1. What is ML? (vs. traditional programming)

2. Types: Supervised, Unsupervised, Semi-supervised, Self-supervised, Reinforcement

3. Training, Validation, Test sets

4. Overfitting & Underfitting

5. Bias-Variance tradeoff

6. Feature engineering

7. Feature scaling (normalization, standardization)

8. One-hot encoding

9. Data augmentation