" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">2.4.6.1.3. </span>Doing Some Calculations<a class="headerlink" href="#doing-some-calculations" title="Permalink to this headline">#</a></h5>
 <p>It’s also common to need to do some calculations while you iterate through a dictionary in Python. Suppose you’ve stored the data for your company’s sales in a dictionary, and now you want to know the total income of the year.</p>
 <p>To solve this problem you could define a variable with an initial value of zero. Then, you can accumulate every value of your dictionary in that variable:</p>
 <div class="cell docutils container">
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 </section>
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-<h3><span class="section-number">3.4.7. </span>Type casting<a class="headerlink" href="#type-casting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">2.4.7. </span>Type casting<a class="headerlink" href="#type-casting" title="Permalink to this headline">#</a></h3>
 <p>There may be times when you want to specify a type to a variable. This can be done with <strong>casting</strong>. Python is an object-orientated language, and as such it uses classes to define data types, including its primitive types. Casting in Python is therefore done using constructor functions.</p>
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@@ -4408,15 +4407,15 @@ <h3><span class="section-number">3.4.7. </span>Type casting<a class="headerlink"
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">3.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">2.5. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Practice the Python programming basics by following this <a class="reference internal" href="../assignments/prerequisites/python-programming-basics.html"><span class="doc std std-doc">assignment</span></a>.</p>
 </section>
 <section id="self-study">
-<h2><span class="section-number">3.6. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">2.6. </span>Self study<a class="headerlink" href="#self-study" title="Permalink to this headline">#</a></h2>
 <p>Here is a list of free/open source learning resources for advanced <a class="reference external" href="https://github.com/ocademy-ai/open-learning-resources/blob/main/README.md#python">Python programming</a>.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">3.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">2.7. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/trekhleb/learn-python">learn-python</a> by <a class="reference external" href="https://github.com/trekhleb">Oleksii Trekhleb</a> and <a class="reference external" href="https://realpython.com/">Real Python</a>. They contribute the majority of the content in this chapter.</p>
 </section>
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+    <title>1. Python programming introduction &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="next" title="3. Python programming basics" href="python-programming-basics.html" />
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+     40.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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-     41.72. Regularized Linear Models
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+     40.83. Counterintuitive Challenges in ML Debugging
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     <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-classification.html">
-     41.84. Case Study: Debugging in Classification
+     40.84. Case Study: Debugging in Classification
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/machine-learning-productionization/debugging-in-regression.html">
-     41.85. Case Study: Debugging in Regression
+     40.85. Case Study: Debugging in Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/study-the-solvers.html">
-     41.86. Study the solvers
+     40.86. Study the solvers
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/build-classification-models.html">
-     41.87. Build classification models
+     40.87. Build classification models
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/build-classification-model.html">
-     41.88. Build Classification Model
+     40.88. Build Classification Model
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/ml-fundamentals/parameter-play.html">
-     41.89. Parameter play
+     40.89. Parameter play
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/cnn/how-to-choose-cnn-architecture-mnist.html">
-     41.90. How to choose cnn architecture mnist
+     40.90. How to choose cnn architecture mnist
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/cnn/sign-language-digits-classification-with-cnn.html">
-     41.92. Sign Language Digits Classification with CNN
+     40.92. Sign Language Digits Classification with CNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/cnn/object-recognition-in-images-using-cnn.html">
-     41.94. Object Recognition in Images using CNN
+     40.94. Object Recognition in Images using CNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/tensorflow/intro_to_tensorflow_for_deeplearning.html">
-     41.95. Intro to TensorFlow for Deep Learning
+     40.95. Intro to TensorFlow for Deep Learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/lstm/bitcoin-lstm-model-with-tweet-volume-and-sentiment.html">
-     41.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/rnn/google-stock-price-prediction-rnn.html">
-     41.99. Google Stock Price Prediction RNN
+     40.99. Google Stock Price Prediction RNN
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/autoencoder/autoencoder.html">
-     41.101. Intro to Autoencoders
+     40.101. Intro to Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/autoencoder/base-denoising-autoencoder-dimension-reduction.html">
-     41.102. Base/Denoising Autoencoder &amp; Dimension Reduction
+     40.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     41.103. Fun with Variational Autoencoders
+     40.103. Fun with Variational Autoencoders
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/time-series-forecasting-assignment.html">
-     41.104. Time Series Forecasting Assignment
+     40.104. Time Series Forecasting Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/nn-for-classification-assignment.html">
-     41.106. Neural Networks for Classification with TensorFlow
+     40.106. Neural Networks for Classification with TensorFlow
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/nn-classify-15-fruits-assignment.html">
-     41.107. NN Classify 15 Fruits Assignment
+     40.107. NN Classify 15 Fruits Assignment
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/dqn/dqn-on-foreign-exchange-market.html">
-     41.112. DQN On Foreign Exchange Market
+     40.112. DQN On Foreign Exchange Market
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/gan/art-by-gan.html">
-     41.113. Art by gan
+     40.113. Art by gan
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/gan/gan-introduction.html">
-     41.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     41.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
    </li>
   </ul>
  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../slides/introduction.html">
-   42. Slides
+   41. Slides
   </a>
   <input class="toctree-checkbox" id="toctree-checkbox-15" name="toctree-checkbox-15" type="checkbox"/>
   <label for="toctree-checkbox-15">
@@ -1211,107 +1211,107 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/python-programming/python-programming-introduction.html">
-     42.1. Python programming introduction
+     41.1. Python programming introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/python-programming/python-programming-basics.html">
-     42.2. Python programming basics
+     41.2. Python programming basics
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/python-programming/python-programming-advanced.html">
-     42.3. Python programming advanced
+     41.3. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/data-science/data-science-introduction.html">
-     42.4. Data Science introduction
+     41.4. Data Science introduction
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/data-science/relational-vs-non-relational-database.html">
-     42.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/data-science/numpy-and-pandas.html">
-     42.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/data-science/data-visualization.html">
-     42.7. Data visualization
+     41.7. Data visualization
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/data-science/data-science-lifecycle.html">
-     42.8. Data Science lifecycle
+     41.8. Data Science lifecycle
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/data-science/data-science-in-the-cloud.html">
-     42.9. Data Science in the cloud
+     41.9. Data Science in the cloud
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/data-science/data-science-in-real-world.html">
-     42.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-fundamentals/ml-overview.html">
-     42.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-fundamentals/linear-regression.html">
-     42.12. Linear Regression
+     41.12. Linear Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-fundamentals/logistic-regression.html">
-     42.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-fundamentals/logistic-regression-condensed.html">
-     42.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-fundamentals/neural-network.html">
-     42.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-fundamentals/build-an-ml-web-app.html">
-     42.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-advanced/unsupervised-learning.html">
-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-advanced/kernel-method.html">
-     42.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/ml-advanced/model-selection.html">
-     42.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../slides/deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1575,167 +1575,141 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
     </div>
     <nav id="bd-toc-nav" aria-label="Page">
         <ul class="visible nav section-nav flex-column">
- <li class="toc-h1 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#">
-   1. Python programming basics
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#why-python">
+   1.1. Why Python?
   </a>
-  <ul class="visible nav section-nav flex-column">
-   <li class="toc-h2 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#python-syntax">
-     1.1. Python syntax
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#environment-setup">
+   1.2. Environment setup
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#installing-python">
+     1.2.1. Installing Python
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#python-shell">
+     1.2.2. Python shell
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#installing-visual-studio-code">
+     1.2.3. Installing Visual Studio Code
     </a>
     <ul class="nav section-nav flex-column">
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#python-indentations">
-       1.1.1. Python indentations
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#how-to-use-visual-studio-code">
+       1.2.3.1. How to use Visual Studio Code
       </a>
      </li>
     </ul>
    </li>
   </ul>
  </li>
- <li class="toc-h1 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#python-programming-introduction">
-   2. Python programming introduction
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#basic-python">
+   1.3. Basic Python
   </a>
-  <ul class="visible nav section-nav flex-column">
-   <li class="toc-h2 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#why-python">
-     2.1. Why Python?
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#python-syntax">
+     1.3.1. Python syntax
     </a>
    </li>
-   <li class="toc-h2 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#environment-setup">
-     2.2. Environment setup
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#python-indentation">
+     1.3.2. Python indentation
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#comments">
+     1.3.3. Comments
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#data-types">
+     1.3.4. Data types
     </a>
     <ul class="nav section-nav flex-column">
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#installing-python">
-       2.2.1. Installing Python
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#number">
+       1.3.4.1. Number
       </a>
      </li>
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#python-shell">
-       2.2.2. Python shell
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#string">
+       1.3.4.2. String
       </a>
      </li>
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#installing-visual-studio-code">
-       2.2.3. Installing Visual Studio Code
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#booleans">
+       1.3.4.3. Booleans
       </a>
-      <ul class="nav section-nav flex-column">
-       <li class="toc-h4 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#how-to-use-visual-studio-code">
-         2.2.3.1. How to use Visual Studio Code
-        </a>
-       </li>
-      </ul>
      </li>
-    </ul>
-   </li>
-   <li class="toc-h2 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#basic-python">
-     2.3. Basic Python
-    </a>
-    <ul class="nav section-nav flex-column">
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#id1">
-       2.3.1. Python syntax
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#list">
+       1.3.4.4. List
       </a>
      </li>
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#python-indentation">
-       2.3.2. Python indentation
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#dictionary">
+       1.3.4.5. Dictionary
       </a>
      </li>
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#comments">
-       2.3.3. Comments
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#tuple">
+       1.3.4.6. Tuple
       </a>
      </li>
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#data-types">
-       2.3.4. Data types
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#set">
+       1.3.4.7. Set
       </a>
-      <ul class="nav section-nav flex-column">
-       <li class="toc-h4 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#number">
-         2.3.4.1. Number
-        </a>
-       </li>
-       <li class="toc-h4 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#string">
-         2.3.4.2. String
-        </a>
-       </li>
-       <li class="toc-h4 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#booleans">
-         2.3.4.3. Booleans
-        </a>
-       </li>
-       <li class="toc-h4 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#list">
-         2.3.4.4. List
-        </a>
-       </li>
-       <li class="toc-h4 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#dictionary">
-         2.3.4.5. Dictionary
-        </a>
-       </li>
-       <li class="toc-h4 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#tuple">
-         2.3.4.6. Tuple
-        </a>
-       </li>
-       <li class="toc-h4 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#set">
-         2.3.4.7. Set
-        </a>
-       </li>
-      </ul>
      </li>
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#control-flow-and-function">
-       2.3.5. Control flow and function
+    </ul>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#control-flow-and-function">
+     1.3.5. Control flow and function
+    </a>
+    <ul class="nav section-nav flex-column">
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#if">
+       1.3.5.1. if
       </a>
-      <ul class="nav section-nav flex-column">
-       <li class="toc-h4 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#if">
-         2.3.5.1. if
-        </a>
-       </li>
-       <li class="toc-h4 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#for">
-         2.3.5.2. for
-        </a>
-       </li>
-       <li class="toc-h4 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#def">
-         2.3.5.3. def
-        </a>
-       </li>
-      </ul>
      </li>
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#python-file">
-       2.3.6. Python file
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#for">
+       1.3.5.2. for
+      </a>
+     </li>
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#def">
+       1.3.5.3. def
       </a>
      </li>
     </ul>
    </li>
-   <li class="toc-h2 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#your-turn">
-     2.4. Your turn! 🚀
-    </a>
-   </li>
-   <li class="toc-h2 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#acknowledgments">
-     2.5. Acknowledgments
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#python-file">
+     1.3.6. Python file
     </a>
    </li>
   </ul>
  </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#your-turn">
+   1.4. Your turn! 🚀
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#acknowledgments">
+   1.5. Acknowledgments
+  </a>
+ </li>
 </ul>
 
     </nav>
@@ -1745,7 +1719,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <div class="col pl-md-3 pl-lg-5 content-container">
             <!-- Table of contents that is only displayed when printing the page -->
             <div id="jb-print-docs-body" class="onlyprint">
-                <h1>Python programming basics</h1>
+                <h1>Python programming introduction</h1>
                 <!-- Table of contents -->
                 <div id="print-main-content">
                     <div id="jb-print-toc">
@@ -1755,167 +1729,141 @@ <h2> Contents </h2>
                         </div>
                         <nav aria-label="Page">
                             <ul class="visible nav section-nav flex-column">
- <li class="toc-h1 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#">
-   1. Python programming basics
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#why-python">
+   1.1. Why Python?
   </a>
-  <ul class="visible nav section-nav flex-column">
-   <li class="toc-h2 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#python-syntax">
-     1.1. Python syntax
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#environment-setup">
+   1.2. Environment setup
+  </a>
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#installing-python">
+     1.2.1. Installing Python
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#python-shell">
+     1.2.2. Python shell
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#installing-visual-studio-code">
+     1.2.3. Installing Visual Studio Code
     </a>
     <ul class="nav section-nav flex-column">
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#python-indentations">
-       1.1.1. Python indentations
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#how-to-use-visual-studio-code">
+       1.2.3.1. How to use Visual Studio Code
       </a>
      </li>
     </ul>
    </li>
   </ul>
  </li>
- <li class="toc-h1 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#python-programming-introduction">
-   2. Python programming introduction
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#basic-python">
+   1.3. Basic Python
   </a>
-  <ul class="visible nav section-nav flex-column">
-   <li class="toc-h2 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#why-python">
-     2.1. Why Python?
+  <ul class="nav section-nav flex-column">
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#python-syntax">
+     1.3.1. Python syntax
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#python-indentation">
+     1.3.2. Python indentation
+    </a>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#comments">
+     1.3.3. Comments
     </a>
    </li>
-   <li class="toc-h2 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#environment-setup">
-     2.2. Environment setup
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#data-types">
+     1.3.4. Data types
     </a>
     <ul class="nav section-nav flex-column">
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#installing-python">
-       2.2.1. Installing Python
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#number">
+       1.3.4.1. Number
       </a>
      </li>
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#python-shell">
-       2.2.2. Python shell
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#string">
+       1.3.4.2. String
       </a>
      </li>
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#installing-visual-studio-code">
-       2.2.3. Installing Visual Studio Code
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#booleans">
+       1.3.4.3. Booleans
       </a>
-      <ul class="nav section-nav flex-column">
-       <li class="toc-h4 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#how-to-use-visual-studio-code">
-         2.2.3.1. How to use Visual Studio Code
-        </a>
-       </li>
-      </ul>
      </li>
-    </ul>
-   </li>
-   <li class="toc-h2 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#basic-python">
-     2.3. Basic Python
-    </a>
-    <ul class="nav section-nav flex-column">
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#id1">
-       2.3.1. Python syntax
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#list">
+       1.3.4.4. List
       </a>
      </li>
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#python-indentation">
-       2.3.2. Python indentation
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#dictionary">
+       1.3.4.5. Dictionary
       </a>
      </li>
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#comments">
-       2.3.3. Comments
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#tuple">
+       1.3.4.6. Tuple
       </a>
      </li>
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#data-types">
-       2.3.4. Data types
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#set">
+       1.3.4.7. Set
       </a>
-      <ul class="nav section-nav flex-column">
-       <li class="toc-h4 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#number">
-         2.3.4.1. Number
-        </a>
-       </li>
-       <li class="toc-h4 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#string">
-         2.3.4.2. String
-        </a>
-       </li>
-       <li class="toc-h4 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#booleans">
-         2.3.4.3. Booleans
-        </a>
-       </li>
-       <li class="toc-h4 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#list">
-         2.3.4.4. List
-        </a>
-       </li>
-       <li class="toc-h4 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#dictionary">
-         2.3.4.5. Dictionary
-        </a>
-       </li>
-       <li class="toc-h4 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#tuple">
-         2.3.4.6. Tuple
-        </a>
-       </li>
-       <li class="toc-h4 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#set">
-         2.3.4.7. Set
-        </a>
-       </li>
-      </ul>
      </li>
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#control-flow-and-function">
-       2.3.5. Control flow and function
+    </ul>
+   </li>
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#control-flow-and-function">
+     1.3.5. Control flow and function
+    </a>
+    <ul class="nav section-nav flex-column">
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#if">
+       1.3.5.1. if
       </a>
-      <ul class="nav section-nav flex-column">
-       <li class="toc-h4 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#if">
-         2.3.5.1. if
-        </a>
-       </li>
-       <li class="toc-h4 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#for">
-         2.3.5.2. for
-        </a>
-       </li>
-       <li class="toc-h4 nav-item toc-entry">
-        <a class="reference internal nav-link" href="#def">
-         2.3.5.3. def
-        </a>
-       </li>
-      </ul>
      </li>
-     <li class="toc-h3 nav-item toc-entry">
-      <a class="reference internal nav-link" href="#python-file">
-       2.3.6. Python file
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#for">
+       1.3.5.2. for
+      </a>
+     </li>
+     <li class="toc-h4 nav-item toc-entry">
+      <a class="reference internal nav-link" href="#def">
+       1.3.5.3. def
       </a>
      </li>
     </ul>
    </li>
-   <li class="toc-h2 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#your-turn">
-     2.4. Your turn! 🚀
-    </a>
-   </li>
-   <li class="toc-h2 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#acknowledgments">
-     2.5. Acknowledgments
+   <li class="toc-h3 nav-item toc-entry">
+    <a class="reference internal nav-link" href="#python-file">
+     1.3.6. Python file
     </a>
    </li>
   </ul>
  </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#your-turn">
+   1.4. Your turn! 🚀
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#acknowledgments">
+   1.5. Acknowledgments
+  </a>
+ </li>
 </ul>
 
                         </nav>
@@ -1926,7 +1874,7 @@ <h2> Contents </h2>
                 
               <div>
                 
-  <div class="cell docutils container">
+  <div class="cell tag_hide-input docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Install the necessary dependencies</span>
 
