Book 4 — Classical Machine Learning

Every algorithm walked through: intuition → math → from-scratch → library → evaluation → failure modes.

Regression

• Linear regression — full deep-dive
• Polynomial regression
• Ridge / Lasso / Elastic Net — full deep-dive

Classification

• Logistic regression — full deep-dive
• KNN — full deep-dive
• Naive Bayes — full deep-dive
• Decision trees — full deep-dive
• Random forests — full deep-dive
• Gradient boosting (XGBoost, LightGBM, CatBoost) — full deep-dive
• SVM — full deep-dive

Status

Complete deep-dive coverage of linear, polynomial, regularized and logistic regression, KNN and Naive Bayes, decision trees, ensemble methods (random forests and gradient boosting), and support vector machines.