Principal Component Analysis (PCA) Explained Simply | Intuition, Math & Visualization
Автор: Debstuti Das
Загружено: 2025-12-29
Просмотров: 42
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
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