MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 18 lectures (1h 40m) | Size: 270.7 MB
The course explains one of the important aspect of machine learning - Principal component analysis and factor analysis in a very easy to understand manner.
Analytics / Machine Learning / Dimensionality Reduction : PCA & Factor Analysis using SAS and R program
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
The course will start with elementary concepts but knowledge of basic statistics will help
For execution - it will help to know basic SAS or R programming
It explains theory as well as demonstrates how to use SAS and R for the purpose.
The course provides entire course content available to 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 n 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
n 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
Analytics Professionals
Research Scholars
Data Scientists
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