Machine Learning – Dimensionality Reduction

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.

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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

Module 1 - Data Series

Introduction to Dimension Reduction
Dimension Reduction Goals

Module 2 - Data Refinement

Principal Component Analysis
Labs

Module 3 - Exploring Data

Exploratory Analysis
Labs

General Informatiln

Self-paced
Flexible enrolment
Audit multiple times

Recommended Existing Skills

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.