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Machine Learning - Dimensionality Reduction Big Data University

About This Course

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.

Requirements

Basic knowledge of operating systems (UNIX/Linux).

Course Syllabus

  • Introduction to Dimension Reduction
  • Principal Component Analysis
  • Exploratory Factor Analysis

Course Staff

Konstantin

Konstantin Tskhay

Konstantin 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. Konstantin has been incredibly successful in academia, publishing a number of first-author papers, presenting at international conferences, and receiving several prestigious awards, but has decided to make a move into the private sector, applying his knowledge and skills within Deloitte’s Human Capital Consulting Practice, starting June 2016. Konstantin holds a Master of Arts degree in Psychology from the University of Toronto and a Bachelor of Arts degree in Psychology from the University of California, Riverside. He is expected to receive the Doctor of Philosophy degree in Psychology with a focus on Charisma and Leadership from the University of Toronto on March 23rd, 2016.

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  1. Course Number

    ML0109EN
  2. Classes Start

    Any Time, Self-Paced
  3. Estimated Effort

    15:00:00
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