ocademy-ai · zhangqi444 · Sep 4, 2023 · Sep 2, 2023 · Sep 3, 2023 · Sep 3, 2023
diff --git a/.github/workflows/pr-awesome-main.yml → .github/workflows/awesome-pr-main.yml b/.github/workflows/pr-awesome-main.yml → .github/workflows/awesome-pr-main.yml
diff --git a/.github/workflows/pr-awesome-release.yml → .github/workflows/awesome-pr-release.yml b/.github/workflows/pr-awesome-release.yml → .github/workflows/awesome-pr-release.yml
diff --git a/.github/workflows/main.yml → .github/workflows/book-main.yml b/.github/workflows/main.yml → .github/workflows/book-main.yml
diff --git a/.github/workflows/release.yml → .github/workflows/book-release.yml b/.github/workflows/release.yml → .github/workflows/book-release.yml
@@ -65,6 +65,7 @@ jobs:
       run: |
         cd ./open-machine-learning-jupyter-book/
         bash ./post-build.sh
+        cp CNAME ./_build/html/
 
     # Push the book's HTML to github-pages
     - name: GitHub Pages action

diff --git a/CNAME b/CNAME
diff --git a/README.md b/README.md
@@ -18,17 +18,22 @@
 
 ## 👍 Ocademy Awesome [![](<https://img.shields.io/badge/-start%20to%20learn%20%E2%86%92-rgb(124,156,55)?style=for-the-badge>)](./awesome/README.md)
 
+A curated list of awesome AI courses, tutorials, books, tools, and other learning resources.
 
-A curated list of awesome AI courses, tutorials, books, tools and other learning resources.
+![ezgif com-video-to-gif](https://github.com/ocademy-ai/machine-learning/assets/5424267/52faa6e0-4cba-4a47-a30d-934d72f805b6)
 
 ## 🤖 Ocademy GenAI [![](<https://img.shields.io/badge/-start%20to%20create%20%E2%86%92-rgb(224,156,55)?style=for-the-badge>)](./generative-ai/prompts/README.md)
 
 Resources about Generative AI, started with a list of prompts for AI Teaching and Learning.
 
+![ezgif com-video-to-gif](https://github.com/ocademy-ai/machine-learning/assets/5424267/5b8e3f70-a88e-4f69-8a17-dcae7856eda6)
+
 ## 📓 Ocademy Open Book [![](<https://img.shields.io/badge/-start%20to%20read%20%E2%86%92-rgb(84,56,255)?style=for-the-badge>)](https://ocademy-ai.github.io/machine-learning/)
 
 An interactive and visual book aims to help millions of busy adults transition into AI.
 
+![ezgif com-video-to-gif (1)](https://github.com/ocademy-ai/machine-learning/assets/5424267/193b78e3-9e58-44c7-8349-74b6f4a354db)
+
 ## 👩‍💻 How to contribute
 
 We welcome all contributions to the community and are excited to welcome you aboard. If you have the passion for Data Science, Machine Learning, and Deep Learning, simply fork the repository and follow our [Guidelines for contributing](https://github.com/ocademy-ai/machine-learning/blob/main/CONTRIBUTING.md) to make your changes.

diff --git a/awesome/CONTRIBUTING.md b/awesome/CONTRIBUTING.md
@@ -20,7 +20,8 @@ The awesome lists are structured into several parts.
 
 Here is the latest ERD of the database.
 
