About this Specialization

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A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space. This Specialization is designed to serve both the data mining expert who would want to implement techniques like collaborative filtering in their job, as well as the data literate marketing professional, who would want to gain more familiarity with these topics. The courses offer interactive, spreadsheet-based exercises to master different algorithms, along with an honors track where you can go into greater depth using the LensKit open source toolkit. By the end of this Specialization, you’ll be able to implement as well as evaluate recommender systems. The Capstone Project brings together the course material with a realistic recommender design and analysis project.
Learner Career Outcomes
60%
Started a new career after completing this specialization.
12%
Got a pay increase or promotion.
Shareable Certificate
Earn a Certificate upon completion
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Set and maintain flexible deadlines.
Intermediate Level
Approx. 5 months to complete
Suggested 3 hours/week
English
Learner Career Outcomes
60%
Started a new career after completing this specialization.
12%
Got a pay increase or promotion.
Shareable Certificate
Earn a Certificate upon completion
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Set and maintain flexible deadlines.
Intermediate Level
Approx. 5 months to complete
Suggested 3 hours/week
English

There are 5 Courses in this Specialization

Course1

Course 1

Introduction to Recommender Systems: Non-Personalized and Content-Based

4.5
stars
555 ratings
117 reviews
Course2

Course 2

Nearest Neighbor Collaborative Filtering

4.3
stars
278 ratings
63 reviews
Course3

Course 3

Recommender Systems: Evaluation and Metrics

4.3
stars
202 ratings
29 reviews
Course4

Course 4

Matrix Factorization and Advanced Techniques

4.3
stars
162 ratings
24 reviews

Offered by

Placeholder

University of Minnesota

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