@@ -1957,26 +1905,8 @@ <h2> Contents </h2>
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.</p>
-</details>
-<section class="tex2jax_ignore mathjax_ignore" id="python-programming-basics">
-<h1><span class="section-number">1. </span>Python programming basics<a class="headerlink" href="#python-programming-basics" title="Permalink to this headline">#</a></h1>
-<section id="python-syntax">
-<h2><span class="section-number">1.1. </span>Python syntax<a class="headerlink" href="#python-syntax" title="Permalink to this headline">#</a></h2>
-<p><strong>Python syntax compared to other programming languages</strong></p>
-<ul class="simple">
-<li><p>Python was designed for readability and has some similarities to the English language with influence from mathematics.</p></li>
-<li><p>Python uses new lines to complete a command, as opposed to other programming languages which often use semicolons or parentheses.</p></li>
-<li><p>Python relies on indentation, using whitespace, to define scope; such as the scope of loops, functions and classes. Other programming languages often use curly brackets for this purpose.</p></li>
-</ul>
-<section id="python-indentations">
-<h3><span class="section-number">1.1.1. </span>Python indentations<a class="headerlink" href="#python-indentations" title="Permalink to this headline">#</a></h3>
-<p>While in other programming languages the indentation in code is for readability only, in Python the indentation is very important.</p>
-<p>Python uses indentation to indicate a block of code.</p>
-</section>
-</section>
-</section>
-<section class="tex2jax_ignore mathjax_ignore" id="python-programming-introduction">
-<h1><span class="section-number">2. </span>Python programming introduction<a class="headerlink" href="#python-programming-introduction" title="Permalink to this headline">#</a></h1>
+</details><section class="tex2jax_ignore mathjax_ignore" id="python-programming-introduction">
+<h1><span class="section-number">1. </span>Python programming introduction<a class="headerlink" href="#python-programming-introduction" title="Permalink to this headline">#</a></h1>
 <p>Python is a high-level programming language for general-purpose programming. It is an open-source, interpreted, objected-oriented programming language. Python was created by a Dutch programmer, Guido van Rossum. The name of the Python programming language was derived from a British sketch comedy series, Month Python’s Flying Circus. The first version was released on February 20, 1991.</p>
 <p>Python is an easy-to-learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python’s elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms.</p>
 <p>It is used for:</p>
@@ -1988,13 +1918,13 @@ <h1><span class="section-number">2. </span>Python programming introduction<a cla
 </ul>
 <p>These Python programming-related sections are designed for beginners and professionals who want to learn Python programming language. If you are in favor of videos, you may get started with this <a class="reference external" href="https://www.youtube.com/watch?v=11OYpBrhdyM">Python for Absolute Beginners video</a>.</p>
 <section id="why-python">
-<h2><span class="section-number">2.1. </span>Why Python?<a class="headerlink" href="#why-python" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">1.1. </span>Why Python?<a class="headerlink" href="#why-python" title="Permalink to this headline">#</a></h2>
 <p>It is a programming language that is very close to human language and because of that, it is easy to learn and use. Python is used by various industries and companies (including Google). It has been used to develop web applications, desktop applications, system administration, and Machine Learning libraries. Python is a highly embraced language in the data science and Machine Learning community. Hope this is enough to convince you to start learning Python. Python is eating the world and you are killing it before it eats you.</p>
 </section>
 <section id="environment-setup">
-<h2><span class="section-number">2.2. </span>Environment setup<a class="headerlink" href="#environment-setup" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">1.2. </span>Environment setup<a class="headerlink" href="#environment-setup" title="Permalink to this headline">#</a></h2>
 <section id="installing-python">
-<h3><span class="section-number">2.2.1. </span>Installing Python<a class="headerlink" href="#installing-python" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">1.2.1. </span>Installing Python<a class="headerlink" href="#installing-python" title="Permalink to this headline">#</a></h3>
 <p>To run a Python script you need to install Python. Let’s <a class="reference external" href="https://www.Python.org/">download</a> Python.</p>
 <p>If you are a windows user. Click the button encircled in red.</p>
 <p><a class="reference external" href="https://www.Python.org/"><img alt="installing on Windows" src="../_images/installing_on_windows.png" /></a></p>
@@ -2006,7 +1936,7 @@ <h3><span class="section-number">2.2.1. </span>Installing Python<a class="header
 <p>As you can see from the terminal, <em>Python 3.7.5</em> version is used at the moment. Your version of Python might be different from mine, but it should be 3.6 or above. If you manage to see the Python version, well done. Python has been installed on your machine. Continue to the next section.</p>
 </section>
 <section id="python-shell">
-<h3><span class="section-number">2.2.2. </span>Python shell<a class="headerlink" href="#python-shell" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">1.2.2. </span>Python shell<a class="headerlink" href="#python-shell" title="Permalink to this headline">#</a></h3>
 <p>Python is an interpreted scripting language, so it does not need to be compiled. It means it executes the code line by line. Python comes with a <em>Python Shell (Python Interactive Shell)</em>. It is used to execute a single Python command and get the result.</p>
 <p>Python Shell waits for the Python code from the user. When you enter the code, it interprets the code and shows the result in the next line. Open your terminal or command prompt(cmd) and write:</p>
 <p>python</p>
@@ -2059,12 +1989,12 @@ <h3><span class="section-number">2.2.2. </span>Python shell<a class="headerlink"
 <p><img alt="Writing String on Python shell" src="../_images/writing_string_on_shell.png" /></p>
 </section>
 <section id="installing-visual-studio-code">
-<h3><span class="section-number">2.2.3. </span>Installing Visual Studio Code<a class="headerlink" href="#installing-visual-studio-code" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">1.2.3. </span>Installing Visual Studio Code<a class="headerlink" href="#installing-visual-studio-code" title="Permalink to this headline">#</a></h3>
 <p>The Python interactive shell is good to try and test small script codes but it will not be for a big project. In the real work environment, developers use different code editors to write code. Will use Visual Studio Code. Visual Studio Code is a very popular open-source text editor. If you are a fan of Visual Studio Code, it is recommended to <a class="reference external" href="https://code.visualstudio.com/">download</a> it. but if you are in favor of other editors, feel free to follow with what you have.</p>
 <p><a class="reference external" href="https://code.visualstudio.com/"><img alt="Visual Studio Code" src="../_images/vscode.png" /></a></p>
 <p>If you installed Visual Studio Code, let us see how to use it. If you prefer a video, you can follow this Visual Studio Code for Python <a class="reference external" href="https://www.youtube.com/watch?v=bn7Cx4z-vSo">Video tutorial</a></p>
 <section id="how-to-use-visual-studio-code">
-<h4><span class="section-number">2.2.3.1. </span>How to use Visual Studio Code<a class="headerlink" href="#how-to-use-visual-studio-code" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">1.2.3.1. </span>How to use Visual Studio Code<a class="headerlink" href="#how-to-use-visual-studio-code" title="Permalink to this headline">#</a></h4>
 <p>Open the Visual Studio Code by double-clicking the visual studio icon. When you open it you will get this kind of interface. Try to interact with the labeled icons.</p>
 <p><img alt="Visual Studio Code" src="../_images/vscode_ui.png" /></p>
 <p>Create a folder named <code class="docutils literal notranslate"><span class="pre">ocademy</span></code> on your desktop. Then open it using Visual Studio Code.</p>
@@ -2079,18 +2009,18 @@ <h4><span class="section-number">2.2.3.1. </span>How to use Visual Studio Code<a
 </section>
 </section>
 <section id="basic-python">
-<h2><span class="section-number">2.3. </span>Basic Python<a class="headerlink" href="#basic-python" title="Permalink to this headline">#</a></h2>
-<section id="id1">
-<h3><span class="section-number">2.3.1. </span>Python syntax<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h2><span class="section-number">1.3. </span>Basic Python<a class="headerlink" href="#basic-python" title="Permalink to this headline">#</a></h2>
+<section id="python-syntax">
+<h3><span class="section-number">1.3.1. </span>Python syntax<a class="headerlink" href="#python-syntax" title="Permalink to this headline">#</a></h3>
 <p>A Python script can be written in Python interactive shell or the code editor. A Python file has an extension <code class="docutils literal notranslate"><span class="pre">.py</span></code>.</p>
 </section>
 <section id="python-indentation">
-<h3><span class="section-number">2.3.2. </span>Python indentation<a class="headerlink" href="#python-indentation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">1.3.2. </span>Python indentation<a class="headerlink" href="#python-indentation" title="Permalink to this headline">#</a></h3>
 <p>An indentation is a white space in a text. Indentation in many languages is used to increase code readability, however, Python uses indentation to create blocks of codes. In other programming languages, curly brackets are used to create blocks of codes instead of an indentation. One of the common bugs when writing Python code is the wrong indentation.</p>
 <p><img alt="Indentation Error" src="../_images/indentation.png" /></p>
 </section>
 <section id="comments">
-<h3><span class="section-number">2.3.3. </span>Comments<a class="headerlink" href="#comments" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">1.3.3. </span>Comments<a class="headerlink" href="#comments" title="Permalink to this headline">#</a></h3>
 <p>Comments are very important to make the code more readable and to leave remarks in our code. Python does not run comment parts of our code. Any text starting with a hash(<code class="docutils literal notranslate"><span class="pre">#</span></code>) in Python is a comment.</p>
 <p><strong>Example: Single Line Comment</strong></p>
 <div class="cell docutils container">
@@ -2121,10 +2051,10 @@ <h3><span class="section-number">2.3.3. </span>Comments<a class="headerlink" hre
 </div>
 </section>
 <section id="data-types">
-<h3><span class="section-number">2.3.4. </span>Data types<a class="headerlink" href="#data-types" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">1.3.4. </span>Data types<a class="headerlink" href="#data-types" title="Permalink to this headline">#</a></h3>
 <p>In Python, there are several types of data types. Let us get started with the most common ones. Different data types will be covered in detail in other sections. For the time being, let us just go through the different data types and get familiar with them. You do not have to have a clear understanding now.</p>
 <section id="number">
-<h4><span class="section-number">2.3.4.1. </span>Number<a class="headerlink" href="#number" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">1.3.4.1. </span>Number<a class="headerlink" href="#number" title="Permalink to this headline">#</a></h4>
 <ul class="simple">
 <li><p>Integer: integer(negative, zero and positive) numbers.
 Example: … -3, -2, -1, 0, 1, 2, 3 …</p></li>
@@ -2152,7 +2082,7 @@ <h4><span class="section-number">2.3.4.1. </span>Number<a class="headerlink" hre
 </div>
 </section>
 <section id="string">
-<h4><span class="section-number">2.3.4.2. </span>String<a class="headerlink" href="#string" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">1.3.4.2. </span>String<a class="headerlink" href="#string" title="Permalink to this headline">#</a></h4>
 <p>A collection of one or more characters under a single or double quote. If a string is more than one sentence then we use a triple quote.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2177,7 +2107,7 @@ <h4><span class="section-number">2.3.4.2. </span>String<a class="headerlink" hre
 </div>
 </section>
 <section id="booleans">
-<h4><span class="section-number">2.3.4.3. </span>Booleans<a class="headerlink" href="#booleans" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">1.3.4.3. </span>Booleans<a class="headerlink" href="#booleans" title="Permalink to this headline">#</a></h4>
 <p>A boolean data type is either a <code class="docutils literal notranslate"><span class="pre">True</span></code> or <code class="docutils literal notranslate"><span class="pre">False</span></code> value. <code class="docutils literal notranslate"><span class="pre">T</span></code> and <code class="docutils literal notranslate"><span class="pre">F</span></code> should be always uppercase.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2207,7 +2137,7 @@ <h4><span class="section-number">2.3.4.3. </span>Booleans<a class="headerlink" h
 </div>
 </section>
 <section id="list">
-<h4><span class="section-number">2.3.4.4. </span>List<a class="headerlink" href="#list" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">1.3.4.4. </span>List<a class="headerlink" href="#list" title="Permalink to this headline">#</a></h4>
 <p>Python list is an ordered collection that allows to store items of different data types. A list is similar to an array in JavaScript.</p>
 <link rel="stylesheet" href="https://ocademy-ai.github.io/machine-learning/_static/style.css">
 <div class='full-width docutils' style='padding-right:20px;'>
@@ -2233,7 +2163,7 @@ <h4><span class="section-number">2.3.4.4. </span>List<a class="headerlink" href=
 </div>
 </section>
 <section id="dictionary">
-<h4><span class="section-number">2.3.4.5. </span>Dictionary<a class="headerlink" href="#dictionary" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">1.3.4.5. </span>Dictionary<a class="headerlink" href="#dictionary" title="Permalink to this headline">#</a></h4>
 <p>A Python dictionary object is an unordered collection of data in a key-value pair format.</p>
 <link rel="stylesheet" href="https://ocademy-ai.github.io/machine-learning/_static/style.css">
 <div class='full-width docutils' style='padding-right:20px;'>
@@ -2269,7 +2199,7 @@ <h4><span class="section-number">2.3.4.5. </span>Dictionary<a class="headerlink"
 </div>
 </div></section>
 <section id="tuple">
-<h4><span class="section-number">2.3.4.6. </span>Tuple<a class="headerlink" href="#tuple" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">1.3.4.6. </span>Tuple<a class="headerlink" href="#tuple" title="Permalink to this headline">#</a></h4>
 <p>A tuple is an ordered collection of different data types like a list, but tuples can not be modified once they are created. They are immutable.</p>
 <div class="cell docutils container">
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@@ -2289,7 +2219,7 @@ <h4><span class="section-number">2.3.4.6. </span>Tuple<a class="headerlink" href
 </div>
 </section>
 <section id="set">
-<h4><span class="section-number">2.3.4.7. </span>Set<a class="headerlink" href="#set" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">1.3.4.7. </span>Set<a class="headerlink" href="#set" title="Permalink to this headline">#</a></h4>
 <p>A set is a collection of data types similar to a list and tuple. Unlike the list and the tuple, a set is not an ordered collection of items. Like in Mathematics, set in Python only stores unique items. In later sections, we will go into detail about every Python data type.</p>
 <link rel="stylesheet" href="https://ocademy-ai.github.io/machine-learning/_static/style.css">
 <div class='full-width docutils' style='padding-right:20px;'>
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 </div></section>
 </section>
 <section id="control-flow-and-function">
-<h3><span class="section-number">2.3.5. </span>Control flow and function<a class="headerlink" href="#control-flow-and-function" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">1.3.5. </span>Control flow and function<a class="headerlink" href="#control-flow-and-function" title="Permalink to this headline">#</a></h3>
 <section id="if">
-<h4><span class="section-number">2.3.5.1. </span>if<a class="headerlink" href="#if" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">1.3.5.1. </span>if<a class="headerlink" href="#if" title="Permalink to this headline">#</a></h4>
 <p>The <code class="docutils literal notranslate"><span class="pre">if</span></code> statement is used for conditional execution. It allows you to execute different code blocks based on the truth or falsehood of a condition.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">condition</span><span class="p">:</span>
   <span class="c1"># Code block to be executed if the condition is true</span>
@@ -2340,7 +2270,7 @@ <h4><span class="section-number">2.3.5.1. </span>if<a class="headerlink" href="#
 You can click <a class="reference external" href="https://ocademy-ai.github.io/machine-learning/prerequisites/python-programming-introduction.html#booleans">here</a> to see sample code in boolean.</p>
 </section>
 <section id="for">
-<h4><span class="section-number">2.3.5.2. </span>for<a class="headerlink" href="#for" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">1.3.5.2. </span>for<a class="headerlink" href="#for" title="Permalink to this headline">#</a></h4>
 <p>The <code class="docutils literal notranslate"><span class="pre">for</span></code> loop is used for iterating over a sequence of elements. It allows you to execute a block of code for each item in the sequence.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">sequence</span><span class="p">:</span>
   <span class="c1"># Code block to be executed for each item</span>
@@ -2350,7 +2280,7 @@ <h4><span class="section-number">2.3.5.2. </span>for<a class="headerlink" href="
 You can click <a class="reference external" href="https://ocademy-ai.github.io/machine-learning/prerequisites/python-programming-introduction.html#list">here</a> to see sample code in list.</p>
 </section>
 <section id="def">
-<h4><span class="section-number">2.3.5.3. </span>def<a class="headerlink" href="#def" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">1.3.5.3. </span>def<a class="headerlink" href="#def" title="Permalink to this headline">#</a></h4>
 <p>The <code class="docutils literal notranslate"><span class="pre">def</span></code> keyword is used to define a function. Functions are blocks of reusable code that perform a specific task when called.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">function_name</span><span class="p">(</span><span class="n">parameters_input</span><span class="p">):</span>
     <span class="c1"># Code block or statements</span>
@@ -2370,7 +2300,7 @@ <h4><span class="section-number">2.3.5.3. </span>def<a class="headerlink" href="
 </section>
 </section>
 <section id="python-file">
-<h3><span class="section-number">2.3.6. </span>Python file<a class="headerlink" href="#python-file" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">1.3.6. </span>Python file<a class="headerlink" href="#python-file" title="Permalink to this headline">#</a></h3>
 <p>First open your project folder, <code class="docutils literal notranslate"><span class="pre">oocademy</span></code>. If you don’t have this folder, create a folder name called <code class="docutils literal notranslate"><span class="pre">ocademy</span></code>. Inside this folder, create a file called <code class="docutils literal notranslate"><span class="pre">helloworld.py</span></code>. Now, let’s do what we did on Python interactive shell using Visual Studio Code.</p>
 <p>The Python interactive shell was printing without using <code class="docutils literal notranslate"><span class="pre">print</span></code> but on Visual Studio Code to see our result we should use a built-in function <code class="docutils literal notranslate"><span class="pre">print()</span></code>. The <code class="docutils literal notranslate"><span class="pre">print()</span></code> built-in function takes one or more arguments as follows <code class="docutils literal notranslate"><span class="pre">print('arument1',</span> <span class="pre">'argument2',</span> <span class="pre">'argument3')</span></code>. See the examples below.</p>
 <link rel="stylesheet" href="https://ocademy-ai.github.io/machine-learning/_static/style.css">
@@ -2418,11 +2348,11 @@ <h3><span class="section-number">2.3.6. </span>Python file<a class="headerlink"
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">2.4. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">1.4. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p>Try to <a class="reference internal" href="../assignments/prerequisites/python-programming-introduction.html"><span class="doc std std-doc">write some simple Python code</span></a> through Python shell, Python file, and Jupyter Notebook.</p>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">2.5. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">1.5. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to <a class="reference external" href="https://github.com/Asabeneh">Asabeneh</a> who helped create this awesome open-source project <a class="reference external" href="https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme.md">30-Days-Of-Python</a> for Python learning. It contributes the majority of the content in this chapter.</p>
 <p>Thanks to <a class="reference external" href="https://pythontutor.com/">pythontutor</a> for providing the ability to visualize the code execution. It also contributes some code to this chapter.</p>
 </section>
@@ -2471,7 +2401,7 @@ <h2><span class="section-number">2.5. </span>Acknowledgments<a class="headerlink
     <a class='right-next' id="next-link" href="python-programming-basics.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">3. </span>Python programming basics</p>
+        <p class="prev-next-title"><span class="section-number">2. </span>Python programming basics</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/search.html b/search.html
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   <a class="reference internal" href="prerequisites/python-programming-introduction.html">
-   1. Python programming basics
+   1. Python programming introduction
   </a>
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   <a class="reference internal" href="prerequisites/python-programming-basics.html">
-   3. Python programming basics
+   2. Python programming basics
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-     5.1. Defining data science
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-     5.2. Data Science ethics
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-     5.3. Defining data
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+     4.4. Introduction to statistics and probability
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     <a class="reference internal" href="data-science/working-with-data/numpy.html">
-     6.3. NumPy
+     5.3. NumPy
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+       5.4.2. Data Selection
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-       6.4.3. Advanced Pandas Techniques
+       5.4.3. Advanced Pandas Techniques
       </a>
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     <a class="reference internal" href="data-science/working-with-data/data-preparation.html">
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+     5.5. Data preparation
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+     6.2. Visualizing proportions
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-     7.3. Visualizing relationships: all about honey 🍯
+     6.3. Visualizing relationships: all about honey 🍯
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-     7.4. Making meaningful visualizations
+     6.4. Making meaningful visualizations
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+     8.3. Data Science in the cloud: The “Azure ML SDK” way
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+   10. Machine Learning overview
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+     12.3. Linear and polynomial regression
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+     12.4. Logistic regression
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-   14. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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-     15.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
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-     17.1. Introduction to clustering
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,201],"y\u00fcksek":35,A:[0,1,4,5,6,7,12,13,14,15,18,19,21,23,26,28,29,32,36,40,41,43,45,47,48,49,50,51,52,56,57,58,59,62,63,64,65,66,68,72,74,75,76,77,78,80,82,85,88,89,90,92,93,99,100,101,102,104,106,108,109,110,112,113,114,116,117,118,119,120,121,122,124,126,127,132,133,134,135,137,138,139,141,142,143,144,146,147,148,149,150,152,153,157,158,160,161,162,165,168,171,174,175,177,178,179,180,183,184,186,187,189,191,194,195,198,199,200,201,202],AND:[92,93,108,119,121,178,179,180],AS:[22,25,45,47,48,92,93,162,178,179,180],And:[31,32,40,41,43,48,49,50,52,56,58,62,68,74,76,78,80,92,100,102,104,112,116,119,126,131,132,135,138,141,142,143,144,145,149,158,160,170,175,179,183,187,191,194,201],As:[1,3,7,8,33,34,36,40,41,43,47,48,49,50,51,52,53,54,56,57,58,59,60,61,68,74,78,80,82,83,99,100,106,109,112,114,116,117,119,122,124,134,135,136,141,142,143,149,152,153,157,158,159,160,161,165,168,169,173,174,175,178,179,180,184,186,189,198,200,201],At:[28,40,48,50,56,59,68,75,80,106,116,119,124,132,142,143,145,149,153,154,159,168,175,177,178,179,186,198,199],BE:[92,93,178,179,180],BUT:[92,93,178,179,180],BY:[101,143],Be:[85,91,104,108,119,162],Being:[43,62,101,104,122],But:[33,38,40,43,48,49,50,52,53,56,57,58,59,61,64,68,74,80,100,104,109,112,124,129,131,139,141,142,143,148,152,153,154,156,158,159,160,161,168,174,177,178,179,180,191,195],By:[7,18,29,40,41,46,49,52,53,54,57,59,68,74,76,77,78,80,99,101,109,114,117,119,122,124,130,139,141,142,144,147,149,150,157,160,165,168,170,173,174,178,189],FOR:[92,93,178,179,180],For:[7,19,29,30,31,32,35,36,38,39,40,41,43,45,46,47,48,49,50,51,54,59,60,61,64,66,67,68,69,71,72,74,75,84,85,86,88,89,90,91,100,101,102,111,112,113,114,116,117,119,120,121,122,126,131,132,135,137,139,141,142,143,144,145,147,148,149,150,152,153,154,157,158,159,160,161,162,165,166,167,168,169,170,173,174,175,177,178,179,180,181,191,195,198,199,201],IN:[25,82,92,93,178,179,180],IS:[22,45,47,48,54,92,93,97,147,178,179,180],IT:[54,99,142],If:[1,7,14,16,18,29,33,34,35,39,40,41,43,45,48,49,50,51,52,53,54,57,58,59,60,62,64,66,68,69,74,76,77,80,82,92,93,95,100,101,103,104,106,108,109,110,111,112,113,114,116,117,118,119,120,121,122,124,126,130,131,132,134,136,138,141,142,143,145,147,149,152,153,154,156,158,159,160,161,162,165,167,168,170,173,174,175,177,178,179,180,187,193,195,198,200,201,202],In:[1,3,7,8,9,11,12,13,14,16,18,19,21,24,28,29,30,31,32,33,36,39,40,41,43,45,46,47,48,49,50,52,53,54,57,58,59,60,61,62,64,66,68,69,71,74,75,76,77,78,80,82,83,88,89,90,92,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,124,125,126,127,128,130,131,132,133,134,135,137,139,141,142,143,144,145,147,148,149,150,152,153,154,155,156,157,158,159,160,161,162,164,165,166,167,168,170,171,173,174,175,176,177,178,179,180,181,184,186,187,188,193,195,196,197,198,199,201,202],Is:[50,93,97,99,103,106,111,112,113,130,142,143,147,162,168,174,175,202],It:[0,1,2,3,4,5,7,8,9,10,11,13,14,15,16,17,18,19,20,23,24,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,72,74,76,77,78,80,82,83,84,85,86,88,89,90,91,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,120,121,122,127,128,129,130,131,132,133,134,135,136,137,138,139,141,142,143,144,145,146,147,148,149,150,152,153,154,155,156,157,158,159,160,161,162,165,166,167,168,170,171,173,175,177,178,179,180,182,183,189,193,194,195,196,198,199,201],Its:[131,152,157,174],NEAR:[61,74],NO:[92,93,178,179,180],NOT:[82,92,93,119,133,178,179,180],Near:[109,185],No:[7,20,29,33,50,54,56,64,82,88,93,98,100,101,110,132,149,158,160,166,170,178,179,182,185],Not:[7,40,43,49,52,54,56,68,80,101,111,117,118,136,153,166,173,174,179,194,201],OF:[22,45,47,48,92,93,133,178,179,180],ON:187,ONE:7,OR:[22,45,47,48,92,93,119,178,179,180],Of:[50,101,102,104,114,160,180,181],On:[49,50,52,57,58,59,60,61,66,68,74,80,101,104,106,143,149,152,153,156,157,158,160,161,162,168,173,177,178,184,194],One:[1,7,11,28,40,43,49,50,52,53,54,55,57,58,59,66,83,98,103,104,106,108,110,114,116,119,127,131,137,143,148,150,152,158,161,168,173,175,178,179,180,184,185,186,191,193,198,201],Or:[32,40,50,58,74,102,104,119,131,132,143,147,150,168,178,179,194,198,201],Such:[1,7,30,40,43,49,50,54,116,143,144,173,178,200],THAT:82,THE:[92,93,178,179,180],TO:[54,92,93,138,178,179,180],That:[31,32,40,43,48,49,50,52,57,61,62,68,74,80,104,109,116,119,124,132,148,150,153,154,160,162,168,170,174,179,180,198],The:[0,3,5,6,7,8,12,13,14,15,16,18,19,24,25,26,28,29,30,31,32,33,34,35,37,39,40,41,45,46,47,48,49,50,52,54,55,56,57,58,59,60,61,62,63,64,65,66,68,71,74,76,78,80,83,84,90,91,92,93,99,102,103,104,106,108,109,110,112,113,114,115,116,118,119,120,121,122,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,147,148,149,150,152,153,154,155,156,157,158,161,162,165,166,168,169,170,171,173,174,180,181,183,184,185,189,190,192,193,194,195,196,197,198,199,202],Then:[7,31,45,47,50,56,66,77,82,101,104,116,119,127,130,136,138,143,144,145,148,149,150,152,153,157,161,162,173,177,178,179,180,194],There:[0,1,3,7,18,28,29,30,32,34,36,37,39,40,41,43,46,47,49,50,51,54,57,59,60,62,64,68,72,74,80,85,100,101,104,106,108,109,110,113,114,116,117,118,119,120,124,126,127,128,131,134,136,138,139,141,142,143,144,145,147,148,150,152,153,156,160,161,162,164,165,167,168,170,173,174,176,177,178,179,180,198,199,200,201],These:[7,30,31,39,41,45,49,54,56,59,60,74,75,77,78,99,101,104,106,109,112,113,118,119,121,122,134,142,148,152,153,157,159,160,161,178,180,182,184,186,187,189,200],To:[0,1,7,14,18,22,29,33,34,36,40,41,45,46,48,49,50,52,54,58,61,62,66,68,74,75,76,82,92,99,100,101,102,104,109,110,111,112,114,116,117,119,120,122,124,129,130,131,134,135,136,139,140,141,142,143,144,145,149,150,153,154,156,157,158,160,161,162,165,168,170,173,174,175,177,178,179,180,184,185,187,190,193,194,198,200,201,202],WITH:[92,93,178,179,180],Will:[145,174,180],With:[7,40,41,43,47,50,56,60,61,62,84,99,103,104,108,109,111,112,113,114,118,119,121,122,124,131,142,143,150,152,153,158,160,161,168,171,178,187,194,199],_0:153,_1:150,_2:150,_:[18,29,31,33,37,41,51,56,81,126,127,130,132,133,134,135,136,138,139,150,153,160,170,178,179,189,195,196,201],____:[3,12,22,24,25,46,92,93,97],_____:[24,92],______:[12,14,25],_______:14,________:14,_________:14,_________________________________________________________________:29,____i:93,__abs__:92,__add__:92,__all__:178,__annotations__:[178,200],__builtins__:178,__cached__:178,__call__:[63,65,128,130,135],__class__:[37,178],__dict__:[170,196,197],__doc__:[178,200],__eq__:92,__file__:178,__finalize__:[120,121],__future__:37,__get__:120,__getitem__:[121,122],__init__:[29,30,31,33,35,36,37,43,55,63,65,81,82,94,120,128,130,132,133,134,135,136,137,154,170,178,195,196,200],__iter__:[33,133],__len__:33,__loader__:178,__main__:[35,132,158,161,162],__mul__:92,__name__:[35,37,132,161,162,178],__operators__:136,__package__:178,__repr__:55,__spec__:178,__str__:92,__sub__:92,__truediv__:92,__version__:[41,132,160,200],_aspp:135,_attach:[118,187],_bin:54,_branch:135,_build_model:35,_bunch:[57,58],_caller:120,_check_indexing_error:122,_concaten:120,_consolidate_inplac:121,_constructor:121,_conv_block:135,_conv_bn_relu:135,_conv_relu:135,_data:121,_decor:120,_deeplabv3:135,_deprecate_mismatched_index:121,_deprecated_arg:121,_engin:122,_etag:[118,187],_fcn_16:135,_fcn_32:135,_fcn_8:135,_format_argument_list:120,_fuse_bn_tensor:134,_get_axi:121,_get_block_manager_axi:121,_get_comb_axi:120,_get_concat_axi:120,_get_join_info:120,_get_list_axi:121,_get_new_ax:120,_get_result_dim:120,_get_slice_axi:121,_get_valu:[121,122],_get_values_for_loc:122,_getbool_axi:121,_getitem_axi:121,_getitem_lowerdim:121,_getitem_tupl:121,_i:[77,78,150,160],_identity_block:135,_ilocindex:121,_index:56,_indicator_pre_merg:120,_info_axi:120,_invalid_index:121,_is_copi:121,_is_scalar_access:121,_items_overlap_with_suffix:120,_j:[150,160],_k:132,_kmean:161,_label:57,_left:120,_lib:[120,122],_locationindex:121,_locindex:121,_m:132,_make_concat_multiindex:120,_make_stag:134,_maybe_cast_for_get_loc:121,_maybe_cast_slice_bound:121,_maybe_check_integr:120,_merge_doc:120,_merge_typ:120,_mergeoper:120,_method:186,_mgr:[120,121],_novalu:186,_other:120,_pad_1x1_to_3x3_tensor:134,_recognized_scalar:121,_reindex_and_concat:120,_rid:[118,187],_right:120,_sec_1:93,_segnet:135,_self:[118,187],_sigmoid:[81,170,196],_skip:3,_slice:121,_static:[121,122],_subplot:74,_sum:186,_t:[118,187],_t_sne:[161,193],_take:121,_take_with_is_copi:121,_takeabl:121,_valid_typ:121,_validate_integ:121,_validate_kei:121,_validate_tuple_index:121,_valu:122,_vendor:132,a0958ad901d7:118,a0:[120,186],a10:122,a1:[119,120,186],a1gkdhua8we2lilmxcctgfiycqfttwx6tljchvsbz6sfau8wquo8541xaz2myyziork:59,a21453:179,a23:[178,200],a2:[119,120,186],a3:120,a3z5kdkfn3tbq:59,a4:120,a5:120,a7yia1n5fo6efhugqfis3dhueyjsa:59,a_:[82,161],a_dict:179,a_i:[82,149],a_list:179,a_n:152,aaaaaa:[158,191],aafter:159,aaron:[29,50,78,133,171],ab:[50,63,65,77,92,93,121,129,133,137,160,178,179,201],abadi:133,abbrevi:[124,130],abc:[93,121,122,179,186,202],abcd:[7,117,121,186],abcdef:121,abcmous:[112,183],abil:[43,52,54,68,76,77,80,108,131,141,152,158,162,168,178,180,191,198],abl:[3,7,10,11,14,16,20,31,40,49,50,52,53,54,57,61,62,74,76,101,104,110,112,114,118,119,122,131,139,142,144,147,153,156,159,161,162,165,173,174,175,177,183,193,196,197,200],abnorm:29,abnorml:66,abo:38,aboslut:159,about:[1,4,7,11,12,13,15,16,17,18,19,22,23,26,28,29,31,40,41,43,46,47,48,49,50,52,53,54,57,58,59,60,61,62,66,68,77,78,80,83,84,90,99,100,101,102,103,104,106,108,109,110,112,114,115,116,117,118,119,120,122,124,125,131,132,135,137,139,140,141,142,143,144,146,147,148,149,152,153,154,156,157,159,160,161,162,164,166,167,168,169,170,171,173,175,176,177,178,179,180,181,183,184,187,195,198,200,202],abov:[0,1,7,11,14,19,26,29,32,36,40,43,45,46,47,48,49,50,51,52,53,54,57,58,59,60,64,66,68,74,80,92,93,104,108,110,114,116,119,120,121,122,124,127,130,131,132,133,134,135,137,139,141,142,143,144,147,148,149,150,152,156,157,159,160,161,165,168,169,173,174,175,176,177,178,179,180,185,194,195],above_cutoff:160,abracadabra:179,abraham:202,abs_vector:[179,201],absenc:[54,161,193],absent:[121,170],absolut:[47,76,78,83,92,116,119,143,152,156,159,178,179,180,201],absolute_error:77,absolute_percentage_error:77,abund:[110,185],ac:160,academ:[112,115,140,183],academi:200,acc:[33,39,47,49,52,57,127,138,199],acc_and_loss:127,acceler:[100,110,111,143,173,185],acceleromet:[116,161],accept:[16,40,57,68,80,83,100,103,106,112,119,121,122,134,143,153,157,168,178,184,198,199],access:[6,14,16,38,41,68,74,80,99,101,102,104,106,108,112,118,121,141,144,145,162,171,173,178,179,183,184,186,194,200,201],accessor:122,accident:153,acclaim:157,accommod:[7,36,47,117,179],accompani:[116,143,173],accomplish:[88,143,153,161,168,193,198],accord:[18,45,50,54,63,65,99,108,109,110,111,116,119,121,134,141,144,147,149,152,157,161,162,166,168,173,174,175,177,193],accordingli:[41,55,127,147,178,200],account:[0,6,8,14,16,50,76,92,101,102,112,116,118,119,145,149,153,173,174,183,186],accumul:[1,50,92,106,139,150,168,179,198],accur:[15,32,33,41,50,54,59,68,69,77,80,90,101,106,112,113,116,131,135,137,141,144,149,156,159,161,165,168,173,174,184],accuraci:[29,33,34,39,40,48,49,50,51,52,54,56,57,60,64,68,69,76,77,78,80,82,83,84,102,112,117,127,135,138,143,144,145,148,149,150,152,153,155,156,158,160,162,166,167,173,174,181,183,189,193],accuracy_metr:127,accuracy_scor:[29,30,39,49,50,51,52,56,57,59,60,68,80,83,152,157,161,162,166,167,174,193,196,197],achiev:[32,33,40,48,50,54,56,59,103,106,119,132,134,135,141,142,143,144,145,152,153,154,155,157,158,159,160,178],achitectur:133,aci_servic:[9,100],aci_service_nam:[9,100],aciconfig:[9,100],acid:48,aciwebservic:[9,100],acm:[112,183],acoust:[146,147,148],acquir:[6,103,106,143,184],acquisit:[3,102,106,114,139,181,183],acro:162,acronym:109,across:[33,43,47,54,68,80,102,112,114,116,119,120,122,124,127,139,141,142,143,153,157,160,178,179,181,183],act:[3,14,22,24,35,53,62,93,104,112,119,122,127,128,132,137,168,173,186,198],act_greedi:35,act_valu:35,action:[0,7,35,40,45,46,92,93,99,103,104,112,114,117,118,119,142,144,162,166,168,178,179,180,183,198],action_prob:132,action_s:35,actions_count:35,activ:[0,29,30,32,33,34,35,36,39,40,41,43,44,45,47,48,56,57,62,82,112,126,127,128,130,132,133,134,135,136,137,138,144,153,159,160,165,183,188,189,194,199],activateion:136,activespac:187,actor:178,actual:[7,38,40,43,46,47,48,50,51,52,56,57,59,60,66,68,74,75,76,77,78,80,82,83,84,92,101,111,113,114,116,117,118,119,121,122,127,130,131,139,141,147,153,155,157,159,160,165,168,171,173,174,178,180,182,184,188,195,198],actual_result:[3,14,22,24,53,93],actual_valu:[38,77],acut:152,ad:[1,7,18,22,29,32,36,38,41,43,45,48,50,52,54,59,64,68,76,80,92,93,111,113,116,118,128,130,134,140,142,143,152,153,154,156,158,161,168,170,173,179,195,201],ada:167,adaboost:[153,167],adaboost_clf:49,adaboostclassifi:[49,56,166,167],adagradoptim:143,adam:[29,30,31,32,33,34,35,36,37,38,39,40,41,42,44,45,47,48,62,126,127,130,133,135,136,189],adamax:36,adamharlei:[127,188],adamoptim:[129,143],adapt:[47,58,62,99,112,134,139,143,153,171,183,199],adaptiveaveragepooling2d:134,add:[1,7,9,14,17,18,30,31,32,33,34,35,36,38,39,41,42,43,44,45,46,47,50,52,54,61,62,63,65,66,76,92,93,108,110,113,116,118,120,124,126,129,130,134,135,136,137,142,152,153,154,156,157,158,159,162,167,168,173,174,175,177,178,179,180,187,189,191,194,195,198,199,201],add_1:136,add_:31,add_artist:[110,185],add_ax:161,add_legend:147,add_selectbox:194,add_slid:194,add_subplot:[35,37,47,75,132],add_trick:178,add_weight:134,addit:[1,7,18,23,32,41,46,54,59,64,66,74,75,76,92,103,104,106,108,112,113,116,117,118,119,124,132,134,135,136,137,139,143,145,149,153,154,156,157,158,160,170,173,179,180,186,187,201,202],addition:[31,117,119,124,142,145,147,149,153,158,187,191],additon:32,address:[75,76,90,103,104,106,112,139,141,142,145,149,156,168,178,183,184],adel:152,adequ:[4,5,6,8,10,11,13,16,17,19,20,21,23,24,26,27,28,69,71,72,85,88,89,90,91,116,143],adher:[48,106,142],adjac:[161,178],adject:[179,201],adjunct:168,adjust:[29,33,36,45,55,57,58,75,134,142,143,152,153,158,161,167],adjusted_mutual_info_scor:161,adjusted_rand_scor:161,admin2:144,admin:187,administr:[145,180],admonit:[120,121,122],adobe_premier:38,adolesc:178,adopt:[59,85,102,112,142,143,149,153,181,183,202],adult:[178,200],advanc:[35,43,66,102,112,116,122,123,125,140,142,143,145,149,161,168,179,187,190],advantag:[40,50,54,68,80,122,134,142,145,152,155,156,158,179],advent:[49,131,142],advers:28,adversari:[36,145,190],advertis:104,advic:143,advis:[7,46,53,58,61,117,152],advoc:112,ae5:161,ae:[31,130],aebf:[118,187],aeroplan:7,aerospik:187,aesthet:22,afb:162,affect:[7,17,33,39,49,52,54,56,58,68,76,80,82,102,104,106,112,116,117,132,134,143,150,156,158,159,170,178,181,183,191],affer:138,affin:[82,147,161],affinity_matrix_:160,affinitypropag:161,afford:[7,74,117,168],african:[112,145,183],afro:[147,148],afropop:[147,148],after:[0,7,14,29,32,33,34,35,36,39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184,195,196,201],combined_imag:36,come:[7,35,43,48,51,57,62,64,66,68,77,80,98,103,104,106,108,109,112,113,114,116,119,120,131,134,139,140,144,147,149,152,154,157,159,161,168,170,173,178,179,180,186,191,193,194],comedi:180,comfort:[7,46,54,117,177],comma:[93,119,167,179,201],command:[47,51,101,112,113,124,132,156,177,179,180,187,201],comment:[45,48,50,104,112,113,128,162,177,183,200],commerc:147,commerci:104,commiss:[17,23],commit:[0,112],committe:62,commom:[60,68,80],common:[7,31,33,40,45,46,47,48,50,54,56,59,62,66,76,92,99,103,104,106,112,113,119,120,122,124,127,128,136,137,139,141,142,143,144,147,148,153,156,158,159,160,161,165,175,176,177,178,180,182,186,201],common_el:179,common_norm:[109,185],common_runtim:29,commonest:47,commonli:[41,54,61,62,68,76,80,99,122,131,141,142,143,144,157,159,161,168,178,199],commun:[28,43,103,105,106,112,114,131,140,142,144,153,168,180,189],comp:138,compact:127,compani:[6,104,113,141,145,153,179,180],company_s:56,company_typ:56,companyx:179,compar:[14,18,21,31,33,41,47,48,50,51,54,60,61,63,64,65,68,74,77,80,90,92,93,101,108,109,111,114,116,119,121,126,130,134,143,144,145,149,150,152,157,161,166,168,174,179,180,198,201],comparis:[63,65],comparison:[8,14,48,50,92,106,111,113,116,118,143,149,153,158,166,178],compat:[15,55,101,119,121,129,132,136,138,140],compatible_format:200,compel:78,compens:[157,160],compet:153,competit:[131,139,145,153,156,157],compexifi:50,compil:[1,7,29,30,32,34,35,38,39,42,44,45,47,48,62,127,135,142,180,186,189],compilaton:40,complaint:[104,112,183],compleletli:[68,80],complementari:139,complet:[1,8,21,24,34,40,41,49,50,52,53,56,57,58,62,68,69,75,80,101,106,108,112,114,116,119,120,122,127,130,134,137,138,139,141,143,145,157,159,160,161,168,173,174,175,178,179,180,182,193,200,201],completeness_scor:161,complex32:119,complex:[0,1,31,32,33,49,57,58,60,61,63,64,65,66,68,76,78,80,111,114,119,124,131,134,137,139,140,141,142,143,145,149,155,158,159,160,165,168,178,180,188,189,191,192,199,202],complex_numb:178,complex_number_1:[179,201],complex_number_2:[179,201],complex_number__1:201,complexnumb:178,complexnumberwithconstructor:178,compli:112,complianc:[22,45,47,48,112,183],compliant:112,complic:[36,50,82,114,119,141,142,143,153,156,159,160,175,189],compon:[75,76,101,102,108,118,126,132,139,141,142,143,144,149,153,156,158,180,181],components_:[160,161,193],compos:[36,37,61,74,82,126,141,142,153,161,195],compose_greet_func:178,compose_greet_func_with_closur:178,composit:[119,152],compound:[179,186,201],compound_stmt:178,comprehend:49,comprehens:[93,108,122,143,173],compress:[29,30,31,106,126,131,134],compris:[39,101,142],compromis:[7,117],comput:[3,7,18,22,29,32,33,36,40,41,43,46,47,49,50,53,54,58,59,66,75,76,77,81,82,84,99,102,103,106,114,116,117,118,120,121,122,126,129,131,133,134,137,138,140,141,142,143,145,149,150,152,153,157,158,159,161,165,168,170,174,177,179,181,182,183,194,195,196,198,201,202],computation:[33,36,50,56,119,121,131,134,139,155],computationn:33,compute_reciproc:186,compute_target:[9,100],con:[7,47,56,101,112,162],concat:[22,30,36,38,42,54,56,66,130,134,135,136,137,139,165,178,179],concat_axi:120,concat_index:120,concaten:[34,38,55,76,119,120,130,135,137,175,179,189,195,196,197,201],concatenated_str:178,concav:130,conceiv:[144,178],concentr:147,concept:[3,18,29,31,47,50,59,75,76,77,101,102,114,116,118,119,122,127,134,140,141,142,143,144,145,150,153,157,158,159,166,174,177,179,188,201],conceptu:153,concern:[7,47,54,58,59,74,106,109,112,117,141,142,145,153,173,185,198],concis:[119,149,168,178,179,201],conclud:[56,59,75,102,108,116,143,150,161],conclus:[24,50,103,112,114,173],concret:[145,161,168,169,193],concurr:[84,100,101],conda:[0,109],condens:132,condit:[3,22,31,39,40,45,47,48,50,54,93,102,112,119,130,134,143,150,152,153,161,173,174,175,178,179,180,200,201],condition2:54,condorcet:149,conduct:[56,100,112,161,183],conf:18,conf_matrix:[52,57],confer:[104,108,120],confid:[40,41,48,68,80,111,134,143,144,147,149,153],config:[9,38,50,66,136,139,149,152,161,193,194],configur:[10,41,45,47,99,101,138,141,142,143,168,175,176,178],confirm:[14,30,45,47,48,59,103,106,112,160,173,183,184],conflict:[93,104,112,122],conform:[113,122,141,143],confus:[7,34,40,50,52,57,60,68,80,83,104,117,119,150,152,157,159,165,178],confusingli:160,confusion_matrix:[34,39,40,51,52,57,59,60,68,80,83,84,166,167,170,174,196,197],confusion_mtx:34,congratul:[100,101,173,174,177,180],conjug:92,conjunct:114,connect:[6,30,32,33,41,43,45,48,62,82,92,93,102,104,112,116,132,134,135,138,143,159,169,178,179,180,189],connectionist:78,connecttimeouterror:132,conquer:153,consciou:7,consecut:[14,32,40,49,157],consent:[112,183],consequ:[28,102,112,132,183],conserv:[109,185],conservationstatu:[109,185],consid:[1,3,7,8,11,14,18,22,24,29,36,39,40,41,45,46,49,50,53,56,62,64,75,76,93,101,103,104,106,108,113,114,115,116,117,126,127,132,134,136,139,142,143,144,145,147,149,150,152,153,154,157,158,159,160,161,162,168,174,177,178,179,191,193,200,201],consider:[59,101,106,112,114,115,122,153,156,160,1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class=\"section-number\">41. </span>Self-paced assignments","<span class=\"section-number\">41.22. </span>Analyzing COVID-19 papers","<span class=\"section-number\">41.27. </span>Analyzing data","<span class=\"section-number\">41.9. </span>Analyzing text about Data Science","<span class=\"section-number\">41.13. </span>Apply your skills","<span class=\"section-number\">41.16. </span>Build your own custom vis","<span class=\"section-number\">41.17. </span>Classifying datasets","<span class=\"section-number\">41.26. </span>Data preparation","<span class=\"section-number\">41.24. </span>Data processing in Python","<span class=\"section-number\">41.41. </span>Data Science in the cloud: The \u201cAzure ML SDK\u201d way","<span class=\"section-number\">41.40. </span>Data Science project using Azure ML SDK","<span class=\"section-number\">41.10. </span>Data Science scenarios","<span class=\"section-number\">41.20. </span>Displaying airport data","<span class=\"section-number\">41.15. </span>Dive into the beehive","<span class=\"section-number\">41.23. </span>Estimation of COVID-19 pandemic","<span class=\"section-number\">41.25. </span>Evaluating data from a form","<span class=\"section-number\">41.36. </span>Explore a planetary computer dataset","<span class=\"section-number\">41.37. </span>Exploring for answers","<span class=\"section-number\">41.19. </span>Introduction to probability and statistics","<span class=\"section-number\">41.12. </span>Lines, scatters and bars","<span class=\"section-number\">41.39. </span>Low code/no code Data Science project on Azure ML","<span class=\"section-number\">41.38. </span>Market research","<span class=\"section-number\">41.29. </span>Matplotlib applied","<span class=\"section-number\">41.28. </span>NYC taxi data in winter and summer","<span class=\"section-number\">41.18. </span>Small diabetes study","<span class=\"section-number\">41.21. </span>Soda profits","<span class=\"section-number\">41.35. </span>Tell a story","<span class=\"section-number\">41.14. </span>Try it in Excel","<span class=\"section-number\">41.11. </span>Write a data ethics case study","<span class=\"section-number\">41.101. </span>Intro to Autoencoders","<span class=\"section-number\">41.102. </span>Base/Denoising Autoencoder &amp; Dimension Reduction","<span class=\"section-number\">41.103. </span>Fun with Variational Autoencoders","<span class=\"section-number\">41.90. </span>How to choose cnn architecture mnist","<span class=\"section-number\">41.94. </span>Object Recognition in Images using CNN","<span class=\"section-number\">41.92. </span>Sign Language Digits Classification with CNN","<span class=\"section-number\">41.112. </span>DQN On Foreign Exchange Market","<span class=\"section-number\">41.113. </span>Art by gan","<span class=\"section-number\">41.115. </span>Generative Adversarial Networks (GANs)","<span class=\"section-number\">41.97. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment","<span class=\"section-number\">41.107. </span>NN Classify 15 Fruits Assignment","<span class=\"section-number\">41.106. </span>Neural Networks for Classification with TensorFlow","<span class=\"section-number\">41.116. </span>Basic classification: Classify images of clothing","<span class=\"section-number\">41.99. </span>Google Stock Price Prediction RNN","<span class=\"section-number\">41.95. </span>Intro to TensorFlow for Deep Learning","<span class=\"section-number\">41.104. </span>Time Series Forecasting Assignment","<span class=\"section-number\">41.83. </span>Counterintuitive Challenges in ML Debugging","<span class=\"section-number\">41.82. </span>Data engineering","<span class=\"section-number\">41.84. </span>Case Study: Debugging in Classification","<span class=\"section-number\">41.85. </span>Case Study: Debugging in Regression","<span class=\"section-number\">41.75. </span>Beyond random forests: more ensemble models","<span class=\"section-number\">41.76. </span>Decision trees","<span class=\"section-number\">41.80. </span>Random Forest Classifier with Feature Importance","<span class=\"section-number\">41.74. </span>Random forests for classification","<span class=\"section-number\">41.73. </span>Random forests intro and regression","<span class=\"section-number\">41.79. </span>Boosting with tuning","<span class=\"section-number\">41.78. </span>Gradient boosting","<span class=\"section-number\">41.77. </span>Hyperparameter tuning gradient boosting","<span class=\"section-number\">41.67. </span>Decision Trees - Classification","<span class=\"section-number\">41.66. </span>Decision Trees - Intro and Regression","<span class=\"section-number\">41.63. </span>Kernel method assignment 1","<span class=\"section-number\">41.65. </span>Support Vector Machines (SVM) - Classification","<span class=\"section-number\">41.64. </span>Support Vector Machines (SVM) - Intro and SVM for Regression","<span class=\"section-number\">41.70. </span>Dropout and Batch Normalization","<span class=\"section-number\">41.71. </span>Lasso and Ridge Regression","<span class=\"section-number\">41.69. </span>Learning Curve To Identify Overfit &amp; Underfit","<span class=\"section-number\">41.68. </span>Model selection assignment 1","<span class=\"section-number\">41.72. </span>Regularized Linear Models","<span class=\"section-number\">41.88. </span>Build Classification Model","<span class=\"section-number\">41.87. </span>Build classification models","<span class=\"section-number\">41.57. </span>Create a regression model","<span class=\"section-number\">41.61. </span>Delicious asian and indian cuisines","<span class=\"section-number\">41.62. </span>Explore classification methods","<span class=\"section-number\">41.55. </span>Exploring visualizations","<span class=\"section-number\">41.58. </span>Linear and polynomial regression","<span class=\"section-number\">41.45. </span>Linear regression - California Housing","<span class=\"section-number\">41.48. </span>Gradient descent","<span class=\"section-number\">41.49. </span>Linear Regression Implementation from Scratch","<span class=\"section-number\">41.46. </span>Linear Regression Metrics","<span class=\"section-number\">41.47. </span>Loss Function","<span class=\"section-number\">41.54. </span>Managing data","<span class=\"section-number\">41.50. </span>ML logistic regression - assignment 1","<span class=\"section-number\">41.51. </span>ML logistic regression - assignment 2","<span class=\"section-number\">41.52. </span>ML neural network - Assignment 1","<span class=\"section-number\">41.42. </span>Machine Learning overview - assignment 1","<span class=\"section-number\">41.43. </span>Machine Learning overview - assignment 2","<span class=\"section-number\">41.89. </span>Parameter play","<span class=\"section-number\">41.60. </span>Pumpkin varieties and color","<span class=\"section-number\">41.53. </span>Regression tools","<span class=\"section-number\">41.44. </span>Regression with Scikit-learn","<span class=\"section-number\">41.59. </span>Retrying some regression","<span class=\"section-number\">41.86. </span>Study the solvers","<span class=\"section-number\">41.56. </span>Try a different model","<span class=\"section-number\">41.8. </span>Python programming advanced","<span class=\"section-number\">41.7. </span>Python programming basics","<span class=\"section-number\">41.6. </span>Python programming introduction","<span class=\"section-number\">41.5. </span>Project Plan\u200b Template","<span class=\"section-number\">41.3. </span>First assignment","<span class=\"section-number\">41.4. </span>Second assignment","<span class=\"section-number\">9. </span>Data Science in the cloud","<span class=\"section-number\">9.1. </span>Introduction","<span class=\"section-number\">9.3. </span>Data Science in the cloud: The \u201cAzure ML SDK\u201d way","<span class=\"section-number\">9.2. </span>The \u201clow code/no code\u201d way","<span class=\"section-number\">10. </span>Data Science in the real world","<span class=\"section-number\">8.2. </span>Analyzing","<span class=\"section-number\">8.3. </span>Communication","<span class=\"section-number\">8. </span>Data Science lifecycle","<span class=\"section-number\">8.1. </span>Introduction to the Data Science lifecycle","<span class=\"section-number\">7. </span>Data visualization","<span class=\"section-number\">7.4. </span>Making meaningful visualizations","<span class=\"section-number\">7.1. </span>Visualizing distributions","<span class=\"section-number\">7.2. </span>Visualizing proportions","<span class=\"section-number\">7.3. </span>Visualizing relationships: all about honey \ud83c\udf6f","<span class=\"section-number\">5.2. </span>Data Science ethics","<span class=\"section-number\">5.3. </span>Defining data","<span class=\"section-number\">5.1. </span>Defining data science","<span class=\"section-number\">5. </span>Introduction","<span class=\"section-number\">5.4. </span>Introduction to statistics and probability","<span class=\"section-number\">6.5. </span>Data preparation","<span class=\"section-number\">6.2. </span>Non-relational data","<span class=\"section-number\">6.3. </span>NumPy","<span class=\"section-number\">6.4.3. </span>Advanced Pandas Techniques","<span class=\"section-number\">6.4.2. </span>Data Selection","<span class=\"section-number\">6.4.1. </span>Introduction and Data Structures","<span class=\"section-number\">6.4. </span>Pandas","<span class=\"section-number\">6.1. </span>Relational databases","<span class=\"section-number\">6. </span>Working with data","<span class=\"section-number\">28. </span>Autoencoder","<span class=\"section-number\">25. </span>Convolutional Neural Networks","<span class=\"section-number\">25.3.1.2. </span>Deepdream in TensorFlow","<span class=\"section-number\">25.3.1.1. </span>Stylenet / Neural-Style","<span class=\"section-number\">34. </span>Diffusion Model","<span class=\"section-number\">24. </span>Intro to Deep Learning","<span class=\"section-number\">31. </span>Deep Q-learning","<span class=\"section-number\">26. </span>Generative adversarial networks","<span class=\"section-number\">32. </span>Image classification","<span class=\"section-number\">33. </span>Image segmentation","<span class=\"section-number\">29. </span>Long-short term memory","<span class=\"section-number\">35. </span>Object detection","<span class=\"section-number\">27. </span>Recurrent Neural Networks","<span class=\"section-number\">30. </span>Time series","Learn AI together, for free","<span class=\"section-number\">38. </span>Data engineering","<span class=\"section-number\">40. </span>Model deployment","<span class=\"section-number\">39. </span>Model training &amp; evaluation","<span class=\"section-number\">36. </span>Overview","<span class=\"section-number\">37. </span>Problem framing","<span class=\"section-number\">17. </span>Clustering models for Machine Learning","<span class=\"section-number\">17.1. </span>Introduction to clustering","<span class=\"section-number\">17.2. </span>K-Means clustering","<span class=\"section-number\">18.1. </span>Bagging","<span class=\"section-number\">18.3. </span>Feature importance","<span class=\"section-number\">18. </span>Getting started with ensemble learning","<span class=\"section-number\">18.2. </span>Random forest","<span class=\"section-number\">19.1. </span>Gradient Boosting","<span class=\"section-number\">19.2. </span>Gradient boosting example","<span class=\"section-number\">19. </span>Introduction to Gradient Boosting","<span class=\"section-number\">19.3. </span>XGBoost","<span class=\"section-number\">19.4. </span>XGBoost + k-fold CV + Feature Importance","<span class=\"section-number\">21. </span>Kernel method","<span class=\"section-number\">23. </span>Model selection","<span class=\"section-number\">20. </span>Unsupervised learning","<span class=\"section-number\">22. </span>Unsupervised learning: PCA and clustering","<span class=\"section-number\">14. </span>Build a web app to use a Machine Learning model","<span class=\"section-number\">15.4. </span>Applied Machine Learning : build a web app","<span class=\"section-number\">15. </span>Getting started with classification","<span class=\"section-number\">15.1. </span>Introduction to classification","<span class=\"section-number\">15.2. </span>More classifiers","<span class=\"section-number\">15.3. </span>Yet other classifiers","<span class=\"section-number\">11. </span>Machine Learning overview","<span class=\"section-number\">12. </span>Summary of machine learning fundamentals","<span class=\"section-number\">16.2. </span>Gradient descent","<span class=\"section-number\">16.1. </span>Loss function","<span class=\"section-number\">16. </span>parameter-optimization","<span class=\"section-number\">13.3. </span>Linear and polynomial regression","<span class=\"section-number\">13.4. </span>Logistic regression","<span class=\"section-number\">13.2. </span>Managing data","<span class=\"section-number\">13. </span>Regression models for Machine Learning","<span class=\"section-number\">13.1. </span>Tools of the trade","<span class=\"section-number\">4. </span>Python programming advanced","<span class=\"section-number\">3. </span>Python programming basics","<span class=\"section-number\">1. </span>Python programming basics","<span class=\"section-number\">42.10. </span>Data Science in real world","<span class=\"section-number\">42.9. </span>Data Science in the cloud","<span class=\"section-number\">42.4. </span>Data Science introduction","<span class=\"section-number\">42.8. </span>Data Science lifecycle","<span class=\"section-number\">42.7. </span>Data visualization","<span class=\"section-number\">42.6. </span>NumPy and Pandas","<span class=\"section-number\">42.5. </span>Relational vs. non-relational database","<span class=\"section-number\">42.20. </span>Convolutional Neural Network","<span class=\"section-number\">42.21. </span>Generative Adversarial Network","<span class=\"section-number\">42. </span>Slides","<span class=\"section-number\">42.18. </span>Kernel method","<span class=\"section-number\">42.19. </span>Model Selection","<span class=\"section-number\">42.17. </span>Unsupervised learning","<span class=\"section-number\">42.16. </span>Build an machine learning web application","<span class=\"section-number\">42.12. </span>Linear Regression","<span class=\"section-number\">42.13. </span>Logistic Regression","<span class=\"section-number\">42.14. </span>Logistic Regression","<span class=\"section-number\">42.11. </span>Machine Learning overview","<span class=\"section-number\">42.15. </span>Neural Network","<span class=\"section-number\">42.3. </span>Python programming advanced","<span class=\"section-number\">42.2. </span>Python programming basics","<span class=\"section-number\">42.1. </span>Python programming introduction"],titleterms:{"0":59,"1":[3,24,30,32,43,49,50,52,53,54,56,57,58,59,60,61,65,68,74,75,80,82,83,104,113,119,134,161,181,182,183,184,185,186,187,188,189,191,195,197,201,202],"10":[40,56,127,134],"100":[51,59,134],"1000":[59,134],"11":56,"12":56,"13":56,"1300131294":158,"15":39,"19":[1,8,14,129],"1d":119,"2":[3,24,30,32,43,44,49,50,51,52,53,54,56,57,58,60,61,68,75,80,81,84,104,113,119,161,181,182,183,184,185,186,187,188,189,191,195,197,201,202],"2d":[30,119,191],"3":[3,24,32,39,43,49,50,52,53,54,56,57,58,60,61,68,74,80,104,119,181,182,183,184,185,186,188,189,195,201,202],"3d":[30,83,108,191],"4":[3,24,32,43,49,50,51,52,53,54,56,57,58,60,61,68,80,104,161,181,182,183,184,185,186,187,201,202],"5":[24,32,43,49,50,53,54,56,57,58,60,61,68,80,83,104,134,161,181,182,183,184,185,187,201,202],"50":56,"500":56,"6":[43,50,52,53,54,56,57,58,60,61,68,80,181,182,183,184,185],"7":[43,50,52,53,56,57,58,60,61,181,184,185],"8":[52,56,184],"9":56,"90":158,"abstract":170,"boolean":[119,179,180,201,202],"break":[92,178,200],"case":[28,45,47,48,50,104,112,175,191],"class":[35,39,47,50,51,63,65,92,171,178,200],"default":[51,59,129,178],"do":[47,114,174,175,179,183,191],"final":[49,74,75,170],"float":[7,179,201],"function":[43,51,59,75,76,78,82,92,93,119,131,132,153,170,171,178,180,186,196,197,199,200],"import":[9,29,33,37,41,44,49,51,52,53,54,56,57,58,59,60,61,64,66,83,117,129,130,143,150,157,177,178,195,196,197,199],"long":136,"new":[56,94,119,122,169,196,197],"null":[7,59,186],"public":37,"return":[94,132,178],"short":136,"static":158,"true":59,"try":[0,27,48,68,75,80,91,178,200],"while":[92,178,200],A:[31,131,159,166,167,173],And:168,At:[46,170,171],But:175,By:153,For:92,Is:145,It:[124,174,187],NOT:174,Not:168,On:35,One:[74,75,199],That:196,The:[9,36,43,51,53,75,77,82,100,101,117,124,159,160,167,175,177,178,179,182,186,187,188,191,200,201],There:175,To:[64,159],With:[34,139,170],about:[3,33,111,165,174,185],absolut:[77,171],acceler:160,access:[93,119,122],accuraci:[32,41,47,59,134,157,161,199],acknowledg:[1,2,3,4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,72,74,80,82,83,84,85,86,88,89,90,91,92,93,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,120,121,122,124,126,127,128,129,130,132,133,134,135,136,137,138,139,140,143,147,148,149,150,152,153,154,156,157,158,162,165,166,167,168,173,174,175,177,178,179,180,188,191,193,194,195,196,199],ackowledg:189,action:132,activ:131,actual:104,ad:[30,31,47,62,66,119,159],adaboost:49,adam:199,add:[48,119,143],addit:122,advanc:[32,92,119,120,178,200],adversari:[37,133,189],after:[30,159,192],ag:24,against:48,agent:[35,132],agglom:[160,161],aggreg:[119,120,183,186],ahead:82,ai:[36,41,140,193],airport:12,aka:64,algorithm:[50,54,132,144,147,152,153,157,160,169],align:122,all:[24,47,74,111,124,173,185,187],an:[9,31,64,66,100,101,104,118,119,162,188,194],anagram:179,analysi:[8,38,49,51,52,53,54,56,57,58,59,60,61,74,77,99,103,161,168,193,198],analyz:[1,2,3,18,103,119,175,184],anchor:[39,51],anim:108,ann:44,annot:[178,200],anomal:47,anomali:29,anoth:37,answer:17,ap:158,api:[34,43],app:[162,163],append:[93,186],appli:[4,22,47,112,163,166,167,183],applic:[50,126,161,194,198],approach:[51,104,124,166,187],ar:179,arbitrari:178,architectur:[32,127,131,188,199],argument:[129,178,200],arithmet:[122,179,201],arrai:[119,122,186],art:36,artifici:198,artwork:36,ascend:[93,94],asian:70,ask:175,assert:51,assign:[0,12,25,39,44,59,65,80,81,82,83,84,96,97,119,120,122,161,197],assist:135,attribut:[59,119,122,186],auc:[56,59],audienc:104,augment:[32,34,130],author:177,autoencod:[29,30,31,126],automl:[9,100,101,143],avail:119,averag:[92,175],avoid:[108,159],axi:119,azur:[9,10,20,100,101,118],b:[75,170],babylonian:93,background:130,backprop:82,bag:[49,149,152,157],balanc:165,bar:[19,22,185],base:[30,92,121,157],basebal:18,baselin:[47,48,168,198],basi:59,basic:[29,32,41,43,93,112,119,122,132,179,180,183,186,201,202],batch:[33,62,160],beehiv:13,begin:104,behind:55,best:[9,32,37,100,145],beta:130,better:[166,174],between:[24,54,56,68,74,80,119,152,171,183,186],beyond:[40,49,158,191],bi:54,bia:[64,152,159,192],bibliographi:[25,170,171],big:199,binai:54,binari:[40,171,174],binder:0,bird:[109,185],bit:[31,119],bitcoin:38,blend:54,bmi:24,boost:[49,54,55,56,153,154,155,157],bootstrap:149,bound:130,boundari:[158,160,191],boxplot:[24,83],bp:24,brain:188,broadcast:[119,186],bug:48,build:[5,29,31,32,36,41,50,51,67,68,80,108,148,162,163,173,174,175,185,194],c:59,cach:194,calcul:[47,92,93,179],california:74,call:200,callabl:121,callback:40,can:[50,56,114,183],candid:56,capac:159,captur:[106,184],cardin:51,cast:[179,201],catalog:141,catboost:54,categor:[7,51,54,57,68,74,80,173,195],categori:93,categorical_crossentropi:199,caus:45,central:[18,116],centroid:160,chain:122,challeng:[1,14,22,45,112,119,131,134,137,183],chang:[34,54],changin:34,channel:104,chart:[108,111,185,19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7,138,139,141,142,143,144,146,147,148,149,150,152,153,157,158,160,161,162,165,168,171,174,175,177,178,179,180,183,184,186,187,189,191,194,195,198,199,200,201,202],AND:[92,93,108,119,121,178,179,180],AS:[22,25,45,47,48,92,93,162,178,179,180],And:[31,32,40,41,43,48,49,50,52,56,58,62,68,74,76,78,80,92,100,102,104,112,116,119,126,131,132,135,138,141,142,143,144,145,149,158,160,170,175,179,183,187,191,194,201],As:[1,3,7,8,33,34,36,40,41,43,47,48,49,50,51,52,53,54,56,57,58,59,60,61,68,74,78,80,82,83,99,100,106,109,112,114,116,117,119,122,124,134,135,136,141,142,143,149,152,153,157,158,159,160,161,165,168,169,173,174,175,178,179,180,184,186,189,198,200,201],At:[28,40,48,50,56,59,68,75,80,106,116,119,124,132,142,143,145,149,153,154,159,168,175,177,178,179,186,198,199],BE:[92,93,178,179,180],BUT:[92,93,178,179,180],BY:[101,143],Be:[85,91,104,108,119,162],Being:[43,62,101,104,122],But:[33,38,40,43,48,49,50,52,53,56,57,58,59,61,64,68,74,80,100,104,109,112,124,129,131,139,141,142,143,148,152,153,154,156,158,159,160,161,168,174,177,178,179,180,191,195],By:[7,18,29,40,41,46,49,52,53,54,57,59,68,74,76,77,78,80,99,101,109,114,117,119,122,124,130,139,141,142,144,147,149,150,157,160,165,168,170,173,174,178,189],FOR:[92,93,178,179,180],For:[7,19,29,30,31,32,35,36,38,39,40,41,43,45,46,47,48,49,50,51,54,59,60,61,64,66,67,68,69,71,72,74,75,84,85,86,88,89,90,91,100,101,102,111,112,113,114,116,117,119,120,121,122,126,131,132,135,137,139,141,142,143,144,145,147,148,149,150,152,153,154,157,158,159,160,161,162,165,166,167,168,169,170,173,174,175,177,178,179,180,181,191,195,198,199,201],IN:[25,82,92,93,178,179,180],IS:[22,45,47,48,54,92,93,97,147,178,179,180],IT:[54,99,142],If:[1,7,14,16,18,29,33,34,35,39,40,41,43,45,48,49,50,51,52,53,54,57,58,59,60,62,64,66,68,69,74,76,77,80,82,92,93,95,100,101,103,104,106,108,109,110,111,112,113,114,116,117,118,119,120,121,122,124,126,130,131,132,134,136,138,141,142,143,145,147,149,152,153,154,156,158,159,160,161,162,165,167,168,170,173,174,175,177,178,179,180,187,193,195,198,200,201,202],In:[1,3,7,8,9,11,12,13,14,16,18,19,21,24,28,29,30,31,32,33,36,39,40,41,43,45,46,47,48,49,50,52,53,54,57,58,59,60,61,62,64,66,68,69,71,74,75,76,77,78,80,82,83,88,89,90,92,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,124,125,126,127,128,130,131,132,133,134,135,137,139,141,142,143,144,145,147,148,149,150,152,153,154,155,156,157,158,159,160,161,162,164,165,166,167,168,170,171,173,174,175,176,177,178,179,180,181,184,186,187,188,193,195,196,197,198,199,201,202],Is:[50,93,97,99,103,106,111,112,113,130,142,143,147,162,168,174,175,202],It:[0,1,2,3,4,5,7,8,9,10,11,13,14,15,16,17,18,19,20,23,24,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,72,74,76,77,78,80,82,83,84,85,86,88,89,90,91,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,120,121,122,127,128,129,130,131,132,133,134,135,136,137,138,139,141,142,143,144,145,146,147,148,149,150,152,153,154,155,156,157,158,159,160,161,162,165,166,167,168,170,171,173,175,177,178,179,180,182,183,189,193,194,195,196,198,199,201],Its:[131,152,157,174],NEAR:[61,74],NO:[92,93,178,179,180],NOT:[82,92,93,119,133,178,179,180],Near:[109,185],No:[7,20,29,33,50,54,56,64,82,88,93,98,100,101,110,132,149,158,160,166,170,178,179,182,185],Not:[7,40,43,49,52,54,56,68,80,101,111,117,118,136,153,166,173,174,179,194,201],OF:[22,45,47,48,92,93,133,178,179,180],ON:187,ONE:7,OR:[22,45,47,48,92,93,119,178,179,180],Of:[50,101,102,104,114,160,180,181],On:[49,50,52,57,58,59,60,61,66,68,74,80,101,104,106,143,149,152,153,156,157,158,160,161,162,168,173,177,178,184,194],One:[1,7,11,28,40,43,49,50,52,53,54,55,57,58,59,66,83,98,103,104,106,108,110,114,116,119,127,131,137,143,148,150,152,158,161,168,173,175,178,179,180,184,185,186,191,193,198,201],Or:[32,40,50,58,74,102,104,119,131,132,143,147,150,168,178,179,194,198,201],Such:[1,7,30,40,43,49,50,54,116,143,144,173,178,200],THAT:82,THE:[92,93,178,179,180],TO:[54,92,93,138,178,179,180],That:[31,32,40,43,48,49,50,52,57,61,62,68,74,80,104,109,116,119,124,132,148,150,153,154,160,162,168,170,174,179,180,198],The:[0,3,5,6,7,8,12,13,14,15,16,18,19,24,25,26,28,29,30,31,32,33,34,35,37,39,40,41,45,46,47,48,49,50,52,54,55,56,57,58,59,60,61,62,63,64,65,66,68,71,74,76,78,80,83,84,90,91,92,93,99,102,103,104,106,108,109,110,112,113,114,115,116,118,119,120,121,122,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,147,148,149,150,152,153,154,155,156,157,158,161,162,165,166,168,169,170,171,173,174,180,181,183,184,185,189,190,192,193,194,195,196,197,198,199,202],Then:[7,31,45,47,50,56,66,77,82,101,104,116,119,127,130,136,138,143,144,145,148,149,150,152,153,157,161,162,173,177,178,179,180,194],There:[0,1,3,7,18,28,29,30,32,34,36,37,39,40,41,43,46,47,49,50,51,54,57,59,60,62,64,68,72,74,80,85,100,101,104,106,108,109,110,113,114,116,117,118,119,120,124,126,127,128,131,134,136,138,139,141,142,143,144,145,147,148,150,152,153,156,160,161,162,164,165,167,168,170,173,174,176,177,178,179,180,198,199,200,201],These:[7,30,31,39,41,45,49,54,56,59,60,74,75,77,78,99,101,104,106,109,112,113,118,119,121,122,134,142,148,152,153,157,159,160,161,178,180,182,184,186,187,189,200],To:[0,1,7,14,18,22,29,33,34,36,40,41,45,46,48,49,50,52,54,58,61,62,66,68,74,75,76,82,92,99,100,101,102,104,109,110,111,112,114,116,117,119,120,122,124,129,130,131,134,135,136,139,140,141,142,143,144,145,149,150,153,154,156,157,158,160,161,162,165,168,170,173,174,175,177,178,179,180,184,185,187,190,193,194,198,200,201,202],WITH:[92,93,178,179,180],Will:[145,174,180],With:[7,40,41,43,47,50,56,60,61,62,84,99,103,104,108,109,111,112,113,114,118,119,121,122,124,131,142,143,150,152,153,158,160,161,168,171,178,187,194,199],_0:153,_1:150,_2:150,_:[18,29,31,33,37,41,51,56,81,126,127,130,132,133,134,135,136,138,139,150,153,160,170,178,179,189,195,196,201],____:[3,12,22,24,25,46,92,93,97],_____:[24,92],______:[12,14,25],_______:14,________:14,_________:14,_________________________________________________________________:29,____i:93,__abs__:92,__add__:92,__all__:178,__annotations__:[178,200],__builtins__:178,__cached__:178,__call__:[63,65,128,130,135],__class__:[37,178],__dict__:[170,196,197],__doc__:[178,200],__eq__:92,__file__:178,__finalize__:[120,121],__future__:37,__get__:120,__getitem__:[121,122],__init__:[29,30,31,33,35,36,37,43,55,63,65,81,82,94,120,128,130,132,133,134,135,136,137,154,170,178,195,196,200],__iter__:[33,133],__len__:33,__loader__:178,__main__:[35,132,158,161,162],__mul__:92,__name__:[35,37,132,161,162,178],__operators__:136,__package__:178,__repr__:55,__spec__:178,__str__:92,__sub__:92,__truediv__:92,__version__:[41,132,160,200],_aspp:135,_attach:[118,187],_bin:54,_branch:135,_build_model:35,_bunch:[57,58],_caller:120,_check_indexing_error:122,_concaten:120,_consolidate_inplac:121,_constructor:121,_conv_block:135,_conv_bn_relu:135,_conv_relu:135,_data:121,_decor:120,_deeplabv3:135,_deprecate_mismatched_index:121,_deprecated_arg:121,_engin:122,_etag:[118,187],_fcn_16:135,_fcn_32:135,_fcn_8:135,_format_argument_list:120,_fuse_bn_tensor:134,_get_axi:121,_get_block_manager_axi:121,_get_comb_axi:120,_get_concat_axi:120,_get_join_info:120,_get_list_axi:121,_get_new_ax:120,_get_result_dim:120,_get_slice_axi:121,_get_valu:[121,122],_get_values_for_loc:122,_getbool_axi:121,_getitem_axi:121,_getitem_lowerdim:121,_getitem_tupl:121,_i:[77,78,150,160],_identity_block:135,_ilocindex:121,_index:56,_indicator_pre_merg:120,_info_axi:120,_invalid_index:121,_is_copi:121,_is_scalar_access:121,_items_overlap_with_suffix:120,_j:[150,160],_k:132,_kmean:161,_label:57,_left:120,_lib:[120,122],_locationindex:121,_locindex:121,_m:132,_make_concat_multiindex:120,_make_stag:134,_maybe_cast_for_get_loc:121,_maybe_cast_slice_bound:121,_maybe_check_integr:120,_merge_doc:120,_merge_typ:120,_mergeoper:120,_method:186,_mgr:[120,121],_novalu:186,_other:120,_pad_1x1_to_3x3_tensor:134,_recognized_scalar:121,_reindex_and_concat:120,_rid:[118,187],_right:120,_sec_1:93,_segnet:135,_self:[118,187],_sigmoid:[81,170,196],_skip:3,_slice:121,_static:[121,122],_subplot:74,_sum:186,_t:[118,187],_t_sne:[161,193],_take:121,_take_with_is_copi:121,_takeabl:121,_valid_typ:121,_validate_integ:121,_validate_kei:121,_validate_tuple_index:121,_valu:122,_vendor:132,a0958ad901d7:118,a0:[120,186],a10:122,a1:[119,120,186],a1gkdhua8we2lilmxcctgfiycqfttwx6tljchvsbz6sfau8wquo8541xaz2myyziork:59,a21453:179,a23:[178,200],a2:[119,120,186],a3:120,a3z5kdkfn3tbq:59,a4:120,a5:120,a7yia1n5fo6efhugqfis3dhueyjsa:59,a_:[82,161],a_dict:179,a_i:[82,149],a_list:179,a_n:152,aaaaaa:[158,191],aafter:159,aaron:[29,50,78,133,171],ab:[50,63,65,77,92,93,121,129,133,137,160,178,179,201],abadi:133,abbrevi:[124,130],abc:[93,121,122,179,186,202],abcd:[7,117,121,186],abcdef:121,abcmous:[112,183],abil:[43,52,54,68,76,77,80,108,131,141,152,158,162,168,178,180,191,198],abl:[3,7,10,11,14,16,20,31,40,49,50,52,53,54,57,61,62,74,76,101,104,110,112,114,118,119,122,131,139,142,144,147,153,156,159,161,162,165,173,174,175,177,183,193,196,197,200],abnorm:29,abnorml:66,abo:38,aboslut:159,about:[1,4,7,11,12,13,15,16,17,18,19,22,23,26,28,29,31,40,41,43,46,47,48,49,50,52,53,54,57,58,59,60,61,62,66,68,77,78,80,83,84,90,99,100,101,102,103,104,106,108,109,110,112,114,115,116,117,118,119,120,122,124,125,131,132,135,137,139,140,141,142,143,144,146,147,148,149,152,153,154,156,157,159,160,161,162,164,166,167,168,169,170,171,173,175,176,177,178,179,180,181,183,184,187,195,198,200,202],abov:[0,1,7,11,14,19,26,29,32,36,40,43,45,46,47,48,49,50,51,52,53,54,57,58,59,60,64,66,68,74,80,92,93,104,108,110,114,116,119,120,121,122,124,127,130,131,132,133,134,135,137,139,141,142,143,144,147,148,149,150,152,156,157,159,160,161,165,168,169,173,174,175,176,177,178,179,180,185,194,195],above_cutoff:160,abracadabra:179,abraham:202,abs_vector:[179,201],absenc:[54,161,193],absent:[121,170],absolut:[47,76,78,83,92,116,119,143,152,156,159,178,179,180,201],absolute_error:77,absolute_percentage_error:77,abund:[110,185],ac:160,academ:[112,115,140,183],academi:200,acc:[33,39,47,49,52,57,127,138,199],acc_and_loss:127,acceler:[100,110,111,143,173,185],acceleromet:[116,161],accept:[16,40,57,68,80,83,100,103,106,112,119,121,122,134,143,153,157,168,178,184,198,199],access:[6,14,16,38,41,68,74,80,99,101,102,104,106,108,112,118,121,141,144,145,162,171,173,178,179,183,184,186,194,200,201],accessor:122,accident:153,acclaim:157,accommod:[7,36,47,117,179],accompani:[116,143,173],accomplish:[88,143,153,161,168,193,198],accord:[18,45,50,54,63,65,99,108,109,110,111,116,119,121,134,141,144,147,149,152,157,161,162,166,168,173,174,175,177,193],accordingli:[41,55,127,147,178,200],account:[0,6,8,14,16,50,76,92,101,102,112,116,118,119,145,149,153,173,174,183,186],accumul:[1,50,92,106,139,150,168,179,198],accur:[15,32,33,41,50,54,59,68,69,77,80,90,101,106,112,113,116,131,135,137,141,144,149,156,159,161,165,168,173,174,184],accuraci:[29,33,34,39,40,48,49,50,51,52,54,56,57,60,64,68,69,76,77,78,80,82,83,84,102,112,117,127,135,138,143,144,145,148,149,150,152,153,155,156,158,160,162,166,167,173,174,181,183,189,193],accuracy_metr:127,accuracy_scor:[29,30,39,49,50,51,52,56,57,59,60,68,80,83,152,157,161,162,166,167,174,193,196,197],achiev:[32,33,40,48,50,54,56,59,103,106,119,132,134,135,141,142,143,144,145,152,153,154,155,157,158,159,160,178],achitectur:133,aci_servic:[9,100],aci_service_nam:[9,100],aciconfig:[9,100],acid:48,aciwebservic:[9,100],acm:[112,183],acoust:[146,147,148],acquir:[6,103,106,143,184],acquisit:[3,102,106,114,139,181,183],acro:162,acronym:109,across:[33,43,47,54,68,80,102,112,114,116,119,120,122,124,127,139,141,142,143,153,157,160,178,179,181,183],act:[3,14,22,24,35,53,62,93,104,112,119,122,127,128,132,137,168,173,186,198],act_greedi:35,act_valu:35,action:[0,7,35,40,45,46,92,93,99,103,104,112,114,117,118,119,142,144,162,166,168,178,179,180,183,198],action_prob:132,action_s:35,actions_count:35,activ:[0,29,30,32,33,34,35,36,39,40,41,43,44,45,47,48,56,57,62,82,112,126,127,128,130,132,133,134,135,136,137,138,144,153,159,160,165,183,188,189,194,199],activateion:136,activespac:187,actor:178,actual:[7,38,40,43,46,47,48,50,51,52,56,57,59,60,66,68,74,75,76,77,78,80,82,83,84,92,101,111,113,114,116,117,118,119,121,122,127,130,131,139,141,147,153,155,157,159,160,165,168,171,173,174,178,180,182,184,188,195,198],actual_result:[3,14,22,24,53,93],actual_valu:[38,77],acut:152,ad:[1,7,18,22,29,32,36,38,41,43,45,48,50,52,54,59,64,68,76,80,92,93,111,113,116,118,128,130,134,140,142,143,152,153,154,156,158,161,168,170,173,179,195,201],ada:167,adaboost:[153,167],adaboost_clf:49,adaboostclassifi:[49,56,166,167],adagradoptim:143,adam:[29,30,31,32,33,34,35,36,37,38,39,40,41,42,44,45,47,48,62,126,127,130,133,135,136,189],adamax:36,adamharlei:[127,188],adamoptim:[129,143],adapt:[47,58,62,99,112,134,139,143,153,171,183,199],adaptiveaveragepooling2d:134,add:[1,7,9,14,17,18,30,31,32,33,34,35,36,38,39,41,42,43,44,45,46,47,50,52,54,61,62,63,65,66,76,92,93,108,110,113,116,118,120,124,126,129,130,134,135,136,137,142,152,153,154,156,157,158,159,162,167,168,173,174,175,177,178,179,180,187,189,191,194,195,198,199,201],add_1:136,add_:31,add_artist:[110,185],add_ax:161,add_legend:147,add_selectbox:194,add_slid:194,add_subplot:[35,37,47,75,132],add_trick:178,add_weight:134,addit:[1,7,18,23,32,41,46,54,59,64,66,74,75,76,92,103,104,106,108,112,113,116,117,118,119,124,132,134,135,136,137,139,143,145,149,153,154,156,157,158,160,170,173,179,180,186,187,201,202],addition:[31,117,119,124,142,145,147,149,153,158,187,191],additon:32,address:[75,76,90,103,104,106,112,139,141,142,145,149,156,168,178,183,184],adel:152,adequ:[4,5,6,8,10,11,13,16,17,19,20,21,23,24,26,27,28,69,71,72,85,88,89,90,91,116,143],adher:[48,106,142],adjac:[161,178],adject:[179,201],adjunct:168,adjust:[29,33,36,45,55,57,58,75,134,142,143,152,153,158,161,167],adjusted_mutual_info_scor:161,adjusted_rand_scor:161,admin2:144,admin:187,administr:[145,180],admonit:[120,121,122],adobe_premier:38,adolesc:178,adopt:[59,85,102,112,142,143,149,153,181,183,202],adult:[178,200],advanc:[35,43,66,102,112,116,122,123,125,140,142,143,145,149,161,168,179,187,190],advantag:[40,50,54,68,80,122,134,142,145,152,155,156,158,179],advent:[49,131,142],advers:28,adversari:[36,145,190],advertis:104,advic:143,advis:[7,46,53,58,61,117,152],advoc:112,ae5:161,ae:[31,130],aebf:[118,187],aeroplan:7,aerospik:187,aesthet:22,afb:162,affect:[7,17,33,39,49,52,54,56,58,68,76,80,82,102,104,106,112,116,117,132,134,143,150,156,158,159,170,178,181,183,191],affer:138,affin:[82,147,161],affinity_matrix_:160,affinitypropag:161,afford:[7,74,117,168],african:[112,145,183],afro:[147,148],afropop:[147,148],after:[0,7,14,29,32,33,34,35,36,39,40,41,47,48,49,50,51,54,55,56,57,60,62,64,74,76,82,104,108,114,116,117,119,124,126,127,132,134,135,136,138,139,142,143,144,147,148,150,152,153,156,157,161,162,167,168,173,175,178,179,180,194,196,197,199,200,201],afterward:[32,119],ag:[9,18,22,50,51,74,88,92,93,100,101,114,116,118,11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67,179,201],command:[47,51,101,112,113,124,132,156,177,179,180,187,201],comment:[45,48,50,104,112,113,128,162,177,183,200],commerc:147,commerci:104,commiss:[17,23],commit:[0,112],committe:62,commom:[60,68,80],common:[7,31,33,40,45,46,47,48,50,54,56,59,62,66,76,92,99,103,104,106,112,113,119,120,122,124,127,128,136,137,139,141,142,143,144,147,148,153,156,158,159,160,161,165,175,176,177,178,180,182,186,201],common_el:179,common_norm:[109,185],common_runtim:29,commonest:47,commonli:[41,54,61,62,68,76,80,99,122,131,141,142,143,144,157,159,161,168,178,199],commun:[28,43,103,105,106,112,114,131,140,142,144,153,168,180,189],comp:138,compact:127,compani:[6,104,113,141,145,153,179,180],company_s:56,company_typ:56,companyx:179,compar:[14,18,21,31,33,41,47,48,50,51,54,60,61,63,64,65,68,74,77,80,90,92,93,101,108,109,111,114,116,119,121,126,130,134,143,144,145,149,150,152,157,161,166,168,174,179,198,201],comparis:[63,65],comparison:[8,14,48,50,92,106,111,113,116,118,143,149,153,158,166,178],compat:[15,55,101,119,121,129,132,136,138,140],compatible_format:200,compel:78,compens:[157,160],compet:153,competit:[131,139,145,153,156,157],compexifi:50,compil:[1,7,29,30,32,34,35,38,39,42,44,45,47,48,62,127,135,142,180,186,189],compilaton:40,complaint:[104,112,183],compleletli:[68,80],complementari:139,complet:[1,8,21,24,34,40,41,49,50,52,53,56,57,58,62,68,69,75,80,101,106,108,112,114,116,119,120,122,127,130,134,137,138,139,141,143,145,157,159,160,161,168,173,174,175,178,179,180,182,193,200,201],completeness_scor:161,complex32:119,complex:[0,1,31,32,33,49,57,58,60,61,63,64,65,66,68,76,78,80,111,114,119,124,131,134,137,139,140,141,142,143,145,149,155,158,159,160,165,168,178,180,188,189,191,192,199,202],complex_numb:178,complex_number_1:[179,201],complex_number_2:[179,201],complex_number__1:201,complexnumb:178,complexnumberwithconstructor:178,compli:112,complianc:[22,45,47,48,112,183],compliant:112,complic:[36,50,82,114,119,141,142,143,153,156,159,160,175,189],compon:[75,76,101,102,108,118,126,132,139,141,142,143,144,149,153,156,158,180,181],components_:[160,161,193],compos:[36,37,61,74,82,126,141,142,153,161,195],compose_greet_func:178,compose_greet_func_with_closur:178,composit:[119,152],compound:[179,186,201],compound_stmt:178,comprehend:49,comprehens:[93,108,122,143,173],compress:[29,30,31,106,126,131,134],compris:[39,101,142],compromis:[7,117],comput:[3,7,18,22,29,32,33,36,40,41,43,46,47,49,50,53,54,58,59,66,75,76,77,81,82,84,99,102,103,106,114,116,117,118,120,121,122,126,129,131,133,134,137,138,140,141,142,143,145,149,150,152,153,157,158,159,161,165,168,170,174,177,179,181,182,183,194,195,196,198,201,202],computation:[33,36,50,56,119,121,131,134,139,155],computationn:33,compute_reciproc:186,compute_target:[9,100],con:[7,47,56,101,112,162],concat:[22,30,36,38,42,54,56,66,130,134,135,136,137,139,165,178,179],concat_axi:120,concat_index:120,concaten:[34,38,55,76,119,120,130,135,137,175,179,189,195,196,197,201],concatenated_str:178,concav:130,conceiv:[144,178],concentr:147,concept:[3,18,29,31,47,50,59,75,76,77,101,102,114,116,118,119,122,127,134,140,141,142,143,144,145,150,153,157,158,159,166,174,177,179,188,201],conceptu:153,concern:[7,47,54,58,59,74,106,109,112,117,141,142,145,153,173,185,198],concis:[119,149,168,178,179,201],conclud:[56,59,75,102,108,116,143,150,161],conclus:[24,50,103,112,114,173],concret:[145,161,168,169,193],concurr:[84,100,101],conda:[0,109],condens:132,condit:[3,22,31,39,40,45,47,48,50,54,93,102,112,119,130,134,143,150,152,153,161,173,174,175,178,179,180,200,201],condition2:54,condorcet:149,conduct:[56,100,112,161,183],conf:18,conf_matrix:[52,57],confer:[104,108,120],confid:[40,41,48,68,80,111,134,143,144,147,149,153],config:[9,38,50,66,136,139,149,152,161,193,194],configur:[10,41,45,47,99,101,138,141,142,143,168,175,176,178],confirm:[14,30,45,47,48,59,103,106,112,160,173,183,184],conflict:[93,104,112,122],conform:[113,122,141,143],confus:[7,34,40,50,52,57,60,68,80,83,104,117,119,150,152,157,159,165,178],confusingli:160,confusion_matrix:[34,39,40,51,52,57,59,60,68,80,83,84,166,167,170,174,196,197],confusion_mtx:34,congratul:[100,101,173,174,177,180],conjug:92,conjunct:114,connect:[6,30,32,33,41,43,45,48,62,82,92,93,102,104,112,116,132,134,135,138,143,159,169,178,179,180,189],connectionist:78,connecttimeouterror:132,conquer:153,consciou:7,consecut:[14,32,40,49,157],consent:[112,183],consequ:[28,102,112,132,183],conserv:[109,185],conservationstatu:[109,185],consid:[1,3,7,8,11,14,18,22,24,29,36,39,40,41,45,46,49,50,53,56,62,64,75,76,93,101,103,104,106,108,113,114,115,116,117,126,127,132,134,136,139,142,143,144,145,147,149,150,152,153,154,157,158,159,160,161,162,168,174,177,178,179,191,193,200,201],consider:[59,101,106,112,114,115,122,153,156,160,183,184],consist:[0,1,3,8,15,30,33,34,36,41,45,49,50,52,54,59,88,106,112,117,119,126,134,135,141,142,143,144,145,150,159,161,168,173,175,179,183,184,187,198],consol:122,consolid:75,conspiraci:62,constant:[50,63,65,75,78,119,128,129,130,132,133,134,138,143,153,157,169,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class=\"section-number\">40. </span>Self-paced assignments","<span class=\"section-number\">40.22. </span>Analyzing COVID-19 papers","<span class=\"section-number\">40.27. </span>Analyzing data","<span class=\"section-number\">40.9. </span>Analyzing text about Data Science","<span class=\"section-number\">40.13. </span>Apply your skills","<span class=\"section-number\">40.16. </span>Build your own custom vis","<span class=\"section-number\">40.17. </span>Classifying datasets","<span class=\"section-number\">40.26. </span>Data preparation","<span class=\"section-number\">40.24. </span>Data processing in Python","<span class=\"section-number\">40.41. </span>Data Science in the cloud: The \u201cAzure ML SDK\u201d way","<span class=\"section-number\">40.40. </span>Data Science project using Azure ML SDK","<span class=\"section-number\">40.10. </span>Data Science scenarios","<span class=\"section-number\">40.20. </span>Displaying airport data","<span class=\"section-number\">40.15. </span>Dive into the beehive","<span class=\"section-number\">40.23. </span>Estimation of COVID-19 pandemic","<span class=\"section-number\">40.25. </span>Evaluating data from a form","<span class=\"section-number\">40.36. </span>Explore a planetary computer dataset","<span class=\"section-number\">40.37. </span>Exploring for answers","<span class=\"section-number\">40.19. </span>Introduction to probability and statistics","<span class=\"section-number\">40.12. </span>Lines, scatters and bars","<span class=\"section-number\">40.39. </span>Low code/no code Data Science project on Azure ML","<span class=\"section-number\">40.38. </span>Market research","<span class=\"section-number\">40.29. </span>Matplotlib applied","<span class=\"section-number\">40.28. </span>NYC taxi data in winter and summer","<span class=\"section-number\">40.18. </span>Small diabetes study","<span class=\"section-number\">40.21. </span>Soda profits","<span class=\"section-number\">40.35. </span>Tell a story","<span class=\"section-number\">40.14. </span>Try it in Excel","<span class=\"section-number\">40.11. </span>Write a data ethics case study","<span class=\"section-number\">40.101. </span>Intro to Autoencoders","<span class=\"section-number\">40.102. </span>Base/Denoising Autoencoder &amp; Dimension Reduction","<span class=\"section-number\">40.103. </span>Fun with Variational Autoencoders","<span class=\"section-number\">40.90. </span>How to choose cnn architecture mnist","<span class=\"section-number\">40.94. </span>Object Recognition in Images using CNN","<span class=\"section-number\">40.92. </span>Sign Language Digits Classification with CNN","<span class=\"section-number\">40.112. </span>DQN On Foreign Exchange Market","<span class=\"section-number\">40.113. </span>Art by gan","<span class=\"section-number\">40.115. </span>Generative Adversarial Networks (GANs)","<span class=\"section-number\">40.97. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment","<span class=\"section-number\">40.107. </span>NN Classify 15 Fruits Assignment","<span class=\"section-number\">40.106. </span>Neural Networks for Classification with TensorFlow","<span class=\"section-number\">40.116. </span>Basic classification: Classify images of clothing","<span class=\"section-number\">40.99. </span>Google Stock Price Prediction RNN","<span class=\"section-number\">40.95. </span>Intro to TensorFlow for Deep Learning","<span class=\"section-number\">40.104. </span>Time Series Forecasting Assignment","<span class=\"section-number\">40.83. </span>Counterintuitive Challenges in ML Debugging","<span class=\"section-number\">40.82. </span>Data engineering","<span class=\"section-number\">40.84. </span>Case Study: Debugging in Classification","<span class=\"section-number\">40.85. </span>Case Study: Debugging in Regression","<span class=\"section-number\">40.75. </span>Beyond random forests: more ensemble models","<span class=\"section-number\">40.76. </span>Decision trees","<span class=\"section-number\">40.80. </span>Random Forest Classifier with Feature Importance","<span class=\"section-number\">40.74. </span>Random forests for classification","<span class=\"section-number\">40.73. </span>Random forests intro and regression","<span class=\"section-number\">40.79. </span>Boosting with tuning","<span class=\"section-number\">40.78. </span>Gradient boosting","<span class=\"section-number\">40.77. </span>Hyperparameter tuning gradient boosting","<span class=\"section-number\">40.67. </span>Decision Trees - Classification","<span class=\"section-number\">40.66. </span>Decision Trees - Intro and Regression","<span class=\"section-number\">40.63. </span>Kernel method assignment 1","<span class=\"section-number\">40.65. </span>Support Vector Machines (SVM) - Classification","<span class=\"section-number\">40.64. </span>Support Vector Machines (SVM) - Intro and SVM for Regression","<span class=\"section-number\">40.70. </span>Dropout and Batch Normalization","<span class=\"section-number\">40.71. </span>Lasso and Ridge Regression","<span class=\"section-number\">40.69. </span>Learning Curve To Identify Overfit &amp; Underfit","<span class=\"section-number\">40.68. </span>Model selection assignment 1","<span class=\"section-number\">40.72. </span>Regularized Linear Models","<span class=\"section-number\">40.88. </span>Build Classification Model","<span class=\"section-number\">40.87. </span>Build classification models","<span class=\"section-number\">40.57. </span>Create a regression model","<span class=\"section-number\">40.61. </span>Delicious asian and indian cuisines","<span class=\"section-number\">40.62. </span>Explore classification methods","<span class=\"section-number\">40.55. </span>Exploring visualizations","<span class=\"section-number\">40.58. </span>Linear and polynomial regression","<span class=\"section-number\">40.45. </span>Linear regression - California Housing","<span class=\"section-number\">40.48. </span>Gradient descent","<span class=\"section-number\">40.49. </span>Linear Regression Implementation from Scratch","<span class=\"section-number\">40.46. </span>Linear Regression Metrics","<span class=\"section-number\">40.47. </span>Loss Function","<span class=\"section-number\">40.54. </span>Managing data","<span class=\"section-number\">40.50. </span>ML logistic regression - assignment 1","<span class=\"section-number\">40.51. </span>ML logistic regression - assignment 2","<span class=\"section-number\">40.52. </span>ML neural network - Assignment 1","<span class=\"section-number\">40.42. </span>Machine Learning overview - assignment 1","<span class=\"section-number\">40.43. </span>Machine Learning overview - assignment 2","<span class=\"section-number\">40.89. </span>Parameter play","<span class=\"section-number\">40.60. </span>Pumpkin varieties and color","<span class=\"section-number\">40.53. </span>Regression tools","<span class=\"section-number\">40.44. </span>Regression with Scikit-learn","<span class=\"section-number\">40.59. </span>Retrying some regression","<span class=\"section-number\">40.86. </span>Study the solvers","<span class=\"section-number\">40.56. </span>Try a different model","<span class=\"section-number\">40.8. </span>Python programming advanced","<span class=\"section-number\">40.7. </span>Python programming basics","<span class=\"section-number\">40.6. </span>Python programming introduction","<span class=\"section-number\">40.5. </span>Project Plan\u200b Template","<span class=\"section-number\">40.3. </span>First assignment","<span class=\"section-number\">40.4. </span>Second assignment","<span class=\"section-number\">8. </span>Data Science in the cloud","<span class=\"section-number\">8.1. </span>Introduction","<span class=\"section-number\">8.3. </span>Data Science in the cloud: The \u201cAzure ML SDK\u201d way","<span class=\"section-number\">8.2. </span>The \u201clow code/no code\u201d way","<span class=\"section-number\">9. </span>Data Science in the real world","<span class=\"section-number\">7.2. </span>Analyzing","<span class=\"section-number\">7.3. </span>Communication","<span class=\"section-number\">7. </span>Data Science lifecycle","<span class=\"section-number\">7.1. </span>Introduction to the Data Science lifecycle","<span class=\"section-number\">6. </span>Data visualization","<span class=\"section-number\">6.4. </span>Making meaningful visualizations","<span class=\"section-number\">6.1. </span>Visualizing distributions","<span class=\"section-number\">6.2. </span>Visualizing proportions","<span class=\"section-number\">6.3. </span>Visualizing relationships: all about honey \ud83c\udf6f","<span class=\"section-number\">4.2. </span>Data Science ethics","<span class=\"section-number\">4.3. </span>Defining data","<span class=\"section-number\">4.1. </span>Defining data science","<span class=\"section-number\">4. </span>Introduction","<span class=\"section-number\">4.4. </span>Introduction to statistics and probability","<span class=\"section-number\">5.5. </span>Data preparation","<span class=\"section-number\">5.2. </span>Non-relational data","<span class=\"section-number\">5.3. </span>NumPy","<span class=\"section-number\">5.4.3. </span>Advanced Pandas Techniques","<span class=\"section-number\">5.4.2. </span>Data Selection","<span class=\"section-number\">5.4.1. </span>Introduction and Data Structures","<span class=\"section-number\">5.4. </span>Pandas","<span class=\"section-number\">5.1. </span>Relational databases","<span class=\"section-number\">5. </span>Working with data","<span class=\"section-number\">27. </span>Autoencoder","<span class=\"section-number\">24. </span>Convolutional Neural Networks","<span class=\"section-number\">24.3.1.2. </span>Deepdream in TensorFlow","<span class=\"section-number\">24.3.1.1. </span>Stylenet / Neural-Style","<span class=\"section-number\">33. </span>Diffusion Model","<span class=\"section-number\">23. </span>Intro to Deep Learning","<span class=\"section-number\">30. </span>Deep Q-learning","<span class=\"section-number\">25. </span>Generative adversarial networks","<span class=\"section-number\">31. </span>Image classification","<span class=\"section-number\">32. </span>Image segmentation","<span class=\"section-number\">28. </span>Long-short term memory","<span class=\"section-number\">34. </span>Object detection","<span class=\"section-number\">26. </span>Recurrent Neural Networks","<span class=\"section-number\">29. </span>Time series","Learn AI together, for free","<span class=\"section-number\">37. </span>Data engineering","<span class=\"section-number\">39. </span>Model deployment","<span class=\"section-number\">38. </span>Model training &amp; evaluation","<span class=\"section-number\">35. </span>Overview","<span class=\"section-number\">36. </span>Problem framing","<span class=\"section-number\">16. </span>Clustering models for Machine Learning","<span class=\"section-number\">16.1. </span>Introduction to clustering","<span class=\"section-number\">16.2. </span>K-Means clustering","<span class=\"section-number\">17.1. </span>Bagging","<span class=\"section-number\">17.3. </span>Feature importance","<span class=\"section-number\">17. </span>Getting started with ensemble learning","<span class=\"section-number\">17.2. </span>Random forest","<span class=\"section-number\">18.1. </span>Gradient Boosting","<span class=\"section-number\">18.2. </span>Gradient boosting example","<span class=\"section-number\">18. </span>Introduction to Gradient Boosting","<span class=\"section-number\">18.3. </span>XGBoost","<span class=\"section-number\">18.4. </span>XGBoost + k-fold CV + Feature Importance","<span class=\"section-number\">20. </span>Kernel method","<span class=\"section-number\">22. </span>Model selection","<span class=\"section-number\">19. </span>Unsupervised learning","<span class=\"section-number\">21. </span>Unsupervised learning: PCA and clustering","<span class=\"section-number\">13. </span>Build a web app to use a Machine Learning model","<span class=\"section-number\">14.4. </span>Applied Machine Learning : build a web app","<span class=\"section-number\">14. </span>Getting started with classification","<span class=\"section-number\">14.1. </span>Introduction to classification","<span class=\"section-number\">14.2. </span>More classifiers","<span class=\"section-number\">14.3. </span>Yet other classifiers","<span class=\"section-number\">10. </span>Machine Learning overview","<span class=\"section-number\">11. </span>Summary of machine learning fundamentals","<span class=\"section-number\">15.2. </span>Gradient descent","<span class=\"section-number\">15.1. </span>Loss function","<span class=\"section-number\">15. </span>parameter-optimization","<span class=\"section-number\">12.3. </span>Linear and polynomial regression","<span class=\"section-number\">12.4. </span>Logistic regression","<span class=\"section-number\">12.2. </span>Managing data","<span class=\"section-number\">12. </span>Regression models for Machine Learning","<span class=\"section-number\">12.1. </span>Tools of the trade","<span class=\"section-number\">3. </span>Python programming advanced","<span class=\"section-number\">2. </span>Python programming basics","<span class=\"section-number\">1. </span>Python programming introduction","<span class=\"section-number\">41.10. </span>Data Science in real world","<span class=\"section-number\">41.9. </span>Data Science in the cloud","<span class=\"section-number\">41.4. </span>Data Science introduction","<span class=\"section-number\">41.8. </span>Data Science lifecycle","<span class=\"section-number\">41.7. </span>Data visualization","<span class=\"section-number\">41.6. </span>NumPy and Pandas","<span class=\"section-number\">41.5. </span>Relational vs. non-relational database","<span class=\"section-number\">41.20. </span>Convolutional Neural Network","<span class=\"section-number\">41.21. </span>Generative Adversarial Network","<span class=\"section-number\">41. </span>Slides","<span class=\"section-number\">41.18. </span>Kernel method","<span class=\"section-number\">41.19. </span>Model Selection","<span class=\"section-number\">41.17. </span>Unsupervised learning","<span class=\"section-number\">41.16. </span>Build an machine learning web application","<span class=\"section-number\">41.12. </span>Linear Regression","<span class=\"section-number\">41.13. </span>Logistic Regression","<span class=\"section-number\">41.14. </span>Logistic Regression","<span class=\"section-number\">41.11. </span>Machine Learning overview","<span class=\"section-number\">41.15. </span>Neural Network","<span class=\"section-number\">41.3. </span>Python programming advanced","<span class=\"section-number\">41.2. </span>Python programming basics","<span class=\"section-number\">41.1. </span>Python programming introduction"],titleterms:{"0":59,"1":[3,24,30,32,43,49,50,52,53,54,56,57,58,59,60,61,65,68,74,75,80,82,83,104,113,119,134,161,181,182,183,184,185,186,187,188,189,191,195,197,201,202],"10":[40,56,127,134],"100":[51,59,134],"1000":[59,134],"11":56,"12":56,"13":56,"1300131294":158,"15":39,"19":[1,8,14,129],"1d":119,"2":[3,24,30,32,43,44,49,50,51,52,53,54,56,57,58,60,61,68,75,80,81,84,104,113,119,161,181,182,183,184,185,186,187,188,189,191,195,197,201,202],"2d":[30,119,191],"3":[3,24,32,39,43,49,50,52,53,54,56,57,58,60,61,68,74,80,104,119,181,182,183,184,185,186,188,189,195,201,202],"3d":[30,83,108,191],"4":[3,24,32,43,49,50,51,52,53,54,56,57,58,60,61,68,80,104,161,181,182,183,184,185,186,187,201,202],"5":[24,32,43,49,50,53,54,56,57,58,60,61,68,80,83,104,134,161,181,182,183,184,185,187,201,202],"50":56,"500":56,"6":[43,50,52,53,54,56,57,58,60,61,68,80,181,182,183,184,185],"7":[43,50,52,53,56,57,58,60,61,181,184,185],"8":[52,56,184],"9":56,"90":158,"abstract":170,"boolean":[119,179,180,201,202],"break":[92,178,200],"case":[28,45,47,48,50,104,112,175,191],"class":[35,39,47,50,51,63,65,92,171,178,200],"default":[51,59,129,178],"do":[47,114,174,175,179,183,191],"final":[49,74,75,170],"float":[7,179,201],"function":[43,51,59,75,76,78,82,92,93,119,131,132,153,170,171,178,180,186,196,197,199,200],"import":[9,29,33,37,41,44,49,51,52,53,54,56,57,58,59,60,61,64,66,83,117,129,130,143,150,157,177,178,195,196,197,199],"long":136,"new":[56,94,119,122,169,196,197],"null":[7,59,186],"public":37,"return":[94,132,178],"short":136,"static":158,"true":59,"try":[0,27,48,68,75,80,91,178,200],"while":[92,178,200],A:[31,131,159,166,167,173],And:168,At:[46,170,171],But:175,By:153,For:92,Is:145,It:[124,174,187],NOT:174,Not:168,On:35,One:[74,75,199],That:196,The:[9,36,43,51,53,75,77,82,100,101,117,124,159,160,167,175,177,178,179,182,186,187,188,191,200,201],There:175,To:[64,159],With:[34,139,170],about:[3,33,111,165,174,185],absolut:[77,171],acceler:160,access:[93,119,122],accuraci:[32,41,47,59,134,157,161,199],acknowledg:[1,2,3,4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,71,72,74,80,82,83,84,85,86,88,89,90,91,92,93,99,100,101,102,103,104,106,108,109,110,111,112,113,114,116,117,118,119,120,121,122,124,126,127,128,129,130,132,133,134,135,136,137,138,139,140,143,147,148,149,150,152,153,154,156,157,158,162,165,166,167,168,173,174,175,177,178,179,180,188,191,193,194,195,196,199],ackowledg:189,action:132,activ:131,actual:104,ad:[30,31,47,62,66,119,159],adaboost:49,adam:199,add:[48,119,143],addit:122,advanc:[32,92,119,120,178,200],adversari:[37,133,189],after:[30,159,192],ag:24,against:48,agent:[35,132],agglom:[160,161],aggreg:[119,120,183,186],ahead:82,ai:[36,41,140,193],airport:12,aka:64,algorithm:[50,54,132,144,147,152,153,157,160,169],align:122,all:[24,47,74,111,124,173,185,187],an:[9,31,64,66,100,101,104,118,119,162,188,194],anagram:179,analysi:[8,38,49,51,52,53,54,56,57,58,59,60,61,74,77,99,103,161,168,193,198],analyz:[1,2,3,18,103,119,175,184],anchor:[39,51],anim:108,ann:44,annot:[178,200],anomal:47,anomali:29,anoth:37,answer:17,ap:158,api:[34,43],app:[162,163],append:[93,186],appli:[4,22,47,112,163,166,167,183],applic:[50,126,161,194,198],approach:[51,104,124,166,187],ar:179,arbitrari:178,architectur:[32,127,131,188,199],argument:[129,178,200],arithmet:[122,179,201],arrai:[119,122,186],art:36,artifici:198,artwork:36,ascend:[93,94],asian:70,ask:175,assert:51,assign:[0,12,25,39,44,59,65,80,81,82,83,84,96,97,119,120,122,161,197],assist:135,attribut:[59,119,122,186],auc:[56,59],audienc:104,augment:[32,34,130],author:177,autoencod:[29,30,31,126],automl:[9,100,101,143],avail:119,averag:[92,175],avoid:[108,159],axi:119,azur:[9,10,20,100,101,118],b:[75,170],babylonian:93,background:130,backprop:82,bag:[49,149,152,157],balanc:165,bar:[19,22,185],base:[30,92,121,157],basebal:18,baselin:[47,48,168,198],basi:59,basic:[29,32,41,43,93,112,119,122,132,179,180,183,186,201,202],batch:[33,62,160],beehiv:13,begin:104,behind:55,best:[9,32,37,100,145],beta:130,better:[166,174],between:[24,54,56,68,74,80,119,152,171,183,186],beyond:[40,49,158,191],bi:54,bia:[64,152,159,192],bibliographi:[25,170,171],big:199,binai:54,binari:[40,171,174],binder:0,bird:[109,185],bit:[31,119],bitcoin:38,blend:54,bmi:24,boost:[49,54,55,56,153,154,155,157],bootstrap:149,bound:130,boundari:[158,160,191],boxplot:[24,83],bp:24,brain:188,broadcast:[119,186],bug:48,build:[5,29,31,32,36,41,50,51,67,68,80,108,148,162,163,173,174,175,185,194],c:59,cach:194,calcul:[47,92,93,179],california:74,call:200,callabl:121,callback:40,can:[50,56,114,183],candid:56,capac:159,captur:[106,184],cardin:51,cast:[179,201],catalog:141,catboost:54,categor:[7,51,54,57,68,74,80,173,195],categori:93,categorical_crossentropi:199,caus:45,central:[18,116],centroid:160,chain:122,challeng:[1,14,22,45,112,119,131,134,137,183],chang:[34,54],changin:34,channel:104,chart:[108,111,185,19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diff --git a/slides/data-science/data-science-in-real-world.html b/slides/data-science/data-science-in-real-world.html
index 0746ef484..51865282b 100644
--- a/slides/data-science/data-science-in-real-world.html
+++ b/slides/data-science/data-science-in-real-world.html
@@ -6,7 +6,7 @@
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-    <title>42.10. Data Science in real world &#8212; Ocademy Open Machine Learning Book</title>
+    <title>41.10. Data Science in real world &#8212; Ocademy Open Machine Learning Book</title>
     
