Dimensionality reduction is the process of reducing the number of random variables impacting your data. Throughout the course of learning, you will be engaging in first-hand practice to analyze this category of machine learning technique to understand data proficiently.
This course allows you to learn how Dimensionality Reduction, a category of unsupervised machine learning techniques, is used to reduce the number of features in a dataset. Dimension reduction can also be used to group similar variables together.
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 using R.
Konstantin Tskhay is an analytic thinker and a Graduate Student Research Scientist (Ph. D.) at the University of Toronto with more than five years of quantitative and qualitative research experience in organizational behavior, impression formation, and leadership.