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
50%
Started a new career after completing this specialization.
33%
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
Approximately 5 months to complete
Suggested pace of 3 hours/week
English
Learner Career Outcomes
50%
Started a new career after completing this specialization.
33%
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
Approximately 5 months to complete
Suggested pace of 3 hours/week
English

How the Specialization Works

Take Courses

A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.

Hands-on Project

Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.

Earn a Certificate

When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.

There are 5 Courses in this Specialization

Course1

Course 1

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

4.5
stars
579 ratings
121 reviews
Course2

Course 2

Nearest Neighbor Collaborative Filtering

4.3
stars
287 ratings
64 reviews
Course3

Course 3

Recommender Systems: Evaluation and Metrics

4.4
stars
212 ratings
30 reviews
Course4

Course 4

Matrix Factorization and Advanced Techniques

4.3
stars
171 ratings
26 reviews

Offered by

Placeholder

University of Minnesota

Frequently Asked Questions

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