Master the bias-variance tradeoff with mathematical derivations, Python implementations, and practical strategies. Learn how to balance model complexity for optimal machine learning performance.
Master unsupervised clustering algorithms including K-means, hierarchical clustering, DBSCAN, and Gaussian mixtures. Learn implementation, evaluation, and practical applications with Python.
Master cross-validation techniques for robust model evaluation. Learn K-fold, stratified, time series, and nested CV with practical Python implementations.
Master dimensionality reduction techniques including PCA, t-SNE, UMAP, and LDA. Learn theory, implementation, and practical applications for high-dimensional data analysis.
