Offered By: IBM
Machine Learning - Dimensionality Reduction
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.
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Machine Learning
3.63k+ EnrolledAt a Glance
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.
About This Course
 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
Course Syllabus
- Introduction to Dimension Reduction
- Principal Component Analysis
- Exploratory Factor Analysis
Course Staff
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.Â
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Estimated Effort
6 Hours
Level
Beginner
Skills You Will Learn
Machine Learning, R
Language
English
Course Code
ML0109EN