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
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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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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
SK
Reviewed on Dec 4, 2017
Awesome course especially for those doing Ph.D in recommender systems
HL
Reviewed on 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)
SL
Reviewed on Sep 11, 2019
It will be great, if we can do honor's track with Python or R
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