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Description

The course explains one of the important aspect of machine learning - Principal component analysis and factor analysis in a very easy to understand manner. It explains theory as well as demonstrates how to use SAS and R for the purpose. 


The course provides entire course content available to download in PDF format, data set and code files. The detail course content is as follows.


Intuitive Understanding of PCA 2D Case
what is the variance in the data in different dimensions?
what is principal component?
Formal definition of PCs
Understand the formal definition of PCA


Properties of Principal Components
Understanding principal component analysis (PCA) definition using a 3D image


Properties of Principal Components
Summarize PCA concepts
Understand why first eigen value is bigger than second, second is bigger than third and so on
Data Treatment for conducting PCA

How to treat ordinal variables?
How to treat numeric variables?


Conduct PCA using SAS: Understand
Correlation Matrix
Eigen value table
Scree plot
How many pricipal components one should keep?
How is principal components getting derived?


Conduct PCA using R


Introduction to Factor Analysis
Introduction to factor analysis
Factor analysis vs PCA side by side


Factor Analysis Using R
Factor Analysis Using SAS
Theory for using PCA for Variable Selection
Demo of using PCA for Variable Selection
Who this course is for:
Analytics Professionals
Research Scholars
Data Scientists

What you'll learn

Understand Principal Component Analysis and Factor Anallysis in crysal clear manner

Will know how to coduct principal component analysis and factor analysis using SAS / R

Will understand, how PCA helps in dimensionality reduction

Will understand the difference and similarity between PCA and factor analysis

Students will be able to use PCA for variable selection

Requirements

  • You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
  • No previous design experience is needed.
  • No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures
Expand All Sections
1-Principal Component Analysis (PCA)
9
1.1-Introduction
1.2-How to consume the contents?
1.3-Intuitive Understanding of PCA 2D Case
1.4-Formal defintion of PCs
1.5-Properties of Principal Components - part 1
1.6-Properties of Principal Components - part 2
1.7-Data Treatment for conducting PCA
1.8-Workshop - conduct principal component analysis using SAS
1.9-Workshop - conduct principal component analysis using R
2-Factor Analysis
4
2.1-Introduction to Factor Analysis
2.2-Workshop - conduct Factor analysis using R - part 1
2.3-Workshop - conduct Factor analysis using R - part 2
2.4-Workshop - conduct Factor analysis using SAS
3-Using Principal Component Analysis for Variable selection
5
3.1-Theory for variable selection using PCA
3.2-Demo for variable selection using PCA - Part 01
3.3-Demo for variable selection using PCA - Part 02
3.4-FAQ (will keep growing overtime based on student's queries)
3.5-Closing Note and PDF of course content