-![Screenshot 2023-08-16 at 1 39 37 PM](https://github.com/ocademy-ai/machine-learning/assets/5424267/194ce266-8f0b-49d9-9210-d8c42d731e33)
+![schema](https://github.com/ocademy-ai/machine-learning/assets/5424267/1a21a701-9404-4d4e-82d6-8fc92445f8d2)
+
 
 ### How to update the data?
 
@@ -30,7 +31,7 @@ The built-in Directus CRM is the recommended way to update the data. But if you'
 
 The Directus is defined by the `awesome/database/docker-compose.yml` by following the [official self-hosting guidance](https://docs.directus.io/self-hosted/quickstart.html), including,
 
-- the login credential,
+- the login credentials,
 - SQLite database location,
 - port,
 - key and secret.
@@ -120,7 +121,7 @@ To update the awesome lists data schema,
    2. automatically update the `createdAt` column and `updatedAt` column.
 7. `knex migrate:latest`
 8. Verify your changes by using Directus, a SQLite editor or any other way you prefer.
-9. Update the database ERD in the [Notes for contributors](#notes-for-contributors) section if needed.
+9. Update the database ERD in the [Notes for contributors](#notes-for-contributors) section if needed by using [DBVisualizer](https://confluence.dbvis.com/display/UG232/Viewing+Entity+Relationships).
 10. Submit PR, a SQLite database diff will be generated automatically by the GitHub action.
 11. Review the GitHub Action build log, and make sure only intended change is included.
 

diff --git a/awesome/README.ipynb b/awesome/README.ipynb
diff --git a/awesome/README.md b/awesome/README.md
diff --git a/awesome/database/data.db b/awesome/database/data.db
diff --git a/awesome/lists/lib/utils/awesome_list.py → awesome/lists/lib/utils/format.py b/awesome/lists/lib/utils/awesome_list.py → awesome/lists/lib/utils/format.py
@@ -103,7 +103,7 @@ def apply_label_style_to_boolean_column(df, column_name):
     if column_name not in df:
         return
 
-    df[column_name] = df[column_name].apply(lambda x: '✅' if x == 'True' else '❌')
+    df[column_name] = df[column_name].apply(lambda x: '✅' if x == 1 else '❌')
 
 
 def apply_label_style_to_column(df, text_column, icon_palette, convert=capitalize_properly):
@@ -139,7 +139,7 @@ def _construct_author(x, author_column, platform_column):
     if x['authorCount'] > 3:
         x[author_column] += ', others'
 
-    if x[platform_column] is not "":
+    if x[platform_column] != "":
         return f"{x[author_column]}@{x[platform_column]}"
 
     return x[author_column]
@@ -185,5 +185,6 @@ def apply_common_style(df, excluded_columns):
     apply_label_style_to_column(df, 'level', level_icon_palette)
 
     apply_label_style_to_boolean_column(df, 'hasCert')
-
+    df.rename(columns={'hasCert': 'Cert'}, inplace=True)
+
     return clean_up(df, excluded_columns)
diff --git a/awesome/sync/utils.js b/awesome/sync/utils.js
@@ -26,7 +26,7 @@ async function fetchLocalData(TABLE_NAME) {
             if (item.hasOwnProperty('hasCert')) {
                 return {
                     ...item,
-                    hasCert: item.hasCert === "True"
+                    hasCert: item.hasCert === 1
                 };
             }
             return item;

diff --git a/open-machine-learning-jupyter-book/CNAME b/open-machine-learning-jupyter-book/CNAME
@@ -0,0 +1 @@
+press.ocademy.cc
diff --git a/open-machine-learning-jupyter-book/_config.yml b/open-machine-learning-jupyter-book/_config.yml
@@ -1,7 +1,7 @@
 # Book settings
 # Learn more at https://jupyterbook.org/customize/config.html
 
-title: An interactive and visual Machine Learning book
+title: Ocademy Open Machine Learning Book
 author: Ocademy
 copyright: 2022-2023
 logo: logo-long.png

