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Ca. 13 Stunden zum Abschließen
Englisch
Untertitel: Englisch

Karriereergebnisse der Lernenden

50%

ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Ca. 13 Stunden zum Abschließen
Englisch
Untertitel: Englisch

von

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University of Minnesota

Lehrplan - Was Sie in diesem Kurs lernen werden

Woche
1

Woche 1

4 Minuten zum Abschließen

Preface

4 Minuten zum Abschließen
1 Video (Gesamt 4 min)
Woche
2

Woche 2

1 Stunde zum Abschließen

Matrix Factorization (Part 1)

1 Stunde zum Abschließen
5 Videos (Gesamt 70 min), 1 Lektüre
5 Videos
Singular Value Decomposition17m
Gradient Descent Techniques17m
Deriving FunkSVD11m
Probabilistic Matrix Factorization10m
1 Lektüre
On Folding-In with Gradient Descent10m
Woche
3

Woche 3

4 Stunden zum Abschließen

Matrix Factorization (Part 2)

4 Stunden zum Abschließen
2 Videos (Gesamt 15 min), 2 Lektüren, 6 Quiz
2 Videos
Programming Matrix Factorization6m
2 Lektüren
Assignment Instructions10m
Intro - Programming Matrix Factorization10m
5 praktische Übungen
Matrix Factorization Assignment Part l10m
Matrix Factorization Assignment Part ll10m
Matrix Factorization Assignment Part lll10m
Matrix Factorization Quiz8m
SVD Programming Eval Quiz6m
Woche
4

Woche 4

2 Stunden zum Abschließen

Hybrid Recommenders

2 Stunden zum Abschließen
6 Videos (Gesamt 96 min)
6 Videos
Hybrids with Robin Burke16m
Hybridization through Matrix Factorization15m
Matrix Factorization Hybrids with George Karypis17m
Interview with Arindam Banerjee15m
Interview with Yehuda Koren22m

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Über den Spezialisierung Empfehlungsdienste

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....
Empfehlungsdienste

Häufig gestellte Fragen

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