Principle Component Analysis (PCA) | Part 2 | Problem Formulation and Step by Step Solution
Channel: CampusX
Date: 2021-05-07

This video breaks down the problem formulation and offers a step-by-step solution guide. Enhance your understanding of PCA and master the techniques for dimensionality reduction in your data.

Code used: https://github.com/campusx-official/100-days-of-machine-learning/tree/main/day47-pca

About Eigen Vectors:
https://www.visiondummy.com/2014/04/geometric-interpretation-covariance-matrix/#:~:text=covariance%20matrix%20captures%20the%20spread%20of%20N%2Ddimensional%20data.&text=Figure%203.,is%20captured%20by%20the%20variance.
https://www.youtube.com/watch?v=PFDu9oVAE-g&t=394s

Plotting tool used:
https://www.geogebra.org/m/YCZa8TAH

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⌚Time Stamps⌚

00:00 - Practical Example on MNIST Dataset
00:33 - Problem Formulation
12:55 - Covariance and Covariance Matrix
23:17 - Eigen Vectors and Eigen Values
25:37 - Visualizing Linear Trasnformations
35:35 - Eigendecompostion of a covariance Matrix
38:04 - How to solve PCA
43:41 - How to transform points?
48:18 - Code Demo with Vizualization
56:00 - Outro