关于此 专项课程

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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.
学生职业成果
60%
完成此 专项课程 后开始了新的职业。
12%
加薪或升职。
可分享的证书
完成后获得证书
100% 在线课程
立即开始,按照自己的计划学习。
灵活的计划
设置并保持灵活的截止日期。
中级
完成时间大约为5 个月
建议 3 小时/周
英语(English)
学生职业成果
60%
完成此 专项课程 后开始了新的职业。
12%
加薪或升职。
可分享的证书
完成后获得证书
100% 在线课程
立即开始,按照自己的计划学习。
灵活的计划
设置并保持灵活的截止日期。
中级
完成时间大约为5 个月
建议 3 小时/周
英语(English)

此专项课程包含 5 门课程

课程1

课程 1

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

4.5
551 个评分
116 条评论
课程2

课程 2

Nearest Neighbor Collaborative Filtering

4.3
276 个评分
63 条评论
课程3

课程 3

Recommender Systems: Evaluation and Metrics

4.3
201 个评分
29 条评论
课程4

课程 4

Matrix Factorization and Advanced Techniques

4.3
160 个评分
24 条评论

提供方

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明尼苏达大学

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