Principal Component Analysis (PCA) and Factor Analysis

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