このコースについて

26,744 最近の表示

受講生の就業成果

60%

コース終了後に新しいキャリアをスタートした

40%

コースが具体的なキャリアアップにつながった

12%

昇給や昇進につながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における5の1コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル
約20時間で修了
英語
字幕:英語

習得するスキル

Summary StatisticsTerm Frequency Inverse Document Frequency (TF-IDF)Microsoft ExcelRecommender Systems

受講生の就業成果

60%

コース終了後に新しいキャリアをスタートした

40%

コースが具体的なキャリアアップにつながった

12%

昇給や昇進につながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における5の1コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル
約20時間で修了
英語
字幕:英語

提供:

ミネソタ大学(University of Minnesota) ロゴ

ミネソタ大学(University of Minnesota)

シラバス - 本コースの学習内容

コンテンツの評価Thumbs Up90%(1,845 件の評価)Info
1

1

1時間で修了

Preface

1時間で修了
2件のビデオ (合計41分), 1 reading
2件のビデオ
Intro to Course and Specialization13 分
1件の学習用教材
Notes on Course Design and Relationship to Prior Courses10 分
3時間で修了

Introducing Recommender Systems

3時間で修了
9件のビデオ (合計147分), 2 readings, 2 quizzes
9件のビデオ
Preferences and Ratings17 分
Predictions and Recommendations16 分
Taxonomy of Recommenders I27 分
Taxonomy of Recommenders II21 分
Tour of Amazon.com21 分
Recommender Systems: Past, Present and Future16 分
Introducing the Honors Track7 分
Honors: Setting up the development environment10 分
2件の学習用教材
About the Honors Track10 分
Downloads and Resources10 分
2の練習問題
Closing Quiz: Introducing Recommender Systems20 分
Honors Track Pre-Quiz2 分
2

2

7時間で修了

Non-Personalized and Stereotype-Based Recommenders

7時間で修了
7件のビデオ (合計111分), 5 readings, 9 quizzes
7件のビデオ
Summary Statistics I16 分
Summary Statistics II22 分
Demographics and Related Approaches13 分
Product Association Recommenders19 分
Assignment #1 Intro Video14 分
Assignment Intro: Programming Non-Personalized Recommenders17 分
5件の学習用教材
External Readings on Ranking and Scoring10 分
Assignment 1 Instructions: Non-Personalized and Stereotype-Based Recommenders10 分
Assignment Intro: Programming Non-Personalized Recommenders10 分
LensKit Resources10 分
Rating Data Information10 分
8の練習問題
Assignment #1: Response #1: Top Movies by Mean Rating10 分
Assignment #1: Response #2: Top Movies by Count10 分
Assignment #1: Response #3: Top Movies by Percent Liking10 分
Assignment #1: Response #4: Association with Toy Story10 分
Assignment #1: Response #5: Correlation with Toy Story10 分
Assignment #1: Response #6: Male-Female Differences in Average Rating10 分
Assignment #1: Response #7: Male-Female differences in Liking8 分
Non-Personalized Recommenders20 分
3

3

3時間で修了

Content-Based Filtering -- Part I

3時間で修了
8件のビデオ (合計156分)
8件のビデオ
TFIDF and Content Filtering24 分
Content-Based Filtering: Deeper Dive26 分
Entree Style Recommenders -- Robin Burke Interview13 分
Case-Based Reasoning -- Interview with Barry Smyth13 分
Dialog-Based Recommenders -- Interview with Pearl Pu21 分
Search, Recommendation, and Target Audiences -- Interview with Sole Pera11 分
Beyond TFIDF -- Interview with Pasquale Lops21 分
4

4

6時間で修了

Content-Based Filtering -- Part II

6時間で修了
2件のビデオ (合計26分), 3 readings, 3 quizzes
2件のビデオ
Honors: Intro to programming assignment10 分
3件の学習用教材
Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)1 時間 20 分
Tools for Content-Based Filtering10 分
CBF Programming Intro10 分
2の練習問題
Assignment #2 Answer Form20 分
Content-Based Filtering20 分
1時間で修了

Course Wrap-up

1時間で修了
2件のビデオ (合計45分), 1 reading
2件のビデオ
Psychology of Preference & Rating -- Interview with Martijn Willemsen31 分
1件の学習用教材
Related Readings10 分

レビュー

INTRODUCTION TO RECOMMENDER SYSTEMS: NON-PERSONALIZED AND CONTENT-BASED からの人気レビュー

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レコメンダシステム専門講座について

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....
レコメンダシステム

よくある質問

  • Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

    • The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

    • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • This specialization is a substantial extension and update of our original introductory course. It involves about 60% new and extended lectures and mostly new assignments and assessments. This course specifically has added material on stereotyped and demographic recommenders and on advanced techniques in content-based recommendation.

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