About This Course
Welcome to this machine learning course on Dimensionality Reduction. Dimensionality Reduction is a category of unsupervised machine learning techniques used to reduce the number of features in a dataset. Dimension reduction can also be used to group similar variables together.
In this course, you will learn the theory behind dimension reduction, and get some hands-on practice using Principal Components Analysis (PCA) and Exploratory Factor Analysis (EFA) on survey data.
The code used in this course is prepared for you in R.
Basic knowledge of operating systems (UNIX/Linux).
- Introduction to Dimension Reduction
- Principal Component Analysis
- Exploratory Factor Analysis
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