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Learner Reviews & Feedback for Matrix Factorization and Advanced Techniques by University of Minnesota
186 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
HL
Jan 2, 2021
Really enjoyed the course!
One suggestion I have is to blend in even more advanced techniques such as using neural networks (e.g. NCF)
LL
Jul 18, 2017
great courses! They invite a lot of interviews to let me understand the sea of recommend system!
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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 9, 2020
Could have been better