1. Regression, Prediction, and Supervised Learning. Section Overview (I)
2. The Traditional Simple Regression Model (II)
3. The Traditional Simple Regression Model (III)
4. Some practical and useful modelling concepts (IV)
5. Some practical and useful modelling concepts (V)
6. Linear Multiple Regression model (VI)
7. Linear Multiple Regression model (VII)
8. Multivariate Polynomial Multiple Regression models (VIII)
9. Multivariate Polynomial Multiple Regression models (VIIII)
10. Regression Regularization, Lasso and Ridge models (X)
11. Decision Tree Regression models (XI)
12. Random Forest Regression (XII)
13. Voting Regression (XIII)
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