Machine Learning 101 with Scikit-learn and StatsModels

Course Curriculum

Chapter 1: Introduction

Lecture 1: What Does the Course Cover?

Chapter 2: Setting Up The Working Environment

Lecture 1: Setting Up the Environment – An Introduction (Do Not Skip, Please)!

Lecture 2: Why Python and Why Jupyter?

Lecture 3: Installing Anaconda

Lecture 4: The Jupyter Dashboard – Part 1

Lecture 5: The Jupyter Dashboard – Part 2

Lecture 6: Jupyter Shortcuts

Lecture 7: Installing sklearn

Lecture 8: Installing Packages – Exercise

Lecture 9: Installing Packages – Solution

Chapter 3: Linear Regression with StatsModels

Lecture 1: Introduction to Regression Analysis

Lecture 2: The Linear Regression Model

Lecture 3: Correlation vs Regression

Lecture 4: Geometrical Representation

Lecture 5: Python Packages Installation

Lecture 6: Simple Linear Regression in Python

Lecture 7: Simple Linear Regression in Python – Exercise

Lecture 8: What is Seaborn?

Lecture 9: What Does the StatsModels Summary Regression Table Tell us?

Lecture 10: SST, SSR, and SSE

Lecture 11: The Ordinary Least Squares (OLS)

Lecture 12: Goodness of Fit: The R-Squared

Lecture 13: The Multiple Linear Regression Model

Lecture 14: Adjusted R-Squared

Lecture 15: Multiple Linear Regression – Exercise

Lecture 16: F-Statistic and F-Test for a Linear Regression

Lecture 17: Assumptions of the OLS Framework

Lecture 18: A1: Linearity

Lecture 19: A2: No Endogeneity

Lecture 20: A3: Normality and Homoscedasticity

Lecture 21: A4: No Autocorrelation

Lecture 22: A5: No Multicollinearity

Lecture 23: Dealing with Categorical Data

Lecture 24: Dealing with Categorical Data – Exercise

Lecture 25: Making Predictions

Chapter 4: Linear Regression with Sklearn

Lecture 1: What is sklearn?

Lecture 2: Game Plan for sklearn

Lecture 3: Simple Linear Regression with sklearn

Lecture 4: Simple Linear Regression with sklearn – Summary Table

Lecture 5: A Note on Normalization

Lecture 6: Simple Linear Regression with sklearn – Exercise

Lecture 7: Multiple Linear Regression with sklearn

Lecture 8: Adjusted R-Squared

Lecture 9: Adjusted R-Squared – Exercise

Lecture 10: Feature Selection through p-values (F-regression)

Lecture 11: A Note on Calculation of P-values with sklearn

Lecture 12: Creating a Summary Table with the p-values

Lecture 13: Multiple Linear Regression – Exercise

Lecture 14: Feature Scaling

Lecture 15: Feature Selection through Standardization

Lecture 16: Making Predictions with Standardized Coefficients

Lecture 17: Feature Scaling – Exercise

Lecture 18: Underfitting and Overfitting

Lecture 19: Training and Testing

Chapter 5: Linear Regression – Practical Example

Lecture 1: Practical Example (Part 1)

Lecture 2: Practical Example (Part 2)

Lecture 3: A Note on Multicollinearity

Lecture 4: Practical Example (Part 3)

Lecture 5: Dummies and VIF – Exercise

Lecture 6: Practical Example (Part 4)

Lecture 7: Dummy Variables Interpretation – Exercise

Lecture 8: Practical Example (Part 5)

Lecture 9: Linear Regression – Exercise

Chapter 6: Logistic Regression

Lecture 1: Introduction to Logistic Regression

Lecture 2: A Simple Example of a Logistic Regression in Python

Lecture 3: What is the Difference Between a Logistic and a Logit Function?

Lecture 4: Your First Logistic Regression

Lecture 5: Your First Logistic Regression – Exercise

Lecture 6: A Coding Tip (optional)

Lecture 7: Going through the Regression Summary Table

Lecture 8: Going through the Regression Summary Table – Exercise

Lecture 9: Interpreting the Odds Ratio

Lecture 10: Dummies in a Logistic Regression

Lecture 11: Dummies in a Logistic Regression – Exercise

Lecture 12: Assessing the Accuracy of a Classification Model

Lecture 13: Assessing the Accuracy of a Classification Model – Exercise

Lecture 14: Underfitting and Overfitting