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
Sponsored by ITC-Infotech
15,603 already enrolled
Gain insight into a topic and learn the fundamentals.
4.3
(186 reviews)
14 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace
Skills you'll gain
- Data Science
- Statistical Machine Learning
- Artificial Intelligence and Machine Learning (AI/ML)
- Machine Learning Methods
- Computer Science
- Applied Machine Learning
- Mathematics and Mathematical Modeling
- Machine Learning
- Linear Algebra
- Advanced Mathematics
- Artificial Intelligence
- Machine Learning Algorithms
- Dimensionality Reduction
- Data Analysis
Details to know
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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
Instructors
Instructor ratings
4.9 (8 ratings)Offered by
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4.3
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Showing 3 of 186
SK
5
Reviewed on Dec 4, 2017
Awesome course especially for those doing Ph.D in recommender systems
SL
4
Reviewed on Sep 11, 2019
It will be great, if we can do honor's track with Python or R
NL
4
Reviewed on Apr 23, 2020
The content is really good, but overall the interviews with experts in the field are the best of this course.
Recommended if you're interested in Data Science
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