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   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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   <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
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     <a class="reference internal" href="../../assignments/set-up-env/first-assignment.html">
-     41.3. First assignment
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-     41.4. Second assignment
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     <a class="reference internal" href="../../assignments/project-plan-template.html">
-     41.5. Project Plan​ Template
+     40.5. Project Plan​ Template
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/prerequisites/python-programming-introduction.html">
-     41.6. Python programming introduction
+     40.6. Python programming introduction
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     <a class="reference internal" href="../../assignments/prerequisites/python-programming-basics.html">
-     41.7. Python programming basics
+     40.7. Python programming basics
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     <a class="reference internal" href="../../assignments/prerequisites/python-programming-advanced.html">
-     41.8. Python programming advanced
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html">
-     41.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html">
-     41.10. Data Science scenarios
+     40.10. Data Science scenarios
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     <a class="reference internal" href="../../assignments/data-science/write-a-data-ethics-case-study.html">
-     41.11. Write a data ethics case study
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     <a class="reference internal" href="../../assignments/data-science/lines-scatters-and-bars.html">
-     41.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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     <a class="reference internal" href="../../assignments/data-science/try-it-in-excel.html">
-     41.14. Try it in Excel
+     40.14. Try it in Excel
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     <a class="reference internal" href="../../assignments/data-science/dive-into-the-beehive.html">
-     41.15. Dive into the beehive
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     <a class="reference internal" href="../../assignments/data-science/build-your-own-custom-vis.html">
-     41.16. Build your own custom vis
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     <a class="reference internal" href="../../assignments/data-science/classifying-datasets.html">
-     41.17. Classifying datasets
+     40.17. Classifying datasets
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     <a class="reference internal" href="../../assignments/data-science/small-diabetes-study.html">
-     41.18. Small diabetes study
+     40.18. Small diabetes study
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     <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
-     41.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
-     41.20. Displaying airport data
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     <a class="reference internal" href="../../assignments/data-science/soda-profits.html">
-     41.21. Soda profits
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     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
-     41.22. Analyzing COVID-19 papers
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
-     41.23. Estimation of COVID-19 pandemic
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     <a class="reference internal" href="../../assignments/data-science/data-processing-in-python.html">
-     41.24. Data processing in Python
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
-     41.25. Evaluating data from a form
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     <a class="reference internal" href="../../assignments/data-science/data-preparation.html">
-     41.26. Data preparation
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-     41.27. Analyzing data
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     <a class="reference internal" href="../../assignments/data-science/nyc-taxi-data-in-winter-and-summer.html">
-     41.28. NYC taxi data in winter and summer
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     <a class="reference internal" href="../../assignments/data-science/matplotlib-applied.html">
-     41.29. Matplotlib applied
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-     41.35. Tell a story
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     <a class="reference internal" href="../../assignments/data-science/explore-a-planetary-computer-dataset.html">
-     41.36. Explore a planetary computer dataset
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     <a class="reference internal" href="../../assignments/data-science/exploring-for-anwser.html">
-     41.37. Exploring for answers
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     <a class="reference internal" href="../../assignments/data-science/market-research.html">
-     41.38. Market research
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-     41.39. Low code/no code Data Science project on Azure ML
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     <a class="reference internal" href="../../assignments/data-science/data-science-project-using-azure-ml-sdk.html">
-     41.40. Data Science project using Azure ML SDK
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     <a class="reference internal" href="../../assignments/data-science/data-science-in-the-cloud-the-azure-ml-sdk-way.html">
-     41.41. Data Science in the cloud: The “Azure ML SDK” way
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     </a>
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
-     41.42. Machine Learning overview - assignment 1
+     40.42. Machine Learning overview - assignment 1
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
-     41.43. Machine Learning overview - assignment 2
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     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-with-scikit-learn.html">
-     41.44. Regression with Scikit-learn
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
-     41.45. Linear regression - California Housing
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-metrics.html">
-     41.46. Linear Regression Metrics
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/loss-function.html">
-     41.47. Loss Function
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/gradient-descent.html">
-     41.48. Gradient descent
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/linear-regression-from-scratch.html">
-     41.49. Linear Regression Implementation from Scratch
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     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
-     41.50. ML logistic regression - assignment 1
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-2.html">
-     41.51. ML logistic regression - assignment 2
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-neural-network-1.html">
-     41.52. ML neural network - Assignment 1
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     <a class="reference internal" href="../../assignments/ml-fundamentals/regression-tools.html">
-     41.53. Regression tools
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-     41.54. Managing data
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-     41.55. Exploring visualizations
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-     41.56. Try a different model
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-     41.57. Create a regression model
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-     41.58. Linear and polynomial regression
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-     41.59. Retrying some regression
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-     41.60. Pumpkin varieties and color
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-     41.61. Delicious asian and indian cuisines
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-     41.62. Explore classification methods
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-     41.63. Kernel method assignment 1
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_regression.html">
-     41.64. Support Vector Machines (SVM) - Intro and SVM for Regression
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     </a>
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/support_vector_machines_for_classification.html">
-     41.65. Support Vector Machines (SVM) - Classification
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     <a class="reference internal" href="../../assignments/ml-advanced/kernel-method/decision_trees_for_regression.html">
-     41.66. Decision Trees - Intro and Regression
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-     41.67. Decision Trees - Classification
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/learning-curve-to-identify-overfit-underfit.html">
-     41.69. Learning Curve To Identify Overfit &amp; Underfit
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/dropout-and-batch-normalization.html">
-     41.70. Dropout and Batch Normalization
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/lasso-and-ridge-regression.html">
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     <a class="reference internal" href="../../assignments/ml-advanced/model-selection/regularized-linear-models.html">
-     41.72. Regularized Linear Models
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     <a class="reference internal" href="../../assignments/ml-advanced/ensemble-learning/random-forests-intro-and-regression.html">
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     <a class="reference internal" href="../../assignments/machine-learning-productionization/debugging-in-classification.html">
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 <section id="data-science-industry">
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+<h2><span class="section-number">41.10.1. </span>1. Data Science + industry<a class="headerlink" href="#data-science-industry" title="Permalink to this headline">#</a></h2>
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 <p><em>1. DataFlair. 6 amazing data science applications – don’t forget to check the 5th one! URL: <a class="reference external" href="https://data-flair.training/blogs/data-science-applications/">https://data-flair.training/blogs/data-science-applications/</a> (visited on 2022).</em></p>
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+<h2><span class="section-number">41.10.2. </span>2. Data Science + research<a class="headerlink" href="#data-science-research" title="Permalink to this headline">#</a></h2>
 <p>Let’s look at one example - the <a class="reference external" href="http://gendershades.org/overview.html">MIT Gender Shades Study</a> from Joy Buolamwini (MIT Media Labs) with a <a class="reference external" href="http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf">signature research paper</a> co-authored with Timnit Gebru (then at Microsoft Research) that focused on,</p>
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+<h2><span class="section-number">41.10.3. </span>3. Data Science + humanities<a class="headerlink" href="#data-science-humanities" title="Permalink to this headline">#</a></h2>
 <p><a class="reference external" href="https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671">“Emily Dickinson and the Meter of Mood”</a> - a great example from <a class="reference external" href="https://twitter.com/jenlooper">Jen Looper</a> that asks how we can use data science to revisit familiar poetry and re-evaluate its meaning and the contributions of its author in new contexts.</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#acquiring-the-dataset"><strong>Data Acquisition</strong></a> - to collect a relevant dataset for analysis. Options include using an API (e.g., <a class="reference external" href="https://poetrydb.org/index.html">Poetry DB API</a>) or scraping web pages (e.g., <a class="reference external" href="https://www.gutenberg.org/files/12242/12242-h/12242-h.htm">Project Gutenberg</a>) using tools like <a class="reference external" href="https://scrapy.org/">Scrapy</a>.</p></li>
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+<h2><span class="section-number">41.10.4. </span>4. Data Science + sustainability<a class="headerlink" href="#data-science-sustainability" title="Permalink to this headline">#</a></h2>
 <p>The <a class="reference external" href="https://sdgs.un.org/2030agenda">2030 Agenda For Sustainable Development</a> - adopted by all United Nations members in 2015 - identifies 17 goals including ones that focus on <strong>Protecting the Planet</strong> from degradation and the impact of climate change.</p>
 <p>The <a class="reference external" href="https://planetarycomputer.microsoft.com/">Planetary Computer</a> initiative provides 4 components to help data scientists and developers in this effort:</p>
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+<h2><span class="section-number">41.10.5. </span>5. Data Science + students<a class="headerlink" href="#data-science-students" title="Permalink to this headline">#</a></h2>
 <p>We’ve talked about real-world applications in industry and research, and explored Data Science application examples in digital humanities and sustainability. So how can you build your skills and share your expertise as a Data Science beginner?</p>
 </section>
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-<h2><span class="section-number">42.10.6. </span>6. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
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 <ol class="simple">
 <li><p><a class="reference external" href="https://builtin.com/data-science/data-science-applications-examples">17 Data Science Applications and Examples</a> - Jul 2021</p></li>
 <li><p><a class="reference external" href="https://myblindbird.com/data-science-applications-real-world/">11 Breathtaking Data Science Applications in Real World</a> - May 2021</p></li>
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 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/data-science-in-the-wild.md">Data Science in the real world</a></p></li>
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+            <p class="prev-next-title"><span class="section-number">41.9. </span>Data Science in the cloud</p>
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+     40.13. Apply your skills
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+     40.14. Try it in Excel
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+   41.9.1. 1. What is the cloud?
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+<h1><span class="section-number">41.9. </span>Data Science in the cloud<a class="headerlink" href="#data-science-in-the-cloud" title="Permalink to this headline">#</a></h1>
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 <p>The Cloud, or Cloud Computing, is the delivery of a wide range of pay-as-you-go computing services hosted on infrastructure over the internet. Services include solutions such as storage, databases, networking, software, analytics, and intelligent services.</p>
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+<h2><span class="section-number">41.9.2. </span>2. Why choose the cloud for Data Science?<a class="headerlink" href="#why-choose-the-cloud-for-data-science" title="Permalink to this headline">#</a></h2>
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+<h2><span class="section-number">41.9.3. </span>3. The “low code/no code” way<a class="headerlink" href="#the-low-code-no-code-way" title="Permalink to this headline">#</a></h2>
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+<h2><span class="section-number">41.9.4. </span>4. Low code/no code Machine Learning<a class="headerlink" href="#low-code-no-code-machine-learning" title="Permalink to this headline">#</a></h2>
 <p>The Low code/No code way is easier to start with as it involves interacting with a GUI (Graphical User Interface), with no prior knowledge of code required.</p>
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+<h2><span class="section-number">41.9.5. </span>5. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/assignments/data-science/market-research.html">Market research</a></p></li>
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/assignments/data-science/low-code-no-code-data-science-project-on-azure-ml.html">Low code/no code Data Science project on Azure ML</a></p></li>
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-    <link rel="prev" title="42.3. Python programming advanced" href="../python-programming/python-programming-advanced.html" />
+    <link rel="next" title="41.5. Relational vs. non-relational database" href="relational-vs-non-relational-database.html" />
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-     41.45. Linear regression - California Housing
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+     41.4.2.2. Ethics concepts
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+     41.4.2.3. Ethics challenges
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    </li>
    <li class="toc-h3 nav-item toc-entry">
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    </li>
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+   41.4.3. 3. Defining data
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+   41.4.4. 4. Introduction to statistics and probability
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#probability-and-random-variables">
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+<h3><span class="section-number">41.4.1.2. </span>What you can do with data<a class="headerlink" href="#what-you-can-do-with-data" title="Permalink to this headline">#</a></h3>
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 <li><p><strong>Data ethics</strong> is a new branch of ethics that “studies and evaluates moral problems related to data, algorithms and corresponding practices”.</p></li>
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 <blockquote>
 <div><p><strong>Probability</strong> is a number between 0 and 1 that expresses how probable an <strong>event</strong> is. And when we talk about events, we use <strong>random variables</strong>.</p>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">42.3. </span>Python programming advanced</p>
+            <p class="prev-next-title"><span class="section-number">41.3. </span>Python programming advanced</p>
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-        <p class="prev-next-title"><span class="section-number">42.5. </span>Relational vs. non-relational database</p>
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-    <link rel="prev" title="42.7. Data visualization" href="data-visualization.html" />
+    <link rel="next" title="41.9. Data Science in the cloud" href="data-science-in-the-cloud.html" />
+    <link rel="prev" title="41.7. Data visualization" href="data-visualization.html" />
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-     41.12. Lines, scatters and bars
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.16. Build your own custom vis
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-     41.17. Classifying datasets
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-     41.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
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-     41.21. Soda profits
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-     41.22. Analyzing COVID-19 papers
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-     41.24. Data processing in Python
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-     41.35. Tell a story
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-     41.36. Explore a planetary computer dataset
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-     41.37. Exploring for answers
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-     41.39. Low code/no code Data Science project on Azure ML
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   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#effective-communication">
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+     41.8.6.1. Effective communication
     </a>
    </li>
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+   41.8.7. 7. Your turn! 🚀
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+   41.8.1. 1. What’s Data Science lifecycle
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  <li class="toc-h2 nav-item toc-entry">
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+   41.8.3. 3. Processing
   </a>
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-   42.8.4. 4. Maintaining
+   41.8.4. 4. Maintaining
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#storing-data">
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+     41.8.4.1. Storing data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
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-     42.8.4.2. Managing data
+     41.8.4.2. Managing data
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    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#securing-the-data">
-     42.8.4.3. Securing the data
+     41.8.4.3. Securing the data
     </a>
    </li>
   </ul>
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+   41.8.5. 5. Analyzing
   </a>
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   <a class="reference internal nav-link" href="#communication">
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     </a>
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+   41.8.7. 7. Your turn! 🚀
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 </div></div>
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 <section class="tex2jax_ignore mathjax_ignore" id="data-science-lifecycle">
-<h1><span class="section-number">42.8. </span>Data Science lifecycle<a class="headerlink" href="#data-science-lifecycle" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.8. </span>Data Science lifecycle<a class="headerlink" href="#data-science-lifecycle" title="Permalink to this headline">#</a></h1>
 <section id="whats-data-science-lifecycle">
-<h2><span class="section-number">42.8.1. </span>1. What’s Data Science lifecycle<a class="headerlink" href="#whats-data-science-lifecycle" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.8.1. </span>1. What’s Data Science lifecycle<a class="headerlink" href="#whats-data-science-lifecycle" title="Permalink to this headline">#</a></h2>
 <p>Data Science is a process which can be broken down into 5 stages:</p>
 <br>
 <div class="row">
@@ -1770,7 +1770,7 @@ <h2><span class="section-number">42.8.1. </span>1. What’s Data Science lifecyc
 <p><em>1. What is data science? (n.d.). UCB-UMT. Retrieved 19 February 2023, from <a class="reference external" href="https://ischoolonline.berkeley.edu/data-science/what-is-data-science/">https://ischoolonline.berkeley.edu/data-science/what-is-data-science/</a></em></p>
 </font></section>
 <section id="capturing">
-<h2><span class="section-number">42.8.2. </span>2. Capturing<a class="headerlink" href="#capturing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.8.2. </span>2. Capturing<a class="headerlink" href="#capturing" title="Permalink to this headline">#</a></h2>
 <p>The capturing is combined by two stages: acquiring the data and defining the purpose and problems that need to be addressed.</p>
 <p>Defining the goals of the project will require deeper context into the problem or question.</p>
 <p>First, we need to identify and acquire those who need their problem solved. These may be stakeholders in a business or sponsors of the project, who can help identify who or what will benefit from this project as well as what, and why they need it. A well-defined goal should be measurable and quantifiable to define an acceptable result.</p>
@@ -1788,7 +1788,7 @@ <h2><span class="section-number">42.8.2. </span>2. Capturing<a class="headerlink
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 </section>
 <section id="processing">
-<h2><span class="section-number">42.8.3. </span>3. Processing<a class="headerlink" href="#processing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.8.3. </span>3. Processing<a class="headerlink" href="#processing" title="Permalink to this headline">#</a></h2>
 <p>The processing stage of the lifecycle focuses on discovering patterns in the data as well as modeling.</p>
 <ul class="simple">
 <li><p>Classification: organizing data into categories for more efficient use.</p></li>
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 </ul>
 </section>
 <section id="maintaining">
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+<h2><span class="section-number">41.8.4. </span>4. Maintaining<a class="headerlink" href="#maintaining" title="Permalink to this headline">#</a></h2>
 <p>In the diagram of the lifecycle, you may have noticed that maintenance sits between capturing and processing.</p>
 <p>Maintenance is an ongoing process of managing, storing, and securing the data throughout the process of a project and should be taken into consideration throughout the entirety of the project.</p>
 <section id="storing-data">
-<h3><span class="section-number">42.8.4.1. </span>Storing data<a class="headerlink" href="#storing-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.8.4.1. </span>Storing data<a class="headerlink" href="#storing-data" title="Permalink to this headline">#</a></h3>
 <p>Considerations of how and where the data is stored can influence the cost of its storage as well as the performance of how fast the data can be accessed</p>
 <ul class="simple">
 <li><p>On premise vs off premise vs public or private cloud</p></li>
@@ -1809,12 +1809,12 @@ <h3><span class="section-number">42.8.4.1. </span>Storing data<a class="headerli
 </ul>
 </section>
 <section id="managing-data">
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+<h3><span class="section-number">41.8.4.2. </span>Managing data<a class="headerlink" href="#managing-data" title="Permalink to this headline">#</a></h3>
 <p>As you work with data you may discover that some of the data needs to be cleaned using some of the techniques covered in the section focused on <a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/working-with-data/data-preparation.html">data preparation</a> building accurate models.</p>
 <p>When new data arrives, it will need some of the same applications to maintain consistency in quality.</p>
 </section>
 <section id="securing-the-data">
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 <p>One of the main goals of securing data is ensuring that those working on it are in control of what is collected and in what context it is being used.</p>
 <ul class="simple">
 <li><p>Confirm that all data is encrypted.
@@ -1826,7 +1826,7 @@ <h3><span class="section-number">42.8.4.3. </span>Securing the data<a class="hea
 </section>
 </section>
 <section id="analyzing">
-<h2><span class="section-number">42.8.5. </span>5. Analyzing<a class="headerlink" href="#analyzing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.8.5. </span>5. Analyzing<a class="headerlink" href="#analyzing" title="Permalink to this headline">#</a></h2>
 <p>Analyzing the data lifecycle confirms that the data can answer the questions that are proposed or solve a particular problem.</p>
 <ul class="simple">
 <li><p>Exploratory data analysis</p></li>
@@ -1837,7 +1837,7 @@ <h2><span class="section-number">42.8.5. </span>5. Analyzing<a class="headerlink
 </ul>
 </section>
 <section id="communication">
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 <p>To communicate is to convey or exchange information.</p>
 <p>Information can be ideas, thoughts, feelings, messages, covert signals, data – anything that a sender (someone sending information) wants a receiver (someone receiving information) to understand.</p>
 <ul class="simple">
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+<h3><span class="section-number">41.8.6.1. </span>Effective communication<a class="headerlink" href="#effective-communication" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Your responsibilities as a communicator</p></li>
 <li><p>Understand your audience, your channel &amp; your communication method</p></li>
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 </section>
 </section>
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 <ol class="simple">
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/data-science-lifecycle/introduction.html#your-turn">Data science lifecycle</a></p></li>
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/data-science-lifecycle/analyzing.html#your-turn">Analyzing</a></p></li>
@@ -1870,7 +1870,7 @@ <h2><span class="section-number">42.8.7. </span>7. Your turn! 🚀<a class="head
 </ol>
 </section>
 <section id="references">
-<h2><span class="section-number">42.8.8. </span>8. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.8.8. </span>8. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/data-science-lifecycle/data-science-lifecycle.html">Data Science lifecycle</a></p></li>
 </ol>
@@ -1914,13 +1914,13 @@ <h2><span class="section-number">42.8.8. </span>8. References<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">42.7. </span>Data visualization</p>
+            <p class="prev-next-title"><span class="section-number">41.7. </span>Data visualization</p>
         </div>
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-        <p class="prev-next-title"><span class="section-number">42.9. </span>Data Science in the cloud</p>
+        <p class="prev-next-title"><span class="section-number">41.9. </span>Data Science in the cloud</p>
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diff --git a/slides/data-science/data-visualization.html b/slides/data-science/data-visualization.html
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-    <link rel="prev" title="42.6. NumPy and Pandas" href="numpy-and-pandas.html" />
+    <link rel="next" title="41.8. Data Science lifecycle" href="data-science-lifecycle.html" />
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+     8.1. Introduction
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+     8.2. The “low code/no code” way
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+     8.3. Data Science in the cloud: The “Azure ML SDK” way
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+     12.3. Linear and polynomial regression
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+     14.3. Yet other classifiers
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+     14.4. Applied Machine Learning : build a web app
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+     15.2. Gradient descent
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-     19.4. XGBoost + k-fold CV + Feature Importance
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+     40.3. First assignment
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+     40.5. Project Plan​ Template
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-     41.6. Python programming introduction
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+     40.7. Python programming basics
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-     41.9. Analyzing text about Data Science
+     40.9. Analyzing text about Data Science
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+     40.10. Data Science scenarios
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-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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-     41.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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+     40.15. Dive into the beehive
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
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-     41.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
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-     41.21. Soda profits
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-     41.22. Analyzing COVID-19 papers
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-     41.24. Data processing in Python
+     40.24. Data processing in Python
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+     40.28. NYC taxi data in winter and summer
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-     41.29. Matplotlib applied
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-     41.35. Tell a story
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-     41.36. Explore a planetary computer dataset
+     40.36. Explore a planetary computer dataset
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-     41.37. Exploring for answers
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-     41.38. Market research
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-     41.39. Low code/no code Data Science project on Azure ML
+     40.39. Low code/no code Data Science project on Azure ML
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-     41.40. Data Science project using Azure ML SDK
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    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#explore-the-dataset-for-distributions-using-text-data">
-     42.7.3.3. Explore the dataset for distributions using text data
+     41.7.3.3. Explore the dataset for distributions using text data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#density-plots">
-     42.7.3.4. Density plots
+     41.7.3.4. Density plots
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualizing-proportions">
-   42.7.4. 4. Visualizing proportions
+   41.7.4. 4. Visualizing proportions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#get-to-know-your-mushrooms">
-     42.7.4.1. Get to know your mushrooms 🍄
+     41.7.4.1. Get to know your mushrooms 🍄
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#pie">
-     42.7.4.2. Pie!
+     41.7.4.2. Pie!
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#donuts">
-     42.7.4.3. Donuts!
+     41.7.4.3. Donuts!
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#waffles">
-     42.7.4.4. Waffles!
+     41.7.4.4. Waffles!
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualizing-relationships-all-about-honey">
-   42.7.5. 5. Visualizing relationships: all about honey
+   41.7.5. 5. Visualizing relationships: all about honey
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#scatterplots">
-     42.7.5.1. Scatterplots
+     41.7.5.1. Scatterplots
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#line-charts">
-     42.7.5.2. Line charts
+     41.7.5.2. Line charts
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#facet-grids">
-     42.7.5.3. Facet grids
+     41.7.5.3. Facet grids
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dual-line-plots">
-     42.7.5.4. Dual-line plots
+     41.7.5.4. Dual-line plots
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   42.7.6. 6. Your turn! 🚀
+   41.7.6. 6. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   42.7.7. 7. References
+   41.7.7. 7. References
   </a>
  </li>
 </ul>
@@ -1712,120 +1712,120 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#whats-data-visualization">
-   42.7.1. 1. What’s data visualization
+   41.7.1. 1. What’s data visualization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualizing-quantities">
-   42.7.2. 2. Visualizing quantities
+   41.7.2. 2. Visualizing quantities
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#build-a-line-plot-about-bird-wingspan-values">
-     42.7.2.1. Build a line plot about bird wingspan values
+     41.7.2.1. Build a line plot about bird wingspan values
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#explore-bar-charts">
-     42.7.2.2. Explore bar charts
+     41.7.2.2. Explore bar charts
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#comparing-data">
-     42.7.2.3. Comparing data
+     41.7.2.3. Comparing data
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualizing-distributions">
-   42.7.3. 3. Visualizing distributions
+   41.7.3. 3. Visualizing distributions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#explore-the-birds-dataset">
-     42.7.3.1. Explore the birds dataset
+     41.7.3.1. Explore the birds dataset
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#working-with-histograms">
-     42.7.3.2. Working with histograms
+     41.7.3.2. Working with histograms
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#explore-the-dataset-for-distributions-using-text-data">
-     42.7.3.3. Explore the dataset for distributions using text data
+     41.7.3.3. Explore the dataset for distributions using text data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#density-plots">
-     42.7.3.4. Density plots
+     41.7.3.4. Density plots
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualizing-proportions">
-   42.7.4. 4. Visualizing proportions
+   41.7.4. 4. Visualizing proportions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#get-to-know-your-mushrooms">
-     42.7.4.1. Get to know your mushrooms 🍄
+     41.7.4.1. Get to know your mushrooms 🍄
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#pie">
-     42.7.4.2. Pie!
+     41.7.4.2. Pie!
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#donuts">
-     42.7.4.3. Donuts!
+     41.7.4.3. Donuts!
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#waffles">
-     42.7.4.4. Waffles!
+     41.7.4.4. Waffles!
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualizing-relationships-all-about-honey">
-   42.7.5. 5. Visualizing relationships: all about honey
+   41.7.5. 5. Visualizing relationships: all about honey
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#scatterplots">
-     42.7.5.1. Scatterplots
+     41.7.5.1. Scatterplots
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#line-charts">
-     42.7.5.2. Line charts
+     41.7.5.2. Line charts
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#facet-grids">
-     42.7.5.3. Facet grids
+     41.7.5.3. Facet grids
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dual-line-plots">
-     42.7.5.4. Dual-line plots
+     41.7.5.4. Dual-line plots
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   42.7.6. 6. Your turn! 🚀
+   41.7.6. 6. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   42.7.7. 7. References
+   41.7.7. 7. References
   </a>
  </li>
 </ul>
@@ -1865,9 +1865,9 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="data-visualization">
-<h1><span class="section-number">42.7. </span>Data visualization<a class="headerlink" href="#data-visualization" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.7. </span>Data visualization<a class="headerlink" href="#data-visualization" title="Permalink to this headline">#</a></h1>
 <section id="whats-data-visualization">
-<h2><span class="section-number">42.7.1. </span>1. What’s data visualization<a class="headerlink" href="#whats-data-visualization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.7.1. </span>1. What’s data visualization<a class="headerlink" href="#whats-data-visualization" title="Permalink to this headline">#</a></h2>
 <p>Visualizing data is one of the most important tasks of a data scientist. Images are worth 1000 words, and a visualization can help you identify all kinds of interesting parts of your data such as,</p>
 <ul class="simple">
 <li><p>spikes,</p></li>
@@ -1878,7 +1878,7 @@ <h2><span class="section-number">42.7.1. </span>1. What’s data visualization<a
 </ul>
 </section>
 <section id="visualizing-quantities">
-<h2><span class="section-number">42.7.2. </span>2. Visualizing quantities<a class="headerlink" href="#visualizing-quantities" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.7.2. </span>2. Visualizing quantities<a class="headerlink" href="#visualizing-quantities" title="Permalink to this headline">#</a></h2>
 <p>An excellent library to create both simple and sophisticated plots and charts of various kinds is <a class="reference external" href="https://matplotlib.org/stable/index.html">Matplotlib</a>.</p>
 <div class="alignRight">
   <img class="removeMargin" height="100" src="../images/matplotlib.svg"/>
@@ -1892,7 +1892,7 @@ <h2><span class="section-number">42.7.2. </span>2. Visualizing quantities<a clas
 <li><p>To show relationships between values: line, scatterplot, bubble</p></li>
 </ul>
 <section id="build-a-line-plot-about-bird-wingspan-values">
-<h3><span class="section-number">42.7.2.1. </span>Build a line plot about bird wingspan values<a class="headerlink" href="#build-a-line-plot-about-bird-wingspan-values" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.2.1. </span>Build a line plot about bird wingspan values<a class="headerlink" href="#build-a-line-plot-about-bird-wingspan-values" title="Permalink to this headline">#</a></h3>
 <div class="cell tag_output-scoll docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
@@ -2056,7 +2056,7 @@ <h3><span class="section-number">42.7.2.1. </span>Build a line plot about bird w
 </div>
 </section>
 <section id="explore-bar-charts">
-<h3><span class="section-number">42.7.2.2. </span>Explore bar charts<a class="headerlink" href="#explore-bar-charts" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.2.2. </span>Explore bar charts<a class="headerlink" href="#explore-bar-charts" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">birds</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
@@ -2092,7 +2092,7 @@ <h3><span class="section-number">42.7.2.2. </span>Explore bar charts<a class="he
 </div>
 </section>
 <section id="comparing-data">
-<h3><span class="section-number">42.7.2.3. </span>Comparing data<a class="headerlink" href="#comparing-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.2.3. </span>Comparing data<a class="headerlink" href="#comparing-data" title="Permalink to this headline">#</a></h3>
 <div class="cell tag_scrolled: true docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">maxlength</span> <span class="o">=</span> <span class="n">birds</span><span class="p">[</span><span class="s1">&#39;MaxLength&#39;</span><span class="p">]</span>
@@ -2126,10 +2126,10 @@ <h3><span class="section-number">42.7.2.3. </span>Comparing data<a class="header
 </section>
 </section>
 <section id="visualizing-distributions">
-<h2><span class="section-number">42.7.3. </span>3. Visualizing distributions<a class="headerlink" href="#visualizing-distributions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.7.3. </span>3. Visualizing distributions<a class="headerlink" href="#visualizing-distributions" title="Permalink to this headline">#</a></h2>
 <p>Another way to dig into data is by looking at its distribution, or how the data is organized along an axis.</p>
 <section id="explore-the-birds-dataset">
-<h3><span class="section-number">42.7.3.1. </span>Explore the birds dataset<a class="headerlink" href="#explore-the-birds-dataset" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.3.1. </span>Explore the birds dataset<a class="headerlink" href="#explore-the-birds-dataset" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">birds</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">&#39;scatter&#39;</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s1">&#39;MaxLength&#39;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">&#39;Order&#39;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
@@ -2148,7 +2148,7 @@ <h3><span class="section-number">42.7.3.1. </span>Explore the birds dataset<a cl
 </div>
 </section>
 <section id="working-with-histograms">
-<h3><span class="section-number">42.7.3.2. </span>Working with histograms<a class="headerlink" href="#working-with-histograms" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.3.2. </span>Working with histograms<a class="headerlink" href="#working-with-histograms" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">birds</span><span class="p">[</span><span class="s1">&#39;MaxBodyMass&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">&#39;hist&#39;</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
@@ -2199,7 +2199,7 @@ <h3><span class="section-number">42.7.3.2. </span>Working with histograms<a clas
 </div>
 </section>
 <section id="explore-the-dataset-for-distributions-using-text-data">
-<h3><span class="section-number">42.7.3.3. </span>Explore the dataset for distributions using text data<a class="headerlink" href="#explore-the-dataset-for-distributions-using-text-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.3.3. </span>Explore the dataset for distributions using text data<a class="headerlink" href="#explore-the-dataset-for-distributions-using-text-data" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x1</span> <span class="o">=</span> <span class="n">filteredBirds</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">filteredBirds</span><span class="o">.</span><span class="n">ConservationStatus</span> <span class="o">==</span> <span class="s1">&#39;EX&#39;</span><span class="p">,</span> <span class="s1">&#39;MinWingspan&#39;</span><span class="p">]</span>
@@ -2229,7 +2229,7 @@ <h3><span class="section-number">42.7.3.3. </span>Explore the dataset for distri
 </div>
 </section>
 <section id="density-plots">
-<h3><span class="section-number">42.7.3.4. </span>Density plots<a class="headerlink" href="#density-plots" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.3.4. </span>Density plots<a class="headerlink" href="#density-plots" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
@@ -2298,7 +2298,7 @@ <h3><span class="section-number">42.7.3.4. </span>Density plots<a class="headerl
 </section>
 </section>
 <section id="visualizing-proportions">
-<h2><span class="section-number">42.7.4. </span>4. Visualizing proportions<a class="headerlink" href="#visualizing-proportions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.7.4. </span>4. Visualizing proportions<a class="headerlink" href="#visualizing-proportions" title="Permalink to this headline">#</a></h2>
 <p>We will use a given dataset about mushrooms to experiment with tasty visualizations such as:</p>
 <ul class="simple">
 <li><p>Pie charts 🥧</p></li>
@@ -2306,7 +2306,7 @@ <h2><span class="section-number">42.7.4. </span>4. Visualizing proportions<a cla
 <li><p>Waffle charts 🧇</p></li>
 </ul>
 <section id="get-to-know-your-mushrooms">
-<h3><span class="section-number">42.7.4.1. </span>Get to know your mushrooms 🍄<a class="headerlink" href="#get-to-know-your-mushrooms" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.4.1. </span>Get to know your mushrooms 🍄<a class="headerlink" href="#get-to-know-your-mushrooms" title="Permalink to this headline">#</a></h3>
 <div class="cell tag_output-scoll docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mushrooms</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;../data/mushrooms.csv&#39;</span><span class="p">)</span>
@@ -2613,7 +2613,7 @@ <h3><span class="section-number">42.7.4.1. </span>Get to know your mushrooms 
 </div>
 </section>
 <section id="pie">
-<h3><span class="section-number">42.7.4.2. </span>Pie!<a class="headerlink" href="#pie" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.4.2. </span>Pie!<a class="headerlink" href="#pie" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Edible&#39;</span><span class="p">,</span> <span class="s1">&#39;Poisonous&#39;</span><span class="p">]</span>
@@ -2629,7 +2629,7 @@ <h3><span class="section-number">42.7.4.2. </span>Pie!<a class="headerlink" href
 </div>
 </section>
 <section id="donuts">
-<h3><span class="section-number">42.7.4.3. </span>Donuts!<a class="headerlink" href="#donuts" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.4.3. </span>Donuts!<a class="headerlink" href="#donuts" title="Permalink to this headline">#</a></h3>
 <div class="cell tag_output-scoll docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">habitat</span> <span class="o">=</span> <span class="n">mushrooms</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;habitat&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
@@ -2903,7 +2903,7 @@ <h3><span class="section-number">42.7.4.3. </span>Donuts!<a class="headerlink" h
 </div>
 </section>
 <section id="waffles">
-<h3><span class="section-number">42.7.4.4. </span>Waffles!<a class="headerlink" href="#waffles" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.7.4.4. </span>Waffles!<a class="headerlink" href="#waffles" title="Permalink to this headline">#</a></h3>
 <div class="cell tag_output-scoll docutils container">
 <div class="cell_input docutils container">
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 <p>Seaborn, which we have used before, as a good library to visualize relationships between variables.</p>
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-            <p class="prev-next-title"><span class="section-number">42.6. </span>NumPy and Pandas</p>
+            <p class="prev-next-title"><span class="section-number">41.6. </span>NumPy and Pandas</p>
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-    <link rel="prev" title="42.5. Relational vs. non-relational database" href="relational-vs-non-relational-database.html" />
+    <link rel="next" title="41.7. Data visualization" href="data-visualization.html" />
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-     41.6. Python programming introduction
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-     41.9. Analyzing text about Data Science
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-     41.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.16. Build your own custom vis
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-     41.17. Classifying datasets
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-     41.19. Introduction to probability and statistics
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-     41.35. Tell a story
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-     41.36. Explore a planetary computer dataset
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-     41.37. Exploring for answers
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-     41.39. Low code/no code Data Science project on Azure ML
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-     41.40. Data Science project using Azure ML SDK
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-     41.45. Linear regression - California Housing
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#the-basics-of-numpy-arrays">
-     42.6.1.1. The basics of NumPy arrays
+     41.6.1.1. The basics of NumPy arrays
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#numpy-array-attributes">
-       42.6.1.1.1. NumPy array attributes
+       41.6.1.1.1. NumPy array attributes
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#computation-on-numpy-arrays-universal-functions">
-     42.6.1.2. Computation on NumPy arrays: universal functions
+     41.6.1.2. Computation on NumPy arrays: universal functions
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploring-numpys-ufuncs">
-     42.6.1.3. Exploring NumPy’s ufuncs
+     41.6.1.3. Exploring NumPy’s ufuncs
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#aggregations-min-max-and-everything-in-between">
-     42.6.1.4. Aggregations: min, max, and everything in between
+     41.6.1.4. Aggregations: min, max, and everything in between
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#minimum-and-maximum">
-       42.6.1.4.1. Minimum and maximum
+       41.6.1.4.1. Minimum and maximum
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#computation-on-arrays-broadcasting">
-     42.6.1.5. Computation on arrays: broadcasting
+     41.6.1.5. Computation on arrays: broadcasting
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#structured-data-numpys-structured-arrays">
-     42.6.1.6. Structured data: NumPy’s structured arrays
+     41.6.1.6. Structured data: NumPy’s structured arrays
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#pandas">
-   42.6.2. 2. Pandas
+   41.6.2. 2. Pandas
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#introducing-pandas-objects">
-     42.6.2.1. Introducing Pandas objects
+     41.6.2.1. Introducing Pandas objects
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#the-pandas-series-object">
-       42.6.2.1.1. The Pandas Series object
+       41.6.2.1.1. The Pandas Series object
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-indexing-and-selection">
-     42.6.2.2. Data indexing and selection
+     41.6.2.2. Data indexing and selection
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#data-selection-in-series">
-       42.6.2.2.1. Data selection in Series
+       41.6.2.2.1. Data selection in Series
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#data-selection-in-dataframe">
-       42.6.2.2.2. Data selection in DataFrame
+       41.6.2.2.2. Data selection in DataFrame
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#dataframe-as-two-dimensional-array">
-       42.6.2.2.3. DataFrame as two-dimensional array
+       41.6.2.2.3. DataFrame as two-dimensional array
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#operating-on-data-in-pandas">
-     42.6.2.3. Operating on data in Pandas
+     41.6.2.3. Operating on data in Pandas
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#ufuncs-index-preservation">
-       42.6.2.3.1. Ufuncs: index preservation
+       41.6.2.3.1. Ufuncs: index preservation
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#handling-missing-data">
-     42.6.2.4. Handling missing data
+     41.6.2.4. Handling missing data
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#none-pythonic-missing-data">
-       42.6.2.4.1.
+       41.6.2.4.1.
        <code class="docutils literal notranslate">
         <span class="pre">
          None
@@ -1876,48 +1876,48 @@ <h2> Contents </h2>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#nan-missing-numerical-data">
-       42.6.2.4.2. NaN: missing numerical data
+       41.6.2.4.2. NaN: missing numerical data
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#nan-and-none-in-pandas">
-       42.6.2.4.3. NaN and None in Pandas
+       41.6.2.4.3. NaN and None in Pandas
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#operating-on-null-values">
-       42.6.2.4.4. Operating on null values
+       41.6.2.4.4. Operating on null values
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#combining-datasets-concat-and-append">
-     42.6.2.5. Combining datasets: concat and append
+     41.6.2.5. Combining datasets: concat and append
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#simple-concatenation-with-pd-concat">
-       42.6.2.5.1. Simple concatenation with pd.concat
+       41.6.2.5.1. Simple concatenation with pd.concat
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#combining-datasets-merge-and-join">
-     42.6.2.6. Combining datasets: merge and join
+     41.6.2.6. Combining datasets: merge and join
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   42.6.3. 3. Your turn! 🚀
+   41.6.3. 3. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   42.6.4. 4. References
+   41.6.4. 4. References
   </a>
  </li>
 </ul>
@@ -1957,14 +1957,14 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="numpy-and-pandas">
-<h1><span class="section-number">42.6. </span>NumPy and Pandas<a class="headerlink" href="#numpy-and-pandas" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.6. </span>NumPy and Pandas<a class="headerlink" href="#numpy-and-pandas" title="Permalink to this headline">#</a></h1>
 <section id="numpy">
-<h2><span class="section-number">42.6.1. </span>1. NumPy<a class="headerlink" href="#numpy" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.6.1. </span>1. NumPy<a class="headerlink" href="#numpy" title="Permalink to this headline">#</a></h2>
 <p>Numpy is a library for working with tensors, i.e. multi-dimensional arrays. Array has values of the same underlying type, and it is simpler than dataframe, but it offers more mathematical operations, and creates less overhead.</p>
 <div class="alignRight">
   <img class="removeMargin" height="100" src="../images/numpy.png"/>
 </div><section id="the-basics-of-numpy-arrays">
-<h3><span class="section-number">42.6.1.1. </span>The basics of NumPy arrays<a class="headerlink" href="#the-basics-of-numpy-arrays" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.1.1. </span>The basics of NumPy arrays<a class="headerlink" href="#the-basics-of-numpy-arrays" title="Permalink to this headline">#</a></h3>
 <p>Data manipulation in Python is nearly synonymous with NumPy array manipulation: even newer tools like Pandas are built around the NumPy array.</p>
 <p>We’ll cover a few categories of basic array manipulations here:</p>
 <ul class="simple">
@@ -1974,7 +1974,7 @@ <h3><span class="section-number">42.6.1.1. </span>The basics of NumPy arrays<a c
 <li><p>Reshaping of arrays: Changing the shape of a given array Joining and splitting of arrays: Combining multiple arrays into one, and splitting one array into many.</p></li>
 </ul>
 <section id="numpy-array-attributes">
-<h4><span class="section-number">42.6.1.1.1. </span>NumPy array attributes<a class="headerlink" href="#numpy-array-attributes" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.1.1.1. </span>NumPy array attributes<a class="headerlink" href="#numpy-array-attributes" title="Permalink to this headline">#</a></h4>
 <p>First let’s discuss some useful array attributes. We’ll start by defining three random arrays, a one-dimensional, two-dimensional, and three-dimensional array.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2033,7 +2033,7 @@ <h4><span class="section-number">42.6.1.1.1. </span>NumPy array attributes<a cla
 </section>
 </section>
 <section id="computation-on-numpy-arrays-universal-functions">
-<h3><span class="section-number">42.6.1.2. </span>Computation on NumPy arrays: universal functions<a class="headerlink" href="#computation-on-numpy-arrays-universal-functions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.1.2. </span>Computation on NumPy arrays: universal functions<a class="headerlink" href="#computation-on-numpy-arrays-universal-functions" title="Permalink to this headline">#</a></h3>
 <p>Computation on NumPy arrays can be very fast, or it can be very slow. The key to making it fast is to use vectorized operations, generally implemented through NumPy’s universal functions (ufuncs).</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2100,7 +2100,7 @@ <h3><span class="section-number">42.6.1.2. </span>Computation on NumPy arrays: u
 </div>
 </section>
 <section id="exploring-numpys-ufuncs">
-<h3><span class="section-number">42.6.1.3. </span>Exploring NumPy’s ufuncs<a class="headerlink" href="#exploring-numpys-ufuncs" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.1.3. </span>Exploring NumPy’s ufuncs<a class="headerlink" href="#exploring-numpys-ufuncs" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
@@ -2126,7 +2126,7 @@ <h3><span class="section-number">42.6.1.3. </span>Exploring NumPy’s ufuncs<a c
 </div>
 </section>
 <section id="aggregations-min-max-and-everything-in-between">
-<h3><span class="section-number">42.6.1.4. </span>Aggregations: min, max, and everything in between<a class="headerlink" href="#aggregations-min-max-and-everything-in-between" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.1.4. </span>Aggregations: min, max, and everything in between<a class="headerlink" href="#aggregations-min-max-and-everything-in-between" title="Permalink to this headline">#</a></h3>
 <p>Often when faced with a large amount of data, a first step is to compute summary statistics for the data in question.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2169,7 +2169,7 @@ <h3><span class="section-number">42.6.1.4. </span>Aggregations: min, max, and ev
 </div>
 </div>
 <section id="minimum-and-maximum">
-<h4><span class="section-number">42.6.1.4.1. </span>Minimum and maximum<a class="headerlink" href="#minimum-and-maximum" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.1.4.1. </span>Minimum and maximum<a class="headerlink" href="#minimum-and-maximum" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">min</span><span class="p">(</span><span class="n">big_array</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">big_array</span><span class="p">)</span>
@@ -2223,7 +2223,7 @@ <h4><span class="section-number">42.6.1.4.1. </span>Minimum and maximum<a class=
 </section>
 </section>
 <section id="computation-on-arrays-broadcasting">
-<h3><span class="section-number">42.6.1.5. </span>Computation on arrays: broadcasting<a class="headerlink" href="#computation-on-arrays-broadcasting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.1.5. </span>Computation on arrays: broadcasting<a class="headerlink" href="#computation-on-arrays-broadcasting" title="Permalink to this headline">#</a></h3>
 <p>Another means of vectorizing operations is to use NumPy’s broadcasting functionality. Broadcasting is simply a set of rules for applying binary ufuncs (e.g., addition, subtraction, multiplication, etc.) on arrays of different sizes.</p>
 <div class="cell docutils container">
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@@ -2282,7 +2282,7 @@ <h3><span class="section-number">42.6.1.5. </span>Computation on arrays: broadca
 </div>
 </section>
 <section id="structured-data-numpys-structured-arrays">
-<h3><span class="section-number">42.6.1.6. </span>Structured data: NumPy’s structured arrays<a class="headerlink" href="#structured-data-numpys-structured-arrays" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.1.6. </span>Structured data: NumPy’s structured arrays<a class="headerlink" href="#structured-data-numpys-structured-arrays" title="Permalink to this headline">#</a></h3>
 <p>While often our data can be well represented by a homogeneous array of values, sometimes this is not the case. This section demonstrates the use of NumPy’s structured arrays and record arrays</p>
 <div class="cell docutils container">
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@@ -2382,16 +2382,16 @@ <h3><span class="section-number">42.6.1.6. </span>Structured data: NumPy’s str
 </section>
 </section>
 <section id="pandas">
-<h2><span class="section-number">42.6.2. </span>2. Pandas<a class="headerlink" href="#pandas" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.6.2. </span>2. Pandas<a class="headerlink" href="#pandas" title="Permalink to this headline">#</a></h2>
 <p>Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool,
 built on top of the Python programming language.</p>
 <div class="alignRight">
   <img class="removeMargin" height="100" src="../images/pandas.png"/>
 </div><section id="introducing-pandas-objects">
-<h3><span class="section-number">42.6.2.1. </span>Introducing Pandas objects<a class="headerlink" href="#introducing-pandas-objects" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.2.1. </span>Introducing Pandas objects<a class="headerlink" href="#introducing-pandas-objects" title="Permalink to this headline">#</a></h3>
 <p>At the very basic level, Pandas objects can be thought of as enhanced versions of NumPy structured arrays in which the rows and columns are identified with labels rather than simple integer indices.</p>
 <section id="the-pandas-series-object">
-<h4><span class="section-number">42.6.2.1.1. </span>The Pandas Series object<a class="headerlink" href="#the-pandas-series-object" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.2.1.1. </span>The Pandas Series object<a class="headerlink" href="#the-pandas-series-object" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
@@ -2453,10 +2453,10 @@ <h4><span class="section-number">42.6.2.1.1. </span>The Pandas Series object<a c
 </section>
 </section>
 <section id="data-indexing-and-selection">
-<h3><span class="section-number">42.6.2.2. </span>Data indexing and selection<a class="headerlink" href="#data-indexing-and-selection" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.2.2. </span>Data indexing and selection<a class="headerlink" href="#data-indexing-and-selection" title="Permalink to this headline">#</a></h3>
 <p>In Pandas, we looked in detail at methods and tools to access, set, and modify values in NumPy arrays. These included indexing (e.g., <code class="docutils literal notranslate"><span class="pre">arr[2,</span> <span class="pre">1]</span></code>), slicing (e.g., <code class="docutils literal notranslate"><span class="pre">arr[:,</span> <span class="pre">1:5]</span></code>), masking (e.g., <code class="docutils literal notranslate"><span class="pre">arr[arr</span> <span class="pre">&gt;</span> <span class="pre">0]</span></code>), fancy indexing (e.g., <code class="docutils literal notranslate"><span class="pre">arr[0,</span> <span class="pre">[1,</span> <span class="pre">5]]</span></code>), and combinations thereof (e.g., <code class="docutils literal notranslate"><span class="pre">arr[:,</span> <span class="pre">[1,</span> <span class="pre">5]]</span></code>).</p>
 <section id="data-selection-in-series">
-<h4><span class="section-number">42.6.2.2.1. </span>Data selection in Series<a class="headerlink" href="#data-selection-in-series" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.2.2.1. </span>Data selection in Series<a class="headerlink" href="#data-selection-in-series" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
@@ -2569,7 +2569,7 @@ <h4><span class="section-number">42.6.2.2.1. </span>Data selection in Series<a c
 </div>
 </section>
 <section id="data-selection-in-dataframe">
-<h4><span class="section-number">42.6.2.2.2. </span>Data selection in DataFrame<a class="headerlink" href="#data-selection-in-dataframe" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.2.2.2. </span>Data selection in DataFrame<a class="headerlink" href="#data-selection-in-dataframe" title="Permalink to this headline">#</a></h4>
 <p>Recall that a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> acts in many ways like a two-dimensional or structured array, and in other ways like a dictionary of <code class="docutils literal notranslate"><span class="pre">Series</span></code> structures sharing the same index.</p>
 <div class="cell docutils container">
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@@ -2689,7 +2689,7 @@ <h4><span class="section-number">42.6.2.2.2. </span>Data selection in DataFrame<
 </div>
 </section>
 <section id="dataframe-as-two-dimensional-array">
-<h4><span class="section-number">42.6.2.2.3. </span>DataFrame as two-dimensional array<a class="headerlink" href="#dataframe-as-two-dimensional-array" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.2.2.3. </span>DataFrame as two-dimensional array<a class="headerlink" href="#dataframe-as-two-dimensional-array" title="Permalink to this headline">#</a></h4>
 <p>As mentioned previously, we can also view the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> as an enhanced two-dimensional array. We can examine the raw underlying data array using the <code class="docutils literal notranslate"><span class="pre">values</span></code> attribute:</p>
 <div class="cell docutils container">
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@@ -2871,10 +2871,10 @@ <h4><span class="section-number">42.6.2.2.3. </span>DataFrame as two-dimensional
 </section>
 </section>
 <section id="operating-on-data-in-pandas">
-<h3><span class="section-number">42.6.2.3. </span>Operating on data in Pandas<a class="headerlink" href="#operating-on-data-in-pandas" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.2.3. </span>Operating on data in Pandas<a class="headerlink" href="#operating-on-data-in-pandas" title="Permalink to this headline">#</a></h3>
 <p>Pandas inherits much of this functionality from NumPy, and the ufuncs are key to this.</p>
 <section id="ufuncs-index-preservation">
-<h4><span class="section-number">42.6.2.3.1. </span>Ufuncs: index preservation<a class="headerlink" href="#ufuncs-index-preservation" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.2.3.1. </span>Ufuncs: index preservation<a class="headerlink" href="#ufuncs-index-preservation" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
@@ -2973,11 +2973,11 @@ <h4><span class="section-number">42.6.2.3.1. </span>Ufuncs: index preservation<a
 </section>
 </section>
 <section id="handling-missing-data">
-<h3><span class="section-number">42.6.2.4. </span>Handling missing data<a class="headerlink" href="#handling-missing-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.2.4. </span>Handling missing data<a class="headerlink" href="#handling-missing-data" title="Permalink to this headline">#</a></h3>
 <p>Real-world data is rarely clean and homogeneous.</p>
 <p>Pandas chose to use sentinels for missing data, and further chose to use two already-existing Python null values: the special floating-point <code class="docutils literal notranslate"><span class="pre">NaN</span></code> value, and the Python <code class="docutils literal notranslate"><span class="pre">None</span></code> object.</p>
 <section id="none-pythonic-missing-data">
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+<h4><span class="section-number">41.6.2.4.1. </span><code class="docutils literal notranslate"><span class="pre">None</span></code>: pythonic missing data<a class="headerlink" href="#none-pythonic-missing-data" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">vals1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
@@ -3035,7 +3035,7 @@ <h4><span class="section-number">42.6.2.4.1. </span><code class="docutils litera
 </div>
 </section>
 <section id="nan-missing-numerical-data">
-<h4><span class="section-number">42.6.2.4.2. </span>NaN: missing numerical data<a class="headerlink" href="#nan-missing-numerical-data" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.2.4.2. </span>NaN: missing numerical data<a class="headerlink" href="#nan-missing-numerical-data" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">vals2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span> 
@@ -3077,7 +3077,7 @@ <h4><span class="section-number">42.6.2.4.2. </span>NaN: missing numerical data<
 </div>
 </section>
 <section id="nan-and-none-in-pandas">
-<h4><span class="section-number">42.6.2.4.3. </span>NaN and None in Pandas<a class="headerlink" href="#nan-and-none-in-pandas" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.2.4.3. </span>NaN and None in Pandas<a class="headerlink" href="#nan-and-none-in-pandas" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="kc">None</span><span class="p">])</span>
@@ -3126,7 +3126,7 @@ <h4><span class="section-number">42.6.2.4.3. </span>NaN and None in Pandas<a cla
 </div>
 </section>
 <section id="operating-on-null-values">
-<h4><span class="section-number">42.6.2.4.4. </span>Operating on null values<a class="headerlink" href="#operating-on-null-values" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.2.4.4. </span>Operating on null values<a class="headerlink" href="#operating-on-null-values" title="Permalink to this headline">#</a></h4>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="s1">&#39;hello&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">])</span>
@@ -3217,7 +3217,7 @@ <h4><span class="section-number">42.6.2.4.4. </span>Operating on null values<a c
 </section>
 </section>
 <section id="combining-datasets-concat-and-append">
-<h3><span class="section-number">42.6.2.5. </span>Combining datasets: concat and append<a class="headerlink" href="#combining-datasets-concat-and-append" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.2.5. </span>Combining datasets: concat and append<a class="headerlink" href="#combining-datasets-concat-and-append" title="Permalink to this headline">#</a></h3>
 <p>Some of the most interesting studies of data come from combining different data sources.</p>
 <div class="cell docutils container">
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@@ -3281,7 +3281,7 @@ <h3><span class="section-number">42.6.2.5. </span>Combining datasets: concat and
 </div></div></div>
 </div>
 <section id="simple-concatenation-with-pd-concat">
-<h4><span class="section-number">42.6.2.5.1. </span>Simple concatenation with pd.concat<a class="headerlink" href="#simple-concatenation-with-pd-concat" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.6.2.5.1. </span>Simple concatenation with pd.concat<a class="headerlink" href="#simple-concatenation-with-pd-concat" title="Permalink to this headline">#</a></h4>
 <p>Pandas has a function, <code class="docutils literal notranslate"><span class="pre">pd.concat()</span></code>, which has a similar syntax to <code class="docutils literal notranslate"><span class="pre">np.concatenate</span></code> but contains a number of options that we’ll discuss momentarily:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">objs</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">join</span><span class="o">=</span><span class="s1">&#39;outer&#39;</span><span class="p">,</span> <span class="n">join_axes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
           <span class="n">keys</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">levels</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verify_integrity</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
@@ -3341,7 +3341,7 @@ <h4><span class="section-number">42.6.2.5.1. </span>Simple concatenation with pd
 </section>
 </section>
 <section id="combining-datasets-merge-and-join">
-<h3><span class="section-number">42.6.2.6. </span>Combining datasets: merge and join<a class="headerlink" href="#combining-datasets-merge-and-join" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.6.2.6. </span>Combining datasets: merge and join<a class="headerlink" href="#combining-datasets-merge-and-join" title="Permalink to this headline">#</a></h3>
 <p>One essential feature offered by Pandas is its high-performance, in-memory join and merge operations.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">pd.merge()</span></code> function implements a number of types of joins: the <em>one-to-one</em>, <em>many-to-one</em>, and <em>many-to-many</em> joins.</p>
 <div class="cell docutils container">
@@ -3523,11 +3523,11 @@ <h3><span class="section-number">42.6.2.6. </span>Combining datasets: merge and
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">42.6.3. </span>3. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.6.3. </span>3. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p><a class="reference external" href="../../data-science/working-with-data/numpy.html#your-turn">Data processing in Python</a></p>
 </section>
 <section id="references">
-<h2><span class="section-number">42.6.4. </span>4. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.6.4. </span>4. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/working-with-data/numpy.html">Working with data - Numpy</a></p></li>
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/working-with-data/pandas.html">Working with data - Pandas</a></p></li>
@@ -3572,13 +3572,13 @@ <h2><span class="section-number">42.6.4. </span>4. References<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">42.5. </span>Relational vs. non-relational database</p>
+            <p class="prev-next-title"><span class="section-number">41.5. </span>Relational vs. non-relational database</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="data-visualization.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">42.7. </span>Data visualization</p>
+        <p class="prev-next-title"><span class="section-number">41.7. </span>Data visualization</p>
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     <i class="fas fa-angle-right"></i>
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diff --git a/slides/data-science/relational-vs-non-relational-database.html b/slides/data-science/relational-vs-non-relational-database.html
index 526eeae80..15bde2a20 100644
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+++ b/slides/data-science/relational-vs-non-relational-database.html
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-    <title>42.5. Relational vs. non-relational database &#8212; Ocademy Open Machine Learning Book</title>
+    <title>41.5. Relational vs. non-relational database &#8212; Ocademy Open Machine Learning Book</title>
     
