Cognitive Class

Machine Learning – Dimensionality Reduction

If life is like a bowl of chocolates, you will never know what you will get, but is there a way to reduce some uncertainty? Dimensionality reduction is the process of reducing the number of random variables impacting your data. Come and explore, but make sure you don't let the chocolates melt.

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About This Course

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

  • Module 1 - Data Series
    1. Introduction to Dimension Reduction
    2. Dimension Reduction Goals
  • Module 2 - Data Refinement
    1. Principal Component Analysis
    2. Labs
  • Module 3 - Exploring Data
    1. Exploratory Analysis
    2. Labs

General Information

  • This course is free.
  • It is self-paced.
  • It can be taken at any time.
  • It can be audited as many times as you wish.

Recommended skills prior to taking this course

  • None

Requirements

  • None

Course Staff

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.

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