Machine Learning – Dimensionality Reduction
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Learn the theory and practice of Dimensionality Reduction in this machine learning course. Discover how to reduce features and group variables using PCA and EFA in R.
At a Glance
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.
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