Principal Component Analysis (PCA) Explained from scratch | Machine Learning from Zero | L.44

Описание: Understand Principal Component Analysis (PCA) from scratch in this lecture. We explain how PCA performs dimensionality reduction, how principal components are derived using eigenvalues and eigenvectors, and how data is projected onto lower-dimensional subspaces while preserving maximum variance.

You’ll learn the complete PCA workflow: data centering, covariance matrix, eigen decomposition, selecting principal components, and transforming data — all with clear intuition and visual explanations.

This video is part of the Machine Learning From Zero series, designed for beginners as well as advanced learners who want strong conceptual clarity.

👉 Full playlist:
   • Machine Learning From Zero  

📌 Prerequisites (Highly Recommended)

Eigenvalues, Eigenvectors & Diagonalization | Linear Algebra Lec 09
   • Eigenvalues, Eigenvectors & Diagonalizatio...  

Projection Vectors & Projection Matrix
   • Projection Vectors & Projection Matrix Exp...  

📌 Topics Covered

What is PCA and Why Dimensionality Reduction?
Variance Maximization Principle
Covariance Matrix Construction
Eigenvalues & Eigenvectors in PCA
Selecting Principal Components
Projection onto Lower Dimensions
Reconstruction Error
Advantages & Limitations of PCA

🎯 Exam & Interview Relevance

Highly useful for GATE DA, university exams, technical interviews, and Machine Learning foundations.