Principle Component Analysis (PCA) | Part 2 | Problem Formulation and Step by Step Solution

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 ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in/s/store ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at [email protected] ⌚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