Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA Versus SVD

The video tutorial explores the relationship between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD), explaining how SVD can be used to perform PCA. It covers the mathematical foundations of SVD, including the decomposition into matrices U, D, and V, and discusses eigenvectors and eigenvalues. The tutorial also demonstrates how SVD can be applied for dimensionality reduction, highlighting its practical implementation in software like MATLAB and Python. Finally, it introduces kernel PCA as a powerful dimensionality reduction technique.

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