Problem Solving on Principal Component Analysis (PCA) | Machine Learning From Zero | L.45

Master problem solving on Principal Component Analysis (PCA) in this lecture. We solve numerical and conceptual PCA questions, covering variance maximization, covariance matrix construction, eigenvalues & eigenvectors, selecting principal components, and data projection. You’ll learn how PCA problems appear in GATE DA / GATE CSE, including common traps, step-by-step calculations, and interpretation of principal components for dimensionality reduction. This video is part of the Machine Learning From Zero series — designed for beginners and serious exam aspirants who want strong conceptual clarity. 👉 Full playlist: https://www.youtube.com/playlist?list=PL8RhRpQueHLsfh-qDGstSB34AnXSbiGY1 📌 Topics Covered • PCA Numerical Problems • Variance & Covariance Matrix • Eigenvalues & Eigenvectors in PCA • Selecting Principal Components • Projection onto Lower Dimensions • Reconstruction Error • Exam-Style Questions & Mistakes 🎯 Exam & Interview Relevance Highly useful for GATE DA, GATE CSE, university exams, interviews, and Machine Learning foundations.