Video Description
Learn Factor extraction using PCA in Excel, R, and Python! This training course will help you understand Factor Analysis and its link to linear regression. See how Principal Components Analysis is a cookie cutter technique to solve factor extraction and how it relates to Machine Learning. Factor analysis helps to cut through the clutter when you have a lot of correlated variables to explain a single effect. Follow along with expert instructors to learn about topics such as Mean and Variance, Eigen Vectors, Covariance Matrices, and so much more! Supplemental Materials included.
Table of Contents
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INTRODUCTION
- You, This Course, and Us! 00:01:46
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FACTOR ANALYSIS PCA
- Factor Analysis and the Link to Regression 00:08:04
- Factor Analysis and PCA 00:07:01
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BASIC STATISTICS REQUIRED FOR PCA
- Mean and Variance 00:06:04
- Covariance and Covariance Matrices 00:11:45
- Covariance vs Correlation 00:03:19
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DIVING INTO PRINCIPAL COMPONENTS ANALYSIS
- The Intuition behind Principal Components 00:05:17
- Finding Principal Components 00:07:10
- Understanding the Results of PCA - Eigen Values 00:04:06
- Using Eigen Vectors to Find Principal Components 00:02:31
- When not to use PCA 00:02:25
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PCA IN EXCEL
- Setting up the data 00:06:52
- Computing Correlation and Covariance Matrices 00:03:27
- PCA using Excel and VBA 00:05:52
- PCA and Regression 00:02:56
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PCA IN R
- Setting up the data 00:05:16
- PCA Regression using Eigen Decomposition 00:03:58
- PCA in R using packages 00:01:56
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PCA IN PYTHON
- PCA and Regression in Python 00:06:43