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   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="42.6. NumPy and Pandas" href="numpy-and-pandas.html" />
-    <link rel="prev" title="42.4. Data Science introduction" href="data-science-introduction.html" />
+    <link rel="next" title="41.6. NumPy and Pandas" href="numpy-and-pandas.html" />
+    <link rel="prev" title="41.4. Data Science introduction" href="data-science-introduction.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -159,17 +159,17 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
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   <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
-   1. Python programming basics
+   1. Python programming introduction
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-     5.1. Defining data science
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     <a class="reference internal" href="../../data-science/introduction/data-science-ethics.html">
-     5.2. Data Science ethics
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-     5.4. Introduction to statistics and probability
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       </a>
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      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#rainfall">
-       42.5.1.3.2. rainfall
+       41.5.1.3.2. rainfall
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#retrieving-the-data">
-     42.5.1.4. Retrieving the data
+     41.5.1.4. Retrieving the data
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#joining-data">
-     42.5.1.5. Joining data
+     41.5.1.5. Joining data
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#non-relational-data">
-   42.5.2. 2. Non-relational data
+   41.5.2. 2. Non-relational data
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#nosql">
-     42.5.2.1. NoSQL
+     41.5.2.1. NoSQL
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#nosql-databasetypes-and-examples-sup-1-sup">
-       42.5.2.1.1. NoSQL databaseTypes and examples
+       41.5.2.1.1. NoSQL databaseTypes and examples
        <sup>
         1
         <sup>
@@ -1740,12 +1740,12 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   42.5.3. 4. Your turn! 🚀
+   41.5.3. 4. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   42.5.4. 5. References
+   41.5.4. 5. References
   </a>
  </li>
 </ul>
@@ -1774,14 +1774,14 @@ <h2> Contents </h2>
 </div></div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="relational-vs-non-relational-database">
-<h1><span class="section-number">42.5. </span>Relational vs. non-relational database<a class="headerlink" href="#relational-vs-non-relational-database" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.5. </span>Relational vs. non-relational database<a class="headerlink" href="#relational-vs-non-relational-database" title="Permalink to this headline">#</a></h1>
 <section id="relational-databases">
-<h2><span class="section-number">42.5.1. </span>1. Relational databases<a class="headerlink" href="#relational-databases" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.5.1. </span>1. Relational databases<a class="headerlink" href="#relational-databases" title="Permalink to this headline">#</a></h2>
 <p>A relational database is a collection of information that organizes data in predefined relationships where data is stored in one or more tables (or “relations”) of columns and rows, making it easy to see and understand how different data structures relate to each other.<sup>[1]</sup></p>
 <font size="4">
 <p><em>1. What is a relational database (Rdbms)? (n.d.). Google Cloud. Retrieved 7 February 2023, from <a class="reference external" href="https://cloud.google.com/learn/what-is-a-relational-database">https://cloud.google.com/learn/what-is-a-relational-database</a></em></p>
 </font><section id="it-all-starts-with-tables">
-<h3><span class="section-number">42.5.1.1. </span>It all starts with tables<a class="headerlink" href="#it-all-starts-with-tables" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.5.1.1. </span>It all starts with tables<a class="headerlink" href="#it-all-starts-with-tables" title="Permalink to this headline">#</a></h3>
 <p>Let’s begin our exploration by starting a table to store information about cities. We might start with their name and country. You could store this in a table as follows:</p>
 <table class="colwidths-auto table">
 <thead>
@@ -1804,7 +1804,7 @@ <h3><span class="section-number">42.5.1.1. </span>It all starts with tables<a cl
 <p>Notice the column names of <code class="docutils literal notranslate"><span class="pre">city</span></code>, <code class="docutils literal notranslate"><span class="pre">country</span></code> and <code class="docutils literal notranslate"><span class="pre">population</span></code> describe the data being stored, and each row has information about one city.</p>
 </section>
 <section id="the-shortcomings-of-a-single-table-approach">
-<h3><span class="section-number">42.5.1.2. </span>The shortcomings of a single table approach<a class="headerlink" href="#the-shortcomings-of-a-single-table-approach" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.5.1.2. </span>The shortcomings of a single table approach<a class="headerlink" href="#the-shortcomings-of-a-single-table-approach" title="Permalink to this headline">#</a></h3>
 <p>Let’s start to add some additional data to our burgeoning database  - annual rainfall (in millimeters). We’ll focus on the years 2018, 2019 and 2020. If we were to add it for Tokyo, it might look something like this:</p>
 <table class="colwidths-auto table">
 <thead>
@@ -1872,7 +1872,7 @@ <h3><span class="section-number">42.5.1.2. </span>The shortcomings of a single t
 <p>This is why we need multiple tables and relationships.</p>
 </section>
 <section id="the-concepts-of-relationships">
-<h3><span class="section-number">42.5.1.3. </span>The concepts of relationships<a class="headerlink" href="#the-concepts-of-relationships" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.5.1.3. </span>The concepts of relationships<a class="headerlink" href="#the-concepts-of-relationships" title="Permalink to this headline">#</a></h3>
 <p>Let’s return to our data and determine how we want to split things up.</p>
 <table class="colwidths-auto table">
 <thead>
@@ -1894,7 +1894,7 @@ <h3><span class="section-number">42.5.1.3. </span>The concepts of relationships<
 </table>
 <p>We need to figure out how to reference each city. We need some form of an identifier, ID or (in technical database terms) a primary key.</p>
 <section id="cities">
-<h4><span class="section-number">42.5.1.3.1. </span>cities<a class="headerlink" href="#cities" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.5.1.3.1. </span>cities<a class="headerlink" href="#cities" title="Permalink to this headline">#</a></h4>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>city_id</p></th>
@@ -1920,7 +1920,7 @@ <h4><span class="section-number">42.5.1.3.1. </span>cities<a class="headerlink"
 <p>With our cities table created, let’s store the rainfall.</p>
 </section>
 <section id="rainfall">
-<h4><span class="section-number">42.5.1.3.2. </span>rainfall<a class="headerlink" href="#rainfall" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.5.1.3.2. </span>rainfall<a class="headerlink" href="#rainfall" title="Permalink to this headline">#</a></h4>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>rainfall_id</p></th>
@@ -1980,7 +1980,7 @@ <h4><span class="section-number">42.5.1.3.2. </span>rainfall<a class="headerlink
 </section>
 </section>
 <section id="retrieving-the-data">
-<h3><span class="section-number">42.5.1.4. </span>Retrieving the data<a class="headerlink" href="#retrieving-the-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.5.1.4. </span>Retrieving the data<a class="headerlink" href="#retrieving-the-data" title="Permalink to this headline">#</a></h3>
 <p>If we are using a relational database such as MySQL, SQL Server or Oracle, we can use a language called <strong>Structured Query Language</strong> or <strong>SQL</strong>.</p>
 <p>To retrieve data you use the command <code class="docutils literal notranslate"><span class="pre">SELECT</span></code>.</p>
 <div class="highlight-sql notranslate"><div class="highlight"><pre><span></span><span class="k">SELECT</span><span class="w"> </span><span class="n">city</span>
@@ -2004,7 +2004,7 @@ <h3><span class="section-number">42.5.1.4. </span>Retrieving the data<a class="h
 </div>
 </section>
 <section id="joining-data">
-<h3><span class="section-number">42.5.1.5. </span>Joining data<a class="headerlink" href="#joining-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.5.1.5. </span>Joining data<a class="headerlink" href="#joining-data" title="Permalink to this headline">#</a></h3>
 <p>Until now we’ve retrieved data from a single table. Now we want to bring the data together from both <code class="docutils literal notranslate"><span class="pre">cities</span></code> and <code class="docutils literal notranslate"><span class="pre">rainfall</span></code>. This is done by <em>joining</em> them together.</p>
 <p>Let’s retrieve the rainfall for 2019 for all our cities.</p>
 <div class="highlight-sql notranslate"><div class="highlight"><pre><span></span><span class="k">SELECT</span><span class="w"> </span><span class="n">cities</span><span class="p">.</span><span class="n">city</span>
@@ -2032,7 +2032,7 @@ <h3><span class="section-number">42.5.1.5. </span>Joining data<a class="headerli
 </section>
 </section>
 <section id="non-relational-data">
-<h2><span class="section-number">42.5.2. </span>2. Non-relational data<a class="headerlink" href="#non-relational-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.5.2. </span>2. Non-relational data<a class="headerlink" href="#non-relational-data" title="Permalink to this headline">#</a></h2>
 <p>A <strong>non-relational database</strong> stores data in a non-tabular form, and tends to be more flexible than the traditional, SQL-based, relational database structures. It does not follow the relational model provided by traditional relational database management systems.<sup>[1]</sup></p>
 <font size="4">
 <p><em>1. What is a non-relational database? (n.d.). MongoDB. Retrieved 9 February 2023, from <a class="reference external" href="https://www.mongodb.com/databases/non-relational">https://www.mongodb.com/databases/non-relational</a></em></p>
@@ -2044,7 +2044,7 @@ <h2><span class="section-number">42.5.2. </span>2. Non-relational data<a class="
 <font size="4">
 <p><em>1. Duca, A. L. (2021, October 5). Relational vs non relational databases. Medium. <a class="reference external" href="https://towardsdatascience.com/relational-vs-non-relational-databases-f2ac792482e3">https://towardsdatascience.com/relational-vs-non-relational-databases-f2ac792482e3</a></em></p>
 </font><section id="nosql">
-<h3><span class="section-number">42.5.2.1. </span>NoSQL<a class="headerlink" href="#nosql" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.5.2.1. </span>NoSQL<a class="headerlink" href="#nosql" title="Permalink to this headline">#</a></h3>
 <p><strong>NoSQL</strong> is an umbrella term for the different ways to store non-relational data and can be interpreted as “non-SQL”, “non-relational” or  “not only SQL”.</p>
 <p>And these types of database systems can be categorized into 4 types.</p>
 <p><a class="reference external" href="https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#keyvalue-data-stores">Key-value</a> databases pair unique keys, which are unique identifiers associated with a value. These pairs are stored using a <a class="reference external" href="https://www.hackerearth.com/practice/data-structures/hash-tables/basics-of-hash-tables/tutorial/">hash table</a> with an appropriate hashing function.</p>
@@ -2082,7 +2082,7 @@ <h3><span class="section-number">42.5.2.1. </span>NoSQL<a class="headerlink" hre
 </pre></div>
 </div>
 <section id="nosql-databasetypes-and-examples-sup-1-sup">
-<h4><span class="section-number">42.5.2.1.1. </span>NoSQL databaseTypes and examples<sup>1<sup><a class="headerlink" href="#nosql-databasetypes-and-examples-sup-1-sup" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.5.2.1.1. </span>NoSQL databaseTypes and examples<sup>1<sup><a class="headerlink" href="#nosql-databasetypes-and-examples-sup-1-sup" title="Permalink to this headline">#</a></h4>
 <table class="colwidths-auto table">
 <thead>
 <tr class="row-odd"><th class="head"><p>Type</p></th>
@@ -2135,7 +2135,7 @@ <h4><span class="section-number">42.5.2.1.1. </span>NoSQL databaseTypes and exam
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">42.5.3. </span>4. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.5.3. </span>4. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/assignments/data-science/displaying-airport-data.html">Displaying airport data</a></p></li>
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/working-with-data/non-relational-data.html#your-turn">Twitter data</a></p></li>
@@ -2143,7 +2143,7 @@ <h2><span class="section-number">42.5.3. </span>4. Your turn! 🚀<a class="head
 </ol>
 </section>
 <section id="references">
-<h2><span class="section-number">42.5.4. </span>5. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.5.4. </span>5. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/working-with-data/relational-databases.html">Relational database</a></p></li>
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/data-science/working-with-data/non-relational-data.html">Non-relational data</a></p></li>
@@ -2188,13 +2188,13 @@ <h2><span class="section-number">42.5.4. </span>5. References<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
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-            <p class="prev-next-title"><span class="section-number">42.4. </span>Data Science introduction</p>
+            <p class="prev-next-title"><span class="section-number">41.4. </span>Data Science introduction</p>
         </div>
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     <a class='right-next' id="next-link" href="numpy-and-pandas.html" title="next page">
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+        <p class="prev-next-title"><span class="section-number">41.6. </span>NumPy and Pandas</p>
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diff --git a/slides/deep-learning/cnn.html b/slides/deep-learning/cnn.html
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+    <title>41.20. Convolutional Neural Network &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="42.21. Generative Adversarial Network" href="gan.html" />
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+    <link rel="next" title="41.21. Generative Adversarial Network" href="gan.html" />
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+   1. Python programming introduction
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+     5.1. Relational databases
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+     40.13. Apply your skills
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+     40.14. Try it in Excel
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-     41.21. Soda profits
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-     41.22. Analyzing COVID-19 papers
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         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">42.19. </span>Model Selection</p>
+            <p class="prev-next-title"><span class="section-number">41.19. </span>Model Selection</p>
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-        <p class="prev-next-title"><span class="section-number">42.21. </span>Generative Adversarial Network</p>
+        <p class="prev-next-title"><span class="section-number">41.21. </span>Generative Adversarial Network</p>
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+     40.14. Try it in Excel
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+   41.21.11. Time to play!
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+   41.21.12. Let’s code a GAN!
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-   42.21.13. Create Model
+   41.21.13. Create Model
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-   42.21.14. Ackowledgement
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-   42.21.4. What is a generative adversarial network (GAN)?
+   41.21.4. What is a generative adversarial network (GAN)?
   </a>
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+   41.21.10. How does a GAN work?
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+   41.21.11. Time to play!
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-   42.21.12. Let’s code a GAN!
+   41.21.12. Let’s code a GAN!
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  </li>
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-   42.21.13. Create Model
+   41.21.13. Create Model
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@@ -1764,16 +1764,16 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="generative-adversarial-network">
-<h1><span class="section-number">42.21. </span>Generative Adversarial Network<a class="headerlink" href="#generative-adversarial-network" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.21. </span>Generative Adversarial Network<a class="headerlink" href="#generative-adversarial-network" title="Permalink to this headline">#</a></h1>
 <section id="what-is-a-gerative-model-1-3">
-<h2><span class="section-number">42.21.1. </span>What is a gerative model? (1/3)<a class="headerlink" href="#what-is-a-gerative-model-1-3" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.1. </span>What is a gerative model? (1/3)<a class="headerlink" href="#what-is-a-gerative-model-1-3" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Many machine learning systems look at some kind of complicated input (say, an image) and produce a simple output (a label like, “cat”)</p></li>
 <li><p>By contrast, the goal of a generative model is something like the opposite: take a small piece of input—perhaps a few random numbers—and produce a complex output, like an image of a realistic-looking face</p></li>
 </ul>
 </section>
 <section id="what-is-a-gerative-model-2-3">
-<h2><span class="section-number">42.21.2. </span>What is a gerative model? (2/3)<a class="headerlink" href="#what-is-a-gerative-model-2-3" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.2. </span>What is a gerative model? (2/3)<a class="headerlink" href="#what-is-a-gerative-model-2-3" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>“Generative” describes a class of statistical models that contrasts with discriminative models.</p></li>
 <li><p>Informally:</p>
@@ -1786,20 +1786,20 @@ <h2><span class="section-number">42.21.2. </span>What is a gerative model? (2/3)
 </ul>
 </section>
 <section id="what-is-a-gerative-model-3-3">
-<h2><span class="section-number">42.21.3. </span>What is a gerative model? (3/3)<a class="headerlink" href="#what-is-a-gerative-model-3-3" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.3. </span>What is a gerative model? (3/3)<a class="headerlink" href="#what-is-a-gerative-model-3-3" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>A generative model can model a distribution by producing convincing “fake” data that looks like it’s drawn from that distribution.</p></li>
 <li><p>A generative model for images might capture correlations like “things that look like boats are probably going to appear near things that look like water” and “eyes are unlikely to appear on foreheads.” These are very complicated distributions.</p></li>
 </ul>
 </section>
 <section id="what-is-a-generative-adversarial-network-gan">
-<h2><span class="section-number">42.21.4. </span>What is a generative adversarial network (GAN)?<a class="headerlink" href="#what-is-a-generative-adversarial-network-gan" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.4. </span>What is a generative adversarial network (GAN)?<a class="headerlink" href="#what-is-a-generative-adversarial-network-gan" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>An especially effective type of generative model, introduced in 2014 by Ian Goodfellow, which has been a subject of intense interest in the machine learning community</p></li>
 </ul>
 </section>
 <section id="how-does-a-gan-work">
-<h2><span class="section-number">42.21.5. </span>How does a GAN work?<a class="headerlink" href="#how-does-a-gan-work" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.5. </span>How does a GAN work?<a class="headerlink" href="#how-does-a-gan-work" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>A generative adversarial network (GAN) has two parts:</p>
 <ul>
@@ -1810,47 +1810,47 @@ <h2><span class="section-number">42.21.5. </span>How does a GAN work?<a class="h
 </ul>
 </section>
 <section id="id1">
-<h2><span class="section-number">42.21.6. </span>How does a GAN work?<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.6. </span>How does a GAN work?<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>When training begins, the generator produces obviously fake data, and the discriminator quickly learns to tell that it’s fake:</p></li>
 </ul>
 <p><img alt="" src="../../_images/gan-g1.svg" /></p>
 </section>
 <section id="id2">
-<h2><span class="section-number">42.21.7. </span>How does a GAN work?<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.7. </span>How does a GAN work?<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>As training progresses, the generator gets closer to producing output that can fool the discriminator:</p></li>
 </ul>
 <p><img alt="" src="../../_images/gan-g2.svg" /></p>
 </section>
 <section id="id3">
-<h2><span class="section-number">42.21.8. </span>How does a GAN work?<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.8. </span>How does a GAN work?<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Finally, if generator training goes well, the discriminator gets worse at telling the difference between real and fake. It starts to classify fake data as real, and its accuracy decreases.</p></li>
 </ul>
 <p><img alt="" src="../../_images/gan-g3.svg" /></p>
 </section>
 <section id="id4">
-<h2><span class="section-number">42.21.9. </span>How does a GAN work?<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.9. </span>How does a GAN work?<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Here’s a picture of the whole system:</p></li>
 </ul>
 <p><img alt="" src="../../_images/gan-g4.svg" /></p>
 </section>
 <section id="id5">
-<h2><span class="section-number">42.21.10. </span>How does a GAN work?<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.10. </span>How does a GAN work?<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Both the generator and the discriminator are neural networks. The generator output is connected directly to the discriminator input. Through backpropagation, the discriminator’s classification provides a signal that the generator uses to update its weights.</p></li>
 </ul>
 </section>
 <section id="time-to-play">
-<h2><span class="section-number">42.21.11. </span>Time to play!<a class="headerlink" href="#time-to-play" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.11. </span>Time to play!<a class="headerlink" href="#time-to-play" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="https://poloclub.github.io/ganlab/">https://poloclub.github.io/ganlab/</a></p></li>
 </ul>
 </section>
 <section id="lets-code-a-gan">
-<h2><span class="section-number">42.21.12. </span>Let’s code a GAN!<a class="headerlink" href="#lets-code-a-gan" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.12. </span>Let’s code a GAN!<a class="headerlink" href="#lets-code-a-gan" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 </div>
 </section>
 <section id="create-model">
-<h2><span class="section-number">42.21.13. </span>Create Model<a class="headerlink" href="#create-model" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.13. </span>Create Model<a class="headerlink" href="#create-model" title="Permalink to this headline">#</a></h2>
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 </div>
 </section>
 <section id="ackowledgement">
-<h2><span class="section-number">42.21.14. </span>Ackowledgement<a class="headerlink" href="#ackowledgement" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.21.14. </span>Ackowledgement<a class="headerlink" href="#ackowledgement" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="https://developers.google.com/machine-learning/gan/gan_structure">https://developers.google.com/machine-learning/gan/gan_structure</a></p></li>
 <li><p><a class="reference external" href="https://www.kaggle.com/code/emreustundag/generative-adversarial-networks-gans-tutorial">https://www.kaggle.com/code/emreustundag/generative-adversarial-networks-gans-tutorial</a></p></li>
@@ -2110,7 +2110,7 @@ <h2><span class="section-number">42.21.14. </span>Ackowledgement<a class="header
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">42.20. </span>Convolutional Neural Network</p>
+            <p class="prev-next-title"><span class="section-number">41.20. </span>Convolutional Neural Network</p>
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+    <title>41. Slides &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="prev" title="41.116. Basic classification: Classify images of clothing" href="../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html" />
+    <link rel="next" title="41.1. Python programming introduction" href="python-programming/python-programming-introduction.html" />
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-     7.3. Visualizing relationships: all about honey 🍯
+     6.3. Visualizing relationships: all about honey 🍯
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+     8.2. The “low code/no code” way
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+     8.3. Data Science in the cloud: The “Azure ML SDK” way
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+     12.3. Linear and polynomial regression
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+     14.3. Yet other classifiers
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-     15.4. Applied Machine Learning : build a web app
+     14.4. Applied Machine Learning : build a web app
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+     15.2. Gradient descent
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+     16.1. Introduction to clustering
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+     17.1. Bagging
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+     17.2. Random forest
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+     18.2. Gradient boosting example
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-     19.4. XGBoost + k-fold CV + Feature Importance
+     18.4. XGBoost + k-fold CV + Feature Importance
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+   21. Unsupervised learning: PCA and clustering
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+     24.3.1.1. Stylenet / Neural-Style
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-     25.3.1.2. Deepdream in TensorFlow
+     24.3.1.2. Deepdream in TensorFlow
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+   30. Deep Q-learning
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+   32. Image segmentation
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+   34. Object detection
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+   38. Model training &amp; evaluation
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+     40.3. First assignment
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+     40.5. Project Plan​ Template
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+     40.6. Python programming introduction
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+     40.7. Python programming basics
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+     40.9. Analyzing text about Data Science
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+     40.10. Data Science scenarios
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-     41.11. Write a data ethics case study
+     40.11. Write a data ethics case study
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-     41.12. Lines, scatters and bars
+     40.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.16. Build your own custom vis
+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
+     40.17. Classifying datasets
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+     40.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
+     40.19. Introduction to probability and statistics
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+     40.20. Displaying airport data
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+     40.21. Soda profits
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-     41.22. Analyzing COVID-19 papers
+     40.22. Analyzing COVID-19 papers
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-<li class="toctree-l1"><a class="reference internal" href="deep-learning/gan.html">42.21. Generative Adversarial Network</a></li>
+<li class="toctree-l1"><a class="reference internal" href="python-programming/python-programming-introduction.html">41.1. Python programming introduction</a></li>
+<li class="toctree-l1"><a class="reference internal" href="python-programming/python-programming-basics.html">41.2. Python programming basics</a></li>
+<li class="toctree-l1"><a class="reference internal" href="python-programming/python-programming-advanced.html">41.3. Python programming advanced</a></li>
+<li class="toctree-l1"><a class="reference internal" href="data-science/data-science-introduction.html">41.4. Data Science introduction</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="data-science/data-science-lifecycle.html">41.8. Data Science lifecycle</a></li>
+<li class="toctree-l1"><a class="reference internal" href="data-science/data-science-in-the-cloud.html">41.9. Data Science in the cloud</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="ml-fundamentals/logistic-regression.html">41.13. Logistic Regression</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="ml-fundamentals/neural-network.html">41.15. Neural Network</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ml-fundamentals/build-an-ml-web-app.html">41.16. Build an machine learning web application</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="ml-advanced/kernel-method.html">41.18. Kernel method</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ml-advanced/model-selection.html">41.19. Model Selection</a></li>
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+            <p class="prev-next-title"><span class="section-number">40.116. </span>Basic classification: Classify images of clothing</p>
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     Margin
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+   41.18.15. Acknowledgement
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 </div>
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-<h1><span class="section-number">42.18. </span>Kernel method<a class="headerlink" href="#kernel-method" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.18. </span>Kernel method<a class="headerlink" href="#kernel-method" title="Permalink to this headline">#</a></h1>
 <ul class="simple">
 <li><p>Support Vector Machines (SVM)</p></li>
 </ul>
 <section id="outline">
-<h2><span class="section-number">42.18.1. </span>Outline<a class="headerlink" href="#outline" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.18.1. </span>Outline<a class="headerlink" href="#outline" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>The Why of Support Vector Machines</p>
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-<h2><span class="section-number">42.18.2. </span>The Why of Support Vector Machines<a class="headerlink" href="#the-why-of-support-vector-machines" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.18.2. </span>The Why of Support Vector Machines<a class="headerlink" href="#the-why-of-support-vector-machines" title="Permalink to this headline">#</a></h2>
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 </div>
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+<h2><span class="section-number">41.18.3. </span>How to draw the separator  line? (1/2)<a class="headerlink" href="#how-to-draw-the-separator-line-1-2" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>A linear discriminant classifier may draw straight lines to separate two sets of data.</p></li>
 <li><p>But there may be multiple split lines that perfectly distinguish between the two categories.</p></li>
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+<h2><span class="section-number">41.18.4. </span>How to draw the separator  line? (2/2)<a class="headerlink" href="#how-to-draw-the-separator-line-2-2" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>All three separators are good if we want to distinguish between these samples</p></li>
 <li><p>Which one is the best?</p></li>
@@ -1867,7 +1867,7 @@ <h2><span class="section-number">42.18.4. </span>How to draw the separator  line
 </ul>
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 <section id="support-vector-machines-maximizing-the-margin">
-<h2><span class="section-number">42.18.5. </span>Support Vector Machines: Maximizing the <em>Margin</em><a class="headerlink" href="#support-vector-machines-maximizing-the-margin" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.18.5. </span>Support Vector Machines: Maximizing the <em>Margin</em><a class="headerlink" href="#support-vector-machines-maximizing-the-margin" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>SVM proposes a new solution algorithm which finds the nearest straight line by applying an appropriate boundary fill to each line</p></li>
 <li><p>Because of its excellent fitting ability, SVM can also be used to handle large amounts of unbalanced data</p></li>
@@ -1893,15 +1893,15 @@ <h2><span class="section-number">42.18.5. </span>Support Vector Machines: Maximi
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-<h2><span class="section-number">42.18.6. </span>SVM for Linear classification : 2D case<a class="headerlink" href="#svm-for-linear-classification-2d-case" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.18.6. </span>SVM for Linear classification : 2D case<a class="headerlink" href="#svm-for-linear-classification-2d-case" title="Permalink to this headline">#</a></h2>
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+<h2><span class="section-number">41.18.7. </span>SVM for Linear classification : 3D case<a class="headerlink" href="#svm-for-linear-classification-3d-case" title="Permalink to this headline">#</a></h2>
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-<h2><span class="section-number">42.18.8. </span>sklearn<a class="headerlink" href="#sklearn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.18.8. </span>sklearn<a class="headerlink" href="#sklearn" title="Permalink to this headline">#</a></h2>
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-<h2><span class="section-number">42.18.9. </span>Insensitivity to distant points<a class="headerlink" href="#insensitivity-to-distant-points" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.18.9. </span>Insensitivity to distant points<a class="headerlink" href="#insensitivity-to-distant-points" title="Permalink to this headline">#</a></h2>
 <p>One of the strengths of the SVM model is that it is insensitive to the exact behaviour of distant points, and increases the number of training points without affecting the classification results of the model.</p>
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+<h2><span class="section-number">41.18.10. </span>Beyond linear boundaries: Kernel SVM<a class="headerlink" href="#beyond-linear-boundaries-kernel-svm" title="Permalink to this headline">#</a></h2>
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+<h2><span class="section-number">41.18.11. </span>Non-linearly separable data<a class="headerlink" href="#non-linearly-separable-data" title="Permalink to this headline">#</a></h2>
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-<h2><span class="section-number">42.18.12. </span>Now let’s do the kernel trick<a class="headerlink" href="#now-lets-do-the-kernel-trick" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.18.12. </span>Now let’s do the kernel trick<a class="headerlink" href="#now-lets-do-the-kernel-trick" title="Permalink to this headline">#</a></h2>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">X</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
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 <section id="svm-v-s-logistic-regression-1-2">
-<h2><span class="section-number">42.18.13. </span>SVM v.s. logistic regression (1/2)<a class="headerlink" href="#svm-v-s-logistic-regression-1-2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.18.13. </span>SVM v.s. logistic regression (1/2)<a class="headerlink" href="#svm-v-s-logistic-regression-1-2" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Support vector machines only consider local points near the interval boundary, whereas logistic regression considers the global (points far from the boundary line also play a role in determining the boundary line). Support vector machines do not cause changes in the separation hyperplane by changing the non-support vector samples</p></li>
 </ul>
 </section>
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-<h2><span class="section-number">42.18.14. </span>SVM v.s. logistic regression (2/2)<a class="headerlink" href="#svm-v-s-logistic-regression-2-2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.18.14. </span>SVM v.s. logistic regression (2/2)<a class="headerlink" href="#svm-v-s-logistic-regression-2-2" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>SVM’s loss function is self-regularising, which is why SVM is a structural risk minimisation algorithm. And LR must additionally add a regular term to the loss function. Structural risk minimisation, meaning seeking a balance between training error and model complexity to prevent overfitting</p></li>
 <li><p>Optimization methods: LR is generally based on gradient descent, SVM on SMO</p></li>
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 <section id="acknowledgement">
-<h2><span class="section-number">42.18.15. </span>Acknowledgement<a class="headerlink" href="#acknowledgement" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.18.15. </span>Acknowledgement<a class="headerlink" href="#acknowledgement" title="Permalink to this headline">#</a></h2>
 <p>Some code comes from <a class="reference external" href="https://jakevdp.github.io/PythonDataScienceHandbook/05.07-support-vector-machines.html">jakevdp</a>, for which the code part is licenced under MIT licence.</p>
 </section>
 </section>
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         <i class="fas fa-angle-left"></i>
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-            <p class="prev-next-title"><span class="section-number">42.17. </span>Unsupervised learning</p>
+            <p class="prev-next-title"><span class="section-number">41.17. </span>Unsupervised learning</p>
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-        <p class="prev-next-title"><span class="section-number">42.19. </span>Model Selection</p>
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diff --git a/slides/ml-advanced/model-selection.html b/slides/ml-advanced/model-selection.html
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-    <link rel="next" title="42.20. Convolutional Neural Network" href="../deep-learning/cnn.html" />
-    <link rel="prev" title="42.18. Kernel method" href="kernel-method.html" />
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+     8.2. The “low code/no code” way
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+     15.2. Gradient descent
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-     19.4. XGBoost + k-fold CV + Feature Importance
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-     41.12. Lines, scatters and bars
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-     41.13. Apply your skills
+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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+     40.15. Dive into the beehive
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-     41.16. Build your own custom vis
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-     41.17. Classifying datasets
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-     41.18. Small diabetes study
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-     41.19. Introduction to probability and statistics
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-     41.20. Displaying airport data
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-     41.21. Soda profits
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-     41.22. Analyzing COVID-19 papers
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-     41.24. Data processing in Python
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-     41.29. Matplotlib applied
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-     41.35. Tell a story
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-     41.36. Explore a planetary computer dataset
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-     41.37. Exploring for answers
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-     41.38. Market research
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-     41.39. Low code/no code Data Science project on Azure ML
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+   41.19.12. L1 and L2 regularization
   </a>
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  <li class="toc-h2 nav-item toc-entry">
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-   42.19.13. L1 and L2 Regularization
+   41.19.13. L1 and L2 Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id9">
-   42.19.14. L1 and L2 Regularization
+   41.19.14. L1 and L2 Regularization
   </a>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id10">
-   42.19.15. L1 and L2 Regularization
+   41.19.15. L1 and L2 Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id11">
-   42.19.16. L1 and L2 Regularization
+   41.19.16. L1 and L2 Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id12">
-   42.19.17. L1 and L2 Regularization
+   41.19.17. L1 and L2 Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#early-stopping">
-   42.19.18. Early stopping
+   41.19.18. Early stopping
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dropout">
-   42.19.19. Dropout
+   41.19.19. Dropout
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#prediction-after-dropout">
-   42.19.20. Prediction after dropout
+   41.19.20. Prediction after dropout
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusions">
-   42.19.21. Conclusions
+   41.19.21. Conclusions
   </a>
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-   42.19.22. Conclusions
+   41.19.22. Conclusions
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  <li class="toc-h2 nav-item toc-entry">
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-   42.19.23. Conclusions
+   41.19.23. Conclusions
   </a>
  </li>
 </ul>
@@ -1711,117 +1711,117 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   42.19.1. Introduction
+   41.19.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
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-   42.19.2. Outline
+   41.19.2. Outline
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#over-fitting-and-under-fitting">
-   42.19.3. Over-fitting and under-fitting
+   41.19.3. Over-fitting and under-fitting
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id1">
-   42.19.4. Over-fitting and under-fitting
+   41.19.4. Over-fitting and under-fitting
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  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id2">
-   42.19.5. Over-fitting and under-fitting
+   41.19.5. Over-fitting and under-fitting
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  <li class="toc-h2 nav-item toc-entry">
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-   42.19.6. Over-fitting and under-fitting
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   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
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-   42.19.7. Over-fitting and under-fitting
+   41.19.7. Over-fitting and under-fitting
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id5">
-   42.19.8. Over-fitting and under-fitting
+   41.19.8. Over-fitting and under-fitting
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
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-   42.19.9. Over-fitting and under-fitting
+   41.19.9. Over-fitting and under-fitting
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#bias-variance-tradeoff">
-   42.19.10. Bias variance tradeoff
+   41.19.10. Bias variance tradeoff
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
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-   42.19.11. Bias variance tradeoff
+   41.19.11. Bias variance tradeoff
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#l1-and-l2-regularization">
-   42.19.12. L1 and L2 regularization
+   41.19.12. L1 and L2 regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id8">
-   42.19.13. L1 and L2 Regularization
+   41.19.13. L1 and L2 Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id9">
-   42.19.14. L1 and L2 Regularization
+   41.19.14. L1 and L2 Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
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-   42.19.15. L1 and L2 Regularization
+   41.19.15. L1 and L2 Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
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-   42.19.16. L1 and L2 Regularization
+   41.19.16. L1 and L2 Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id12">
-   42.19.17. L1 and L2 Regularization
+   41.19.17. L1 and L2 Regularization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#early-stopping">
-   42.19.18. Early stopping
+   41.19.18. Early stopping
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#dropout">
-   42.19.19. Dropout
+   41.19.19. Dropout
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#prediction-after-dropout">
-   42.19.20. Prediction after dropout
+   41.19.20. Prediction after dropout
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#conclusions">
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+   41.19.21. Conclusions
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
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+   41.19.22. Conclusions
   </a>
  </li>
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+   41.19.23. Conclusions
   </a>
  </li>
 </ul>
@@ -1852,16 +1852,16 @@ <h2> Contents </h2>
 </div></div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="model-selection">
-<h1><span class="section-number">42.19. </span>Model Selection<a class="headerlink" href="#model-selection" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.19. </span>Model Selection<a class="headerlink" href="#model-selection" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">42.19.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Model Selection is the process of choosing the best model among all the potential candidate models for a given problem.</p></li>
 <li><p>The aim of the model selection process is to select a machine learning algorithm that evaluates to perform well against all the different parameters.</p></li>
 </ul>
 </section>
 <section id="outline">
-<h2><span class="section-number">42.19.2. </span>Outline<a class="headerlink" href="#outline" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.2. </span>Outline<a class="headerlink" href="#outline" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Over-fitting and under-fitting</p></li>
 <li><p>Bias variance tradeoff</p></li>
@@ -1872,14 +1872,14 @@ <h2><span class="section-number">42.19.2. </span>Outline<a class="headerlink" hr
 </ul>
 </section>
 <section id="over-fitting-and-under-fitting">
-<h2><span class="section-number">42.19.3. </span>Over-fitting and under-fitting<a class="headerlink" href="#over-fitting-and-under-fitting" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.3. </span>Over-fitting and under-fitting<a class="headerlink" href="#over-fitting-and-under-fitting" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Regression</p></li>
 </ul>
 <p><img alt="" src="../../_images/under_over_justalright.png" /></p>
 </section>
 <section id="id1">
-<h2><span class="section-number">42.19.4. </span>Over-fitting and under-fitting<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.4. </span>Over-fitting and under-fitting<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Regression</p>
 <ul>
@@ -1890,7 +1890,7 @@ <h2><span class="section-number">42.19.4. </span>Over-fitting and under-fitting<
 <p><img alt="" src="../../_images/bias-variance-datapoints.jpg" /></p>
 </section>
 <section id="id2">
-<h2><span class="section-number">42.19.5. </span>Over-fitting and under-fitting<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.5. </span>Over-fitting and under-fitting<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Regression</p>
 <ul>
@@ -1901,7 +1901,7 @@ <h2><span class="section-number">42.19.5. </span>Over-fitting and under-fitting<
 <p><img alt="" src="../../_images/bias-variance-overfitting.jpg" /></p>
 </section>
 <section id="id3">
-<h2><span class="section-number">42.19.6. </span>Over-fitting and under-fitting<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.6. </span>Over-fitting and under-fitting<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Regression</p>
 <ul>
@@ -1912,7 +1912,7 @@ <h2><span class="section-number">42.19.6. </span>Over-fitting and under-fitting<
 <p><img alt="" src="../../_images/bias-variance-overfitting-testdata.jpg" /></p>
 </section>
 <section id="id4">
-<h2><span class="section-number">42.19.7. </span>Over-fitting and under-fitting<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.7. </span>Over-fitting and under-fitting<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Regression</p>
 <ul>
@@ -1923,7 +1923,7 @@ <h2><span class="section-number">42.19.7. </span>Over-fitting and under-fitting<
 <p><img alt="" src="../../_images/bias-variance-underfitting.jpg" /></p>
 </section>
 <section id="id5">
-<h2><span class="section-number">42.19.8. </span>Over-fitting and under-fitting<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.8. </span>Over-fitting and under-fitting<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Regression</p>
 <ul>
@@ -1934,84 +1934,84 @@ <h2><span class="section-number">42.19.8. </span>Over-fitting and under-fitting<
 <p><img alt="" src="../../_images/bias-variance-underfitting-test-data.jpg" /></p>
 </section>
 <section id="id6">
-<h2><span class="section-number">42.19.9. </span>Over-fitting and under-fitting<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.9. </span>Over-fitting and under-fitting<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Classification</p></li>
 </ul>
 <p><img alt="" src="../../_images/classification.png" /></p>
 </section>
 <section id="bias-variance-tradeoff">
-<h2><span class="section-number">42.19.10. </span>Bias variance tradeoff<a class="headerlink" href="#bias-variance-tradeoff" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.10. </span>Bias variance tradeoff<a class="headerlink" href="#bias-variance-tradeoff" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Graphical illustration of variance and bias</p></li>
 </ul>
 <p><img alt="" src="../../_images/graphicalillustration.png" /></p>
 </section>
 <section id="id7">
-<h2><span class="section-number">42.19.11. </span>Bias variance tradeoff<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.11. </span>Bias variance tradeoff<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Model complexity v.s. error</p></li>
 </ul>
 <p><img alt="" src="../../_images/total_error.png" /></p>
 </section>
 <section id="l1-and-l2-regularization">
-<h2><span class="section-number">42.19.12. </span>L1 and L2 regularization<a class="headerlink" href="#l1-and-l2-regularization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.12. </span>L1 and L2 regularization<a class="headerlink" href="#l1-and-l2-regularization" title="Permalink to this headline">#</a></h2>
 <div class="math notranslate nohighlight">
 \[L2\ Loss = Loss + {\lambda}\sum_{i} w_i^2\]</div>
 <div class="math notranslate nohighlight">
 \[L1\ Loss = Loss +  {\lambda}\sum_{i} \lvert w \rvert\]</div>
 </section>
 <section id="id8">
-<h2><span class="section-number">42.19.13. </span>L1 and L2 Regularization<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.13. </span>L1 and L2 Regularization<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/circlesquare.png" /></p>
 </section>
 <section id="id9">
-<h2><span class="section-number">42.19.14. </span>L1 and L2 Regularization<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.14. </span>L1 and L2 Regularization<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/L1L2contour.png" /></p>
 </section>
 <section id="id10">
-<h2><span class="section-number">42.19.15. </span>L1 and L2 Regularization<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.15. </span>L1 and L2 Regularization<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/ridgelassoItayEvron.gif" /></p>
 </section>
 <section id="id11">
-<h2><span class="section-number">42.19.16. </span>L1 and L2 Regularization<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.16. </span>L1 and L2 Regularization<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/p-norm_balls.webp" /></p>
 </section>
 <section id="id12">
-<h2><span class="section-number">42.19.17. </span>L1 and L2 Regularization<a class="headerlink" href="#id12" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.17. </span>L1 and L2 Regularization<a class="headerlink" href="#id12" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>The impact of the value of <span class="math notranslate nohighlight">\(\lambda\)</span></p></li>
 </ul>
 <p><img alt="" src="../../_images/lagrange-animation.gif" /></p>
 </section>
 <section id="early-stopping">
-<h2><span class="section-number">42.19.18. </span>Early stopping<a class="headerlink" href="#early-stopping" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.18. </span>Early stopping<a class="headerlink" href="#early-stopping" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/traintestoverfitting.png" /></p>
 </section>
 <section id="dropout">
-<h2><span class="section-number">42.19.19. </span>Dropout<a class="headerlink" href="#dropout" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.19. </span>Dropout<a class="headerlink" href="#dropout" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/dropoutgif.gif" /></p>
 </section>
 <section id="prediction-after-dropout">
-<h2><span class="section-number">42.19.20. </span>Prediction after dropout<a class="headerlink" href="#prediction-after-dropout" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.20. </span>Prediction after dropout<a class="headerlink" href="#prediction-after-dropout" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/kUc8r.jpg" /></p>
 </section>
 <section id="conclusions">
-<h2><span class="section-number">42.19.21. </span>Conclusions<a class="headerlink" href="#conclusions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.21. </span>Conclusions<a class="headerlink" href="#conclusions" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Training size matters</p></li>
 </ul>
 <p><img alt="" src="../../_images/ZahidHasan.png" /></p>
 </section>
 <section id="id13">
-<h2><span class="section-number">42.19.22. </span>Conclusions<a class="headerlink" href="#id13" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.22. </span>Conclusions<a class="headerlink" href="#id13" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>How to choose a good model</p></li>
 </ul>
 <p><img alt="" src="../../_images/steps.png" /></p>
 </section>
 <section id="id14">
-<h2><span class="section-number">42.19.23. </span>Conclusions<a class="headerlink" href="#id14" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.19.23. </span>Conclusions<a class="headerlink" href="#id14" title="Permalink to this headline">#</a></h2>
 <p><img alt="" src="../../_images/Bias-vs.webp" /></p>
 </section>
 </section>
@@ -2053,13 +2053,13 @@ <h2><span class="section-number">42.19.23. </span>Conclusions<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">42.18. </span>Kernel method</p>
+            <p class="prev-next-title"><span class="section-number">41.18. </span>Kernel method</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../deep-learning/cnn.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">42.20. </span>Convolutional Neural Network</p>
+        <p class="prev-next-title"><span class="section-number">41.20. </span>Convolutional Neural Network</p>
     </div>
     <i class="fas fa-angle-right"></i>
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diff --git a/slides/ml-advanced/unsupervised-learning.html b/slides/ml-advanced/unsupervised-learning.html
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@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>42.17. Unsupervised learning &#8212; Ocademy Open Machine Learning Book</title>
+    <title>41.17. Unsupervised learning &#8212; Ocademy Open Machine Learning Book</title>
     
