Linear regression, logistic regression, decision trees, random forests, gradient boosting, SVMs, k-nearest neighbors, k-means clustering, PCA, cross-validation, bias-variance tradeoff, feature engineering, model selection, hyperparameter tuning, evaluation metrics, and the full ML project lifecycle.
