Decision Trees and Ensemble Methods
From single decision trees to random forests and gradient boosting - how combining models reduces error and when to use each approach.
Machine-Learning
Linear Regression
The simplest predictive model - how it works, how to fit it, what can go wrong, and how to fix it.
Model Evaluation
Loss functions, metrics, cross-validation, and diagnostic curves - how to measure whether your model actually works.
Regularization and Feature Selection
Preventing overfitting and choosing the right features - bias-variance trade-off, Ridge, Lasso, and feature selection methods.