關於此 專項課程

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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.
學生職業成果
60%
完成此 專項課程 後開始了新的職業。
12%
加薪或升職。
可分享的證書
完成後獲得證書
100% 在線課程
立即開始,按照自己的計劃學習。
靈活的計劃
設置並保持靈活的截止日期。
中級
完成時間大約為5 個月
建議 3 小時/週
英語(English)
字幕:英語(English)
學生職業成果
60%
完成此 專項課程 後開始了新的職業。
12%
加薪或升職。
可分享的證書
完成後獲得證書
100% 在線課程
立即開始,按照自己的計劃學習。
靈活的計劃
設置並保持靈活的截止日期。
中級
完成時間大約為5 個月
建議 3 小時/週
英語(English)
字幕:英語(English)

此專項課程包含 5 門課程

課程1

課程 1

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

4.5
531 個評分
110 條評論
課程2

課程 2

Nearest Neighbor Collaborative Filtering

4.2
264 個評分
61 條評論
課程3

課程 3

Recommender Systems: Evaluation and Metrics

4.4
191 個評分
29 條評論
課程4

課程 4

Matrix Factorization and Advanced Techniques

4.3
155 個評分
24 條評論

提供方

明尼苏达大学 徽標

明尼苏达大学

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  • 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.

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