Principal Component Analysis (PCA) Explained Simply | Intuition, Math & Visualization скачать в хорошем качестве

Principal Component Analysis (PCA) Explained Simply | Intuition, Math & Visualization

Principal Component Analysis (PCA) is one of the most important techniques in machine learning and data science — but it’s often taught in a confusing, math-heavy way. In this video, we explain PCA from first principles: • Why PCA rotates the coordinate system • How PCA finds new axes (principal components) • The geometric intuition behind variance maximization • Eigenvectors and eigenvalues explained visually • How PCA reduces dimensions with minimal information loss This video focuses on intuition and visualization first, then connects the ideas to the math behind PCA. 📌 Topics covered: – What is Principal Component Analysis (PCA)? – Why PCA works – PCA geometry and rotation – Variance and principal components – Eigenvectors in PCA – Dimensionality reduction explained 🎯 This video is part of the “Foundations of Machine Learning” playlist. If you want to truly understand PCA — not just memorize formulas — this video is for you. #PCA #PrincipalComponentAnalysis #MachineLearning #DataScience #LinearAlgebra #DimensionalityReduction