De broglie wave equation

A practical guide to dimensionality reduction using PCA. Learn how to compress high-dimensional datasets (100+ features) into 2-3 principal components without losing critical variance. Includes: mathematical intuition (covariance matrices, eigenvectors), implementation patterns (scikit-learn, TensorFlow), when to use PCA vs. other reduction techniques (t-SNE, UMAP), and production gotchas (scaling requirements, interpretation challenges). Real examples: image compression, feature engineering for faster models, visualization of complex datasets. Code snippets for training/inference pipelines.