Principal Component Analysis (PCA) | Dimensionality Reduction | Explained with Example
About this lesson
๐ Notes:- https://robosathi.com/docs/machine_learning/unsupervised/dimensionality_reduction/pca/ ๐ฅ Next Video: t- SNE :- https://youtu.be/kVdMM51bfRM ๐ฅ Related Video: Constrained Optimization :- https://youtu.be/BDogM9wBQPo ๐ In this video, we will understand Principal Component Analysis (PCA) from intuition to optimization, understanding why it works and what it actually optimizes. ๐ฏ Learning Objectives โ Understand uses of PCA โ Explain PCA using example โ Formulate PCA as an optimization problem โ Understand the PCA algorithm โ Identify limitations of PCA ๐ Maths for ML Playlist: https://www.youtube.com/playlist?list=PLnpa6KP2ZQxePOg6k6vAkcg5Y50EAZds9 ๐ Time Stamp ๐ 00:00:00 - 00:00:22 Introduction 00:00:23 - 00:02:05 Uses of Principal Component Analysis (PCA) 00:02:06 - 00:05:03 Intuition for PCA 00:05:04 - 00:09:31 Explained PCA with Example 00:09:32 - 00:09:53 What is PCA? 00:09:54 - 00:11:16 Goal - Find the set of Orthogonal axes 00:11:17 - 00:15:33 PCA as Optimization Problem 00:15:34 - 00:20:07 Mathematical projection 00:20:08 - 00:26:51 Constrained Optimization 00:26:51 - 00:28:05 Maximum Variance 00:28:06 - 00:34:24 PCA Algorithm 00:34:25 - 00:35:11 Limitations of PCA 00:35:12 - 00:35:43 What's Next? ๐ค #ml #ai #pca #algorithmicoptimization #constrained #pca
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