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Resources: Module 03 — Supervised Learning

  1. scikit-learn User Guide: Supervised Learning (https://scikit-learn.org/stable/supervised_learning.html) Authoritative documentation for every algorithm in this module with mathematical descriptions.

  2. "Hands-On Machine Learning" by Aurélien Géron — Chapters 4–7 Covers linear models, SVMs, decision trees, and ensembles with practical sklearn code.

  3. "An Introduction to Statistical Learning" by James, Witten, Hastie, Tibshirani (https://www.statlearning.com) Free online. Rigorous but accessible treatment of linear/logistic regression, trees, and SVMs. The gold standard ML textbook for applied statistics.

  4. XGBoost documentation (https://xgboost.readthedocs.io) The most widely used gradient boosting library. Essential reading before using GBT in production.