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Learner Reviews & Feedback for Matrix Factorization and Advanced Techniques by University of Minnesota
190 ratings
About the Course
In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.
Top reviews
NL
Apr 24, 2020
The content is really good, but overall the interviews with experts in the field are the best of this course.
AG
Jun 10, 2018
Programming Assignments are not clear enough and the quiz for the last one seems to be a bit off.
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26 - 27 of 27 Reviews for Matrix Factorization and Advanced Techniques
By Moustafa M
•Apr 18, 2020
The HWs for the Honor track had mistakes
By PRATIK K C
•Jun 10, 2020
Could have been better