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   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
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+    <link rel="next" title="41.18. Kernel method" href="kernel-method.html" />
+    <link rel="prev" title="41.16. Build an machine learning web application" href="../ml-fundamentals/build-an-ml-web-app.html" />
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-     41.9. Analyzing text about Data Science
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 <li><p>Can help with data visualization</p></li>
 <li><p>Can help deal with the multicollinearity of your data and prepare the data for a supervised learning method</p></li>
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 <blockquote>
 <div><p>“To deal with hyper-planes in a 14 dimensional space, visualize a 3D space and say ‘fourteen’ very loudly. Everyone does it.” - Geoffrey Hinton</p>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">digits</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">genfromtxt</span><span class="p">(</span><span class="s1">&#39;../data/mnist_8x8.csv&#39;</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">&#39;,&#39;</span><span class="p">,</span> <span class="n">skip_header</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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 <ul class="simple">
 <li><p>Basically, “I have these points here, and I can see that they organize into groups. It would be nice to describe these things more concretely, and, when a new point comes in, assign it to the correct group.”</p></li>
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 <p>K-means algorithm is the most popular and yet simplest of all the clustering algorithms. Here is how it works:</p>
 <ol class="simple">
 <li><p>Select the number of clusters <span class="math notranslate nohighlight">\(k\)</span> that you think is the optimal number.</p></li>
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 <p>Another “feature” of this algorithm is its sensitivity to the initial positions of the cluster centroids. You can run the algorithm several times and then average all the centroid results.</p>
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 <section id="acknowledgments">
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+<h2><span class="section-number">41.17.11. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to YURY KASHNITSKY for creating the open-source <a class="reference external" href="https://www.kaggle.com/code/kashnitsky/topic-7-unsupervised-learning-pca-and-clustering">Kaggle jupyter notebook</a>, licensed under Apache 2.0. It inspires the majority of the content of this slides.</p>
 </section>
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-            <p class="prev-next-title"><span class="section-number">42.16. </span>Build an machine learning web application</p>
+            <p class="prev-next-title"><span class="section-number">41.16. </span>Build an machine learning web application</p>
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     <a class="reference internal" href="../../assignments/data-science/analyzing-text-about-data-science.html">
-     41.9. Analyzing text about Data Science
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     <a class="reference internal" href="../../assignments/data-science/data-science-scenarios.html">
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     <a class="reference internal" href="../../assignments/data-science/write-a-data-ethics-case-study.html">
-     41.11. Write a data ethics case study
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     <a class="reference internal" href="../../assignments/data-science/try-it-in-excel.html">
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-     41.15. Dive into the beehive
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-     41.17. Classifying datasets
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-     41.18. Small diabetes study
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     <a class="reference internal" href="../../assignments/data-science/introduction-to-statistics-and-probability.html">
-     41.19. Introduction to probability and statistics
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     <a class="reference internal" href="../../assignments/data-science/displaying-airport-data.html">
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-     41.21. Soda profits
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     <a class="reference internal" href="../../assignments/data-science/analyzing-COVID-19-papers.html">
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     <a class="reference internal" href="../../assignments/data-science/estimation-of-COVID-19-pandemic.html">
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     <a class="reference internal" href="../../assignments/data-science/evaluating-data-from-a-form.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-iris.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-overview-mnist-digits.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/linear-regression/california_housing.html">
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     <a class="reference internal" href="../../assignments/ml-fundamentals/ml-logistic-regression-1.html">
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+     41.1. Python programming introduction
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+     41.16. Build an machine learning web application
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-     42.17. Unsupervised learning
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-     42.18. Kernel method
+     41.18. Kernel method
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-     42.19. Model Selection
+     41.19. Model Selection
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-     42.20. Convolutional Neural Network
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-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
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@@ -1575,34 +1575,34 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
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-   42.16.1. Introduction
+   41.16.1. Introduction
   </a>
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-   42.16.2. Streamlit
+   41.16.2. Streamlit
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-   42.16.3. Display and style data
+   41.16.3. Display and style data
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-     42.16.3.1. Use magic
+     41.16.3.1. Use magic
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-     42.16.5.1. Styling
+     41.16.5.1. Styling
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+   41.16.6. Draw a line chart
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-   42.16.7. Plot a map
+   41.16.7. Plot a map
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-   42.16.8. Widgets
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-   42.16.16. Show progress
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-   42.16.17. Themes
+   41.16.17. Themes
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-   42.16.18. Caching
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-   42.16.19. Acknowledgments
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@@ -1735,34 +1735,34 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
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-   42.16.1. Introduction
+   41.16.1. Introduction
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-   42.16.2. Streamlit
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+   41.16.3. Display and style data
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+     41.16.3.1. Use magic
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-     42.16.5.1. Styling
+     41.16.5.1. Styling
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+   41.16.6. Draw a line chart
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+   41.16.7. Plot a map
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-   42.16.12. Use a selectbox for options
+   41.16.12. Use a selectbox for options
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+   41.16.13. Layout
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-   42.16.18. Caching
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+   41.16.19. Acknowledgments
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 </ul>
@@ -1907,9 +1907,9 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="build-an-machine-learning-web-application">
-<h1><span class="section-number">42.16. </span>Build an machine learning web application<a class="headerlink" href="#build-an-machine-learning-web-application" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.16. </span>Build an machine learning web application<a class="headerlink" href="#build-an-machine-learning-web-application" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">42.16.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.16.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Imagine that you’re working on a machine learning project and you’ve found your champion model. What happens next?</p></li>
 <li><p>Let clients run your Jupyter notebook? Not a good idea!</p></li>
@@ -1918,16 +1918,16 @@ <h2><span class="section-number">42.16.1. </span>Introduction<a class="headerlin
 </ul>
 </section>
 <section id="streamlit">
-<h2><span class="section-number">42.16.2. </span>Streamlit<a class="headerlink" href="#streamlit" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.16.2. </span>Streamlit<a class="headerlink" href="#streamlit" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>A Python-based library that allows the creation and deployment of machine learning web applications</p></li>
 <li><p>Fast and flexible, turning application development time from days into hours</p></li>
 </ul>
 </section>
 <section id="display-and-style-data">
-<h2><span class="section-number">42.16.3. </span>Display and style data<a class="headerlink" href="#display-and-style-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.16.3. </span>Display and style data<a class="headerlink" href="#display-and-style-data" title="Permalink to this headline">#</a></h2>
 <section id="use-magic">
-<h3><span class="section-number">42.16.3.1. </span>Use magic<a class="headerlink" href="#use-magic" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.16.3.1. </span>Use magic<a class="headerlink" href="#use-magic" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
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@@ -1959,9 +1959,9 @@ <h3><span class="section-number">42.16.3.1. </span>Use magic<a class="headerlink
 </section>
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-<h2><span class="section-number">42.16.4. </span>Display and style data<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.16.4. </span>Display and style data<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
 <section id="st-write">
-<h3><span class="section-number">42.16.4.1. </span><code class="docutils literal notranslate"><span class="pre">st.write()</span></code><a class="headerlink" href="#st-write" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.16.4.1. </span><code class="docutils literal notranslate"><span class="pre">st.write()</span></code><a class="headerlink" href="#st-write" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">st.write()</span></code> is Streamlit’s “Swiss Army knife”</p></li>
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@@ -1982,9 +1982,9 @@ <h3><span class="section-number">42.16.4.1. </span><code class="docutils literal
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+<h2><span class="section-number">41.16.5. </span>Display and style data<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
 <section id="styling">
-<h3><span class="section-number">42.16.5.1. </span>Styling<a class="headerlink" href="#styling" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.16.5.1. </span>Styling<a class="headerlink" href="#styling" title="Permalink to this headline">#</a></h3>
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@@ -1999,7 +1999,7 @@ <h3><span class="section-number">42.16.5.1. </span>Styling<a class="headerlink"
 </section>
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-<h2><span class="section-number">42.16.6. </span>Draw a line chart<a class="headerlink" href="#draw-a-line-chart" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.16.6. </span>Draw a line chart<a class="headerlink" href="#draw-a-line-chart" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>You can easily add a line chart to your app with
 <code class="docutils literal notranslate"><span class="pre">st.line_chart()</span></code></p></li>
@@ -2017,7 +2017,7 @@ <h2><span class="section-number">42.16.6. </span>Draw a line chart<a class="head
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-<h2><span class="section-number">42.16.7. </span>Plot a map<a class="headerlink" href="#plot-a-map" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.16.7. </span>Plot a map<a class="headerlink" href="#plot-a-map" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>With <code class="docutils literal notranslate"><span class="pre">st.map()</span></code> you can display data points on a map</p></li>
 </ul>
@@ -2034,13 +2034,13 @@ <h2><span class="section-number">42.16.7. </span>Plot a map<a class="headerlink"
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-<h2><span class="section-number">42.16.8. </span>Widgets<a class="headerlink" href="#widgets" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.16.8. </span>Widgets<a class="headerlink" href="#widgets" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
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+<h2><span class="section-number">41.16.19. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
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 </section>
 </section>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">42.15. </span>Neural Network</p>
+            <p class="prev-next-title"><span class="section-number">41.15. </span>Neural Network</p>
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     <a class='right-next' id="next-link" href="../ml-advanced/unsupervised-learning.html" title="next page">
     <div class="prev-next-info">
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-        <p class="prev-next-title"><span class="section-number">42.17. </span>Unsupervised learning</p>
+        <p class="prev-next-title"><span class="section-number">41.17. </span>Unsupervised learning</p>
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-    <link rel="prev" title="42.11. Machine Learning overview" href="ml-overview.html" />
+    <link rel="next" title="41.13. Logistic Regression" href="logistic-regression.html" />
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+   13. Build a web app to use a Machine Learning model
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-     41.45. Linear regression - California Housing
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   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-the-multiple-linear-regression-model-on-the-training-set">
-   42.12.15. Training the Multiple Linear Regression model on the Training set
+   41.12.15. Training the Multiple Linear Regression model on the Training set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id4">
-   42.12.16. Predicting the Test set results
+   41.12.16. Predicting the Test set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#example-3">
-   42.12.17. Example 3
+   41.12.17. Example 3
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id5">
-   42.12.18. Importing the libraries
+   41.12.18. Importing the libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id6">
-   42.12.19. Importing the dataset
+   41.12.19. Importing the dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-the-linear-regression-model-on-the-whole-dataset">
-   42.12.20. Training the Linear Regression model on the whole dataset
+   41.12.20. Training the Linear Regression model on the whole dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-the-polynomial-regression-model-on-the-whole-dataset">
-   42.12.21. Training the Polynomial Regression model on the whole dataset
+   41.12.21. Training the Polynomial Regression model on the whole dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualising-the-polynomial-linear-regression-results">
-   42.12.22. Visualising the Polynomial Linear Regression results
+   41.12.22. Visualising the Polynomial Linear Regression results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#implementation-of-linear-regression-for-scratch">
-   42.12.23. Implementation of Linear Regression for scratch
+   41.12.23. Implementation of Linear Regression for scratch
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#traning-model-with-our-own-linear-regression">
-   42.12.24. Traning model with our own Linear Regression
+   41.12.24. Traning model with our own Linear Regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id7">
-   42.12.25. Visualising the Training set results
+   41.12.25. Visualising the Training set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   42.12.26. Acknowledgments
+   41.12.26. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1777,183 +1777,183 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#what-is-linear-regression">
-   42.12.1. What is Linear Regression?
+   41.12.1. What is Linear Regression?
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#example">
-     42.12.1.1. Example
+     41.12.1.1. Example
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#linear-model">
-     42.12.1.2. Linear Model
+     41.12.1.2. Linear Model
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#hypothesis">
-       42.12.1.2.1. Hypothesis
+       41.12.1.2.1. Hypothesis
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#design-matrix">
-       42.12.1.2.2. Design Matrix
+       41.12.1.2.2. Design Matrix
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#univariate-example">
-       42.12.1.2.3. Univariate Example
+       41.12.1.2.3. Univariate Example
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#what-is-a-good-fit">
-     42.12.1.3. What is a Good Fit?
+     41.12.1.3. What is a Good Fit?
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#optimization-problem">
-       42.12.1.3.1. Optimization Problem
+       41.12.1.3.1. Optimization Problem
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#normal-equations">
-       42.12.1.3.2. Normal Equations
+       41.12.1.3.2. Normal Equations
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#normalization">
-     42.12.1.4. Normalization
+     41.12.1.4. Normalization
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#example-1">
-   42.12.2. Example 1
+   41.12.2. Example 1
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-libraries">
-   42.12.3. Importing the libraries
+   41.12.3. Importing the libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-dataset">
-   42.12.4. Importing the dataset
+   41.12.4. Importing the dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#splitting-the-dataset-into-the-training-set-and-test-set">
-   42.12.5. Splitting the dataset into the Training set and Test set
+   41.12.5. Splitting the dataset into the Training set and Test set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-the-simple-linear-regression-model-on-the-training-set">
-   42.12.6. Training the Simple Linear Regression model on the Training set
+   41.12.6. Training the Simple Linear Regression model on the Training set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#predicting-the-test-set-results">
-   42.12.7. Predicting the Test set results
+   41.12.7. Predicting the Test set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualising-the-training-set-results">
-   42.12.8. Visualising the Training set results
+   41.12.8. Visualising the Training set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualising-the-test-set-results">
-   42.12.9. Visualising the Test set results
+   41.12.9. Visualising the Test set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#example-2">
-   42.12.10. Example 2
+   41.12.10. Example 2
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id1">
-   42.12.11. Importing the libraries
+   41.12.11. Importing the libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id2">
-   42.12.12. Importing the dataset
+   41.12.12. Importing the dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#encoding-categorical-data">
-   42.12.13. Encoding categorical data
+   41.12.13. Encoding categorical data
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id3">
-   42.12.14. Splitting the dataset into the Training set and Test set
+   41.12.14. Splitting the dataset into the Training set and Test set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-the-multiple-linear-regression-model-on-the-training-set">
-   42.12.15. Training the Multiple Linear Regression model on the Training set
+   41.12.15. Training the Multiple Linear Regression model on the Training set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id4">
-   42.12.16. Predicting the Test set results
+   41.12.16. Predicting the Test set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#example-3">
-   42.12.17. Example 3
+   41.12.17. Example 3
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id5">
-   42.12.18. Importing the libraries
+   41.12.18. Importing the libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id6">
-   42.12.19. Importing the dataset
+   41.12.19. Importing the dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-the-linear-regression-model-on-the-whole-dataset">
-   42.12.20. Training the Linear Regression model on the whole dataset
+   41.12.20. Training the Linear Regression model on the whole dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-the-polynomial-regression-model-on-the-whole-dataset">
-   42.12.21. Training the Polynomial Regression model on the whole dataset
+   41.12.21. Training the Polynomial Regression model on the whole dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualising-the-polynomial-linear-regression-results">
-   42.12.22. Visualising the Polynomial Linear Regression results
+   41.12.22. Visualising the Polynomial Linear Regression results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#implementation-of-linear-regression-for-scratch">
-   42.12.23. Implementation of Linear Regression for scratch
+   41.12.23. Implementation of Linear Regression for scratch
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#traning-model-with-our-own-linear-regression">
-   42.12.24. Traning model with our own Linear Regression
+   41.12.24. Traning model with our own Linear Regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id7">
-   42.12.25. Visualising the Training set results
+   41.12.25. Visualising the Training set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   42.12.26. Acknowledgments
+   41.12.26. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1989,12 +1989,12 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="linear-regression">
-<h1><span class="section-number">42.12. </span>Linear Regression<a class="headerlink" href="#linear-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.12. </span>Linear Regression<a class="headerlink" href="#linear-regression" title="Permalink to this headline">#</a></h1>
 <section id="what-is-linear-regression">
-<h2><span class="section-number">42.12.1. </span>What is Linear Regression?<a class="headerlink" href="#what-is-linear-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.1. </span>What is Linear Regression?<a class="headerlink" href="#what-is-linear-regression" title="Permalink to this headline">#</a></h2>
 <p>Finding a straight line of best fit through the data. This works well when the true underlying function is linear.</p>
 <section id="example">
-<h3><span class="section-number">42.12.1.1. </span>Example<a class="headerlink" href="#example" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.12.1.1. </span>Example<a class="headerlink" href="#example" title="Permalink to this headline">#</a></h3>
 <p>We use features <span class="math notranslate nohighlight">\(\mathbf{x}\)</span> to predict a “response” <span class="math notranslate nohighlight">\(y\)</span>. For example we might want to regress <code class="docutils literal notranslate"><span class="pre">num_hours_studied</span></code> onto <code class="docutils literal notranslate"><span class="pre">exam_score</span></code> - in other words we predict exam score from number of hours studied.</p>
 <p>Let’s generate some example data for this case and examine the relationship between <span class="math notranslate nohighlight">\(\mathbf{x}\)</span> and <span class="math notranslate nohighlight">\(y\)</span>.</p>
 <div class="cell docutils container">
@@ -2017,9 +2017,9 @@ <h3><span class="section-number">42.12.1.1. </span>Example<a class="headerlink"
 <p>We will now build a linear model to fit this data.</p>
 </section>
 <section id="linear-model">
-<h3><span class="section-number">42.12.1.2. </span>Linear Model<a class="headerlink" href="#linear-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.12.1.2. </span>Linear Model<a class="headerlink" href="#linear-model" title="Permalink to this headline">#</a></h3>
 <section id="hypothesis">
-<h4><span class="section-number">42.12.1.2.1. </span>Hypothesis<a class="headerlink" href="#hypothesis" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.12.1.2.1. </span>Hypothesis<a class="headerlink" href="#hypothesis" title="Permalink to this headline">#</a></h4>
 <p>A linear model makes a “hypothesis” about the true nature of the underlying function - that it is linear. We express this hypothesis in the univariate case as</p>
 <div class="math notranslate nohighlight">
 \[h_\theta(x) = ax + b\]</div>
@@ -2029,7 +2029,7 @@ <h4><span class="section-number">42.12.1.2.1. </span>Hypothesis<a class="headerl
 <p>Here <span class="math notranslate nohighlight">\(\mathbf{a}\)</span> is a vector of learned parameters, and <span class="math notranslate nohighlight">\(\mathbf{X}\)</span> is the “design matrix” with all the data points. In this formulation the intercept term has been added to the design matrix as the first column (of all ones).</p>
 </section>
 <section id="design-matrix">
-<h4><span class="section-number">42.12.1.2.2. </span>Design Matrix<a class="headerlink" href="#design-matrix" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.12.1.2.2. </span>Design Matrix<a class="headerlink" href="#design-matrix" title="Permalink to this headline">#</a></h4>
 <p>In general with <span class="math notranslate nohighlight">\(n\)</span> data points and <span class="math notranslate nohighlight">\(p\)</span> features our design matrix will have <span class="math notranslate nohighlight">\(n\)</span> rows and <span class="math notranslate nohighlight">\(p\)</span> columns.</p>
 <p>Returning to our exam score regression example, let’s add one more feature - number of hours slept the night before the exam. If we have 4 data points and 2 features, then our matrix will be of shape <span class="math notranslate nohighlight">\(4 \times 3\)</span> (remember we add a bias column). It might look like</p>
 <div class="math notranslate nohighlight">
@@ -2044,7 +2044,7 @@ <h4><span class="section-number">42.12.1.2.2. </span>Design Matrix<a class="head
 <p>Notice we do <strong>not</strong> include the response (label/target) in the design matrix.</p>
 </section>
 <section id="univariate-example">
-<h4><span class="section-number">42.12.1.2.3. </span>Univariate Example<a class="headerlink" href="#univariate-example" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.12.1.2.3. </span>Univariate Example<a class="headerlink" href="#univariate-example" title="Permalink to this headline">#</a></h4>
 <p>Let’s now see what our univariate example looks like</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -2082,7 +2082,7 @@ <h4><span class="section-number">42.12.1.2.3. </span>Univariate Example<a class=
 </section>
 </section>
 <section id="what-is-a-good-fit">
-<h3><span class="section-number">42.12.1.3. </span>What is a Good Fit?<a class="headerlink" href="#what-is-a-good-fit" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.12.1.3. </span>What is a Good Fit?<a class="headerlink" href="#what-is-a-good-fit" title="Permalink to this headline">#</a></h3>
 <p>Typically we use “mean squared error” to measure the goodness of fit in a regression problem.</p>
 <div class="math notranslate nohighlight">
 \[\text{MSE} = \frac{1}{n} \sum_{i=1}^n (y^{(i)} - h_\theta^{(i)})^2\]</div>
@@ -2090,14 +2090,14 @@ <h3><span class="section-number">42.12.1.3. </span>What is a Good Fit?<a class="
 <p><img alt="" src="../../_images/residual.webp" /></p>
 <p>This function is then taken to be our “loss” function - a measure of how badly we are doing. In general we want to minimize this.</p>
 <section id="optimization-problem">
-<h4><span class="section-number">42.12.1.3.1. </span>Optimization Problem<a class="headerlink" href="#optimization-problem" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.12.1.3.1. </span>Optimization Problem<a class="headerlink" href="#optimization-problem" title="Permalink to this headline">#</a></h4>
 <p>The typical recipe for machine learning algorithms is to define a loss function of the parameters of a hypothesis, then to minimize the loss function. In our case we have the optimization problem</p>
 <div class="math notranslate nohighlight">
 \[\min_{\mathbf{a}} \frac{1}{n} \sum_{i=1}^n (y^{(i)} - \mathbf{a}^\top\mathbf{X}^{(i)})^2\]</div>
 <p>Note that we have added the bias into the design matrix.</p>
 </section>
 <section id="normal-equations">
-<h4><span class="section-number">42.12.1.3.2. </span>Normal Equations<a class="headerlink" href="#normal-equations" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.12.1.3.2. </span>Normal Equations<a class="headerlink" href="#normal-equations" title="Permalink to this headline">#</a></h4>
 <p>Linear regression actually has a closed-form solution - the normal equation. It is beyond our scope to show the derivation, but here it is:</p>
 <div class="math notranslate nohighlight">
 \[\mathbf{a}^* = (\mathbf{X}^\top\mathbf{X})^{-1}\mathbf{X}^\top \mathbf{y}\]</div>
@@ -2105,18 +2105,18 @@ <h4><span class="section-number">42.12.1.3.2. </span>Normal Equations<a class="h
 </section>
 </section>
 <section id="normalization">
-<h3><span class="section-number">42.12.1.4. </span>Normalization<a class="headerlink" href="#normalization" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.12.1.4. </span>Normalization<a class="headerlink" href="#normalization" title="Permalink to this headline">#</a></h3>
 <p>It is generally a good idea to normalize all the values in the design matrix. This means all values should be in the range <span class="math notranslate nohighlight">\((0, 1)\)</span> and centered around zero.</p>
 <p><img alt="" src="../../_images/normalization.jpeg" /></p>
 <p>Normalization usually helps the learning algorithm perform better.</p>
 </section>
 </section>
 <section id="example-1">
-<h2><span class="section-number">42.12.2. </span>Example 1<a class="headerlink" href="#example-1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.2. </span>Example 1<a class="headerlink" href="#example-1" title="Permalink to this headline">#</a></h2>
 <p>Simple Linear Regression</p>
 </section>
 <section id="importing-the-libraries">
-<h2><span class="section-number">42.12.3. </span>Importing the libraries<a class="headerlink" href="#importing-the-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.3. </span>Importing the libraries<a class="headerlink" href="#importing-the-libraries" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -2129,7 +2129,7 @@ <h2><span class="section-number">42.12.3. </span>Importing the libraries<a class
 </div>
 </section>
 <section id="importing-the-dataset">
-<h2><span class="section-number">42.12.4. </span>Importing the dataset<a class="headerlink" href="#importing-the-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.4. </span>Importing the dataset<a class="headerlink" href="#importing-the-dataset" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;../../assets/data/Salary_Data.csv&#39;</span><span class="p">)</span>
@@ -2141,7 +2141,7 @@ <h2><span class="section-number">42.12.4. </span>Importing the dataset<a class="
 </div>
 </section>
 <section id="splitting-the-dataset-into-the-training-set-and-test-set">
-<h2><span class="section-number">42.12.5. </span>Splitting the dataset into the Training set and Test set<a class="headerlink" href="#splitting-the-dataset-into-the-training-set-and-test-set" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.5. </span>Splitting the dataset into the Training set and Test set<a class="headerlink" href="#splitting-the-dataset-into-the-training-set-and-test-set" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
@@ -2153,7 +2153,7 @@ <h2><span class="section-number">42.12.5. </span>Splitting the dataset into the
 </div>
 </section>
 <section id="training-the-simple-linear-regression-model-on-the-training-set">
-<h2><span class="section-number">42.12.6. </span>Training the Simple Linear Regression model on the Training set<a class="headerlink" href="#training-the-simple-linear-regression-model-on-the-training-set" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.6. </span>Training the Simple Linear Regression model on the Training set<a class="headerlink" href="#training-the-simple-linear-regression-model-on-the-training-set" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
@@ -2166,7 +2166,7 @@ <h2><span class="section-number">42.12.6. </span>Training the Simple Linear Regr
 </div>
 </section>
 <section id="predicting-the-test-set-results">
-<h2><span class="section-number">42.12.7. </span>Predicting the Test set results<a class="headerlink" href="#predicting-the-test-set-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.7. </span>Predicting the Test set results<a class="headerlink" href="#predicting-the-test-set-results" title="Permalink to this headline">#</a></h2>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">regressor</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
@@ -2176,7 +2176,7 @@ <h2><span class="section-number">42.12.7. </span>Predicting the Test set results
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 <section id="visualising-the-training-set-results">
-<h2><span class="section-number">42.12.8. </span>Visualising the Training set results<a class="headerlink" href="#visualising-the-training-set-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.8. </span>Visualising the Training set results<a class="headerlink" href="#visualising-the-training-set-results" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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@@ -2191,7 +2191,7 @@ <h2><span class="section-number">42.12.8. </span>Visualising the Training set re
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 <section id="visualising-the-test-set-results">
-<h2><span class="section-number">42.12.9. </span>Visualising the Test set results<a class="headerlink" href="#visualising-the-test-set-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.9. </span>Visualising the Test set results<a class="headerlink" href="#visualising-the-test-set-results" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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@@ -2206,11 +2206,11 @@ <h2><span class="section-number">42.12.9. </span>Visualising the Test set result
 </div>
 </section>
 <section id="example-2">
-<h2><span class="section-number">42.12.10. </span>Example 2<a class="headerlink" href="#example-2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.10. </span>Example 2<a class="headerlink" href="#example-2" title="Permalink to this headline">#</a></h2>
 <p>Multiple Linear Regression</p>
 </section>
 <section id="id1">
-<h2><span class="section-number">42.12.11. </span>Importing the libraries<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.11. </span>Importing the libraries<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -2223,7 +2223,7 @@ <h2><span class="section-number">42.12.11. </span>Importing the libraries<a clas
 </div>
 </section>
 <section id="id2">
-<h2><span class="section-number">42.12.12. </span>Importing the dataset<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.12. </span>Importing the dataset<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;../../assets/data/50_Startups.csv&#39;</span><span class="p">)</span>
@@ -2249,7 +2249,7 @@ <h2><span class="section-number">42.12.12. </span>Importing the dataset<a class=
 </div>
 </section>
 <section id="encoding-categorical-data">
-<h2><span class="section-number">42.12.13. </span>Encoding categorical data<a class="headerlink" href="#encoding-categorical-data" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.13. </span>Encoding categorical data<a class="headerlink" href="#encoding-categorical-data" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.compose</span> <span class="kn">import</span> <span class="n">ColumnTransformer</span>
@@ -2269,7 +2269,7 @@ <h2><span class="section-number">42.12.13. </span>Encoding categorical data<a cl
 </div>
 </section>
 <section id="id3">
-<h2><span class="section-number">42.12.14. </span>Splitting the dataset into the Training set and Test set<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.14. </span>Splitting the dataset into the Training set and Test set<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
@@ -2280,7 +2280,7 @@ <h2><span class="section-number">42.12.14. </span>Splitting the dataset into the
 </div>
 </section>
 <section id="training-the-multiple-linear-regression-model-on-the-training-set">
-<h2><span class="section-number">42.12.15. </span>Training the Multiple Linear Regression model on the Training set<a class="headerlink" href="#training-the-multiple-linear-regression-model-on-the-training-set" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.15. </span>Training the Multiple Linear Regression model on the Training set<a class="headerlink" href="#training-the-multiple-linear-regression-model-on-the-training-set" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
@@ -2292,7 +2292,7 @@ <h2><span class="section-number">42.12.15. </span>Training the Multiple Linear R
 </div>
 </section>
 <section id="id4">
-<h2><span class="section-number">42.12.16. </span>Predicting the Test set results<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.16. </span>Predicting the Test set results<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">regressor</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
@@ -2304,11 +2304,11 @@ <h2><span class="section-number">42.12.16. </span>Predicting the Test set result
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 </section>
 <section id="example-3">
-<h2><span class="section-number">42.12.17. </span>Example 3<a class="headerlink" href="#example-3" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.17. </span>Example 3<a class="headerlink" href="#example-3" title="Permalink to this headline">#</a></h2>
 <p>Polynomial Linear Regression</p>
 </section>
 <section id="id5">
-<h2><span class="section-number">42.12.18. </span>Importing the libraries<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.18. </span>Importing the libraries<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -2321,7 +2321,7 @@ <h2><span class="section-number">42.12.18. </span>Importing the libraries<a clas
 </div>
 </section>
 <section id="id6">
-<h2><span class="section-number">42.12.19. </span>Importing the dataset<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.19. </span>Importing the dataset<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h2>
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 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;../../assets/data/Position_Salaries.csv&#39;</span><span class="p">)</span>
@@ -2333,7 +2333,7 @@ <h2><span class="section-number">42.12.19. </span>Importing the dataset<a class=
 </div>
 </section>
 <section id="training-the-linear-regression-model-on-the-whole-dataset">
-<h2><span class="section-number">42.12.20. </span>Training the Linear Regression model on the whole dataset<a class="headerlink" href="#training-the-linear-regression-model-on-the-whole-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.20. </span>Training the Linear Regression model on the whole dataset<a class="headerlink" href="#training-the-linear-regression-model-on-the-whole-dataset" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
@@ -2345,7 +2345,7 @@ <h2><span class="section-number">42.12.20. </span>Training the Linear Regression
 </div>
 </section>
 <section id="training-the-polynomial-regression-model-on-the-whole-dataset">
-<h2><span class="section-number">42.12.21. </span>Training the Polynomial Regression model on the whole dataset<a class="headerlink" href="#training-the-polynomial-regression-model-on-the-whole-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.21. </span>Training the Polynomial Regression model on the whole dataset<a class="headerlink" href="#training-the-polynomial-regression-model-on-the-whole-dataset" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">PolynomialFeatures</span>
@@ -2359,7 +2359,7 @@ <h2><span class="section-number">42.12.21. </span>Training the Polynomial Regres
 </div>
 </section>
 <section id="visualising-the-polynomial-linear-regression-results">
-<h2><span class="section-number">42.12.22. </span>Visualising the Polynomial Linear Regression results<a class="headerlink" href="#visualising-the-polynomial-linear-regression-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.22. </span>Visualising the Polynomial Linear Regression results<a class="headerlink" href="#visualising-the-polynomial-linear-regression-results" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;red&#39;</span><span class="p">)</span>
@@ -2374,7 +2374,7 @@ <h2><span class="section-number">42.12.22. </span>Visualising the Polynomial Lin
 </div>
 </section>
 <section id="implementation-of-linear-regression-for-scratch">
-<h2><span class="section-number">42.12.23. </span>Implementation of Linear Regression for scratch<a class="headerlink" href="#implementation-of-linear-regression-for-scratch" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.23. </span>Implementation of Linear Regression for scratch<a class="headerlink" href="#implementation-of-linear-regression-for-scratch" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyOwnLinearRegression</span><span class="p">:</span>
@@ -2412,7 +2412,7 @@ <h2><span class="section-number">42.12.23. </span>Implementation of Linear Regre
 </div>
 </section>
 <section id="traning-model-with-our-own-linear-regression">
-<h2><span class="section-number">42.12.24. </span>Traning model with our own Linear Regression<a class="headerlink" href="#traning-model-with-our-own-linear-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.24. </span>Traning model with our own Linear Regression<a class="headerlink" href="#traning-model-with-our-own-linear-regression" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;../../assets/data/Salary_Data.csv&#39;</span><span class="p">)</span>
@@ -2428,7 +2428,7 @@ <h2><span class="section-number">42.12.24. </span>Traning model with our own Lin
 </div>
 </section>
 <section id="id7">
-<h2><span class="section-number">42.12.25. </span>Visualising the Training set results<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.25. </span>Visualising the Training set results<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;red&#39;</span><span class="p">)</span>
@@ -2443,7 +2443,7 @@ <h2><span class="section-number">42.12.25. </span>Visualising the Training set r
 </div>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">42.12.26. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.12.26. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to TIM NIVEN for creating the open-source <a class="reference external" href="https://www.kaggle.com/code/timniven/linear-regression-tutorial">Kaggle jupyter notebook</a>, licensed under Apache 2.0. It inspires the majority of the content of this assignment.</p>
 </section>
 </section>
@@ -2485,13 +2485,13 @@ <h2><span class="section-number">42.12.26. </span>Acknowledgments<a class="heade
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">42.11. </span>Machine Learning overview</p>
+            <p class="prev-next-title"><span class="section-number">41.11. </span>Machine Learning overview</p>
         </div>
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     <a class='right-next' id="next-link" href="logistic-regression.html" title="next page">
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-        <p class="prev-next-title"><span class="section-number">42.13. </span>Logistic Regression</p>
+        <p class="prev-next-title"><span class="section-number">41.13. </span>Logistic Regression</p>
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diff --git a/slides/ml-fundamentals/logistic-regression-condensed.html b/slides/ml-fundamentals/logistic-regression-condensed.html
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-    <title>42.14. Logistic Regression &#8212; Ocademy Open Machine Learning Book</title>
+    <title>41.14. Logistic Regression &#8212; Ocademy Open Machine Learning Book</title>
     
