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.
This course is part of the Recommender Systems Specialization
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Course 4 of 5 in the
Approx. 15 hours to complete
English
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Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 4 of 5 in the
Approx. 15 hours to complete
English
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessOffered by
Syllabus - What you will learn from this course
4 minutes to complete
Preface
4 minutes to complete
1 video (Total 4 min)
1 hour to complete
Matrix Factorization (Part 1)
1 hour to complete
5 videos (Total 70 min), 1 reading
6 hours to complete
Matrix Factorization (Part 2)
6 hours to complete
2 videos (Total 15 min), 2 readings, 6 quizzes
2 hours to complete
Hybrid Recommenders
2 hours to complete
6 videos (Total 96 min)
Reviews
- 5 stars53.51%
- 4 stars32.97%
- 3 stars8.10%
- 2 stars4.32%
- 1 star1.08%
TOP REVIEWS FROM MATRIX FACTORIZATION AND ADVANCED TECHNIQUES
by LLJul 18, 2017
great courses! They invite a lot of interviews to let me understand the sea of recommend system!
by SLSep 11, 2019
It will be great, if we can do honor's track with Python or R
by SKDec 4, 2017
Awesome course especially for those doing Ph.D in recommender systems
by AAAug 13, 2017
Interview with Francesco Ricci
is very knowledgeable about context aware Recommender System.
About the Recommender Systems Specialization

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