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
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Matrix Factorization and Advanced Techniques
This course is part of Recommender Systems Specialization
Instructors: Michael D. Ekstrand
15,800 already enrolled
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4.3
(189 reviews)
14 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace
Skills you'll gain
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Shareable certificate
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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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There are 6 modules in this course
What's included
1 video
This is a two-part, two-week module on matrix factorization recommender techniques. It includes an assignment and quiz (both due in the second week), and an honors assignment (also due in the second week). Please pace yourself carefully -- it will be difficult to finish in two weeks unless you start the assignments during the first week.
What's included
5 videos1 reading
What's included
2 videos2 readings5 assignments1 programming assignment
This is a three-part, two-week module on hybrid and machine learning recommendaton algorithms and advanced recommender techniques. It includes a quiz (due in the second week), and an honors assignment (also due in the second week). Please pace yourself carefully -- it will be difficult to finish the honors track in two weeks unless you start the assignments during the first week.
What's included
6 videos
What's included
3 videos
What's included
7 videos1 reading2 assignments1 programming assignment
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Instructors
Instructor ratings
4.9 (8 ratings)Offered by
Explore more from Machine Learning
Status: Free TrialUniversity of Minnesota
Status: Free TrialUniversity of Minnesota
University of Minnesota
EIT Digital
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4.3
189 reviews
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Showing 3 of 189
SK
5
Reviewed on Dec 4, 2017
Awesome course especially for those doing Ph.D in recommender systems
HL
5
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)
AK
4
Reviewed on Aug 13, 2017
Interview with Francesco Ricci is very knowledgeable about context aware Recommender System.
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