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.
Matrix Factorization and Advanced Techniques
Matrix Factorization and Advanced Techniques
This course is part of Recommender Systems Specialization
Instructors: Michael D. Ekstrand
Access provided by University of Minnesota
16,099 already enrolled
Gain insight into a topic and learn the fundamentals.
190 reviews
1 week to complete
at 10 hours a week
Flexible schedule
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Assessments
7 assignments
Taught in English
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This course is part of the Recommender Systems Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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Showing 3 of 190
DD
Reviewed on Jan 9, 2021
Very good. Per closing comments, it probably needs an update (since 2016) as this is active, progressive area.
LL
Reviewed on Jul 18, 2017
great courses! They invite a lot of interviews to let me understand the sea of recommend system!
AG
Reviewed on Jun 9, 2018
Programming Assignments are not clear enough and the quiz for the last one seems to be a bit off.