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
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4.3
(186 reviews)
14 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace
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Assessments
7 assignments
Taught in English
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This course is part of the Recommender Systems 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
Instructors
Instructor ratings
4.9 (8 ratings)Offered by
Recommended if you're interested in Machine Learning
DeepLearning.AI
Imperial College London
The University of Sydney
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