Machine Learning

8 topics across 7 chapters

Chapter 1

Math prerequisites for ML (linear algebra, calculus basics, probability)

Chapter 2

Python + ML tooling (NumPy, pandas, scikit-learn, notebooks)

Chapter 3

Supervised learning fundamentals (regression, classification, trees, SVM)

Chapter 4

Unsupervised learning (clustering, dimensionality reduction, anomaly detection)

Chapter 5

Model evaluation & tuning (train/val/test, metrics, cross-validation, hyperparameters)

Chapter 6

Neural networks & deep learning basics (MLP, CNN/RNN/Transformers overview)

Chapter 7

Practical ML workflow & deployment (data pipelines, ML ops basics, ethics/fairness)