關於此 專項課程
8,854 次近期查看

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2 個月

建議 9 小時/週

英語(English)

字幕:英語(English)

您將學到的內容有

  • Check

    Build recommendation systems

  • Check

    Implement collaborative filtering

  • Check

    Master spreadsheet based tools

  • Check

    Use project-association recommenders

您將獲得的技能

Collaborative FilteringRecommender SystemsEvaluationLensKitMatrix Factorization
學習Specialization的學生是
  • Data Scientists
  • Machine Learning Engineers
  • Chief Technology Officers (CTOs)
  • Data Engineers
  • Research Assistants

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2 個月

建議 9 小時/週

英語(English)

字幕:英語(English)

專項課程的運作方式

加入課程

Coursera 專項課程是幫助您掌握一門技能的一系列課程。若要開始學習,請直接註冊專項課程,或預覽專項課程並選擇您要首先開始學習的課程。當您訂閱專項課程的部分課程時,您將自動訂閱整個專項課程。您可以只完成一門課程,您可以隨時暫停學習或結束訂閱。訪問您的學生面板,跟踪您的課程註冊情況和進度。

實踐項目

每個專項課程都包括實踐項目。您需要成功完成這個(些)項目才能完成專項課程並獲得證書。如果專項課程中包括單獨的實踐項目課程,則需要在開始之前完成其他所有課程。

獲得證書

在結束每門課程並完成實踐項目之後,您會獲得一個證書,您可以向您的潛在雇主展示該證書並在您的職業社交網絡中分享。

how it works

此專項課程包含 5 門課程

課程1

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

4.5
432 個評分
87 個審閱
課程2

Nearest Neighbor Collaborative Filtering

4.3
214 個評分
50 個審閱
課程3

Recommender Systems: Evaluation and Metrics

4.3
155 個評分
23 個審閱
課程4

Matrix Factorization and Advanced Techniques

4.3
135 個評分
20 個審閱

講師

Avatar

Joseph A Konstan

Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
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Michael D. Ekstrand

Assistant Professor
Dept. of Computer Science, Boise State University

關於 明尼苏达大学

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....

常見問題

  • 可以!点击您感兴趣的课程卡开始注册即可。注册并完成课程后,您可以获得可共享的证书,或者您也可以旁听该课程免费查看课程资料。如果您订阅的课程是某专项课程的一部分,系统会自动为您订阅完整的专项课程。访问您的学生面板,跟踪您的进度。

  • 此课程完全在线学习,无需到教室现场上课。您可以通过网络或移动设备随时随地访问课程视频、阅读材料和作业。

  • Most learners should be able to complete the specialization in 20-26 weeks.

  • 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.

還有其他問題嗎?請訪問 學生幫助中心