此课程适用人群: This course is appropriate for learners who have a basic understanding of statistics. It can be useful both for those exploring applied machine learning and data mining, and for those focused on technology-supported marketing and commerce.


制作方:   University of Minnesota

  • Joseph A Konstan

    教学方:    Joseph A Konstan, Distinguished McKnight Professor and Distinguished University Teaching Professor

    Computer Science and Engineering

  • Michael D. Ekstrand

    教学方:    Michael D. Ekstrand, Assistant Professor

    Dept. of Computer Science, Boise State University
基本信息
Course 1 of 5 in the Recommender Systems Specialization.
级别Intermediate
承诺学习时间4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track.
语言
English
如何通过通过所有计分作业以完成课程。
用户评分
4.5 stars
Average User Rating 4.5查看学生的留言
Course 1 of Specialization
授课大纲

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运作方式
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课程作业

每门课程都像是一本互动的教科书,具有预先录制的视频、测验和项目。

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制作方
University of Minnesota
The University of Minnesota is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation’s most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations.
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评分和审阅
已评分 4.5,总共 5 个 79 评分

Un profesor excelente y un temario muy bueno. También me han gustado mucho las entrevistas y los recorridos por las páginas web que tienen recomendadores.

Overall, the class is perfect. But if you could supply a sample of honour class when we have finished honour codes, it would be perfect.

Exceptional quality.The course content is comprehensive and practical enough applied at workplaces.

Guest lectures are super helpful and assignments are very practical yet make you think.

Thank you Coursera and Minnesota professors for this amazing course and wonderful opportunity for people like me with no background in recommendation systems learn the best research methods and practices in this field.

As a software engineer with computer science background I found that course enhancing my knowledge. I'm going to continue the specialization.