Singular Value Decomposition (SVD) is a powerful matrix factorization technique used across machine learning, computer vision, and data science. From transforming non-square matrices to enabling PCA without explicitly computing the covariance matrix, SVD simplifies complex transformations into elegant geometric steps. This blog unpacks its meaning, mechanics, and visual intuition with real-world applications.