Principal Component Analysis: Dimensionality Reduction

This lecture introduces Principal Component Analysis (PCA) as a method for dimensionality reduction. It covers the concepts of projecting high-dimensional data onto lower-dimensional subspaces to find patterns and clusters. The limitations of PCA, applications in data compression, denoising, and regression are discussed. The lecture also explores the Wine Dataset and demonstrates how PCA can be used for visualization and linear subspaces. The importance of choosing the right number of principal components in PCA and Principal Component Regression is highlighted.