What is ML? (vs. traditional programming)

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

Training, Validation, Test sets

Overfitting & Underfitting

Bias-Variance tradeoff

Feature engineering

Feature scaling (normalization, standardization)

One-hot encoding

Data augmentation