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Course Details

 Course Code: ML0109EN

 Audience: Anyone interested in Machine Learning

 Course Level: Beginner

 Time to Complete: 2 hours

 Language: English

About the Course

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.

Course Syllabus

  • Introduction to Dimension Reduction
  • Dimension Reduction Goals
  • Principal Component Analysis
  • Labs
  • Exploratory Analysis
  • Labs

General Informatiln

  • Self-paced
  • Flexible enrolment
  • Audit multiple times

Recommended Existing Skills

  • None


  • None

Course Staff

Petro Verkhogliad

Konstantin Tskhay

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.