- 01. Machine Learning Models
- 02. The Bias-Variance Trade-off
- 03. Cross-Validation
- 04. Exercise 1: Cross-Validation and the Train-Test Split
- 05. Exercise 1 Solution
- 06. Model Performance and Loss
- 07. Identifying and Mitigating Overfitting and Underfitting
- 08. Dimensionality Reduction
- 09. Addressing Overfitting with Regularization
- 10. Regularization in Distance-Based Models
- 11. Regularization in Tree-Based Models
- 12. Exercise 2: Regularizing a Decision Tree Model
- 13. Exercise 2 Solution
- 14. Ensemble Methods
- 15. Demo 1: Cross-Validation and the Train-Test Split
- 16. Demo 2: Dimensionality Reduction with Principal Components Analysis (PCA)
- 17. Demo 3: Optimizing k-Nearest Neighbors (k-NN) Algorithms
- 18. Demo 4: Optimizing Decision Tree Models