Created my custom website

2023-10-11-Regression-Algorithm-in-machine-learning-:-A-Comprehensive-Overview.md

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+# What is regression
+ Regression is a statisticalmodeling technique that aims to establish a relationship between a dependent variable 
+ (also known as the target or outcome variable) and one or more independent variables (also known as predictors or features). 
+ The goal of regression analysis is to predict the value of the dependent variable based on the given independent variables.
+
+ # Types of Regression Algorithms:
+
+ ## LinearRegression:
+ Linear regression is the simplest and most commonly used regression algorithm.
+ It assumes a linear relationship between the dependent variable and the independent variables.
+ The algorithm calculates the best-fit line that minimizes the sum of squared errors between the predicted and actual values.
+ Linear regression can be further categorized into simple linear regression 
+ (with one independent variable) and multiple linear regression (with multiple independent variables).
+
+ ## PolynomialRegression:
+ Polynomial regression extends linear regression by introducing polynomial terms to capture non-linear relationships between variables. 
+ It fits a curve rather than a straight line to the data points, allowing for more flexible modeling.
+
+ ## RidgeRegression:
+ Ridge regression is a regularization technique used when dealing with multicollinearity (high correlation among independent variables).
+ It adds a penalty term to the linear regression objective function, controlling the model's complexity and reducing overfitting.
+
+ ## LassoRegression:
+ Similar to ridge regression, lasso regression also addresses multicollinearity. 
+ However, it uses the L1 regularization technique, which tends to produce sparse models by shrinking some coefficients to zero.
+ This feature makes lasso regression useful for feature selection.
+
+ ## DecisionTree Regression:
+ Decision tree regression builds a decision tree by recursively splitting the data based on the independent variables. 
+ Each leaf node represents a predicted outcome value. 
+ Decision tree regression is advantageous in handling non-linear relationships and capturing interactions between variables.
+
+ ## RandomForest Regression:
+ Random forest regression combines multiple decision trees to make predictions.
+ It creates an ensemble of decision trees and aggregates their predictions, resulting in more accurate and robust models.

_config.yml

@@ -24,9 +24,9 @@
 # You can create any custom variable you would like, and they will be accessible
 # in the templates via {{ site.myvariable }}.
 
-title: Your awesome title
-email: your-email@domain.com
-author: GitHub User
+title: Richard Kilea
+email: Richardkilea@gmail.com
+author: Richard
 
 # Copyright setting
 # You can use any html code, currently below placeholders are available:
@@ -43,9 +43,10 @@ author: GitHub User
 copyright: "Unpublished Work (cleft) 2017-{currentYear} {author}"
 
 description: >- # this means to ignore newlines until "baseurl:"
-  Write an awesome description for your new site here. You can edit this
-  line in _config.yml. It will appear in your document head meta (for
-  Google search results) and in your feed.xml site description.
+  i extract, analyse, visualize and interpret large amounts of data from a range of sources. This includes-
+  Structured Data, Semi-Stuctured Data and Unstructured Data. I use different machine learning algorithms to 
+  build models that capture the underlying patterns in the data. once i train the model, it
+  can can start predicting using unseen data.
 
 baseurl: "" # the subpath of your site, e.g. /blog
 url: "" # the base hostname & protocol for your site, e.g. http://example.com

_data/defaults.yml

@@ -1,4 +1,4 @@
 home:
-  heading: "your awesome title"
-  subheading: "your awesome subheading"
+  heading: "Richard Kilea"
+  subheading: "Data Scientist"
   banner: "your awesome url"

_includes/views/footer.html

@@ -12,10 +12,6 @@
             | replace: '(cleft)', '<span class="copyleft">&copy;</span>'
         -%}
       <div>{{ copyright }}</div>
-      <div>Powered by <a title="Jekyll is a simple, blog-aware, static site
-      generator." href="http://jekyllrb.com/">Jekyll</a> &amp; <a title="Yat, yet
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-      <div class="footer-col rss-subscribe">Subscribe <a href="{{ "/feed.xml" | relative_url }}">via RSS</a></div>
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_posts/2023-10-03-my-first-post-this-year.md

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+---
+layout: post
+title: Introduction to the world of machine learning
+subtitle: Quick summary
+categories: machine learning
+tags: [Python, Data, AI, Machine Learning]
+---
+
+
+# Artificial Intelligence and Machine Learning
+AI and machine learning heavily rely on data for training and improvement. 
+These technologies analyze massive datasets to recognize patterns and make predictions, often outperforming human capabilities. 
+The potential for AI to understand and manipulate data could lead to new breakthroughs in various fields.
+
+Here are ways that data plays a crucial role:
+
+### Training data
+For AI systems to learn patterns, they need to be exposed to vast amounts of training data.
+This data is labeled, meaning it is annotated with the correct answers or outcomes.
+For instance, to teach a machine to recognize cats, it needs to see numerous images of cats along with their labels.
+
+### Feature Extraction
+In machine learning, features are the relevant characteristics or attributes of the data that influence the model's predictions. 
+The quality and relevance of features significantly impact the model's performance.
+AI and ML algorithms automatically extract features from the data to build meaningful representations.
+
+### Model Building
+Using the training data, AI/ML algorithms build models that capture the underlying patterns in the data.
+These models can be decision trees, neural networks, support vector machines, and more.
+The better the data quality, the more accurate and robust the models become.
+
+### Generalization
+Once trained, the models can generalize their learning to new, unseen data.
+This is a basis for making predictions or classifications on real-world data.
+The more diverse and representative the training data, the better the model's generalization.
+

about.md

@@ -3,6 +3,14 @@ layout: about
 title: About
 ---
 
-## About
+## Welcome to My Tech blog
 
-Hi, nice to meet you.
+### What i do
+1. Build customized predictive models.
+2. Investigate new data sources, perform statistical analyses, and document findings.
+3. Perform analysis on a wide variety of large.
+4. Create presentations and data visualizations to effectively communicate results to clients.
+5. Apply and combine current and emerging techniques and tools in novel ways.
+6. Develop and combine complex data analysis techniques and methods for a specific/custom application.
+7. Prepare and aggregate large datasets. 
+8. Maintain machine learning models and evaluation.