About this Specialization

9,991 recent views
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. 2 months to complete

Suggested 9 hours/week

English

Subtitles: 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. 2 months to complete

Suggested 9 hours/week

English

Subtitles: 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.

how it works

There are 5 Courses in this Specialization

Course1

Course 1

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

4.5
stars
488 ratings
100 reviews
Course2

Course 2

Nearest Neighbor Collaborative Filtering

4.2
stars
245 ratings
58 reviews
Course3

Course 3

Recommender Systems: Evaluation and Metrics

4.3
stars
178 ratings
25 reviews
Course4

Course 4

Matrix Factorization and Advanced Techniques

4.3
stars
145 ratings
21 reviews

Offered by

University of Minnesota logo

University of Minnesota

Frequently Asked Questions

  • Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

  • This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

  • Most learners should be able to complete the specialization in 20-26 weeks.

  • Basic statistics or college algebra, and an ability to work with spreadsheets. For the honors track, you should also be comfortable implementing software in Java.

  • While each component can be useful by itself, the courses do build on each other and should be taken in order.

  • The University of Minnesota does not offer credit for completing this specialization. If you are enrolled elsewhere, you may wish to speak with your advisor or program staff to find out whether this specialization could be used for independent study credit.

  • You will understand and be able to apply the major families of recommender algorithms: non-personalized, product association, content-based, nearest-neighbor, and matrix factorization. You will know and be able to apply a variety of recommender metrics, and will be able to use this knowledge to match the correct recommender system to appplications.

  • The honors track is an optional track where learners add programming recommenders in the open source LensKit toolkit. You should be comfortable with basic data structures, algorithms, and Java to attempt the honors track.

  • This specialization is an extended and updated version of the two prior versions of Introduction to Recommender Systems that we've offered through Coursera. About 50% of the video and 80% of the assessment material are new, and there is an honors track with programming assignments (which existed in the first version of the course only, and have been re-done for this specialization). The Capstone is entirely new.

More questions? Visit the Learner Help Center.