课程信息

100% 在线

立即开始,按照自己的计划学习。

第 5 门课程(共 5 门)

可灵活调整截止日期

根据您的日程表重置截止日期。

完成时间大约为8 小时

建议:1-3 weeks of study, 3-5 hours per week...

英语(English)

字幕:英语(English)

100% 在线

立即开始,按照自己的计划学习。

第 5 门课程(共 5 门)

可灵活调整截止日期

根据您的日程表重置截止日期。

完成时间大约为8 小时

建议:1-3 weeks of study, 3-5 hours per week...

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1

1

完成时间为 4 小时

Capstone Project

完成时间为 4 小时
2 个视频 (总计 6 分钟), 2 个阅读材料, 3 个测验
2 个视频
Capstone Wrap-Up1分钟
2 个阅读材料
Capstone Assignment (all versions combined)10分钟
Thank you!10分钟
1 个练习
Certification for honors track2分钟

提供方

明尼苏达大学 徽标

明尼苏达大学

关于 推荐系统 专项课程

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....
推荐系统

常见问题

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