03 - Decision Trees and Random Forest: Classification and Regression
Discover decision trees (interpretable, easy to understand) and random forests (ensemble of trees fo…
01 - Linear Algebra for ML: Vectors, Matrices, and Transformations
Essential fundamentals: vectors, matrices, determinants, eigenvalues, SVD decomposition. NO abstract concepts—only what's needed for ML. How data flows: input → weight matrices → output. Geometric visualizations, NumPy implementation, when each decomposition matters.
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Essential fundamentals: vectors, matrices, determinants, eigenvalues, SVD decomposition. NO abstract concepts—only what's needed for ML. How data flows: input → weight matrices → output. Geometric visualizations, NumPy implementation, when each decomposition matters.
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