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-    <link rel="next" title="42.15. Neural Network" href="neural-network.html" />
-    <link rel="prev" title="42.13. Logistic Regression" href="logistic-regression.html" />
+    <link rel="next" title="41.15. Neural Network" href="neural-network.html" />
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@@ -159,17 +159,17 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
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+   1. Python programming introduction
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  <li class="toctree-l1">
   <a class="reference internal" href="../../prerequisites/python-programming-basics.html">
-   3. Python programming basics
+   2. Python programming basics
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   <a class="reference internal" href="../../prerequisites/python-programming-advanced.html">
-   4. Python programming advanced
+   3. Python programming advanced
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@@ -181,7 +181,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
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-   5. Introduction
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   <ul>
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     <a class="reference internal" href="../../data-science/introduction/defining-data-science.html">
-     5.1. Defining data science
+     4.1. Defining data science
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/introduction/data-science-ethics.html">
-     5.2. Data Science ethics
+     4.2. Data Science ethics
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/introduction/defining-data.html">
-     5.3. Defining data
+     4.3. Defining data
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     <a class="reference internal" href="../../data-science/introduction/introduction-to-statistics-and-probability.html">
-     5.4. Introduction to statistics and probability
+     4.4. Introduction to statistics and probability
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  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../data-science/working-with-data/working-with-data.html">
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-     6.1. Relational databases
+     5.1. Relational databases
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+     40.13. Apply your skills
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     <a class="reference internal" href="../data-science/data-science-in-the-cloud.html">
-     42.9. Data Science in the cloud
+     41.9. Data Science in the cloud
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-in-real-world.html">
-     42.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview.html">
-     42.11. Machine Learning overview
+     41.11. Machine Learning overview
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="linear-regression.html">
-     42.12. Linear Regression
+     41.12. Linear Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="logistic-regression.html">
-     42.13. Logistic Regression
+     41.13. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2 current active">
     <a class="current reference internal" href="#">
-     42.14. Logistic Regression
+     41.14. Logistic Regression
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="neural-network.html">
-     42.15. Neural Network
+     41.15. Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="build-an-ml-web-app.html">
-     42.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/kernel-method.html">
-     42.18. Kernel method
+     41.18. Kernel method
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection.html">
-     42.19. Model Selection
+     41.19. Model Selection
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1575,77 +1575,77 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   42.14.1. Introduction
+   41.14.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-libraries">
-   42.14.2. Importing the libraries
+   41.14.2. Importing the libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#sigmoid-function">
-   42.14.3. Sigmoid function
+   41.14.3. Sigmoid function
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-dataset">
-   42.14.4. Importing the dataset
+   41.14.4. Importing the dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#splitting-the-dataset-into-the-training-set-and-test-set">
-   42.14.5. Splitting the dataset into the Training set and Test set
+   41.14.5. Splitting the dataset into the Training set and Test set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#feature-scaling">
-   42.14.6. Feature Scaling
+   41.14.6. Feature Scaling
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-the-logistic-regression-model-on-the-training-set">
-   42.14.7. Training the Logistic Regression model on the Training set
+   41.14.7. Training the Logistic Regression model on the Training set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#predicting-a-new-result">
-   42.14.8. Predicting a new result
+   41.14.8. Predicting a new result
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#predicting-the-test-set-results">
-   42.14.9. Predicting the Test set results
+   41.14.9. Predicting the Test set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#making-the-confusion-matrix">
-   42.14.10. Making the Confusion Matrix
+   41.14.10. Making the Confusion Matrix
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualising-the-training-set-results">
-   42.14.11. Visualising the Training set results
+   41.14.11. Visualising the Training set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualising-the-test-set-results">
-   42.14.12. Visualising the Test set results
+   41.14.12. Visualising the Test set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#linear-regression-v-s-logistic-regression">
-   42.14.13. Linear Regression v.s. Logistic Regression
+   41.14.13. Linear Regression v.s. Logistic Regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#assignment-1">
-   42.14.14. Assignment - 1
+   41.14.14. Assignment - 1
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#assignment-2">
-   42.14.15. Assignment - 2
+   41.14.15. Assignment - 2
   </a>
  </li>
 </ul>
@@ -1669,77 +1669,77 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   42.14.1. Introduction
+   41.14.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-libraries">
-   42.14.2. Importing the libraries
+   41.14.2. Importing the libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#sigmoid-function">
-   42.14.3. Sigmoid function
+   41.14.3. Sigmoid function
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-dataset">
-   42.14.4. Importing the dataset
+   41.14.4. Importing the dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#splitting-the-dataset-into-the-training-set-and-test-set">
-   42.14.5. Splitting the dataset into the Training set and Test set
+   41.14.5. Splitting the dataset into the Training set and Test set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#feature-scaling">
-   42.14.6. Feature Scaling
+   41.14.6. Feature Scaling
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-the-logistic-regression-model-on-the-training-set">
-   42.14.7. Training the Logistic Regression model on the Training set
+   41.14.7. Training the Logistic Regression model on the Training set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#predicting-a-new-result">
-   42.14.8. Predicting a new result
+   41.14.8. Predicting a new result
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#predicting-the-test-set-results">
-   42.14.9. Predicting the Test set results
+   41.14.9. Predicting the Test set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#making-the-confusion-matrix">
-   42.14.10. Making the Confusion Matrix
+   41.14.10. Making the Confusion Matrix
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualising-the-training-set-results">
-   42.14.11. Visualising the Training set results
+   41.14.11. Visualising the Training set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualising-the-test-set-results">
-   42.14.12. Visualising the Test set results
+   41.14.12. Visualising the Test set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#linear-regression-v-s-logistic-regression">
-   42.14.13. Linear Regression v.s. Logistic Regression
+   41.14.13. Linear Regression v.s. Logistic Regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#assignment-1">
-   42.14.14. Assignment - 1
+   41.14.14. Assignment - 1
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#assignment-2">
-   42.14.15. Assignment - 2
+   41.14.15. Assignment - 2
   </a>
  </li>
 </ul>
@@ -1775,9 +1775,9 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="logistic-regression">
-<h1><span class="section-number">42.14. </span>Logistic Regression<a class="headerlink" href="#logistic-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.14. </span>Logistic Regression<a class="headerlink" href="#logistic-regression" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">42.14.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>In fact, logistic regression is a  <u>classification</u> algorithm, unlike other regression models.</p></li>
 <li><p>Logistic Regression is very important for entering deep learning.</p></li>
@@ -1785,7 +1785,7 @@ <h2><span class="section-number">42.14.1. </span>Introduction<a class="headerlin
 </ul>
 </section>
 <section id="importing-the-libraries">
-<h2><span class="section-number">42.14.2. </span>Importing the libraries<a class="headerlink" href="#importing-the-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.2. </span>Importing the libraries<a class="headerlink" href="#importing-the-libraries" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -1798,7 +1798,7 @@ <h2><span class="section-number">42.14.2. </span>Importing the libraries<a class
 </div>
 </section>
 <section id="sigmoid-function">
-<h2><span class="section-number">42.14.3. </span>Sigmoid function<a class="headerlink" href="#sigmoid-function" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.3. </span>Sigmoid function<a class="headerlink" href="#sigmoid-function" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">sigmoid</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
@@ -1817,7 +1817,7 @@ <h2><span class="section-number">42.14.3. </span>Sigmoid function<a class="heade
 </div>
 </section>
 <section id="importing-the-dataset">
-<h2><span class="section-number">42.14.4. </span>Importing the dataset<a class="headerlink" href="#importing-the-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.4. </span>Importing the dataset<a class="headerlink" href="#importing-the-dataset" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;../../assets/data/Social_Network_Ads.csv&#39;</span><span class="p">)</span>
@@ -1831,7 +1831,7 @@ <h2><span class="section-number">42.14.4. </span>Importing the dataset<a class="
 </div>
 </section>
 <section id="splitting-the-dataset-into-the-training-set-and-test-set">
-<h2><span class="section-number">42.14.5. </span>Splitting the dataset into the Training set and Test set<a class="headerlink" href="#splitting-the-dataset-into-the-training-set-and-test-set" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.5. </span>Splitting the dataset into the Training set and Test set<a class="headerlink" href="#splitting-the-dataset-into-the-training-set-and-test-set" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
@@ -1842,7 +1842,7 @@ <h2><span class="section-number">42.14.5. </span>Splitting the dataset into the
 </div>
 </section>
 <section id="feature-scaling">
-<h2><span class="section-number">42.14.6. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.6. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
@@ -1855,7 +1855,7 @@ <h2><span class="section-number">42.14.6. </span>Feature Scaling<a class="header
 </div>
 </section>
 <section id="training-the-logistic-regression-model-on-the-training-set">
-<h2><span class="section-number">42.14.7. </span>Training the Logistic Regression model on the Training set<a class="headerlink" href="#training-the-logistic-regression-model-on-the-training-set" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.7. </span>Training the Logistic Regression model on the Training set<a class="headerlink" href="#training-the-logistic-regression-model-on-the-training-set" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
@@ -1867,7 +1867,7 @@ <h2><span class="section-number">42.14.7. </span>Training the Logistic Regressio
 </div>
 </section>
 <section id="predicting-a-new-result">
-<h2><span class="section-number">42.14.8. </span>Predicting a new result<a class="headerlink" href="#predicting-a-new-result" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.8. </span>Predicting a new result<a class="headerlink" href="#predicting-a-new-result" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">transform</span><span class="p">([[</span><span class="mi">30</span><span class="p">,</span> <span class="mi">87000</span><span class="p">],</span> <span class="p">[</span><span class="mi">65</span><span class="p">,</span> <span class="mi">990000</span><span class="p">]])))</span>
@@ -1877,7 +1877,7 @@ <h2><span class="section-number">42.14.8. </span>Predicting a new result<a class
 </div>
 </section>
 <section id="predicting-the-test-set-results">
-<h2><span class="section-number">42.14.9. </span>Predicting the Test set results<a class="headerlink" href="#predicting-the-test-set-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.9. </span>Predicting the Test set results<a class="headerlink" href="#predicting-the-test-set-results" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
@@ -1888,7 +1888,7 @@ <h2><span class="section-number">42.14.9. </span>Predicting the Test set results
 </div>
 </section>
 <section id="making-the-confusion-matrix">
-<h2><span class="section-number">42.14.10. </span>Making the Confusion Matrix<a class="headerlink" href="#making-the-confusion-matrix" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.10. </span>Making the Confusion Matrix<a class="headerlink" href="#making-the-confusion-matrix" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">confusion_matrix</span><span class="p">,</span> <span class="n">accuracy_score</span>
@@ -1901,7 +1901,7 @@ <h2><span class="section-number">42.14.10. </span>Making the Confusion Matrix<a
 </div>
 </section>
 <section id="visualising-the-training-set-results">
-<h2><span class="section-number">42.14.11. </span>Visualising the Training set results<a class="headerlink" href="#visualising-the-training-set-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.11. </span>Visualising the Training set results<a class="headerlink" href="#visualising-the-training-set-results" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib.colors</span> <span class="kn">import</span> <span class="n">ListedColormap</span>
@@ -1925,7 +1925,7 @@ <h2><span class="section-number">42.14.11. </span>Visualising the Training set r
 </div>
 </section>
 <section id="visualising-the-test-set-results">
-<h2><span class="section-number">42.14.12. </span>Visualising the Test set results<a class="headerlink" href="#visualising-the-test-set-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.12. </span>Visualising the Test set results<a class="headerlink" href="#visualising-the-test-set-results" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib.colors</span> <span class="kn">import</span> <span class="n">ListedColormap</span>
@@ -1949,7 +1949,7 @@ <h2><span class="section-number">42.14.12. </span>Visualising the Test set resul
 </div>
 </section>
 <section id="linear-regression-v-s-logistic-regression">
-<h2><span class="section-number">42.14.13. </span>Linear Regression v.s. Logistic Regression<a class="headerlink" href="#linear-regression-v-s-logistic-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.13. </span>Linear Regression v.s. Logistic Regression<a class="headerlink" href="#linear-regression-v-s-logistic-regression" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_classification</span>
@@ -2005,13 +2005,13 @@ <h2><span class="section-number">42.14.13. </span>Linear Regression v.s. Logisti
 </div>
 </section>
 <section id="assignment-1">
-<h2><span class="section-number">42.14.14. </span>Assignment - 1<a class="headerlink" href="#assignment-1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.14. </span>Assignment - 1<a class="headerlink" href="#assignment-1" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Build classification models：Predict the price range</p></li>
 </ul>
 </section>
 <section id="assignment-2">
-<h2><span class="section-number">42.14.15. </span>Assignment - 2<a class="headerlink" href="#assignment-2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.14.15. </span>Assignment - 2<a class="headerlink" href="#assignment-2" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Logistic Regression from scratch</p></li>
 </ul>
@@ -2055,13 +2055,13 @@ <h2><span class="section-number">42.14.15. </span>Assignment - 2<a class="header
         <i class="fas fa-angle-left"></i>
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-     41.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
+     40.97. Bitcoin LSTM Model with Tweet Volume and Sentiment
     </a>
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-     41.99. Google Stock Price Prediction RNN
+     40.99. Google Stock Price Prediction RNN
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-     41.102. Base/Denoising Autoencoder &amp; Dimension Reduction
+     40.102. Base/Denoising Autoencoder &amp; Dimension Reduction
     </a>
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     <a class="reference internal" href="../../assignments/deep-learning/autoencoder/variational-autoencoder-and-faces-generation.html">
-     41.103. Fun with Variational Autoencoders
+     40.103. Fun with Variational Autoencoders
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-     41.104. Time Series Forecasting Assignment
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-     41.106. Neural Networks for Classification with TensorFlow
+     40.106. Neural Networks for Classification with TensorFlow
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-     41.107. NN Classify 15 Fruits Assignment
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-     41.112. DQN On Foreign Exchange Market
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-     41.113. Art by gan
+     40.113. Art by gan
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     <a class="reference internal" href="../../assignments/deep-learning/gan/gan-introduction.html">
-     41.115. Generative Adversarial Networks (GANs)
+     40.115. Generative Adversarial Networks (GANs)
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     <a class="reference internal" href="../../assignments/deep-learning/overview/basic-classification-classify-images-of-clothing.html">
-     41.116. Basic classification: Classify images of clothing
+     40.116. Basic classification: Classify images of clothing
     </a>
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   </ul>
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@@ -1209,107 +1209,107 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
   <ul class="current">
    <li class="toctree-l2">
     <a class="reference internal" href="../python-programming/python-programming-introduction.html">
-     42.1. Python programming introduction
+     41.1. Python programming introduction
     </a>
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-     42.2. Python programming basics
+     41.2. Python programming basics
     </a>
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-     42.3. Python programming advanced
+     41.3. Python programming advanced
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/data-science-introduction.html">
-     42.4. Data Science introduction
+     41.4. Data Science introduction
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../data-science/relational-vs-non-relational-database.html">
-     42.5. Relational vs. non-relational database
+     41.5. Relational vs. non-relational database
     </a>
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    <li class="toctree-l2">
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-     42.6. NumPy and Pandas
+     41.6. NumPy and Pandas
     </a>
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+     41.7. Data visualization
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-     42.8. Data Science lifecycle
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-     42.9. Data Science in the cloud
+     41.9. Data Science in the cloud
     </a>
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-     42.10. Data Science in real world
+     41.10. Data Science in real world
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="ml-overview.html">
-     42.11. Machine Learning overview
+     41.11. Machine Learning overview
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    </li>
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-     42.12. Linear Regression
+     41.12. Linear Regression
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-     42.13. Logistic Regression
+     41.13. Logistic Regression
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-     42.14. Logistic Regression
+     41.14. Logistic Regression
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-     42.15. Neural Network
+     41.15. Neural Network
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-     42.16. Build an machine learning web application
+     41.16. Build an machine learning web application
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/unsupervised-learning.html">
-     42.17. Unsupervised learning
+     41.17. Unsupervised learning
     </a>
    </li>
    <li class="toctree-l2">
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-     42.18. Kernel method
+     41.18. Kernel method
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../ml-advanced/model-selection.html">
-     42.19. Model Selection
+     41.19. Model Selection
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
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     <a class="reference internal" href="../deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
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@@ -1575,101 +1575,101 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
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-   42.13.1. Introduction
+   41.13.1. Introduction
   </a>
  </li>
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   <a class="reference internal nav-link" href="#importing-the-libraries">
-   42.13.2. Importing the libraries
+   41.13.2. Importing the libraries
   </a>
  </li>
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-   42.13.3. Sigmoid function
+   41.13.3. Sigmoid function
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-   42.13.4. Importing the dataset
+   41.13.4. Importing the dataset
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-   42.13.5. Splitting the dataset into the Training set and Test set
+   41.13.5. Splitting the dataset into the Training set and Test set
   </a>
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-   42.13.6. Feature Scaling
+   41.13.6. Feature Scaling
   </a>
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   <a class="reference internal nav-link" href="#training-the-logistic-regression-model-on-the-training-set">
-   42.13.7. Training the Logistic Regression model on the Training set
+   41.13.7. Training the Logistic Regression model on the Training set
   </a>
  </li>
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   <a class="reference internal nav-link" href="#predicting-a-new-result">
-   42.13.8. Predicting a new result
+   41.13.8. Predicting a new result
   </a>
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   <a class="reference internal nav-link" href="#predicting-the-test-set-results">
-   42.13.9. Predicting the Test set results
+   41.13.9. Predicting the Test set results
   </a>
  </li>
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   <a class="reference internal nav-link" href="#making-the-confusion-matrix">
-   42.13.10. Making the Confusion Matrix
+   41.13.10. Making the Confusion Matrix
   </a>
  </li>
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-   42.13.11. Visualising the Training set results
+   41.13.11. Visualising the Training set results
   </a>
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-   42.13.12. Visualising the Test set results
+   41.13.12. Visualising the Test set results
   </a>
  </li>
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-   42.13.13. Linear Regression v.s. Logistic Regression
+   41.13.13. Linear Regression v.s. Logistic Regression
   </a>
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-   42.13.14. Logistic Regression from scratch
+   41.13.14. Logistic Regression from scratch
   </a>
  </li>
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-   42.13.15. Loss function for logistic regression
+   41.13.15. Loss function for logistic regression
   </a>
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#cross-entropy">
-     42.13.15.1. Cross-entropy
+     41.13.15.1. Cross-entropy
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#linear-separability">
-   42.13.16. Linear separability
+   41.13.16. Linear separability
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#thats-when-neural-network-comes-into-play">
-     42.13.16.1. That’s when Neural Network comes into play!
+     41.13.16.1. That’s when Neural Network comes into play!
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   42.13.17. Acknowledgments
+   41.13.17. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1693,101 +1693,101 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#introduction">
-   42.13.1. Introduction
+   41.13.1. Introduction
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-libraries">
-   42.13.2. Importing the libraries
+   41.13.2. Importing the libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#sigmoid-function">
-   42.13.3. Sigmoid function
+   41.13.3. Sigmoid function
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-dataset">
-   42.13.4. Importing the dataset
+   41.13.4. Importing the dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#splitting-the-dataset-into-the-training-set-and-test-set">
-   42.13.5. Splitting the dataset into the Training set and Test set
+   41.13.5. Splitting the dataset into the Training set and Test set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#feature-scaling">
-   42.13.6. Feature Scaling
+   41.13.6. Feature Scaling
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#training-the-logistic-regression-model-on-the-training-set">
-   42.13.7. Training the Logistic Regression model on the Training set
+   41.13.7. Training the Logistic Regression model on the Training set
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#predicting-a-new-result">
-   42.13.8. Predicting a new result
+   41.13.8. Predicting a new result
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#predicting-the-test-set-results">
-   42.13.9. Predicting the Test set results
+   41.13.9. Predicting the Test set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#making-the-confusion-matrix">
-   42.13.10. Making the Confusion Matrix
+   41.13.10. Making the Confusion Matrix
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualising-the-training-set-results">
-   42.13.11. Visualising the Training set results
+   41.13.11. Visualising the Training set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#visualising-the-test-set-results">
-   42.13.12. Visualising the Test set results
+   41.13.12. Visualising the Test set results
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#linear-regression-v-s-logistic-regression">
-   42.13.13. Linear Regression v.s. Logistic Regression
+   41.13.13. Linear Regression v.s. Logistic Regression
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#logistic-regression-from-scratch">
-   42.13.14. Logistic Regression from scratch
+   41.13.14. Logistic Regression from scratch
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loss-function-for-logistic-regression">
-   42.13.15. Loss function for logistic regression
+   41.13.15. Loss function for logistic regression
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#cross-entropy">
-     42.13.15.1. Cross-entropy
+     41.13.15.1. Cross-entropy
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#linear-separability">
-   42.13.16. Linear separability
+   41.13.16. Linear separability
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#thats-when-neural-network-comes-into-play">
-     42.13.16.1. That’s when Neural Network comes into play!
+     41.13.16.1. That’s when Neural Network comes into play!
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   42.13.17. Acknowledgments
+   41.13.17. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1823,9 +1823,9 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="logistic-regression">
-<h1><span class="section-number">42.13. </span>Logistic Regression<a class="headerlink" href="#logistic-regression" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.13. </span>Logistic Regression<a class="headerlink" href="#logistic-regression" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">42.13.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>In fact, logistic regression is a  <u>classification</u> algorithm, unlike other regression models.</p></li>
 <li><p>Logistic Regression is very important for entering deep learning.</p></li>
@@ -1833,7 +1833,7 @@ <h2><span class="section-number">42.13.1. </span>Introduction<a class="headerlin
 </ul>
 </section>
 <section id="importing-the-libraries">
-<h2><span class="section-number">42.13.2. </span>Importing the libraries<a class="headerlink" href="#importing-the-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.2. </span>Importing the libraries<a class="headerlink" href="#importing-the-libraries" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -1846,7 +1846,7 @@ <h2><span class="section-number">42.13.2. </span>Importing the libraries<a class
 </div>
 </section>
 <section id="sigmoid-function">
-<h2><span class="section-number">42.13.3. </span>Sigmoid function<a class="headerlink" href="#sigmoid-function" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.3. </span>Sigmoid function<a class="headerlink" href="#sigmoid-function" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">sigmoid</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
@@ -1865,7 +1865,7 @@ <h2><span class="section-number">42.13.3. </span>Sigmoid function<a class="heade
 </div>
 </section>
 <section id="importing-the-dataset">
-<h2><span class="section-number">42.13.4. </span>Importing the dataset<a class="headerlink" href="#importing-the-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.4. </span>Importing the dataset<a class="headerlink" href="#importing-the-dataset" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;../../assets/data/Social_Network_Ads.csv&#39;</span><span class="p">)</span>
@@ -1879,7 +1879,7 @@ <h2><span class="section-number">42.13.4. </span>Importing the dataset<a class="
 </div>
 </section>
 <section id="splitting-the-dataset-into-the-training-set-and-test-set">
-<h2><span class="section-number">42.13.5. </span>Splitting the dataset into the Training set and Test set<a class="headerlink" href="#splitting-the-dataset-into-the-training-set-and-test-set" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.5. </span>Splitting the dataset into the Training set and Test set<a class="headerlink" href="#splitting-the-dataset-into-the-training-set-and-test-set" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
@@ -1890,7 +1890,7 @@ <h2><span class="section-number">42.13.5. </span>Splitting the dataset into the
 </div>
 </section>
 <section id="feature-scaling">
-<h2><span class="section-number">42.13.6. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.6. </span>Feature Scaling<a class="headerlink" href="#feature-scaling" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
@@ -1903,7 +1903,7 @@ <h2><span class="section-number">42.13.6. </span>Feature Scaling<a class="header
 </div>
 </section>
 <section id="training-the-logistic-regression-model-on-the-training-set">
-<h2><span class="section-number">42.13.7. </span>Training the Logistic Regression model on the Training set<a class="headerlink" href="#training-the-logistic-regression-model-on-the-training-set" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.7. </span>Training the Logistic Regression model on the Training set<a class="headerlink" href="#training-the-logistic-regression-model-on-the-training-set" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
@@ -1915,7 +1915,7 @@ <h2><span class="section-number">42.13.7. </span>Training the Logistic Regressio
 </div>
 </section>
 <section id="predicting-a-new-result">
-<h2><span class="section-number">42.13.8. </span>Predicting a new result<a class="headerlink" href="#predicting-a-new-result" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.8. </span>Predicting a new result<a class="headerlink" href="#predicting-a-new-result" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">transform</span><span class="p">([[</span><span class="mi">30</span><span class="p">,</span> <span class="mi">87000</span><span class="p">],</span> <span class="p">[</span><span class="mi">65</span><span class="p">,</span> <span class="mi">990000</span><span class="p">]])))</span>
@@ -1925,7 +1925,7 @@ <h2><span class="section-number">42.13.8. </span>Predicting a new result<a class
 </div>
 </section>
 <section id="predicting-the-test-set-results">
-<h2><span class="section-number">42.13.9. </span>Predicting the Test set results<a class="headerlink" href="#predicting-the-test-set-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.9. </span>Predicting the Test set results<a class="headerlink" href="#predicting-the-test-set-results" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
@@ -1936,7 +1936,7 @@ <h2><span class="section-number">42.13.9. </span>Predicting the Test set results
 </div>
 </section>
 <section id="making-the-confusion-matrix">
-<h2><span class="section-number">42.13.10. </span>Making the Confusion Matrix<a class="headerlink" href="#making-the-confusion-matrix" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.10. </span>Making the Confusion Matrix<a class="headerlink" href="#making-the-confusion-matrix" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">confusion_matrix</span><span class="p">,</span> <span class="n">accuracy_score</span>
@@ -1949,7 +1949,7 @@ <h2><span class="section-number">42.13.10. </span>Making the Confusion Matrix<a
 </div>
 </section>
 <section id="visualising-the-training-set-results">
-<h2><span class="section-number">42.13.11. </span>Visualising the Training set results<a class="headerlink" href="#visualising-the-training-set-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.11. </span>Visualising the Training set results<a class="headerlink" href="#visualising-the-training-set-results" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib.colors</span> <span class="kn">import</span> <span class="n">ListedColormap</span>
@@ -1973,7 +1973,7 @@ <h2><span class="section-number">42.13.11. </span>Visualising the Training set r
 </div>
 </section>
 <section id="visualising-the-test-set-results">
-<h2><span class="section-number">42.13.12. </span>Visualising the Test set results<a class="headerlink" href="#visualising-the-test-set-results" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.12. </span>Visualising the Test set results<a class="headerlink" href="#visualising-the-test-set-results" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib.colors</span> <span class="kn">import</span> <span class="n">ListedColormap</span>
@@ -1997,7 +1997,7 @@ <h2><span class="section-number">42.13.12. </span>Visualising the Test set resul
 </div>
 </section>
 <section id="linear-regression-v-s-logistic-regression">
-<h2><span class="section-number">42.13.13. </span>Linear Regression v.s. Logistic Regression<a class="headerlink" href="#linear-regression-v-s-logistic-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.13. </span>Linear Regression v.s. Logistic Regression<a class="headerlink" href="#linear-regression-v-s-logistic-regression" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_classification</span>
@@ -2053,7 +2053,7 @@ <h2><span class="section-number">42.13.13. </span>Linear Regression v.s. Logisti
 </div>
 </section>
 <section id="logistic-regression-from-scratch">
-<h2><span class="section-number">42.13.14. </span>Logistic Regression from scratch<a class="headerlink" href="#logistic-regression-from-scratch" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.14. </span>Logistic Regression from scratch<a class="headerlink" href="#logistic-regression-from-scratch" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyOwnLogisticRegression</span><span class="p">:</span>
@@ -2122,9 +2122,9 @@ <h2><span class="section-number">42.13.14. </span>Logistic Regression from scrat
 </div>
 </section>
 <section id="loss-function-for-logistic-regression">
-<h2><span class="section-number">42.13.15. </span>Loss function for logistic regression<a class="headerlink" href="#loss-function-for-logistic-regression" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.15. </span>Loss function for logistic regression<a class="headerlink" href="#loss-function-for-logistic-regression" title="Permalink to this headline">#</a></h2>
 <section id="cross-entropy">
-<h3><span class="section-number">42.13.15.1. </span>Cross-entropy<a class="headerlink" href="#cross-entropy" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.13.15.1. </span>Cross-entropy<a class="headerlink" href="#cross-entropy" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">loss</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
@@ -2136,7 +2136,7 @@ <h3><span class="section-number">42.13.15.1. </span>Cross-entropy<a class="heade
 </section>
 </section>
 <section id="linear-separability">
-<h2><span class="section-number">42.13.16. </span>Linear separability<a class="headerlink" href="#linear-separability" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.16. </span>Linear separability<a class="headerlink" href="#linear-separability" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_moons</span>
@@ -2160,11 +2160,11 @@ <h2><span class="section-number">42.13.16. </span>Linear separability<a class="h
 </div>
 </div>
 <section id="thats-when-neural-network-comes-into-play">
-<h3><span class="section-number">42.13.16.1. </span>That’s when Neural Network comes into play!<a class="headerlink" href="#thats-when-neural-network-comes-into-play" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.13.16.1. </span>That’s when Neural Network comes into play!<a class="headerlink" href="#thats-when-neural-network-comes-into-play" title="Permalink to this headline">#</a></h3>
 </section>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">42.13.17. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.13.17. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to ERENCAN  for creating the open-source <a class="reference external" href="https://www.kaggle.com/code/erncncbk/logistic-regression-tutorial">Kaggle jupyter notebook</a>, licensed under Apache 2.0. It inspires the majority of the content of this assignment.</p>
 </section>
 </section>
@@ -2206,13 +2206,13 @@ <h2><span class="section-number">42.13.17. </span>Acknowledgments<a class="heade
         <i class="fas fa-angle-left"></i>
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             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">42.12. </span>Linear Regression</p>
+            <p class="prev-next-title"><span class="section-number">41.12. </span>Linear Regression</p>
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-        <p class="prev-next-title"><span class="section-number">42.14. </span>Logistic Regression</p>
+        <p class="prev-next-title"><span class="section-number">41.14. </span>Logistic Regression</p>
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diff --git a/slides/ml-fundamentals/ml-overview.html b/slides/ml-fundamentals/ml-overview.html
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@@ -6,7 +6,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>42.11. Machine Learning overview &#8212; Ocademy Open Machine Learning Book</title>
+    <title>41.11. Machine Learning overview &#8212; Ocademy Open Machine Learning Book</title>
     
