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。
11%
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)
學生職業成果
60%
完成此 專項課程 後開始了新的職業。
11%
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)

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
559 個評分
118 條評論
課程2

課程 2

Nearest Neighbor Collaborative Filtering

4.3
281 個評分
63 條評論
課程3

課程 3

Recommender Systems: Evaluation and Metrics

4.3
206 個評分
29 條評論
課程4

課程 4

Matrix Factorization and Advanced Techniques

4.3
163 個評分
24 條評論

提供方

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明尼苏达大学

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