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UMN - Recommender Systems Specialization 

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Recommender Systems Specialization
 at 
Coursera 
Overview

Duration

5 months

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Total fee

Free

Mode of learning

Online

Official Website

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Credential

Certificate

Recommender Systems Specialization
 at 
Coursera 
Highlights

  • Earn a Certificate upon completion from university of Minnesota



    Financial aid available
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Recommender Systems Specialization
 at 
Coursera 
Course details

More about this course
  • 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
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Recommender Systems Specialization
 at 
Coursera 
Curriculum

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

Nearest Neighbor Collaborative Filtering

Recommender Systems: Evaluation and Metrics

Matrix Factorization and Advanced Techniques

Recommender Systems Specialization
 at 
Coursera 
Admission Process

    Important Dates

    May 25, 2024
    Course Commencement Date

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    Recommender Systems Specialization
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