diff --git a/...hine-learning-jupyter-book/assignments/data-science/estimation-of-COVID-19-pandemic.ipynb b/...hine-learning-jupyter-book/assignments/data-science/estimation-of-COVID-19-pandemic.ipynb
@@ -857,7 +857,9 @@
     "jupyter": {
      "source_hidden": true
     },
-    "tags": []
+    "tags": [
+     "hide-input"
+    ]
    },
    "outputs": [],
    "source": [
@@ -1222,7 +1224,9 @@
     "jupyter": {
      "source_hidden": true
     },
-    "tags": []
+    "tags": [
+     "hide-input"
+    ]
    },
    "outputs": [],
    "source": [
@@ -1669,7 +1673,9 @@
     "jupyter": {
      "source_hidden": true
     },
-    "tags": []
+    "tags": [
+     "hide-input"
+    ]
    },
    "outputs": [],
    "source": [

diff --git a/open-machine-learning-jupyter-book/intro.md b/open-machine-learning-jupyter-book/intro.md
@@ -25,7 +25,7 @@ An interactive and visual Machine Learning book - with code and assignment
 ````
 `````
 
-# Learn with us
+# Learn AI together, for free
 
 ````{grid} 1 1 2 3
 :class-container: text-center

diff --git a/...e-learning-jupyter-book/ml-fundamentals/regression/linear-and-polynomial-regression.ipynb b/...e-learning-jupyter-book/ml-fundamentals/regression/linear-and-polynomial-regression.ipynb
@@ -1,25 +1,26 @@
 {
  "cells": [
   {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "95868c2f-5abf-4fc4-bd6b-256d55d051eb",
+   "cell_type": "markdown",
+   "id": "cb578cb6-9845-4eb3-94ab-51c5e677031a",
    "metadata": {
     "editable": true,
     "slideshow": {
-     "slide_type": ""
+     "slide_type": "skip"
     },
-    "tags": [
-     "hide-cell"
-    ]
+    "tags": []
    },
-   "outputs": [],
    "source": [
-    "# Install the necessary dependencies\n",
-    "\n",
-    "import os\n",
-    "import sys\n",
-    "!{sys.executable} -m pip install --quiet pandas scikit-learn numpy matplotlib jupyterlab_myst\n"
+    "---\n",
+    "license:\n",
+    "    code: MIT\n",
+    "    content: CC-BY-4.0\n",
+    "github: https://github.com/ocademy-ai/machine-learning\n",
+    "venue: By Ocademy\n",
+    "open_access: true\n",
+    "bibliography:\n",
+    "  - https://raw.githubusercontent.com/ocademy-ai/machine-learning/main/open-machine-learning-jupyter-book/references.bib\n",
+    "---"
    ]
   },
   {
@@ -41,7 +42,23 @@
     "width: 100%\n",
     "---\n",
     "Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)\n",
-    ":::\n"
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed080429-60c9-4f56-a3b5-7f3c533ec961",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "```{seealso}\n",
+    "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 your visual interpretation of the scatterplot? Does that change if you use more fine-grained measure instead of `Month`, eg. *day of the year* (i.e. number of days since the beginning of the year)?\n",
+    "```"
    ]
   },
   {
@@ -58,6 +75,28 @@
     "## Build a regression model using Scikit-learn: regression four ways"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "13f15a2c-b2c3-4877-afba-8d207246d0af",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Install the necessary dependencies\n",
+    "\n",
+    "import os\n",
+    "import sys\n",
+    "!{sys.executable} -m pip install --quiet pandas scikit-learn numpy matplotlib jupyterlab_myst ipython\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 35,
@@ -1692,6 +1731,20 @@
     ":filter: docname in docnames\n",
     ":::"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "98aaaa3a-4a48-4d3e-8aa2-ba6328731d82",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

diff --git a/tutorials/llm-app/LLM.ipynb b/tutorials/llm-app/LLM.ipynb
diff --git a/tutorials/llm-app/LLMappstack.png b/tutorials/llm-app/LLMappstack.png
diff --git a/tutorials/visualize-code-execution-by-python-tutor.ipynb b/tutorials/visualize-code-execution-by-python-tutor.ipynb
diff --git a/tutorials/visualize-pandas-code-by-pandas-tutor.ipynb b/tutorials/visualize-pandas-code-by-pandas-tutor.ipynb