In this course, you will see how to use advanced machine learning techniques to build more sophisticated recommender systems. Machine Learning is able to provide recommendations and make better predictions, by taking advantage of historical opinions from users and building up the model automatically, without the need for you to think about all the details of the model.
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Advanced Recommender Systems
EIT DigitalAbout this Course
Basic knowledge of recommender systems. Basic notions of linear algebra.
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Try Coursera for BusinessWhat you will learn
You will be able to use some machine learning techniques in order to build more sophisticated recommender systems.
You will learn how to combine different basic approaches into a hybrid recommender system, in order to improve the quality of recommendations.
You will know how to integrate different kinds of side information (about content or context) in a recommender system.
You'll learn how to use factorization machines and represent the input data, mixing together different kinds of filtering techniques.
Basic knowledge of recommender systems. Basic notions of linear algebra.
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessOffered by
Syllabus - What you will learn from this course
ADVANCED COLLABORATIVE FILTERING
SINGULAR VALUE DECOMPOSITION TECHNIQUES - SVD
HYBRID AND CONTEXT AWARE RECOMMENDER SYSTEMS
FACTORIZATION MACHINES
Reviews
- 5 stars55.55%
- 4 stars16.66%
- 2 stars5.55%
- 1 star22.22%
TOP REVIEWS FROM ADVANCED RECOMMENDER SYSTEMS
Great course to overview advanced techniques to build recommender system.
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