PCA: Dimensionality Reduction

Finding the directions of maximum variance in your data

Principal Component Analysis is eigenvalues applied to data. You compute the covariance matrix of your dataset, find its eigenvectors (the principal components), and project onto the top-k directions of maximum variance. This module is the bridge from abstract eigenvalue theory to practical ML: you'll reduce a real dataset from high dimensions to 2D and visualize the clusters that emerge. Mini-lab: Run PCA on the Iris dataset -- reduce 4 features to 2, plot the result, and see the species separate.

Estimated time: 60 minutes

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