   <!-- Loaded before other Sphinx assets -->
   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="42.12. Linear Regression" href="linear-regression.html" />
-    <link rel="prev" title="42.10. Data Science in real world" href="../data-science/data-science-in-real-world.html" />
+    <link rel="next" title="41.12. Linear Regression" href="linear-regression.html" />
+    <link rel="prev" title="41.10. Data Science in real world" href="../data-science/data-science-in-real-world.html" />
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-     41.13. Apply your skills
+     40.13. Apply your skills
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-     41.14. Try it in Excel
+     40.14. Try it in Excel
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-     41.20. Displaying airport data
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-     41.21. Soda profits
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-     41.35. Tell a story
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-     41.36. Explore a planetary computer dataset
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-     41.45. Linear regression - California Housing
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-     41.47. Loss Function
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-     42.11.16.1. Labels
+     41.11.16.1. Labels
     </a>
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+     41.11.17.1. Features
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   </ul>
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-     42.11.18.1. Examples
+     41.11.18.1. Examples
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-     42.11.19.1. Examples
+     41.11.19.1. Examples
     </a>
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-     42.11.20.1. Models
+     41.11.20.1. Models
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-   42.11.21. Types of Machine Learning Systems
+   41.11.21. Types of Machine Learning Systems
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-     42.11.22.1. Supervised Learning
+     41.11.22.1. Supervised Learning
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-     42.11.23.1. Supervised Learning
+     41.11.23.1. Supervised Learning
     </a>
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-     42.11.24.1. Supervised Learning
+     41.11.24.1. Supervised Learning
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-     42.11.25.1. Unsupervised Learning
+     41.11.25.1. Unsupervised Learning
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     <a class="reference internal nav-link" href="#reinforcement-learning">
-     42.11.26.1. Reinforcement Learning
+     41.11.26.1. Reinforcement Learning
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    </li>
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-   42.11.27. Typical Machine Learning Workflow
+   41.11.27. Typical Machine Learning Workflow
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-     42.11.28.1. Defining and formulating a problem
+     41.11.28.1. Defining and formulating a problem
     </a>
    </li>
   </ul>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id24">
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-     42.11.29.1. Collecting Data
+     41.11.29.1. Collecting Data
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   </ul>
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   <a class="reference internal nav-link" href="#id25">
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+   41.11.30. Typical Machine Learning Workflow
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     <a class="reference internal nav-link" href="#establishing-a-baseline">
-     42.11.30.1. Establishing a Baseline
+     41.11.30.1. Establishing a Baseline
     </a>
    </li>
   </ul>
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+   41.11.31. Typical Machine Learning Workflow
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     <a class="reference internal nav-link" href="#exploratory-data-analysis-eda">
-     42.11.31.1. Exploratory data analysis(EDA)
+     41.11.31.1. Exploratory data analysis(EDA)
     </a>
    </li>
   </ul>
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+   41.11.32. Typical Machine Learning Workflow
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     <a class="reference internal nav-link" href="#data-preprocessing">
-     42.11.32.1. Data Preprocessing
+     41.11.32.1. Data Preprocessing
     </a>
    </li>
   </ul>
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   <a class="reference internal nav-link" href="#id28">
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+   41.11.33. Typical Machine Learning Workflow
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     <a class="reference internal nav-link" href="#selecting-and-training-a-model">
-     42.11.33.1. Selecting and Training a Model
+     41.11.33.1. Selecting and Training a Model
     </a>
    </li>
   </ul>
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-     42.11.34.1. Performing Error Analysis
+     41.11.34.1. Performing Error Analysis
     </a>
    </li>
   </ul>
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-     42.11.35.1. Deploying a Model
+     41.11.35.1. Deploying a Model
     </a>
    </li>
   </ul>
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   <a class="reference internal nav-link" href="#thank-you">
-   42.11.36. Thank you!
+   41.11.36. Thank you!
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 </ul>
@@ -1916,322 +1916,322 @@ <h2> Contents </h2>
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+   41.11.1. Table of Content
   </a>
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   <a class="reference internal nav-link" href="#what-is-machine-learning">
-   42.11.2. What is Machine Learning
+   41.11.2. What is Machine Learning
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+   41.11.4. What is Machine Learning
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-   42.11.5. What is Machine Learning
+   41.11.5. What is Machine Learning
   </a>
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   <a class="reference internal nav-link" href="#artificial-intelligence-data-science-machine-learning-and-deep-learning">
-   42.11.6. Artificial Intelligence, Data Science, Machine Learning, and Deep Learning
+   41.11.6. Artificial Intelligence, Data Science, Machine Learning, and Deep Learning
   </a>
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   <a class="reference internal nav-link" href="#id4">
-   42.11.7. Artificial Intelligence, Data Science, Machine Learning, and Deep Learning
+   41.11.7. Artificial Intelligence, Data Science, Machine Learning, and Deep Learning
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-   42.11.8. Artificial Intelligence, Data Science, Machine Learning, and Deep Learning
+   41.11.8. Artificial Intelligence, Data Science, Machine Learning, and Deep Learning
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-   42.11.9. Artificial Intelligence, Data Science, Machine Learning, and Deep Learning
+   41.11.9. Artificial Intelligence, Data Science, Machine Learning, and Deep Learning
   </a>
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-   42.11.10. Ordinary Programming vs Machine Learning
+   41.11.10. Ordinary Programming vs Machine Learning
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-     42.11.10.1. Ordinary Programming
+     41.11.10.1. Ordinary Programming
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+   41.11.11. Ordinary Programming vs Machine Learning
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-     42.11.11.1. Machine Learning
+     41.11.11.1. Machine Learning
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+   41.11.12. Ordinary Programming vs Machine Learning
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+   41.11.13. Ordinary Programming vs Machine Learning
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-   42.11.14. Ordinary Programming vs Machine Learning
+   41.11.14. Ordinary Programming vs Machine Learning
   </a>
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-   42.11.15. Applications of Machine Learning
+   41.11.15. Applications of Machine Learning
   </a>
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-   42.11.16. Key terminology
+   41.11.16. Key terminology
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     <a class="reference internal nav-link" href="#labels">
-     42.11.16.1. Labels
+     41.11.16.1. Labels
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+   41.11.17. Key terminology
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-     42.11.17.1. Features
+     41.11.17.1. Features
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-   42.11.18. Key terminology
+   41.11.18. Key terminology
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+     41.11.18.1. Examples
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+   41.11.19. Key terminology
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     <a class="reference internal nav-link" href="#id14">
-     42.11.19.1. Examples
+     41.11.19.1. Examples
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-   42.11.20. Key terminology
+   41.11.20. Key terminology
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     <a class="reference internal nav-link" href="#models">
-     42.11.20.1. Models
+     41.11.20.1. Models
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    </li>
   </ul>
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-   42.11.21. Types of Machine Learning Systems
+   41.11.21. Types of Machine Learning Systems
   </a>
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-   42.11.22. Types of Machine Learning Systems
+   41.11.22. Types of Machine Learning Systems
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     <a class="reference internal nav-link" href="#supervised-learning">
-     42.11.22.1. Supervised Learning
+     41.11.22.1. Supervised Learning
     </a>
    </li>
   </ul>
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  <li class="toc-h2 nav-item toc-entry">
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-   42.11.23. Types of Machine Learning Systems
+   41.11.23. Types of Machine Learning Systems
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     <a class="reference internal nav-link" href="#id18">
-     42.11.23.1. Supervised Learning
+     41.11.23.1. Supervised Learning
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   </ul>
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+   41.11.24. Types of Machine Learning Systems
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     <a class="reference internal nav-link" href="#id20">
-     42.11.24.1. Supervised Learning
+     41.11.24.1. Supervised Learning
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    </li>
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+   41.11.25. Types of Machine Learning Systems
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     <a class="reference internal nav-link" href="#unsupervised-learning">
-     42.11.25.1. Unsupervised Learning
+     41.11.25.1. Unsupervised Learning
     </a>
    </li>
   </ul>
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   <a class="reference internal nav-link" href="#id22">
-   42.11.26. Types of Machine Learning Systems
+   41.11.26. Types of Machine Learning Systems
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#reinforcement-learning">
-     42.11.26.1. Reinforcement Learning
+     41.11.26.1. Reinforcement Learning
     </a>
    </li>
   </ul>
  </li>
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   <a class="reference internal nav-link" href="#typical-machine-learning-workflow">
-   42.11.27. Typical Machine Learning Workflow
+   41.11.27. Typical Machine Learning Workflow
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+   41.11.28. Typical Machine Learning Workflow
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-     42.11.28.1. Defining and formulating a problem
+     41.11.28.1. Defining and formulating a problem
     </a>
    </li>
   </ul>
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+   41.11.29. Typical Machine Learning Workflow
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     <a class="reference internal nav-link" href="#collecting-data">
-     42.11.29.1. Collecting Data
+     41.11.29.1. Collecting Data
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    </li>
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+   41.11.30. Typical Machine Learning Workflow
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     <a class="reference internal nav-link" href="#establishing-a-baseline">
-     42.11.30.1. Establishing a Baseline
+     41.11.30.1. Establishing a Baseline
     </a>
    </li>
   </ul>
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+   41.11.31. Typical Machine Learning Workflow
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#exploratory-data-analysis-eda">
-     42.11.31.1. Exploratory data analysis(EDA)
+     41.11.31.1. Exploratory data analysis(EDA)
     </a>
    </li>
   </ul>
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+   41.11.32. Typical Machine Learning Workflow
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     <a class="reference internal nav-link" href="#data-preprocessing">
-     42.11.32.1. Data Preprocessing
+     41.11.32.1. Data Preprocessing
     </a>
    </li>
   </ul>
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-   42.11.33. Typical Machine Learning Workflow
+   41.11.33. Typical Machine Learning Workflow
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#selecting-and-training-a-model">
-     42.11.33.1. Selecting and Training a Model
+     41.11.33.1. Selecting and Training a Model
     </a>
    </li>
   </ul>
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-   42.11.34. Typical Machine Learning Workflow
+   41.11.34. Typical Machine Learning Workflow
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    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#performing-error-analysis">
-     42.11.34.1. Performing Error Analysis
+     41.11.34.1. Performing Error Analysis
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id30">
-   42.11.35. Typical Machine Learning Workflow
+   41.11.35. Typical Machine Learning Workflow
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#deploying-a-model">
-     42.11.35.1. Deploying a Model
+     41.11.35.1. Deploying a Model
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#thank-you">
-   42.11.36. Thank you!
+   41.11.36. Thank you!
   </a>
  </li>
 </ul>
@@ -2260,9 +2260,9 @@ <h2> Contents </h2>
 </div></div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="machine-learning-overview">
-<h1><span class="section-number">42.11. </span>Machine Learning overview<a class="headerlink" href="#machine-learning-overview" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.11. </span>Machine Learning overview<a class="headerlink" href="#machine-learning-overview" title="Permalink to this headline">#</a></h1>
 <section id="table-of-content">
-<h2><span class="section-number">42.11.1. </span>Table of Content<a class="headerlink" href="#table-of-content" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.1. </span>Table of Content<a class="headerlink" href="#table-of-content" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>What is machine learning?</p></li>
 <li><p>Difference between AI, data science, ML and DL</p></li>
@@ -2274,14 +2274,14 @@ <h2><span class="section-number">42.11.1. </span>Table of Content<a class="heade
 </ul>
 </section>
 <section id="what-is-machine-learning">
-<h2><span class="section-number">42.11.2. </span>What is Machine Learning<a class="headerlink" href="#what-is-machine-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.2. </span>What is Machine Learning<a class="headerlink" href="#what-is-machine-learning" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Machine learning is a new programming paradigm in which instead of explicitly programming computers to perform some tasks, we let them learn from data in order to find the underlying patterns in the data</p></li>
 <li><p>In few words, machine learning is the science of giving the machine the ability to reason about the data</p></li>
 </ul>
 </section>
 <section id="id1">
-<h2><span class="section-number">42.11.3. </span>What is Machine Learning<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.3. </span>What is Machine Learning<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>The term <a class="reference external" href="https://en.wikipedia.org/wiki/Machine_learning">machine learning</a> was coined by Arthur Samuel in 1959. At that time, Arthur defined machine learning as a:</p></li>
 </ul>
@@ -2290,7 +2290,7 @@ <h2><span class="section-number">42.11.3. </span>What is Machine Learning<a clas
 </div></blockquote>
 </section>
 <section id="id2">
-<h2><span class="section-number">42.11.4. </span>What is Machine Learning<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.4. </span>What is Machine Learning<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>A more technical definition of machine learning was provided by Tom M. Mitchell in 1997. Here is how Tom defined machine learning:</p></li>
 </ul>
@@ -2299,7 +2299,7 @@ <h2><span class="section-number">42.11.4. </span>What is Machine Learning<a clas
 </div></blockquote>
 </section>
 <section id="id3">
-<h2><span class="section-number">42.11.5. </span>What is Machine Learning<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.5. </span>What is Machine Learning<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Wikipedia provides a much clearer definition of machine learning:</p></li>
 </ul>
@@ -2311,34 +2311,34 @@ <h2><span class="section-number">42.11.5. </span>What is Machine Learning<a clas
 </ul>
 </section>
 <section id="artificial-intelligence-data-science-machine-learning-and-deep-learning">
-<h2><span class="section-number">42.11.6. </span>Artificial Intelligence, Data Science, Machine Learning, and Deep Learning<a class="headerlink" href="#artificial-intelligence-data-science-machine-learning-and-deep-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.6. </span>Artificial Intelligence, Data Science, Machine Learning, and Deep Learning<a class="headerlink" href="#artificial-intelligence-data-science-machine-learning-and-deep-learning" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>AI is a branch of computer science concerned with building intelligent machines capable of performing tasks at the level of human. AI seeks to mimic human. AI is an interdisciplinary field that involves machine learning, programming, robotics, data science, etc…</p></li>
 </ul>
 </section>
 <section id="id4">
-<h2><span class="section-number">42.11.7. </span>Artificial Intelligence, Data Science, Machine Learning, and Deep Learning<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.7. </span>Artificial Intelligence, Data Science, Machine Learning, and Deep Learning<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Machine learning on the other hand is the branch of AI and as we saw, it is concerned with giving the machine the ability to learn from data. Machine learning algorithms consists of shallow or classical algorithms such as decision trees and deep learning algorithms such as convolutional neural networks</p></li>
 </ul>
 </section>
 <section id="id5">
-<h2><span class="section-number">42.11.8. </span>Artificial Intelligence, Data Science, Machine Learning, and Deep Learning<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.8. </span>Artificial Intelligence, Data Science, Machine Learning, and Deep Learning<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Deep learning is a branch of machine learning that deals with the study of artificial neural networks and it was inspired by the human brain. Classical machine learning algorithms needs a lot of feature engineering, but deep learning algorithms can extract features in huge amount of data such as images themselves</p></li>
 </ul>
 </section>
 <section id="id6">
-<h2><span class="section-number">42.11.9. </span>Artificial Intelligence, Data Science, Machine Learning, and Deep Learning<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.9. </span>Artificial Intelligence, Data Science, Machine Learning, and Deep Learning<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>The difference between AI, machine learning and deep learning</p></li>
 </ul>
 <p><img alt="" src="../../_images/ai-ml-dl.png" /></p>
 </section>
 <section id="ordinary-programming-vs-machine-learning">
-<h2><span class="section-number">42.11.10. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#ordinary-programming-vs-machine-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.10. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#ordinary-programming-vs-machine-learning" title="Permalink to this headline">#</a></h2>
 <section id="ordinary-programming">
-<h3><span class="section-number">42.11.10.1. </span>Ordinary Programming<a class="headerlink" href="#ordinary-programming" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.10.1. </span>Ordinary Programming<a class="headerlink" href="#ordinary-programming" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>In ordinary programming, the job of the programmer is to clearly write every single rule that makes up the task he/she is trying to accomplish</p></li>
 <li><p>In order to get the results, she/he must write all rules that acts up on the data</p></li>
@@ -2346,55 +2346,55 @@ <h3><span class="section-number">42.11.10.1. </span>Ordinary Programming<a class
 </section>
 </section>
 <section id="id7">
-<h2><span class="section-number">42.11.11. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.11. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h2>
 <section id="machine-learning">
-<h3><span class="section-number">42.11.11.1. </span>Machine Learning<a class="headerlink" href="#machine-learning" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.11.1. </span>Machine Learning<a class="headerlink" href="#machine-learning" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Machine learning flips that. Instead of having to write the rules that makes up a particular application, we can feed data and results(or labels) to the machine learning model, and its job can be to determine the set of rules that map the data and labels</p></li>
 </ul>
 </section>
 </section>
 <section id="id8">
-<h2><span class="section-number">42.11.12. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.12. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Traditional Programming vs Deep Learning</p></li>
 </ul>
 <p><img alt="" src="../../_images/traditional-ml.png" /></p>
 </section>
 <section id="id9">
-<h2><span class="section-number">42.11.13. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.13. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Let’s take a real world example. If you wanted to build an application that given a picture of person can determine if he/she is wearing or not wearing a facemask, you can just feed a bunch of images of people with and without facemasks to the machine learning model, and the model can learn the rules or patterns that map the images to whether they have a facemask or not</p></li>
 <li><p>You can even extend that further and use those learned rules to recognize facemasks in the images that were never seen by the that model</p></li>
 </ul>
 </section>
 <section id="id10">
-<h2><span class="section-number">42.11.14. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.14. </span>Ordinary Programming vs Machine Learning<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Approaching a facemask recognition problem with rule based programming would really be a hard problem. You would have to write lots of code that would later turn out to not work typically because your program will be tested on different kinds of facemasks and people in various scenarios and it’s almost impossible to express that in rules</p></li>
 <li><p>Whereas with machine learning, all you need is a bunch of images of people with and without facemasks, and there you are few steps away from getting an effective facemask recognizer</p></li>
 </ul>
 </section>
 <section id="applications-of-machine-learning">
-<h2><span class="section-number">42.11.15. </span>Applications of Machine Learning<a class="headerlink" href="#applications-of-machine-learning" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.15. </span>Applications of Machine Learning<a class="headerlink" href="#applications-of-machine-learning" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Machine learning has transformed many industries, from banking, manufacturing, streaming, autonomous vehicles, agriculture, etc…In fact, most of the tech products and services we use daily possess some sorts of machine learning algorithms running in their backgrounds</p></li>
 <li><p>Fraud detection, Loan repayment prediction, Diagnozing diseases and predicting the survival rate, Detecting defects in industry, Churn prediction, Spam detection, Autonomous vehicles, etc.</p></li>
 </ul>
 </section>
 <section id="key-terminology">
-<h2><span class="section-number">42.11.16. </span>Key terminology<a class="headerlink" href="#key-terminology" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.16. </span>Key terminology<a class="headerlink" href="#key-terminology" title="Permalink to this headline">#</a></h2>
 <section id="labels">
-<h3><span class="section-number">42.11.16.1. </span>Labels<a class="headerlink" href="#labels" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.16.1. </span>Labels<a class="headerlink" href="#labels" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>A <code class="docutils literal notranslate"><span class="pre">label</span></code> is the thing we’re predicting—the <code class="docutils literal notranslate"><span class="pre">y</span></code> variable in simple linear regression. The label could be the future price of wheat, the kind of animal shown in a picture, the meaning of an audio clip, or just about anything.</p></li>
 </ul>
 </section>
 </section>
 <section id="id11">
-<h2><span class="section-number">42.11.17. </span>Key terminology<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.17. </span>Key terminology<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h2>
 <section id="features">
-<h3><span class="section-number">42.11.17.1. </span>Features<a class="headerlink" href="#features" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.17.1. </span>Features<a class="headerlink" href="#features" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>A <code class="docutils literal notranslate"><span class="pre">feature</span></code> is an input variable—the <code class="docutils literal notranslate"><span class="pre">x</span></code> variable in simple linear regression. A simple machine learning project might use a single feature, while a more sophisticated machine learning project could use millions of features, specified as:</p></li>
 </ul>
@@ -2403,9 +2403,9 @@ <h3><span class="section-number">42.11.17.1. </span>Features<a class="headerlink
 </section>
 </section>
 <section id="id12">
-<h2><span class="section-number">42.11.18. </span>Key terminology<a class="headerlink" href="#id12" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.18. </span>Key terminology<a class="headerlink" href="#id12" title="Permalink to this headline">#</a></h2>
 <section id="examples">
-<h3><span class="section-number">42.11.18.1. </span>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.18.1. </span>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>An <code class="docutils literal notranslate"><span class="pre">example</span></code> is a particular instance of data, <strong><code class="docutils literal notranslate"><span class="pre">x</span></code></strong>. (We put <strong><code class="docutils literal notranslate"><span class="pre">x</span></code></strong> in boldface to indicate that it is a vector.) We break examples into two categories:</p>
 <ul>
@@ -2417,9 +2417,9 @@ <h3><span class="section-number">42.11.18.1. </span>Examples<a class="headerlink
 </section>
 </section>
 <section id="id13">
-<h2><span class="section-number">42.11.19. </span>Key terminology<a class="headerlink" href="#id13" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.19. </span>Key terminology<a class="headerlink" href="#id13" title="Permalink to this headline">#</a></h2>
 <section id="id14">
-<h3><span class="section-number">42.11.19.1. </span>Examples<a class="headerlink" href="#id14" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.19.1. </span>Examples<a class="headerlink" href="#id14" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>A <strong><code class="docutils literal notranslate"><span class="pre">labeled</span> <span class="pre">example</span></code></strong> includes both feature(s) and the label. That is:</p></li>
 </ul>
@@ -2432,9 +2432,9 @@ <h3><span class="section-number">42.11.19.1. </span>Examples<a class="headerlink
 </section>
 </section>
 <section id="id15">
-<h2><span class="section-number">42.11.20. </span>Key terminology<a class="headerlink" href="#id15" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.20. </span>Key terminology<a class="headerlink" href="#id15" title="Permalink to this headline">#</a></h2>
 <section id="models">
-<h3><span class="section-number">42.11.20.1. </span>Models<a class="headerlink" href="#models" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.20.1. </span>Models<a class="headerlink" href="#models" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>A model defines the relationship between features and label. For example, a spam detection model might associate certain features strongly with “spam”. Let’s highlight two phases of a model’s life:</p>
 <ul>
@@ -2446,7 +2446,7 @@ <h3><span class="section-number">42.11.20.1. </span>Models<a class="headerlink"
 </section>
 </section>
 <section id="types-of-machine-learning-systems">
-<h2><span class="section-number">42.11.21. </span>Types of Machine Learning Systems<a class="headerlink" href="#types-of-machine-learning-systems" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.21. </span>Types of Machine Learning Systems<a class="headerlink" href="#types-of-machine-learning-systems" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>In broad, there are 3 main types of machine learning systems that are:</p>
 <ul>
@@ -2458,9 +2458,9 @@ <h2><span class="section-number">42.11.21. </span>Types of Machine Learning Syst
 </ul>
 </section>
 <section id="id16">
-<h2><span class="section-number">42.11.22. </span>Types of Machine Learning Systems<a class="headerlink" href="#id16" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.22. </span>Types of Machine Learning Systems<a class="headerlink" href="#id16" title="Permalink to this headline">#</a></h2>
 <section id="supervised-learning">
-<h3><span class="section-number">42.11.22.1. </span>Supervised Learning<a class="headerlink" href="#supervised-learning" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.22.1. </span>Supervised Learning<a class="headerlink" href="#supervised-learning" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Most machine learning tasks fall into supervised learning type</p></li>
 <li><p>Trained with input data along with some form of guidance that we can call labels</p></li>
@@ -2470,9 +2470,9 @@ <h3><span class="section-number">42.11.22.1. </span>Supervised Learning<a class=
 </section>
 </section>
 <section id="id17">
-<h2><span class="section-number">42.11.23. </span>Types of Machine Learning Systems<a class="headerlink" href="#id17" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.23. </span>Types of Machine Learning Systems<a class="headerlink" href="#id17" title="Permalink to this headline">#</a></h2>
 <section id="id18">
-<h3><span class="section-number">42.11.23.1. </span>Supervised Learning<a class="headerlink" href="#id18" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.23.1. </span>Supervised Learning<a class="headerlink" href="#id18" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>there are 2 main kinds of supervised learning problems:</p>
 <ul>
@@ -2484,16 +2484,16 @@ <h3><span class="section-number">42.11.23.1. </span>Supervised Learning<a class=
 </section>
 </section>
 <section id="id19">
-<h2><span class="section-number">42.11.24. </span>Types of Machine Learning Systems<a class="headerlink" href="#id19" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.24. </span>Types of Machine Learning Systems<a class="headerlink" href="#id19" title="Permalink to this headline">#</a></h2>
 <section id="id20">
-<h3><span class="section-number">42.11.24.1. </span>Supervised Learning<a class="headerlink" href="#id20" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.24.1. </span>Supervised Learning<a class="headerlink" href="#id20" title="Permalink to this headline">#</a></h3>
 <p><img alt="" src="../../_images/class-reg.png" /></p>
 </section>
 </section>
 <section id="id21">
-<h2><span class="section-number">42.11.25. </span>Types of Machine Learning Systems<a class="headerlink" href="#id21" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.25. </span>Types of Machine Learning Systems<a class="headerlink" href="#id21" title="Permalink to this headline">#</a></h2>
 <section id="unsupervised-learning">
-<h3><span class="section-number">42.11.25.1. </span>Unsupervised Learning<a class="headerlink" href="#unsupervised-learning" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.25.1. </span>Unsupervised Learning<a class="headerlink" href="#unsupervised-learning" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Unsupervised learning algorithms are trained on unlabelled data</p></li>
 <li><p>Unsupervised learning algorithms are primarily used for:</p>
@@ -2506,9 +2506,9 @@ <h3><span class="section-number">42.11.25.1. </span>Unsupervised Learning<a clas
 </section>
 </section>
 <section id="id22">
-<h2><span class="section-number">42.11.26. </span>Types of Machine Learning Systems<a class="headerlink" href="#id22" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.26. </span>Types of Machine Learning Systems<a class="headerlink" href="#id22" title="Permalink to this headline">#</a></h2>
 <section id="reinforcement-learning">
-<h3><span class="section-number">42.11.26.1. </span>Reinforcement Learning<a class="headerlink" href="#reinforcement-learning" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.26.1. </span>Reinforcement Learning<a class="headerlink" href="#reinforcement-learning" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Mostly applied in robotics and games</p></li>
 <li><p>A learning system called <em>an agent</em> can perceives the environment, performs some actions, and gets <em>rewarded</em> or <em>penalized</em> depending on how it is performing. The main goal of the agent is to accumulate as much as rewards as possible.</p></li>
@@ -2517,7 +2517,7 @@ <h3><span class="section-number">42.11.26.1. </span>Reinforcement Learning<a cla
 </section>
 </section>
 <section id="typical-machine-learning-workflow">
-<h2><span class="section-number">42.11.27. </span>Typical Machine Learning Workflow<a class="headerlink" href="#typical-machine-learning-workflow" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.27. </span>Typical Machine Learning Workflow<a class="headerlink" href="#typical-machine-learning-workflow" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Overall, a typical machine learning project workflow consists of:</p>
 <ul>
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 </ul>
 </section>
 <section id="id23">
-<h2><span class="section-number">42.11.28. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id23" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.28. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id23" title="Permalink to this headline">#</a></h2>
 <section id="defining-and-formulating-a-problem">
-<h3><span class="section-number">42.11.28.1. </span>Defining and formulating a problem<a class="headerlink" href="#defining-and-formulating-a-problem" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.28.1. </span>Defining and formulating a problem<a class="headerlink" href="#defining-and-formulating-a-problem" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Understanding the problem is all about diving deep into the details of the problem at hand and asking the right questions</p></li>
 <li><p>First, it’s important to narrow down the problem until you can have a simple and a well-defined goal. Here are examples of simple goals: To <em>classify</em> products into different categories, to <em>predict</em> the price of a used car given its features (such as brand, age, etc…), to <em>recognize</em> if a person is wearing a facemask, to <em>divide</em> customers into different groups that share similar behaviors, etc…</p></li>
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 </section>
 </section>
 <section id="id24">
-<h2><span class="section-number">42.11.29. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id24" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.29. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id24" title="Permalink to this headline">#</a></h2>
 <section id="collecting-data">
-<h3><span class="section-number">42.11.29.1. </span>Collecting Data<a class="headerlink" href="#collecting-data" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.29.1. </span>Collecting Data<a class="headerlink" href="#collecting-data" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
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 </section>
 </section>
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-<h2><span class="section-number">42.11.30. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id25" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.30. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id25" title="Permalink to this headline">#</a></h2>
 <section id="establishing-a-baseline">
-<h3><span class="section-number">42.11.30.1. </span>Establishing a Baseline<a class="headerlink" href="#establishing-a-baseline" title="Permalink to this headline">#</a></h3>
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 <ul class="simple">
 <li><p>Without a benchmark, you won’t know how to evaluate your results properly</p></li>
 <li><p>The single most purpose of a baseline is to act as a reference point when comparing the actual model with the baseline</p></li>
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 </section>
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-<h2><span class="section-number">42.11.31. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id26" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.31. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id26" title="Permalink to this headline">#</a></h2>
 <section id="exploratory-data-analysis-eda">
-<h3><span class="section-number">42.11.31.1. </span>Exploratory data analysis(EDA)<a class="headerlink" href="#exploratory-data-analysis-eda" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.31.1. </span>Exploratory data analysis(EDA)<a class="headerlink" href="#exploratory-data-analysis-eda" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Before manipulating the data, it is quite important to learn about the dataset</p></li>
 <li><p>Go through some values, plot some features, and try to understand the correlation between them</p></li>
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 </section>
 </section>
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+<h2><span class="section-number">41.11.32. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id27" title="Permalink to this headline">#</a></h2>
 <section id="data-preprocessing">
-<h3><span class="section-number">42.11.32.1. </span>Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.32.1. </span>Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Perhaps the biggest part of any machine learning project</p></li>
 <li><p>Convert the raw data to go in a format that can be accepted by the machine learning algorithms</p></li>
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 </section>
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-<h2><span class="section-number">42.11.33. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id28" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.33. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id28" title="Permalink to this headline">#</a></h2>
 <section id="selecting-and-training-a-model">
-<h3><span class="section-number">42.11.33.1. </span>Selecting and Training a Model<a class="headerlink" href="#selecting-and-training-a-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.33.1. </span>Selecting and Training a Model<a class="headerlink" href="#selecting-and-training-a-model" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Depending on your problem, you can choose different relevant model</p></li>
 <li><p>The scope of the problem</p></li>
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 </section>
 </section>
 <section id="id29">
-<h2><span class="section-number">42.11.34. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id29" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.34. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id29" title="Permalink to this headline">#</a></h2>
 <section id="performing-error-analysis">
-<h3><span class="section-number">42.11.34.1. </span>Performing Error Analysis<a class="headerlink" href="#performing-error-analysis" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.34.1. </span>Performing Error Analysis<a class="headerlink" href="#performing-error-analysis" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Performing error analysis will guide you throughout the process of improving the results of the model</p></li>
 <li><p>The improvement can either be from the data or the model</p></li>
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 </section>
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 <section id="id30">
-<h2><span class="section-number">42.11.35. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id30" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.35. </span>Typical Machine Learning Workflow<a class="headerlink" href="#id30" title="Permalink to this headline">#</a></h2>
 <section id="deploying-a-model">
-<h3><span class="section-number">42.11.35.1. </span>Deploying a Model<a class="headerlink" href="#deploying-a-model" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.11.35.1. </span>Deploying a Model<a class="headerlink" href="#deploying-a-model" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Model deployment is the last part in this workflow.</p></li>
 <li><p>When all the previous steps has gone right, and you are happy about the results of the model on the test set, the next step will be to deploy the model so that the users can start to make requests and get predictions or enhanced services</p></li>
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-<h2><span class="section-number">42.11.36. </span>Thank you!<a class="headerlink" href="#thank-you" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.11.36. </span>Thank you!<a class="headerlink" href="#thank-you" title="Permalink to this headline">#</a></h2>
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+            <p class="prev-next-title"><span class="section-number">41.10. </span>Data Science in real world</p>
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-    <link rel="next" title="42.16. Build an machine learning web application" href="build-an-ml-web-app.html" />
-    <link rel="prev" title="42.14. Logistic Regression" href="logistic-regression-condensed.html" />
+    <link rel="next" title="41.16. Build an machine learning web application" href="build-an-ml-web-app.html" />
+    <link rel="prev" title="41.14. Logistic Regression" href="logistic-regression-condensed.html" />
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-     41.45. Linear regression - California Housing
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   <a class="reference internal nav-link" href="#importing-the-libraries">
-   42.15.2. Importing the libraries
+   41.15.2. Importing the libraries
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#importing-the-dataset">
-   42.15.3. Importing the dataset
+   41.15.3. Importing the dataset
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-visualization">
-   42.15.4. Data visualization
+   41.15.4. Data visualization
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#designing-model-architecture-using-keras">
-   42.15.5. Designing model architecture using Keras
+   41.15.5. Designing model architecture using Keras
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#import-keras-layers">
-   42.15.6. Import Keras layers
+   41.15.6. Import Keras layers
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#compute-the-number-of-labels">
-   42.15.7. Compute the number of labels
+   41.15.7. Compute the number of labels
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#one-hot-encoding">
-   42.15.8. One-Hot Encoding
+   41.15.8. One-Hot Encoding
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-preprocessing">
-   42.15.9. Data Preprocessing
+   41.15.9. Data Preprocessing
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#setting-network-parameters">
-   42.15.10. Setting network parameters
+   41.15.10. Setting network parameters
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#designing-the-model-architecture">
-   42.15.11. Designing the model architecture
+   41.15.11. Designing the model architecture
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#view-model-summary">
-   42.15.12. View model summary
+   41.15.12. View model summary
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#how-big-is-our-model-number-of-parameters">
-   42.15.13. How big is our model (number of parameters)?
+   41.15.13. How big is our model (number of parameters)?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#compile-the-model-with-compile-method">
-   42.15.14. Compile the model with compile() method
+   41.15.14. Compile the model with compile() method
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#loss-function-categorical-crossentropy">
-   42.15.15. Loss function (categorical_crossentropy)
+   41.15.15. Loss function (categorical_crossentropy)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#optimization-optimizer-adam">
-   42.15.16. Optimization (optimizer adam)
+   41.15.16. Optimization (optimizer adam)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#metrics-accuracy">
-   42.15.17. Metrics (accuracy)
+   41.15.17. Metrics (accuracy)
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#train-the-model-with-fit-method">
-   42.15.18. Train the model with fit() method
+   41.15.18. Train the model with fit() method
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#evaluating-model-performance-with-evaluate-method">
-   42.15.19. Evaluating model performance with evaluate() method
+   41.15.19. Evaluating model performance with evaluate() method
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#neural-network-from-scratch">
-   42.15.20. Neural Network from scratch
+   41.15.20. Neural Network from scratch
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#acknowledgments">
-   42.15.21. Acknowledgments
+   41.15.21. Acknowledgments
   </a>
  </li>
 </ul>
@@ -1835,9 +1835,9 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="neural-network">
-<h1><span class="section-number">42.15. </span>Neural Network<a class="headerlink" href="#neural-network" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.15. </span>Neural Network<a class="headerlink" href="#neural-network" title="Permalink to this headline">#</a></h1>
 <section id="introduction">
-<h2><span class="section-number">42.15.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.1. </span>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>In fact, Logistic Regression (that have learned in our last session) is the simplest form of Neural Network; Artificial neural networks can be viewed as an extension of Logistic Regression</p></li>
 <li><p>Logistic Regression: results in decision boundaries that are a straight line</p></li>
@@ -1847,7 +1847,7 @@ <h2><span class="section-number">42.15.1. </span>Introduction<a class="headerlin
 </ul>
 </section>
 <section id="importing-the-libraries">
-<h2><span class="section-number">42.15.2. </span>Importing the libraries<a class="headerlink" href="#importing-the-libraries" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.2. </span>Importing the libraries<a class="headerlink" href="#importing-the-libraries" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
@@ -1864,7 +1864,7 @@ <h2><span class="section-number">42.15.2. </span>Importing the libraries<a class
 </div>
 </section>
 <section id="importing-the-dataset">
-<h2><span class="section-number">42.15.3. </span>Importing the dataset<a class="headerlink" href="#importing-the-dataset" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.3. </span>Importing the dataset<a class="headerlink" href="#importing-the-dataset" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>MNIST is a collection of handwritten digits ranging from the number 0 to 9.</p></li>
 <li><p>It has a training set of 60,000 images, and 10,000 test images that are classified into corresponding categories or labels.</p></li>
@@ -1895,7 +1895,7 @@ <h2><span class="section-number">42.15.3. </span>Importing the dataset<a class="
 </div>
 </section>
 <section id="data-visualization">
-<h2><span class="section-number">42.15.4. </span>Data visualization<a class="headerlink" href="#data-visualization" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.4. </span>Data visualization<a class="headerlink" href="#data-visualization" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Let’s sample the 25 random MNIST digits and visualize them.</p></li>
 </ul>
@@ -1921,10 +1921,10 @@ <h2><span class="section-number">42.15.4. </span>Data visualization<a class="hea
 </div>
 </section>
 <section id="designing-model-architecture-using-keras">
-<h2><span class="section-number">42.15.5. </span>Designing model architecture using Keras<a class="headerlink" href="#designing-model-architecture-using-keras" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.5. </span>Designing model architecture using Keras<a class="headerlink" href="#designing-model-architecture-using-keras" title="Permalink to this headline">#</a></h2>
 </section>
 <section id="import-keras-layers">
-<h2><span class="section-number">42.15.6. </span>Import Keras layers<a class="headerlink" href="#import-keras-layers" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.6. </span>Import Keras layers<a class="headerlink" href="#import-keras-layers" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">keras.models</span> <span class="kn">import</span> <span class="n">Sequential</span>
@@ -1936,7 +1936,7 @@ <h2><span class="section-number">42.15.6. </span>Import Keras layers<a class="he
 </div>
 </section>
 <section id="compute-the-number-of-labels">
-<h2><span class="section-number">42.15.7. </span>Compute the number of labels<a class="headerlink" href="#compute-the-number-of-labels" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.7. </span>Compute the number of labels<a class="headerlink" href="#compute-the-number-of-labels" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">num_labels</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y_train</span><span class="p">))</span>
@@ -1946,7 +1946,7 @@ <h2><span class="section-number">42.15.7. </span>Compute the number of labels<a
 </div>
 </section>
 <section id="one-hot-encoding">
-<h2><span class="section-number">42.15.8. </span>One-Hot Encoding<a class="headerlink" href="#one-hot-encoding" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.8. </span>One-Hot Encoding<a class="headerlink" href="#one-hot-encoding" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>At this point, the labels are in digits format, 0 to 9.</p></li>
 <li><p>A more suitable format is called a one-hot vector, a 10-dim vector with all elements 0, except for the index of the digit class.</p></li>
@@ -1963,7 +1963,7 @@ <h2><span class="section-number">42.15.8. </span>One-Hot Encoding<a class="heade
 </div>
 </section>
 <section id="data-preprocessing">
-<h2><span class="section-number">42.15.9. </span>Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.9. </span>Data Preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># image dimensions (assumed square)</span>
@@ -1987,7 +1987,7 @@ <h2><span class="section-number">42.15.9. </span>Data Preprocessing<a class="hea
 </div>
 </section>
 <section id="setting-network-parameters">
-<h2><span class="section-number">42.15.10. </span>Setting network parameters<a class="headerlink" href="#setting-network-parameters" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.10. </span>Setting network parameters<a class="headerlink" href="#setting-network-parameters" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>The <strong>batch_size</strong> argument indicates the number of data that we will use for each update of the model parameters.</p></li>
 <li><p><strong>Hidden_units</strong> shows the number of hidden units.</p></li>
@@ -2005,7 +2005,7 @@ <h2><span class="section-number">42.15.10. </span>Setting network parameters<a c
 </div>
 </section>
 <section id="designing-the-model-architecture">
-<h2><span class="section-number">42.15.11. </span>Designing the model architecture<a class="headerlink" href="#designing-the-model-architecture" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.11. </span>Designing the model architecture<a class="headerlink" href="#designing-the-model-architecture" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># model is a 3-layer MLP with ReLU and dropout after each layer</span>
@@ -2024,7 +2024,7 @@ <h2><span class="section-number">42.15.11. </span>Designing the model architectu
 </div>
 </section>
 <section id="view-model-summary">
-<h2><span class="section-number">42.15.12. </span>View model summary<a class="headerlink" href="#view-model-summary" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.12. </span>View model summary<a class="headerlink" href="#view-model-summary" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Keras library provides us <code class="docutils literal notranslate"><span class="pre">summary()</span></code> method to check the model description.</p></li>
 </ul>
@@ -2037,7 +2037,7 @@ <h2><span class="section-number">42.15.12. </span>View model summary<a class="he
 </div>
 </section>
 <section id="how-big-is-our-model-number-of-parameters">
-<h2><span class="section-number">42.15.13. </span>How big is our model (number of parameters)?<a class="headerlink" href="#how-big-is-our-model-number-of-parameters" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.13. </span>How big is our model (number of parameters)?<a class="headerlink" href="#how-big-is-our-model-number-of-parameters" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>From input to Dense layer: 784 × 256 + 256 = 200,960.</p></li>
 <li><p>From first Dense to second Dense: 256 × 256 + 256 = 65,792.</p></li>
@@ -2046,7 +2046,7 @@ <h2><span class="section-number">42.15.13. </span>How big is our model (number o
 </ul>
 </section>
 <section id="compile-the-model-with-compile-method">
-<h2><span class="section-number">42.15.14. </span>Compile the model with compile() method<a class="headerlink" href="#compile-the-model-with-compile-method" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.14. </span>Compile the model with compile() method<a class="headerlink" href="#compile-the-model-with-compile-method" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> 
@@ -2058,7 +2058,7 @@ <h2><span class="section-number">42.15.14. </span>Compile the model with compile
 </div>
 </section>
 <section id="loss-function-categorical-crossentropy">
-<h2><span class="section-number">42.15.15. </span>Loss function (categorical_crossentropy)<a class="headerlink" href="#loss-function-categorical-crossentropy" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.15. </span>Loss function (categorical_crossentropy)<a class="headerlink" href="#loss-function-categorical-crossentropy" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>How far the predicted tensor is from the one-hot ground truth vector is called <strong>loss</strong>.</p></li>
 <li><p>In this example, we use <strong>categorical_crossentropy</strong> as the loss function. It is the negative of the sum of the product of the target and the logarithm of the prediction.</p></li>
@@ -2067,7 +2067,7 @@ <h2><span class="section-number">42.15.15. </span>Loss function (categorical_cro
 </ul>
 </section>
 <section id="optimization-optimizer-adam">
-<h2><span class="section-number">42.15.16. </span>Optimization (optimizer adam)<a class="headerlink" href="#optimization-optimizer-adam" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.16. </span>Optimization (optimizer adam)<a class="headerlink" href="#optimization-optimizer-adam" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>With optimization, the objective is to minimize the loss function. The idea is that if the loss is reduced to an acceptable level, the model has indirectly learned the function mapping input to output.</p></li>
 <li><p>In Keras, there are several choices for optimizers. The most commonly used optimizers are; <strong>Stochastic Gradient Descent (SGD)</strong>, <strong>Adaptive Moments (Adam)</strong> and <strong>Root Mean Squared Propagation (RMSprop)</strong>.</p></li>
@@ -2076,7 +2076,7 @@ <h2><span class="section-number">42.15.16. </span>Optimization (optimizer adam)<
 </ul>
 </section>
 <section id="metrics-accuracy">
-<h2><span class="section-number">42.15.17. </span>Metrics (accuracy)<a class="headerlink" href="#metrics-accuracy" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.17. </span>Metrics (accuracy)<a class="headerlink" href="#metrics-accuracy" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Performance metrics are used to determine if a model has learned the underlying data distribution. The default metric in Keras is loss.</p></li>
 <li><p>During training, validation, and testing, other metrics such as <strong>accuracy</strong> can also be included.</p></li>
@@ -2084,7 +2084,7 @@ <h2><span class="section-number">42.15.17. </span>Metrics (accuracy)<a class="he
 </ul>
 </section>
 <section id="train-the-model-with-fit-method">
-<h2><span class="section-number">42.15.18. </span>Train the model with fit() method<a class="headerlink" href="#train-the-model-with-fit-method" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.18. </span>Train the model with fit() method<a class="headerlink" href="#train-the-model-with-fit-method" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span>
@@ -2094,7 +2094,7 @@ <h2><span class="section-number">42.15.18. </span>Train the model with fit() met
 </div>
 </section>
 <section id="evaluating-model-performance-with-evaluate-method">
-<h2><span class="section-number">42.15.19. </span>Evaluating model performance with evaluate() method<a class="headerlink" href="#evaluating-model-performance-with-evaluate-method" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.19. </span>Evaluating model performance with evaluate() method<a class="headerlink" href="#evaluating-model-performance-with-evaluate-method" title="Permalink to this headline">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">loss</span><span class="p">,</span> <span class="n">acc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span>
@@ -2105,13 +2105,13 @@ <h2><span class="section-number">42.15.19. </span>Evaluating model performance w
 </div>
 </section>
 <section id="neural-network-from-scratch">
-<h2><span class="section-number">42.15.20. </span>Neural Network from scratch<a class="headerlink" href="#neural-network-from-scratch" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.20. </span>Neural Network from scratch<a class="headerlink" href="#neural-network-from-scratch" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>It’s for tomorrow!</p></li>
 </ul>
 </section>
 <section id="acknowledgments">
-<h2><span class="section-number">42.15.21. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.15.21. </span>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Permalink to this headline">#</a></h2>
 <p>Thanks to PRASHANT BANERJEE for creating the open-source <a class="reference external" href="https://www.kaggle.com/code/prashant111/mnist-deep-neural-network-with-keras">Kaggle jupyter notebook</a>, licensed under Apache 2.0. It inspires the majority of the content of this slides.</p>
 </section>
 </section>
@@ -2153,13 +2153,13 @@ <h2><span class="section-number">42.15.21. </span>Acknowledgments<a class="heade
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">42.14. </span>Logistic Regression</p>
+            <p class="prev-next-title"><span class="section-number">41.14. </span>Logistic Regression</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="build-an-ml-web-app.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">42.16. </span>Build an machine learning web application</p>
+        <p class="prev-next-title"><span class="section-number">41.16. </span>Build an machine learning web application</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
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-    <title>42.3. Python programming advanced &#8212; Ocademy Open Machine Learning Book</title>
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  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#functions">
-   42.3.10. Functions
+   41.3.10. Functions
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id11">
-   42.3.11. Functions
+   41.3.11. Functions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#defining-a-function">
-     42.3.11.1. Defining a function
+     41.3.11.1. Defining a function
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#calling-the-function">
-     42.3.11.2. Calling the function
+     41.3.11.2. Calling the function
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id12">
-   42.3.12. Functions
+   41.3.12. Functions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#scopes-of-variables-inside-functions">
-     42.3.12.1. Scopes of variables inside functions
+     41.3.12.1. Scopes of variables inside functions
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id13">
-   42.3.13. Functions
+   41.3.13. Functions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id14">
-     42.3.13.1. Scopes of variables inside functions
+     41.3.13.1. Scopes of variables inside functions
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#unpacking-argument-lists-and-statements">
-   42.3.14. Unpacking argument lists (
+   41.3.14. Unpacking argument lists (
    <code class="docutils literal notranslate">
     <span class="pre">
      *
@@ -2120,14 +2120,14 @@ <h2> Contents </h2>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#code-output">
-     42.3.14.1. Code output:
+     41.3.14.1. Code output:
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id15">
-   42.3.15. Unpacking argument lists (
+   41.3.15. Unpacking argument lists (
    <code class="docutils literal notranslate">
     <span class="pre">
      *
@@ -2144,80 +2144,80 @@ <h2> Contents </h2>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id16">
-   42.3.16. Functions
+   41.3.16. Functions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#documentation-strings-docstring">
-     42.3.16.1. Documentation strings (docstring)
+     41.3.16.1. Documentation strings (docstring)
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id17">
-   42.3.17. Functions
+   41.3.17. Functions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#function-annotations">
-     42.3.17.1. Function annotations
+     41.3.17.1. Function annotations
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id18">
-   42.3.18. Functions
+   41.3.18. Functions
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#function-decorators">
-     42.3.18.1. Function decorators
+     41.3.18.1. Function decorators
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#classes">
-   42.3.19. Classes
+   41.3.19. Classes
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#id19">
-   42.3.20. Classes
+   41.3.20. Classes
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#inheritance">
-     42.3.20.1. Inheritance
+     41.3.20.1. Inheritance
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#modules">
-   42.3.21. Modules
+   41.3.21. Modules
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#lets-try-with-our-own-module">
-   42.3.22. Let’s try with our own module
+   41.3.22. Let’s try with our own module
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#errors-and-exceptions">
-   42.3.23. Errors and exceptions
+   41.3.23. Errors and exceptions
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   42.3.24. Your turn! 🚀
+   41.3.24. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   42.3.25. References:
+   41.3.25. References:
   </a>
  </li>
 </ul>
@@ -2258,9 +2258,9 @@ <h2> Contents </h2>
 </div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="python-programming-advanced">
-<h1><span class="section-number">42.3. </span>Python programming advanced<a class="headerlink" href="#python-programming-advanced" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.3. </span>Python programming advanced<a class="headerlink" href="#python-programming-advanced" title="Permalink to this headline">#</a></h1>
 <section id="table-of-content">
-<h2><span class="section-number">42.3.1. </span>Table of Content<a class="headerlink" href="#table-of-content" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.1. </span>Table of Content<a class="headerlink" href="#table-of-content" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Control flow</p>
 <ul>
@@ -2287,9 +2287,9 @@ <h2><span class="section-number">42.3.1. </span>Table of Content<a class="header
 </ul>
 </section>
 <section id="control-flow">
-<h2><span class="section-number">42.3.2. </span>Control flow<a class="headerlink" href="#control-flow" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.2. </span>Control flow<a class="headerlink" href="#control-flow" title="Permalink to this headline">#</a></h2>
 <section id="the-if-statement">
-<h3><span class="section-number">42.3.2.1. </span>The <code class="docutils literal notranslate"><span class="pre">if</span></code> statement<a class="headerlink" href="#the-if-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.2.1. </span>The <code class="docutils literal notranslate"><span class="pre">if</span></code> statement<a class="headerlink" href="#the-if-statement" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Used for conditional execution</p></li>
 <li><p>If a condition is true, we run a block of statements</p></li>
@@ -2320,9 +2320,9 @@ <h3><span class="section-number">42.3.2.1. </span>The <code class="docutils lite
 </section>
 </section>
 <section id="id1">
-<h2><span class="section-number">42.3.3. </span>Control flow<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.3. </span>Control flow<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h2>
 <section id="the-for-statement">
-<h3><span class="section-number">42.3.3.1. </span>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement<a class="headerlink" href="#the-for-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.3.1. </span>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement<a class="headerlink" href="#the-for-statement" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Sequence: an ordered collection of items</p></li>
 <li><p>A looping statement which iterates over a sequence of objects, i.e. go through each item in a sequence</p></li>
@@ -2369,9 +2369,9 @@ <h3><span class="section-number">42.3.3.1. </span>The <code class="docutils lite
 </section>
 </section>
 <section id="id2">
-<h2><span class="section-number">42.3.4. </span>Control flow<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.4. </span>Control flow<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h2>
 <section id="id3">
-<h3><span class="section-number">42.3.4.1. </span>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.4.1. </span>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
 <p>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement can be used in tandem with:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">range()</span></code>: to iterate over a sequence of numbers</p></li>
@@ -2396,9 +2396,9 @@ <h3><span class="section-number">42.3.4.1. </span>The <code class="docutils lite
 </section>
 </section>
 <section id="id4">
-<h2><span class="section-number">42.3.5. </span>Control flow<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.5. </span>Control flow<a class="headerlink" href="#id4" title="Permalink to this headline">#</a></h2>
 <section id="id5">
-<h3><span class="section-number">42.3.5.1. </span>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.5.1. </span>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement<a class="headerlink" href="#id5" title="Permalink to this headline">#</a></h3>
 <p>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement can be used in tandem with:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">enumerate()</span></code>: to get a counter and the value from the iterable at the same time</p></li>
@@ -2424,9 +2424,9 @@ <h3><span class="section-number">42.3.5.1. </span>The <code class="docutils lite
 </section>
 </section>
 <section id="id6">
-<h2><span class="section-number">42.3.6. </span>Control flow<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.6. </span>Control flow<a class="headerlink" href="#id6" title="Permalink to this headline">#</a></h2>
 <section id="id7">
-<h3><span class="section-number">42.3.6.1. </span>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.6.1. </span>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement<a class="headerlink" href="#id7" title="Permalink to this headline">#</a></h3>
 <p>The <code class="docutils literal notranslate"><span class="pre">for</span></code> statement can be used in tandem with:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">items()</span></code>: to loop through dictionaries</p></li>
@@ -2457,9 +2457,9 @@ <h3><span class="section-number">42.3.6.1. </span>The <code class="docutils lite
 </section>
 </section>
 <section id="id8">
-<h2><span class="section-number">42.3.7. </span>Control flow<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.7. </span>Control flow<a class="headerlink" href="#id8" title="Permalink to this headline">#</a></h2>
 <section id="the-while-statement">
-<h3><span class="section-number">42.3.7.1. </span>The <code class="docutils literal notranslate"><span class="pre">while</span></code> statement<a class="headerlink" href="#the-while-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.7.1. </span>The <code class="docutils literal notranslate"><span class="pre">while</span></code> statement<a class="headerlink" href="#the-while-statement" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Repeatedly executes a block of statements as long as a condition is true</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">True</span></code> and <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
@@ -2484,9 +2484,9 @@ <h3><span class="section-number">42.3.7.1. </span>The <code class="docutils lite
 </section>
 </section>
 <section id="id9">
-<h2><span class="section-number">42.3.8. </span>Control flow<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.8. </span>Control flow<a class="headerlink" href="#id9" title="Permalink to this headline">#</a></h2>
 <section id="the-break-statement">
-<h3><span class="section-number">42.3.8.1. </span>The <code class="docutils literal notranslate"><span class="pre">break</span></code> statement<a class="headerlink" href="#the-break-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.8.1. </span>The <code class="docutils literal notranslate"><span class="pre">break</span></code> statement<a class="headerlink" href="#the-break-statement" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Breaks out of the innermost enclosing “for” or “while” loop</p></li>
 </ul>
@@ -2512,9 +2512,9 @@ <h3><span class="section-number">42.3.8.1. </span>The <code class="docutils lite
 </section>
 </section>
 <section id="id10">
-<h2><span class="section-number">42.3.9. </span>Control flow<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.9. </span>Control flow<a class="headerlink" href="#id10" title="Permalink to this headline">#</a></h2>
 <section id="the-continue-statement">
-<h3><span class="section-number">42.3.9.1. </span>The <code class="docutils literal notranslate"><span class="pre">continue</span></code> statement<a class="headerlink" href="#the-continue-statement" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.9.1. </span>The <code class="docutils literal notranslate"><span class="pre">continue</span></code> statement<a class="headerlink" href="#the-continue-statement" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Continues with the next iteration of the loop</p></li>
 </ul>
@@ -2540,7 +2540,7 @@ <h3><span class="section-number">42.3.9.1. </span>The <code class="docutils lite
 </section>
 </section>
 <section id="functions">
-<h2><span class="section-number">42.3.10. </span>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.10. </span>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>A function is a group of statements that exist within a program for the purpose of performing a specific task</p></li>
 <li><p>Since the beginning of the session, we have been using a number of Python’s built-in functions, including:</p>
@@ -2554,9 +2554,9 @@ <h2><span class="section-number">42.3.10. </span>Functions<a class="headerlink"
 </ul>
 </section>
 <section id="id11">
-<h2><span class="section-number">42.3.11. </span>Functions<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.11. </span>Functions<a class="headerlink" href="#id11" title="Permalink to this headline">#</a></h2>
 <section id="defining-a-function">
-<h3><span class="section-number">42.3.11.1. </span>Defining a function<a class="headerlink" href="#defining-a-function" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.11.1. </span>Defining a function<a class="headerlink" href="#defining-a-function" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">myfunction</span><span class="p">():</span>
@@ -2567,7 +2567,7 @@ <h3><span class="section-number">42.3.11.1. </span>Defining a function<a class="
 </div>
 </section>
 <section id="calling-the-function">
-<h3><span class="section-number">42.3.11.2. </span>Calling the function<a class="headerlink" href="#calling-the-function" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.11.2. </span>Calling the function<a class="headerlink" href="#calling-the-function" title="Permalink to this headline">#</a></h3>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">myfunction</span><span class="p">()</span>
@@ -2662,9 +2662,9 @@ <h3><span class="section-number">42.3.11.2. </span>Calling the function<a class=
 </section>
 </section>
 <section id="id12">
-<h2><span class="section-number">42.3.12. </span>Functions<a class="headerlink" href="#id12" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.12. </span>Functions<a class="headerlink" href="#id12" title="Permalink to this headline">#</a></h2>
 <section id="scopes-of-variables-inside-functions">
-<h3><span class="section-number">42.3.12.1. </span>Scopes of variables inside functions<a class="headerlink" href="#scopes-of-variables-inside-functions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.12.1. </span>Scopes of variables inside functions<a class="headerlink" href="#scopes-of-variables-inside-functions" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Local variable:</p>
 <ul>
@@ -2694,9 +2694,9 @@ <h3><span class="section-number">42.3.12.1. </span>Scopes of variables inside fu
 </section>
 </section>
 <section id="id13">
-<h2><span class="section-number">42.3.13. </span>Functions<a class="headerlink" href="#id13" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.13. </span>Functions<a class="headerlink" href="#id13" title="Permalink to this headline">#</a></h2>
 <section id="id14">
-<h3><span class="section-number">42.3.13.1. </span>Scopes of variables inside functions<a class="headerlink" href="#id14" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.13.1. </span>Scopes of variables inside functions<a class="headerlink" href="#id14" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Global Variables:</p>
 <ul>
@@ -2882,7 +2882,7 @@ <h3><span class="section-number">42.3.13.1. </span>Scopes of variables inside fu
 </section>
 </section>
 <section id="unpacking-argument-lists-and-statements">
-<h2><span class="section-number">42.3.14. </span>Unpacking argument lists (<code class="docutils literal notranslate"><span class="pre">*</span></code> and <code class="docutils literal notranslate"><span class="pre">**</span></code> statements)<a class="headerlink" href="#unpacking-argument-lists-and-statements" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.14. </span>Unpacking argument lists (<code class="docutils literal notranslate"><span class="pre">*</span></code> and <code class="docutils literal notranslate"><span class="pre">**</span></code> statements)<a class="headerlink" href="#unpacking-argument-lists-and-statements" title="Permalink to this headline">#</a></h2>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">fun</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">d</span><span class="p">):</span>
     <span class="nb">print</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span>
 
@@ -2893,12 +2893,12 @@ <h2><span class="section-number">42.3.14. </span>Unpacking argument lists (<code
 </pre></div>
 </div>
 <section id="code-output">
-<h3><span class="section-number">42.3.14.1. </span>Code output:<a class="headerlink" href="#code-output" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.14.1. </span>Code output:<a class="headerlink" href="#code-output" title="Permalink to this headline">#</a></h3>
 <p><strong>TypeError</strong>: <code class="docutils literal notranslate"><span class="pre">fun()</span></code> takes exactly 4 arguments (1 given)</p>
 </section>
 </section>
 <section id="id15">
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+<h2><span class="section-number">41.3.15. </span>Unpacking argument lists (<code class="docutils literal notranslate"><span class="pre">*</span></code> and <code class="docutils literal notranslate"><span class="pre">**</span></code> statements)<a class="headerlink" href="#id15" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">*args</span></code> (Non-keyword arguments)</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">**kwargs</span></code> (Keyword arguments)</p></li>
@@ -3132,9 +3132,9 @@ <h2><span class="section-number">42.3.15. </span>Unpacking argument lists (<code
 </div>
 </section>
 <section id="id16">
-<h2><span class="section-number">42.3.16. </span>Functions<a class="headerlink" href="#id16" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.16. </span>Functions<a class="headerlink" href="#id16" title="Permalink to this headline">#</a></h2>
 <section id="documentation-strings-docstring">
-<h3><span class="section-number">42.3.16.1. </span>Documentation strings (docstring)<a class="headerlink" href="#documentation-strings-docstring" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.16.1. </span>Documentation strings (docstring)<a class="headerlink" href="#documentation-strings-docstring" title="Permalink to this headline">#</a></h3>
 <blockquote>
 <div><p>“Code is more often read than written.”</p>
 <p>— Guido van Rossum</p>
@@ -3244,9 +3244,9 @@ <h3><span class="section-number">42.3.16.1. </span>Documentation strings (docstr
 </section>
 </section>
 <section id="id17">
-<h2><span class="section-number">42.3.17. </span>Functions<a class="headerlink" href="#id17" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.17. </span>Functions<a class="headerlink" href="#id17" title="Permalink to this headline">#</a></h2>
 <section id="function-annotations">
-<h3><span class="section-number">42.3.17.1. </span>Function annotations<a class="headerlink" href="#function-annotations" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.17.1. </span>Function annotations<a class="headerlink" href="#function-annotations" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Function annotations are completely optional metadata information about the types used by user-defined functions.</p></li>
 </ul>
@@ -3270,9 +3270,9 @@ <h3><span class="section-number">42.3.17.1. </span>Function annotations<a class=
 </section>
 </section>
 <section id="id18">
-<h2><span class="section-number">42.3.18. </span>Functions<a class="headerlink" href="#id18" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.18. </span>Functions<a class="headerlink" href="#id18" title="Permalink to this headline">#</a></h2>
 <section id="function-decorators">
-<h3><span class="section-number">42.3.18.1. </span>Function decorators<a class="headerlink" href="#function-decorators" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.18.1. </span>Function decorators<a class="headerlink" href="#function-decorators" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Wrappers to existing functions</p></li>
 <li><p>Dynamically alter the functionality of a function</p></li>
@@ -3361,7 +3361,7 @@ <h3><span class="section-number">42.3.18.1. </span>Function decorators<a class="
 </section>
 </section>
 <section id="classes">
-<h2><span class="section-number">42.3.19. </span>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.19. </span>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>To group data and functions belonging together to form custom data types</p></li>
 <li><p>A Class is like an object constructor or a “blueprint” for creating objects</p></li>
@@ -3399,9 +3399,9 @@ <h2><span class="section-number">42.3.19. </span>Classes<a class="headerlink" hr
 </div>
 </section>
 <section id="id19">
-<h2><span class="section-number">42.3.20. </span>Classes<a class="headerlink" href="#id19" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.20. </span>Classes<a class="headerlink" href="#id19" title="Permalink to this headline">#</a></h2>
 <section id="inheritance">
-<h3><span class="section-number">42.3.20.1. </span>Inheritance<a class="headerlink" href="#inheritance" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.3.20.1. </span>Inheritance<a class="headerlink" href="#inheritance" title="Permalink to this headline">#</a></h3>
 <ul class="simple">
 <li><p>Allows a derived class to reuse the same code and modify it accordingly</p></li>
 <li><p>Derived classes may override methods of their base classes.</p></li>
@@ -3469,7 +3469,7 @@ <h3><span class="section-number">42.3.20.1. </span>Inheritance<a class="headerli
 </section>
 </section>
 <section id="modules">
-<h2><span class="section-number">42.3.21. </span>Modules<a class="headerlink" href="#modules" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.21. </span>Modules<a class="headerlink" href="#modules" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>As your program gets longer, you may want to split it into several files for easier maintenance</p></li>
 <li><p>Python has a way to put definitions in a file and use them in a script or in an interactive instance of the interpreter</p></li>
@@ -3543,7 +3543,7 @@ <h2><span class="section-number">42.3.21. </span>Modules<a class="headerlink" hr
 </div>
 </section>
 <section id="lets-try-with-our-own-module">
-<h2><span class="section-number">42.3.22. </span>Let’s try with our own module<a class="headerlink" href="#lets-try-with-our-own-module" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.22. </span>Let’s try with our own module<a class="headerlink" href="#lets-try-with-our-own-module" title="Permalink to this headline">#</a></h2>
 <p>We write those code into the file <code class="docutils literal notranslate"><span class="pre">my_module.py</span></code>:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">my_sum</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
     <span class="k">return</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
@@ -3587,7 +3587,7 @@ <h2><span class="section-number">42.3.22. </span>Let’s try with our own module
 </div>
 </section>
 <section id="errors-and-exceptions">
-<h2><span class="section-number">42.3.23. </span>Errors and exceptions<a class="headerlink" href="#errors-and-exceptions" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.23. </span>Errors and exceptions<a class="headerlink" href="#errors-and-exceptions" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Even if a statement or expression is syntactically correct, it may cause an error when an attempt is made to execute it</p></li>
 <li><p>Errors detected during execution are called exceptions and are not unconditionally fatal</p></li>
@@ -3632,13 +3632,13 @@ <h2><span class="section-number">42.3.23. </span>Errors and exceptions<a class="
 </div>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">42.3.24. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.24. </span>Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Practice the Python programming (advanced) by following the assignment</p></li>
 </ul>
 </section>
 <section id="references">
-<h2><span class="section-number">42.3.25. </span>References:<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.3.25. </span>References:<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p><a class="reference external" href="https://www.geeksforgeeks.org/packing-and-unpacking-arguments-in-python/">https://www.geeksforgeeks.org/packing-and-unpacking-arguments-in-python/</a></p></li>
 <li><p><a class="reference external" href="https://cs.nyu.edu/courses/summer16/CSCI-UA.0002-002/slides/Python_Functions.pdf">https://cs.nyu.edu/courses/summer16/CSCI-UA.0002-002/slides/Python_Functions.pdf</a></p></li>
@@ -3685,13 +3685,13 @@ <h2><span class="section-number">42.3.25. </span>References:<a class="headerlink
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">42.2. </span>Python programming basics</p>
+            <p class="prev-next-title"><span class="section-number">41.2. </span>Python programming basics</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="../data-science/data-science-introduction.html" title="next page">
     <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title"><span class="section-number">42.4. </span>Data Science introduction</p>
+        <p class="prev-next-title"><span class="section-number">41.4. </span>Data Science introduction</p>
     </div>
     <i class="fas fa-angle-right"></i>
     </a>
diff --git a/slides/python-programming/python-programming-basics.html b/slides/python-programming/python-programming-basics.html
index 266a3d21c..7f7f48901 100644
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+++ b/slides/python-programming/python-programming-basics.html
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-    <title>42.2. Python programming basics &#8212; Ocademy Open Machine Learning Book</title>
+    <title>41.2. Python programming basics &#8212; Ocademy Open Machine Learning Book</title>
     
