Machine Learning 101 with Scikit-learn and StatsModels

Table of contents

1. Chapter 1 : Introduction
   1. What Does the Course Cover?
2. Chapter 2 : Setting Up the Working Environment
   1. Setting Up the Environment - An Introduction (Do Not Skip, Please)!
   2. Why Python and Why Jupyter?
   3. Installing Anaconda
   4. The Jupyter Dashboard - Part 1
   5. The Jupyter Dashboard - Part 2
   6. Installing sklearn
3. Chapter 3 : Linear Regression with StatsModels
   1. Introduction to Regression Analysis
   2. The Linear Regression Model
   3. Correlation vs Regression
   4. Geometrical Representation
   5. Python Packages Installation
   6. Simple Linear Regression in Python
   7. What is Seaborn?
   8. What Does the StatsModels Summary Regression Table Tell us?
   9. SST, SSR, and SSE
   10. The Ordinary Least Squares (OLS)
   11. Goodness of Fit: The R-Squared
   12. The Multiple Linear Regression Model
   13. Adjusted R-Squared
   14. F-Statistic and F-Test for a Linear Regression
   15. Assumptions of the OLS Framework
   16. A1: Linearity
   17. A2: No Endogeneity
   18. A3: Normality and Homoscedasticity
   19. A4: No Autocorrelation
   20. A5: No Multicollinearity
   21. Dealing with Categorical Data
   22. Making Predictions
4. Chapter 4 : Linear Regression with Sklearn
   1. What is sklearn?
   2. Game Plan for sklearn
   3. Simple Linear Regression with sklearn
   4. Simple Linear Regression with sklearn - Summary Table
   5. Multiple Linear Regression with sklearn
   6. Adjusted R-Squared
   7. Feature Selection through p-values (F-regression)
   8. Creating a Summary Table with the p-values
   9. Feature Scaling
   10. Feature Selection through Standardization
   11. Making Predictions with Standardized Coefficients
   12. Underfitting and Overfitting
   13. Training and Testing
5. Chapter 5 : Linear Regression - Practical Example
   1. Practical Example (Part 1)
   2. Practical Example (Part 2)
   3. Practical Example (Part 3)
   4. Practical Example (Part 4)
   5. Practical Example (Part 5)
6. Chapter 6 : Logistic Regression
   1. Introduction to Logistic Regression
   2. A Simple Example of a Logistic Regression in Python
   3. What is the Difference Between a Logistic and a Logit Function?
   4. Your First Logistic Regression
   5. A Coding Tip (optional)
   6. Going through the Regression Summary Table
   7. Interpreting the Odds Ratio
   8. Dummies in a Logistic Regression
   9. Assessing the Accuracy of a Classification Model
   10. Underfitting and Overfitting
   11. Testing our Model and Bulding a Confusion Matrix
7. Chapter 7 : Cluster Analysis
   1. Introduction to Cluster Analysis
   2. Examples of Clustering
   3. Classification vs Clustering
   4. Math Concepts Needed to Proceed
   5. K-Means Clustering
   6. A Hands-on Example of K-Means
   7. Categorical Data in Cluster Analysis
   8. The Elbow Method or How to Choose the Number of Clusters
   9. Pros and Cons of K-Means
   10. Standardization of Features when Clustering
   11. Cluster Analysis and Regression Analysis
   12. Practical Example: Market Segmentation (Part 1)
   13. Practical Example: Market Segmentation (Part 2)
   14. What Can be Done with Cluster Analysis?
8. Chapter 8 : Cluster Analysis: Additional Topics
   1. Other Types of Clustering
   2. The Dendrogram
   3. Heatmaps