Machine Learning — Coursera

My working notes and exercises for Andrew Ng's Machine Learning Specialization.

Layout

• Note/ — handwritten/typed lecture notes and screenshots from the videos.
• Regression & Classification/ — the original Coursera lab notebooks (Course 1).
• tutorials/ — my own from-scratch implementations, organized by week and topic. Each folder contains:
  • scaffold_loop.py — loop-based implementation (for building intuition)
  • scaffold_vectorized.py — NumPy-vectorized implementation
  • test_scaffold.py — tests, including sklearn baselines where applicable
  • README.md — math notes and what the scaffold is asking me to implement

Progress

• Week 1 — Linear regression (univariate and multivariate)
• Week 2 — Feature scaling, polynomial regression, sklearn comparison