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   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="42.3. Python programming advanced" href="python-programming-advanced.html" />
-    <link rel="prev" title="42.1. Python programming introduction" href="python-programming-introduction.html" />
+    <link rel="next" title="41.3. Python programming advanced" href="python-programming-advanced.html" />
+    <link rel="prev" title="41.1. Python programming introduction" href="python-programming-introduction.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -159,17 +159,17 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
-   1. Python programming basics
+   1. Python programming introduction
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../prerequisites/python-programming-basics.html">
-   3. Python programming basics
+   2. Python programming basics
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../prerequisites/python-programming-advanced.html">
-   4. Python programming advanced
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@@ -181,7 +181,7 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
 <ul class="nav bd-sidenav">
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../data-science/introduction/introduction.html">
-   5. Introduction
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/introduction/defining-data-science.html">
-     5.1. Defining data science
+     4.1. Defining data science
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/introduction/data-science-ethics.html">
-     5.2. Data Science ethics
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     <a class="reference internal" href="../../data-science/introduction/defining-data.html">
-     5.3. Defining data
+     4.3. Defining data
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     <a class="reference internal" href="../../data-science/introduction/introduction-to-statistics-and-probability.html">
-     5.4. Introduction to statistics and probability
+     4.4. Introduction to statistics and probability
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    </li>
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   <a class="reference internal" href="../../data-science/working-with-data/working-with-data.html">
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     <a class="reference internal" href="../../data-science/working-with-data/relational-databases.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/non-relational-data.html">
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/numpy.html">
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    <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas/pandas.html">
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       <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
-       6.4.1. Introduction and Data Structures
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      <li class="toctree-l3">
       <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
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       <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
-       6.4.3. Advanced Pandas Techniques
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     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-visualization/visualization-distributions.html">
-     7.1. Visualizing distributions
+     6.1. Visualizing distributions
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-visualization/visualization-proportions.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-visualization/visualization-relationships.html">
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+     6.3. Visualizing relationships: all about honey 🍯
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-visualization/meaningful-visualizations.html">
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   <ul>
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     <a class="reference internal" href="../../data-science/data-science-lifecycle/introduction.html">
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     <a class="reference internal" href="../../data-science/data-science-lifecycle/analyzing.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/data-science-lifecycle/communication.html">
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     <a class="reference internal" href="../../data-science/data-science-in-the-cloud/introduction.html">
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     <a class="reference internal" href="../../data-science/data-science-in-the-cloud/the-low-code-no-code-way.html">
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     <a class="reference internal" href="../../data-science/data-science-in-the-cloud/the-azure-ml-sdk-way.html">
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     </a>
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  <li class="toctree-l1">
   <a class="reference internal" href="../../data-science/data-science-in-the-wild.html">
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  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/ml-overview.html">
-   11. Machine Learning overview
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  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../ml-fundamentals/ml-summary.html">
-   12. Summary of machine learning fundamentals
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  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../ml-fundamentals/regression/regression-models-for-machine-learning.html">
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   <ul>
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     <a class="reference internal" href="../../ml-fundamentals/regression/tools-of-the-trade.html">
-     13.1. Tools of the trade
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/managing-data.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/linear-and-polynomial-regression.html">
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    <li class="toctree-l2">
     <a class="reference internal" href="../../ml-fundamentals/regression/logistic-regression.html">
-     13.4. Logistic regression
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   <a class="reference internal" href="../../ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.html">
-   14. Build a web app to use a Machine Learning model
+   13. Build a web app to use a Machine Learning model
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   <a class="reference internal" href="../../ml-fundamentals/classification/getting-started-with-classification.html">
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-     41.14. Try it in Excel
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-     41.45. Linear regression - California Housing
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     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#list-type">
-       42.2.4.3.1. List type
+       41.2.4.3.1. List type
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#list-methods">
-       42.2.4.3.2. List methods
+       41.2.4.3.2. List methods
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#the-del-statement">
-       42.2.4.3.3. The del statement
+       41.2.4.3.3. The del statement
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#list-comprehensions">
-       42.2.4.3.4. List comprehensions
+       41.2.4.3.4. List comprehensions
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#nested-list-comprehensions">
-       42.2.4.3.5. Nested list comprehensions
+       41.2.4.3.5. Nested list comprehensions
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dictionaries">
-     42.2.4.4. Dictionaries
+     41.2.4.4. Dictionaries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#type-casting">
-     42.2.4.5. Type casting
+     41.2.4.5. Type casting
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   42.2.5. 4. Your turn! 🚀
+   41.2.5. 4. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   42.2.6. 5. References
+   41.2.6. 5. References
   </a>
  </li>
 </ul>
@@ -1768,118 +1768,118 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#python-syntax">
-   42.2.1. 1. Python syntax
+   41.2.1. 1. Python syntax
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#python-indentations">
-     42.2.1.1. Python indentations
+     41.2.1.1. Python indentations
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#comments">
-     42.2.1.2. Comments
+     41.2.1.2. Comments
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#docstrings">
-     42.2.1.3. Docstrings
+     41.2.1.3. Docstrings
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#variables">
-   42.2.2. 2. Variables
+   41.2.2. 2. Variables
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#operators">
-   42.2.3. 3. Operators
+   41.2.3. 3. Operators
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#arithmetic-operators">
-     42.2.3.1. Arithmetic operators
+     41.2.3.1. Arithmetic operators
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#comparison-operators">
-     42.2.3.2. Comparison operators
+     41.2.3.2. Comparison operators
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#data-types">
-   42.2.4. 4. Data types
+   41.2.4. 4. Data types
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#numbers-including-booleans">
-     42.2.4.1. Numbers (including booleans)
+     41.2.4.1. Numbers (including booleans)
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#integers">
-       42.2.4.1.1. Integers
+       41.2.4.1.1. Integers
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#booleans">
-       42.2.4.1.2. Booleans
+       41.2.4.1.2. Booleans
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#floats">
-       42.2.4.1.3. Floats
+       41.2.4.1.3. Floats
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#complexes">
-       42.2.4.1.4. Complexes
+       41.2.4.1.4. Complexes
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#number-operation">
-       42.2.4.1.5. Number operation
+       41.2.4.1.5. Number operation
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#strings-and-their-methods">
-     42.2.4.2. Strings and their methods
+     41.2.4.2. Strings and their methods
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string-type">
-       42.2.4.2.1. String type
+       41.2.4.2.1. String type
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string-operations">
-       42.2.4.2.2. String operations
+       41.2.4.2.2. String operations
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string-methods">
-       42.2.4.2.3. String methods
+       41.2.4.2.3. String methods
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string-formatting">
-       42.2.4.2.4. String formatting
+       41.2.4.2.4. String formatting
       </a>
       <ul class="nav section-nav flex-column">
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#formatted-string-literals">
-         42.2.4.2.4.1. Formatted string literals
+         41.2.4.2.4.1. Formatted string literals
         </a>
        </li>
        <li class="toc-h5 nav-item toc-entry">
         <a class="reference internal nav-link" href="#the-string-format-method">
-         42.2.4.2.4.2. The string format() method
+         41.2.4.2.4.2. The string format() method
         </a>
        </li>
       </ul>
@@ -1888,56 +1888,56 @@ <h2> Contents </h2>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#lists-and-their-methods-including-list-comprehensions">
-     42.2.4.3. Lists and their methods (including list comprehensions)
+     41.2.4.3. Lists and their methods (including list comprehensions)
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#list-type">
-       42.2.4.3.1. List type
+       41.2.4.3.1. List type
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#list-methods">
-       42.2.4.3.2. List methods
+       41.2.4.3.2. List methods
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#the-del-statement">
-       42.2.4.3.3. The del statement
+       41.2.4.3.3. The del statement
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#list-comprehensions">
-       42.2.4.3.4. List comprehensions
+       41.2.4.3.4. List comprehensions
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#nested-list-comprehensions">
-       42.2.4.3.5. Nested list comprehensions
+       41.2.4.3.5. Nested list comprehensions
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#dictionaries">
-     42.2.4.4. Dictionaries
+     41.2.4.4. Dictionaries
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#type-casting">
-     42.2.4.5. Type casting
+     41.2.4.5. Type casting
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   42.2.5. 4. Your turn! 🚀
+   41.2.5. 4. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   42.2.6. 5. References
+   41.2.6. 5. References
   </a>
  </li>
 </ul>
@@ -1966,16 +1966,16 @@ <h2> Contents </h2>
 </div></div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="python-programming-basics">
-<h1><span class="section-number">42.2. </span>Python programming basics<a class="headerlink" href="#python-programming-basics" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.2. </span>Python programming basics<a class="headerlink" href="#python-programming-basics" title="Permalink to this headline">#</a></h1>
 <section id="python-syntax">
-<h2><span class="section-number">42.2.1. </span>1. Python syntax<a class="headerlink" href="#python-syntax" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.2.1. </span>1. Python syntax<a class="headerlink" href="#python-syntax" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Python was designed for readability and has some similarities to the English language.</p></li>
 <li><p>Python uses new lines to complete a command, not semicolons or parentheses.</p></li>
 <li><p>Python relies on indentation, using whitespace, to define scope, not curly brackets.</p></li>
 </ul>
 <section id="python-indentations">
-<h3><span class="section-number">42.2.1.1. </span>Python indentations<a class="headerlink" href="#python-indentations" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.2.1.1. </span>Python indentations<a class="headerlink" href="#python-indentations" title="Permalink to this headline">#</a></h3>
 <p>Python uses indentation to indicate a block of code.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="mi">5</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
     <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Five is greater than two!&quot;</span><span class="p">)</span>
@@ -1983,7 +1983,7 @@ <h3><span class="section-number">42.2.1.1. </span>Python indentations<a class="h
 </div>
 </section>
 <section id="comments">
-<h3><span class="section-number">42.2.1.2. </span>Comments<a class="headerlink" href="#comments" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.2.1.2. </span>Comments<a class="headerlink" href="#comments" title="Permalink to this headline">#</a></h3>
 <p>Python has the commenting capability for the purpose of in-code documentation.</p>
 <p>Comments start with a <code class="docutils literal notranslate"><span class="pre">#</span></code>, and Python will render the rest of the line as a comment:</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="c1"># This is a comment.</span>
@@ -1992,7 +1992,7 @@ <h3><span class="section-number">42.2.1.2. </span>Comments<a class="headerlink"
 </div>
 </section>
 <section id="docstrings">
-<h3><span class="section-number">42.2.1.3. </span>Docstrings<a class="headerlink" href="#docstrings" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.2.1.3. </span>Docstrings<a class="headerlink" href="#docstrings" title="Permalink to this headline">#</a></h3>
 <p>Python also has extended capability for multiline documentation, called docstrings.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;This is a </span>
 <span class="sd">multiline docstring.&quot;&quot;&quot;</span>
@@ -2002,7 +2002,7 @@ <h3><span class="section-number">42.2.1.3. </span>Docstrings<a class="headerlink
 </section>
 </section>
 <section id="variables">
-<h2><span class="section-number">42.2.2. </span>2. Variables<a class="headerlink" href="#variables" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.2.2. </span>2. Variables<a class="headerlink" href="#variables" title="Permalink to this headline">#</a></h2>
 <p>Python is completely <strong>object-oriented</strong>, and <strong>not “statically typed”</strong>. You <strong>do not need to declare</strong> variables or their type before using them. Every variable in Python is an <strong>object</strong>.</p>
 <p>Rules for Python variables:</p>
 <ul class="simple">
@@ -2020,7 +2020,7 @@ <h2><span class="section-number">42.2.2. </span>2. Variables<a class="headerlink
 </div>
 </section>
 <section id="operators">
-<h2><span class="section-number">42.2.3. </span>3. Operators<a class="headerlink" href="#operators" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.2.3. </span>3. Operators<a class="headerlink" href="#operators" title="Permalink to this headline">#</a></h2>
 <p>Operators are used to performing operations on variables and values. Python divides the operators into the following groups:</p>
 <ul class="simple">
 <li><p>Arithmetic operators</p></li>
@@ -2032,7 +2032,7 @@ <h2><span class="section-number">42.2.3. </span>3. Operators<a class="headerlink
 <li><p>Bitwise operators</p></li>
 </ul>
 <section id="arithmetic-operators">
-<h3><span class="section-number">42.2.3.1. </span>Arithmetic operators<a class="headerlink" href="#arithmetic-operators" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.2.3.1. </span>Arithmetic operators<a class="headerlink" href="#arithmetic-operators" title="Permalink to this headline">#</a></h3>
 <p>Arithmetic operators are used with numeric values to perform common mathematical operations.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="c1"># Addition.</span>
 <span class="k">assert</span> <span class="mi">5</span> <span class="o">+</span> <span class="mi">3</span> <span class="o">==</span> <span class="mi">8</span>
@@ -2063,7 +2063,7 @@ <h3><span class="section-number">42.2.3.1. </span>Arithmetic operators<a class="
 </div>
 </section>
 <section id="comparison-operators">
-<h3><span class="section-number">42.2.3.2. </span>Comparison operators<a class="headerlink" href="#comparison-operators" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.2.3.2. </span>Comparison operators<a class="headerlink" href="#comparison-operators" title="Permalink to this headline">#</a></h3>
 <p>Comparison operators are used to compare two values.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="c1"># Equal.</span>
 <span class="n">number</span> <span class="o">=</span> <span class="mi">5</span>
@@ -2095,7 +2095,7 @@ <h3><span class="section-number">42.2.3.2. </span>Comparison operators<a class="
 </section>
 </section>
 <section id="data-types">
-<h2><span class="section-number">42.2.4. </span>4. Data types<a class="headerlink" href="#data-types" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.2.4. </span>4. Data types<a class="headerlink" href="#data-types" title="Permalink to this headline">#</a></h2>
 <p>In programming, data type is an important concept. Variables can store data of different types, and different types can do different things. Python has the following data types built in by default, in these categories:</p>
 <table class="colwidths-auto table">
 <thead>
@@ -2131,7 +2131,7 @@ <h2><span class="section-number">42.2.4. </span>4. Data types<a class="headerlin
 </tbody>
 </table>
 <section id="numbers-including-booleans">
-<h3><span class="section-number">42.2.4.1. </span>Numbers (including booleans)<a class="headerlink" href="#numbers-including-booleans" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.2.4.1. </span>Numbers (including booleans)<a class="headerlink" href="#numbers-including-booleans" title="Permalink to this headline">#</a></h3>
 <p>There are three numeric types in Python:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">int</span></code></p></li>
@@ -2139,7 +2139,7 @@ <h3><span class="section-number">42.2.4.1. </span>Numbers (including booleans)<a
 <li><p><code class="docutils literal notranslate"><span class="pre">complex</span></code></p></li>
 </ul>
 <section id="integers">
-<h4><span class="section-number">42.2.4.1.1. </span>Integers<a class="headerlink" href="#integers" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.1.1. </span>Integers<a class="headerlink" href="#integers" title="Permalink to this headline">#</a></h4>
 <p>Int, or integer, is a whole number, positive or negative, without decimals, of unlimited length.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">positive_integer</span> <span class="o">=</span> <span class="mi">1</span>
 <span class="n">negative_integer</span> <span class="o">=</span> <span class="o">-</span><span class="mi">3255522</span>
@@ -2152,7 +2152,7 @@ <h4><span class="section-number">42.2.4.1.1. </span>Integers<a class="headerlink
 </div>
 </section>
 <section id="booleans">
-<h4><span class="section-number">42.2.4.1.2. </span>Booleans<a class="headerlink" href="#booleans" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.1.2. </span>Booleans<a class="headerlink" href="#booleans" title="Permalink to this headline">#</a></h4>
 <p>Booleans represent the truth values <code class="docutils literal notranslate"><span class="pre">False</span></code> and <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">true_boolean</span> <span class="o">=</span> <span class="kc">True</span>
 <span class="n">false_boolean</span> <span class="o">=</span> <span class="kc">False</span>
@@ -2170,7 +2170,7 @@ <h4><span class="section-number">42.2.4.1.2. </span>Booleans<a class="headerlink
 </div>
 </section>
 <section id="floats">
-<h4><span class="section-number">42.2.4.1.3. </span>Floats<a class="headerlink" href="#floats" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.1.3. </span>Floats<a class="headerlink" href="#floats" title="Permalink to this headline">#</a></h4>
 <p>Float, or “floating point number” is a number, positive or negative, containing one or more decimals.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">float_number</span> <span class="o">=</span> <span class="mf">7.0</span>
 <span class="c1"># Another way of declaring float is using float() function.</span>
@@ -2193,7 +2193,7 @@ <h4><span class="section-number">42.2.4.1.3. </span>Floats<a class="headerlink"
 </div>
 </section>
 <section id="complexes">
-<h4><span class="section-number">42.2.4.1.4. </span>Complexes<a class="headerlink" href="#complexes" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.1.4. </span>Complexes<a class="headerlink" href="#complexes" title="Permalink to this headline">#</a></h4>
 <p>A complex number has two parts, a real part and an imaginary part.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">complex_number_1</span> <span class="o">=</span> <span class="mi">5</span> <span class="o">+</span> <span class="mi">6</span><span class="n">j</span>
 <span class="n">complex_number_2</span> <span class="o">=</span> <span class="mi">3</span> <span class="o">-</span> <span class="mi">2</span><span class="n">j</span>
@@ -2205,7 +2205,7 @@ <h4><span class="section-number">42.2.4.1.4. </span>Complexes<a class="headerlin
 </div>
 </section>
 <section id="number-operation">
-<h4><span class="section-number">42.2.4.1.5. </span>Number operation<a class="headerlink" href="#number-operation" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.1.5. </span>Number operation<a class="headerlink" href="#number-operation" title="Permalink to this headline">#</a></h4>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="c1"># Addition.</span>
 <span class="k">assert</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">4</span> <span class="o">==</span> <span class="mi">6</span>
 
@@ -2231,7 +2231,7 @@ <h4><span class="section-number">42.2.4.1.5. </span>Number operation<a class="he
 </section>
 </section>
 <section id="strings-and-their-methods">
-<h3><span class="section-number">42.2.4.2. </span>Strings and their methods<a class="headerlink" href="#strings-and-their-methods" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.2.4.2. </span>Strings and their methods<a class="headerlink" href="#strings-and-their-methods" title="Permalink to this headline">#</a></h3>
 <p>Besides numbers, Python can also manipulate strings, which can be expressed in several ways. They can be enclosed in single quotes <code class="docutils literal notranslate"><span class="pre">''</span></code> or double quotes <code class="docutils literal notranslate"><span class="pre">&quot;&quot;</span></code> with the same result.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="c1"># String with double quotes.</span>
 <span class="n">name_1</span> <span class="o">=</span> <span class="s2">&quot;John&quot;</span>
@@ -2245,7 +2245,7 @@ <h3><span class="section-number">42.2.4.2. </span>Strings and their methods<a cl
 </pre></div>
 </div>
 <section id="string-type">
-<h4><span class="section-number">42.2.4.2.1. </span>String type<a class="headerlink" href="#string-type" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.2.1. </span>String type<a class="headerlink" href="#string-type" title="Permalink to this headline">#</a></h4>
 <p><code class="docutils literal notranslate"><span class="pre">\</span></code> can be used to escape quotes. Use <code class="docutils literal notranslate"><span class="pre">\'</span></code> to escape the single quote or use double quotes instead.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">single_quote_string</span> <span class="o">=</span> <span class="s1">&#39;doesn</span><span class="se">\&#39;</span><span class="s1">t&#39;</span>
 <span class="n">double_quote_string</span> <span class="o">=</span> <span class="s2">&quot;doesn&#39;t&quot;</span>
@@ -2311,7 +2311,7 @@ <h4><span class="section-number">42.2.4.2.1. </span>String type<a class="headerl
 </div>
 </section>
 <section id="string-operations">
-<h4><span class="section-number">42.2.4.2.2. </span>String operations<a class="headerlink" href="#string-operations" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.2.2. </span>String operations<a class="headerlink" href="#string-operations" title="Permalink to this headline">#</a></h4>
 <p>Strings can be concatenated (glued together) with the <code class="docutils literal notranslate"><span class="pre">+</span></code> operator, and repeated with <code class="docutils literal notranslate"><span class="pre">*</span></code>.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="k">assert</span> <span class="mi">3</span> <span class="o">*</span> <span class="s1">&#39;un&#39;</span> <span class="o">+</span> <span class="s1">&#39;ium&#39;</span> <span class="o">==</span> <span class="s1">&#39;unununium&#39;</span>
 <span class="k">assert</span> <span class="s1">&#39;Py&#39;</span> <span class="s1">&#39;thon&#39;</span> <span class="o">==</span> <span class="s1">&#39;Python&#39;</span>
@@ -2332,7 +2332,7 @@ <h4><span class="section-number">42.2.4.2.2. </span>String operations<a class="h
 </div>
 </section>
 <section id="string-methods">
-<h4><span class="section-number">42.2.4.2.3. </span>String methods<a class="headerlink" href="#string-methods" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.2.3. </span>String methods<a class="headerlink" href="#string-methods" title="Permalink to this headline">#</a></h4>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">hello_world_string</span> <span class="o">=</span> <span class="s2">&quot;Hello, World!&quot;</span>
 </pre></div>
 </div>
@@ -2364,10 +2364,10 @@ <h4><span class="section-number">42.2.4.2.3. </span>String methods<a class="head
 </div>
 </section>
 <section id="string-formatting">
-<h4><span class="section-number">42.2.4.2.4. </span>String formatting<a class="headerlink" href="#string-formatting" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.2.4. </span>String formatting<a class="headerlink" href="#string-formatting" title="Permalink to this headline">#</a></h4>
 <p>Often you’ll want more control over the formatting of your output than simply printing space-separated values. There are several ways to format output.</p>
 <section id="formatted-string-literals">
-<h5><span class="section-number">42.2.4.2.4.1. </span>Formatted string literals<a class="headerlink" href="#formatted-string-literals" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">41.2.4.2.4.1. </span>Formatted string literals<a class="headerlink" href="#formatted-string-literals" title="Permalink to this headline">#</a></h5>
 <p>Formatted string literals (also called f-strings for short) let you include the value of Python expressions inside a string by prefixing the string with <code class="docutils literal notranslate"><span class="pre">f</span></code> or <code class="docutils literal notranslate"><span class="pre">F</span></code> and writing expressions as <code class="docutils literal notranslate"><span class="pre">{expression}</span></code>.</p>
 <p>An optional format specifier can follow the expression. This allows greater control over how the value is formatted. The following example rounds pi to three places after the decimal.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">pi_value</span> <span class="o">=</span> <span class="mf">3.14159</span>
@@ -2387,7 +2387,7 @@ <h5><span class="section-number">42.2.4.2.4.1. </span>Formatted string literals<
 </div>
 </section>
 <section id="the-string-format-method">
-<h5><span class="section-number">42.2.4.2.4.2. </span>The string format() method<a class="headerlink" href="#the-string-format-method" title="Permalink to this headline">#</a></h5>
+<h5><span class="section-number">41.2.4.2.4.2. </span>The string format() method<a class="headerlink" href="#the-string-format-method" title="Permalink to this headline">#</a></h5>
 <p>Basic usage of the <code class="docutils literal notranslate"><span class="pre">str.format()</span></code> method looks like this:</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="k">assert</span> <span class="s1">&#39;We are </span><span class="si">{}</span><span class="s1"> who say &quot;</span><span class="si">{}</span><span class="s1">!&quot;&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;knights&#39;</span><span class="p">,</span> <span class="s1">&#39;Ni&#39;</span><span class="p">)</span> <span class="o">==</span> <span class="s1">&#39;We are knights who say &quot;Ni!&quot;&#39;</span>
 </pre></div>
@@ -2433,10 +2433,10 @@ <h5><span class="section-number">42.2.4.2.4.2. </span>The string format() method
 </section>
 </section>
 <section id="lists-and-their-methods-including-list-comprehensions">
-<h3><span class="section-number">42.2.4.3. </span>Lists and their methods (including list comprehensions)<a class="headerlink" href="#lists-and-their-methods-including-list-comprehensions" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.2.4.3. </span>Lists and their methods (including list comprehensions)<a class="headerlink" href="#lists-and-their-methods-including-list-comprehensions" title="Permalink to this headline">#</a></h3>
 <p>Python knows several compound data types, used to group together other values. The most versatile is the list, which can be written as a list of comma-separated values (items) between square brackets.</p>
 <section id="list-type">
-<h4><span class="section-number">42.2.4.3.1. </span>List type<a class="headerlink" href="#list-type" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.3.1. </span>List type<a class="headerlink" href="#list-type" title="Permalink to this headline">#</a></h4>
 <p>Lists are very similar to arrays. They can contain any type of variable, and they can contain as many variables as you wish. Lists can also be iterated over in a very simple manner.</p>
 <p>Here is an example of how to build a list.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">squares</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">25</span><span class="p">]</span>
@@ -2494,7 +2494,7 @@ <h4><span class="section-number">42.2.4.3.1. </span>List type<a class="headerlin
 </div>
 </section>
 <section id="list-methods">
-<h4><span class="section-number">42.2.4.3.2. </span>List methods<a class="headerlink" href="#list-methods" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.3.2. </span>List methods<a class="headerlink" href="#list-methods" title="Permalink to this headline">#</a></h4>
 <p><code class="docutils literal notranslate"><span class="pre">list.remove(x)</span></code> removes the first item from the list whose value is equal to x. It raises a <code class="docutils literal notranslate"><span class="pre">ValueError</span></code> if there is no such item.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="k">assert</span> <span class="n">fruits</span> <span class="o">==</span> <span class="p">[</span><span class="s1">&#39;orange&#39;</span><span class="p">,</span> <span class="s1">&#39;apple&#39;</span><span class="p">,</span> <span class="s1">&#39;pear&#39;</span><span class="p">,</span> <span class="s1">&#39;banana&#39;</span><span class="p">,</span> <span class="s1">&#39;kiwi&#39;</span><span class="p">,</span> <span class="s1">&#39;apple&#39;</span><span class="p">,</span> <span class="s1">&#39;banana&#39;</span><span class="p">,</span> <span class="s1">&#39;grape&#39;</span><span class="p">]</span>
 
@@ -2571,7 +2571,7 @@ <h4><span class="section-number">42.2.4.3.2. </span>List methods<a class="header
 </div>
 </section>
 <section id="the-del-statement">
-<h4><span class="section-number">42.2.4.3.3. </span>The del statement<a class="headerlink" href="#the-del-statement" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.3.3. </span>The del statement<a class="headerlink" href="#the-del-statement" title="Permalink to this headline">#</a></h4>
 <p>There is a way to remove an item from a list given its index instead of its value: the <code class="docutils literal notranslate"><span class="pre">del</span></code> statement.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pytest</span>
 
@@ -2586,7 +2586,7 @@ <h4><span class="section-number">42.2.4.3.3. </span>The del statement<a class="h
 </div>
 </section>
 <section id="list-comprehensions">
-<h4><span class="section-number">42.2.4.3.4. </span>List comprehensions<a class="headerlink" href="#list-comprehensions" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.3.4. </span>List comprehensions<a class="headerlink" href="#list-comprehensions" title="Permalink to this headline">#</a></h4>
 <p>List comprehensions provide a concise way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence or iterable or to create a subsequence of those elements that satisfy a certain condition.</p>
 <p>For example, assume we want to create a list of squares, like:</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">squares</span> <span class="o">=</span> <span class="p">[]</span>
@@ -2659,7 +2659,7 @@ <h4><span class="section-number">42.2.4.3.4. </span>List comprehensions<a class=
 </div>
 </section>
 <section id="nested-list-comprehensions">
-<h4><span class="section-number">42.2.4.3.5. </span>Nested list comprehensions<a class="headerlink" href="#nested-list-comprehensions" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.2.4.3.5. </span>Nested list comprehensions<a class="headerlink" href="#nested-list-comprehensions" title="Permalink to this headline">#</a></h4>
 <p>The initial expression in a list comprehension can be any arbitrary expression, including another list comprehension.</p>
 <p>Consider the following example of a 3x4 matrix implemented as a list of 3 lists of length 4:</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">matrix</span> <span class="o">=</span> <span class="p">[</span>
@@ -2721,7 +2721,7 @@ <h4><span class="section-number">42.2.4.3.5. </span>Nested list comprehensions<a
 </section>
 </section>
 <section id="dictionaries">
-<h3><span class="section-number">42.2.4.4. </span>Dictionaries<a class="headerlink" href="#dictionaries" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.2.4.4. </span>Dictionaries<a class="headerlink" href="#dictionaries" title="Permalink to this headline">#</a></h3>
 <p>A dictionary is a collection that is unordered, changeable and indexed. In Python, dictionaries are written with curly brackets, and they have keys and values.</p>
 <p>It is best to think of a dictionary as a set of key: value pairs, with the requirement that the keys are unique (within one dictionary). A pair of braces creates an empty dictionary: <code class="docutils literal notranslate"><span class="pre">{}</span></code>. Placing a comma-separated list of key-value pairs within the braces adds initial key-value pairs to the dictionary; this is also the way dictionaries are written on output.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">fruits_dictionary</span> <span class="o">=</span> <span class="p">{</span>
@@ -2787,7 +2787,7 @@ <h3><span class="section-number">42.2.4.4. </span>Dictionaries<a class="headerli
 </div>
 </section>
 <section id="type-casting">
-<h3><span class="section-number">42.2.4.5. </span>Type casting<a class="headerlink" href="#type-casting" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.2.4.5. </span>Type casting<a class="headerlink" href="#type-casting" title="Permalink to this headline">#</a></h3>
 <p>There may be times when you want to specify a type on to a variable. This can be done with <strong>casting</strong>. Python is an object-orientated language, and as such it uses classes to define data types, including its primitive types.</p>
 <p>Casting in python is therefore done using constructor functions.</p>
 <ul class="simple">
@@ -2821,11 +2821,11 @@ <h3><span class="section-number">42.2.4.5. </span>Type casting<a class="headerli
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">42.2.5. </span>4. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.2.5. </span>4. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/prerequisites/python-programming-basics.html#your-turn">Python programming basics - Your turn</a></p>
 </section>
 <section id="references">
-<h2><span class="section-number">42.2.6. </span>5. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.2.6. </span>5. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/prerequisites/python-programming-basic.html">Python programming basic</a></p></li>
 </ol>
@@ -2869,13 +2869,13 @@ <h2><span class="section-number">42.2.6. </span>5. References<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">42.1. </span>Python programming introduction</p>
+            <p class="prev-next-title"><span class="section-number">41.1. </span>Python programming introduction</p>
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-        <p class="prev-next-title"><span class="section-number">42.3. </span>Python programming advanced</p>
+        <p class="prev-next-title"><span class="section-number">41.3. </span>Python programming advanced</p>
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     <i class="fas fa-angle-right"></i>
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diff --git a/slides/python-programming/python-programming-introduction.html b/slides/python-programming/python-programming-introduction.html
index 3508b0b00..dcd99bff1 100644
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+++ b/slides/python-programming/python-programming-introduction.html
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     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
-    <title>42.1. Python programming introduction &#8212; Ocademy Open Machine Learning Book</title>
+    <title>41.1. Python programming introduction &#8212; Ocademy Open Machine Learning Book</title>
     
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   <link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
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     <link rel="shortcut icon" href="../../_static/favicon.ico"/>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="42.2. Python programming basics" href="python-programming-basics.html" />
-    <link rel="prev" title="42. Slides" href="../introduction.html" />
+    <link rel="next" title="41.2. Python programming basics" href="python-programming-basics.html" />
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 <ul class="nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="../../prerequisites/python-programming-introduction.html">
-   1. Python programming basics
+   1. Python programming introduction
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="../../prerequisites/python-programming-basics.html">
-   3. Python programming basics
+   2. Python programming basics
   </a>
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  <li class="toctree-l1">
   <a class="reference internal" href="../../prerequisites/python-programming-advanced.html">
-   4. Python programming advanced
+   3. Python programming advanced
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 <ul class="nav bd-sidenav">
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-   5. Introduction
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   <ul>
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     <a class="reference internal" href="../../data-science/introduction/defining-data-science.html">
-     5.1. Defining data science
+     4.1. Defining data science
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/introduction/data-science-ethics.html">
-     5.2. Data Science ethics
+     4.2. Data Science ethics
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/introduction/defining-data.html">
-     5.3. Defining data
+     4.3. Defining data
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/introduction/introduction-to-statistics-and-probability.html">
-     5.4. Introduction to statistics and probability
+     4.4. Introduction to statistics and probability
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  </li>
  <li class="toctree-l1 has-children">
   <a class="reference internal" href="../../data-science/working-with-data/working-with-data.html">
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   <ul>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/relational-databases.html">
-     6.1. Relational databases
+     5.1. Relational databases
     </a>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/non-relational-data.html">
-     6.2. Non-relational data
+     5.2. Non-relational data
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    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/numpy.html">
-     6.3. NumPy
+     5.3. NumPy
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    <li class="toctree-l2 has-children">
     <a class="reference internal" href="../../data-science/working-with-data/pandas/pandas.html">
-     6.4. Pandas
+     5.4. Pandas
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     <ul>
      <li class="toctree-l3">
       <a class="reference internal" href="../../data-science/working-with-data/pandas/introduction-and-data-structures.html">
-       6.4.1. Introduction and Data Structures
+       5.4.1. Introduction and Data Structures
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      <li class="toctree-l3">
       <a class="reference internal" href="../../data-science/working-with-data/pandas/data-selection.html">
-       6.4.2. Data Selection
+       5.4.2. Data Selection
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       <a class="reference internal" href="../../data-science/working-with-data/pandas/advanced-pandas-techniques.html">
-       6.4.3. Advanced Pandas Techniques
+       5.4.3. Advanced Pandas Techniques
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     </ul>
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    <li class="toctree-l2">
     <a class="reference internal" href="../../data-science/working-with-data/data-preparation.html">
-     6.5. Data preparation
+     5.5. Data preparation
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   <a class="reference internal" href="../../data-science/data-visualization/data-visualization.html">
-   7. Data visualization
+   6. Data visualization
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+     40.9. Analyzing text about Data Science
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+     40.13. Apply your skills
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+     40.14. Try it in Excel
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+     40.16. Build your own custom vis
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-     41.17. Classifying datasets
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-     41.24. Data processing in Python
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     <a class="reference internal" href="../deep-learning/cnn.html">
-     42.20. Convolutional Neural Network
+     41.20. Convolutional Neural Network
     </a>
    </li>
    <li class="toctree-l2">
     <a class="reference internal" href="../deep-learning/gan.html">
-     42.21. Generative Adversarial Network
+     41.21. Generative Adversarial Network
     </a>
    </li>
   </ul>
@@ -1575,96 +1575,96 @@ <h1 class="site-logo" id="site-title">Ocademy Open Machine Learning Book</h1>
         <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#whats-python">
-   42.1.1. 1. What’s Python?
+   41.1.1. 1. What’s Python?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#why-python">
-   42.1.2. 2. Why Python?
+   41.1.2. 2. Why Python?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#basic-python">
-   42.1.3. 3. Basic Python
+   41.1.3. 3. Basic Python
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#indentation">
-     42.1.3.1. Indentation
+     41.1.3.1. Indentation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#comments">
-     42.1.3.2. Comments
+     41.1.3.2. Comments
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-types">
-     42.1.3.3. Data types
+     41.1.3.3. Data types
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#number">
-       42.1.3.3.1. Number
+       41.1.3.3.1. Number
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string">
-       42.1.3.3.2. String
+       41.1.3.3.2. String
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#booleans">
-       42.1.3.3.3. Booleans
+       41.1.3.3.3. Booleans
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#list">
-       42.1.3.3.4. List
+       41.1.3.3.4. List
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#dictionary">
-       42.1.3.3.5. Dictionary
+       41.1.3.3.5. Dictionary
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#tuple">
-       42.1.3.3.6. Tuple
+       41.1.3.3.6. Tuple
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#set">
-       42.1.3.3.7. Set
+       41.1.3.3.7. Set
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-data-types">
-     42.1.3.4. Checking data types
+     41.1.3.4. Checking data types
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#python-file">
-     42.1.3.5. Python file
+     41.1.3.5. Python file
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     42.1.3.6. Python file
+     41.1.3.6. Python file
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   42.1.4. 4. Your turn! 🚀
+   41.1.4. 4. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   42.1.5. 5. References
+   41.1.5. 5. References
   </a>
  </li>
 </ul>
@@ -1688,96 +1688,96 @@ <h2> Contents </h2>
                             <ul class="visible nav section-nav flex-column">
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#whats-python">
-   42.1.1. 1. What’s Python?
+   41.1.1. 1. What’s Python?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#why-python">
-   42.1.2. 2. Why Python?
+   41.1.2. 2. Why Python?
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#basic-python">
-   42.1.3. 3. Basic Python
+   41.1.3. 3. Basic Python
   </a>
   <ul class="nav section-nav flex-column">
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#indentation">
-     42.1.3.1. Indentation
+     41.1.3.1. Indentation
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#comments">
-     42.1.3.2. Comments
+     41.1.3.2. Comments
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#data-types">
-     42.1.3.3. Data types
+     41.1.3.3. Data types
     </a>
     <ul class="nav section-nav flex-column">
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#number">
-       42.1.3.3.1. Number
+       41.1.3.3.1. Number
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#string">
-       42.1.3.3.2. String
+       41.1.3.3.2. String
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#booleans">
-       42.1.3.3.3. Booleans
+       41.1.3.3.3. Booleans
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#list">
-       42.1.3.3.4. List
+       41.1.3.3.4. List
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#dictionary">
-       42.1.3.3.5. Dictionary
+       41.1.3.3.5. Dictionary
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#tuple">
-       42.1.3.3.6. Tuple
+       41.1.3.3.6. Tuple
       </a>
      </li>
      <li class="toc-h4 nav-item toc-entry">
       <a class="reference internal nav-link" href="#set">
-       42.1.3.3.7. Set
+       41.1.3.3.7. Set
       </a>
      </li>
     </ul>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#checking-data-types">
-     42.1.3.4. Checking data types
+     41.1.3.4. Checking data types
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#python-file">
-     42.1.3.5. Python file
+     41.1.3.5. Python file
     </a>
    </li>
    <li class="toc-h3 nav-item toc-entry">
     <a class="reference internal nav-link" href="#id1">
-     42.1.3.6. Python file
+     41.1.3.6. Python file
     </a>
    </li>
   </ul>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#your-turn">
-   42.1.4. 4. Your turn! 🚀
+   41.1.4. 4. Your turn! 🚀
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#references">
-   42.1.5. 5. References
+   41.1.5. 5. References
   </a>
  </li>
 </ul>
@@ -1806,9 +1806,9 @@ <h2> Contents </h2>
 </div></div>
 </div>
 <section class="tex2jax_ignore mathjax_ignore" id="python-programming-introduction">
-<h1><span class="section-number">42.1. </span>Python programming introduction<a class="headerlink" href="#python-programming-introduction" title="Permalink to this headline">#</a></h1>
+<h1><span class="section-number">41.1. </span>Python programming introduction<a class="headerlink" href="#python-programming-introduction" title="Permalink to this headline">#</a></h1>
 <section id="whats-python">
-<h2><span class="section-number">42.1.1. </span>1. What’s Python?<a class="headerlink" href="#whats-python" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.1.1. </span>1. What’s Python?<a class="headerlink" href="#whats-python" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Python is a high-level programming language for general-purpose programming.</p></li>
 <li><p>Python is open-source.</p></li>
@@ -1819,7 +1819,7 @@ <h2><span class="section-number">42.1.1. </span>1. What’s Python?<a class="hea
   <img class="removeMargin" src="https://www.python.org/static/img/python-logo.png"/>
 </div></section>
 <section id="why-python">
-<h2><span class="section-number">42.1.2. </span>2. Why Python?<a class="headerlink" href="#why-python" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.1.2. </span>2. Why Python?<a class="headerlink" href="#why-python" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>Easy to learn.</p></li>
 <li><p>Well adopted.</p></li>
@@ -1833,13 +1833,13 @@ <h2><span class="section-number">42.1.2. </span>2. Why Python?<a class="headerli
     </div>
 </div></section>
 <section id="basic-python">
-<h2><span class="section-number">42.1.3. </span>3. Basic Python<a class="headerlink" href="#basic-python" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.1.3. </span>3. Basic Python<a class="headerlink" href="#basic-python" title="Permalink to this headline">#</a></h2>
 <ul class="simple">
 <li><p>A Python script can be written in Python interactive shell or the code editor.</p></li>
 <li><p>A Python file has an extension <code class="docutils literal notranslate"><span class="pre">.py</span></code>.</p></li>
 </ul>
 <section id="indentation">
-<h3><span class="section-number">42.1.3.1. </span>Indentation<a class="headerlink" href="#indentation" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.1.3.1. </span>Indentation<a class="headerlink" href="#indentation" title="Permalink to this headline">#</a></h3>
 <p>An indentation is a white space in a text. Indentation in many languages is used to increase code readability, however, Python uses indentation to create blocks of codes.</p>
 <div class="cell tag_raises-exception docutils container">
 <div class="cell_input docutils container">
@@ -1856,7 +1856,7 @@ <h3><span class="section-number">42.1.3.1. </span>Indentation<a class="headerlin
 </div>
 </section>
 <section id="comments">
-<h3><span class="section-number">42.1.3.2. </span>Comments<a class="headerlink" href="#comments" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.1.3.2. </span>Comments<a class="headerlink" href="#comments" title="Permalink to this headline">#</a></h3>
 <p>Comments are very important to make the code more readable and to leave remarks in our code. Python does not run comment parts of our code.</p>
 <p><strong>Example: Single Line Comment</strong></p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="c1"># This is the first comment</span>
@@ -1873,9 +1873,9 @@ <h3><span class="section-number">42.1.3.2. </span>Comments<a class="headerlink"
 </div>
 </section>
 <section id="data-types">
-<h3><span class="section-number">42.1.3.3. </span>Data types<a class="headerlink" href="#data-types" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.1.3.3. </span>Data types<a class="headerlink" href="#data-types" title="Permalink to this headline">#</a></h3>
 <section id="number">
-<h4><span class="section-number">42.1.3.3.1. </span>Number<a class="headerlink" href="#number" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.1.3.3.1. </span>Number<a class="headerlink" href="#number" title="Permalink to this headline">#</a></h4>
 <ul class="simple">
 <li><p>Integer: integer(negative, zero and positive) numbers.
 Example: … -3, -2, -1, 0, 1, 2, 3 …</p></li>
@@ -1886,7 +1886,7 @@ <h4><span class="section-number">42.1.3.3.1. </span>Number<a class="headerlink"
 </ul>
 </section>
 <section id="string">
-<h4><span class="section-number">42.1.3.3.2. </span>String<a class="headerlink" href="#string" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.1.3.3.2. </span>String<a class="headerlink" href="#string" title="Permalink to this headline">#</a></h4>
 <p>A collection of one or more characters under a single or double quote. If a string is more than one sentence then we use a triple quote.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="s1">&#39;Asabeneh&#39;</span>
 <span class="s1">&#39;Finland&#39;</span>
@@ -1897,14 +1897,14 @@ <h4><span class="section-number">42.1.3.3.2. </span>String<a class="headerlink"
 </div>
 </section>
 <section id="booleans">
-<h4><span class="section-number">42.1.3.3.3. </span>Booleans<a class="headerlink" href="#booleans" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.1.3.3.3. </span>Booleans<a class="headerlink" href="#booleans" title="Permalink to this headline">#</a></h4>
 <div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kc">True</span>  <span class="c1">#  Is the light on? If it is on, then the value is True</span>
 <span class="kc">False</span> <span class="c1"># I</span>
 </pre></div>
 </div>
 </section>
 <section id="list">
-<h4><span class="section-number">42.1.3.3.4. </span>List<a class="headerlink" href="#list" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.1.3.3.4. </span>List<a class="headerlink" href="#list" title="Permalink to this headline">#</a></h4>
 <p>Python list is an ordered collection that allows to store items of different data types. A list is similar to an array in JavaScript.</p>
 <p><strong>Example:</strong></p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]</span>  <span class="c1"># all are the same data types - a list of numbers</span>
@@ -1915,7 +1915,7 @@ <h4><span class="section-number">42.1.3.3.4. </span>List<a class="headerlink" hr
 </div>
 </section>
 <section id="dictionary">
-<h4><span class="section-number">42.1.3.3.5. </span>Dictionary<a class="headerlink" href="#dictionary" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.1.3.3.5. </span>Dictionary<a class="headerlink" href="#dictionary" title="Permalink to this headline">#</a></h4>
 <p>A Python dictionary object is an unordered collection of data in a key-value pair format.</p>
 <p><strong>Example:</strong></p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="p">{</span>
@@ -1930,7 +1930,7 @@ <h4><span class="section-number">42.1.3.3.5. </span>Dictionary<a class="headerli
 </div>
 </section>
 <section id="tuple">
-<h4><span class="section-number">42.1.3.3.6. </span>Tuple<a class="headerlink" href="#tuple" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.1.3.3.6. </span>Tuple<a class="headerlink" href="#tuple" title="Permalink to this headline">#</a></h4>
 <p>A tuple is an ordered collection of different data types like a list, but tuples can not be modified once they are created. They are immutable.</p>
 <p><strong>Example:</strong></p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="s1">&#39;Asabeneh&#39;</span><span class="p">,</span> <span class="s1">&#39;Pawel&#39;</span><span class="p">,</span> <span class="s1">&#39;Brook&#39;</span><span class="p">,</span> <span class="s1">&#39;Abraham&#39;</span><span class="p">,</span> <span class="s1">&#39;Lidiya&#39;</span><span class="p">)</span> <span class="c1"># Names</span>
@@ -1941,7 +1941,7 @@ <h4><span class="section-number">42.1.3.3.6. </span>Tuple<a class="headerlink" h
 </div>
 </section>
 <section id="set">
-<h4><span class="section-number">42.1.3.3.7. </span>Set<a class="headerlink" href="#set" title="Permalink to this headline">#</a></h4>
+<h4><span class="section-number">41.1.3.3.7. </span>Set<a class="headerlink" href="#set" title="Permalink to this headline">#</a></h4>
 <p>A set is a collection of data types similar to a list and tuple. Unlike the list and the tuple, a set is not an ordered collection of items. Like in Mathematics, set in Python only stores unique items. In later sections, we will go into detail about every Python data type.</p>
 <p><strong>Example:</strong></p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="p">{</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">}</span>
@@ -1951,7 +1951,7 @@ <h4><span class="section-number">42.1.3.3.7. </span>Set<a class="headerlink" hre
 </section>
 </section>
 <section id="checking-data-types">
-<h3><span class="section-number">42.1.3.4. </span>Checking data types<a class="headerlink" href="#checking-data-types" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.1.3.4. </span>Checking data types<a class="headerlink" href="#checking-data-types" title="Permalink to this headline">#</a></h3>
 <p>To check the data type of certain data/variables we use the <code class="docutils literal notranslate"><span class="pre">type</span></code> function.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
@@ -1991,7 +1991,7 @@ <h3><span class="section-number">42.1.3.4. </span>Checking data types<a class="h
 </div>
 </section>
 <section id="python-file">
-<h3><span class="section-number">42.1.3.5. </span>Python file<a class="headerlink" href="#python-file" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.1.3.5. </span>Python file<a class="headerlink" href="#python-file" title="Permalink to this headline">#</a></h3>
 <p>A sample Python file <code class="docutils literal notranslate"><span class="pre">helloworld.py</span></code>.</p>
 <div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">3</span><span class="p">)</span>             <span class="c1"># addition(+)</span>
 <span class="nb">print</span><span class="p">(</span><span class="mi">3</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>             <span class="c1"># subtraction(-)</span>
@@ -2014,15 +2014,15 @@ <h3><span class="section-number">42.1.3.5. </span>Python file<a class="headerlin
 </div>
 </section>
 <section id="id1">
-<h3><span class="section-number">42.1.3.6. </span>Python file<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<h3><span class="section-number">41.1.3.6. </span>Python file<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
 </section>
 </section>
 <section id="your-turn">
-<h2><span class="section-number">42.1.4. </span>4. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.1.4. </span>4. Your turn! 🚀<a class="headerlink" href="#your-turn" title="Permalink to this headline">#</a></h2>
 <p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/prerequisites/python-programming-introduction.html#your-turn">Python programming introduction - Your turn</a></p>
 </section>
 <section id="references">
-<h2><span class="section-number">42.1.5. </span>5. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
+<h2><span class="section-number">41.1.5. </span>5. References<a class="headerlink" href="#references" title="Permalink to this headline">#</a></h2>
 <ol class="simple">
 <li><p><a class="reference external" href="https://ocademy-ai.github.io/machine-learning/prerequisites/python-programming-introduction.html">Python programming introduction</a></p></li>
 </ol>
@@ -2066,13 +2066,13 @@ <h2><span class="section-number">42.1.5. </span>5. References<a class="headerlin
         <i class="fas fa-angle-left"></i>
         <div class="prev-next-info">
             <p class="prev-next-subtitle">previous</p>
-            <p class="prev-next-title"><span class="section-number">42. </span>Slides</p>
+            <p class="prev-next-title"><span class="section-number">41. </span>Slides</p>
         </div>
     </a>
     <a class='right-next' id="next-link" href="python-programming-basics.html" title="next page">
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