Über dieses Spezialisierung

9,555 kürzliche Aufrufe

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.

Karriereergebnisse der Lernenden
60%
Ich nahm nach Abschluss dieses Spezialisierung einen neuen Beruf auf.
12%
Ich erhielt eine Gehaltserhöhung oder Beförderung.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
Kurse, die komplett online stattfinden
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Flexibler Zeitplan
Festlegen und Einhalten flexibler Termine.
Stufe „Mittel“
Ca. 5 Monate zum Abschließen
Empfohlen werden 3 Stunden/Woche
Englisch
Untertitel: Englisch
Karriereergebnisse der Lernenden
60%
Ich nahm nach Abschluss dieses Spezialisierung einen neuen Beruf auf.
12%
Ich erhielt eine Gehaltserhöhung oder Beförderung.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
Kurse, die komplett online stattfinden
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Flexibler Zeitplan
Festlegen und Einhalten flexibler Termine.
Stufe „Mittel“
Ca. 5 Monate zum Abschließen
Empfohlen werden 3 Stunden/Woche
Englisch
Untertitel: Englisch

Es gibt 5 Kurse in dieser Spezialisierung

Kurs1

Kurs 1

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

4.5
Sterne
530 Bewertungen
110 Bewertungen
Kurs2

Kurs 2

Nearest Neighbor Collaborative Filtering

4.2
Sterne
264 Bewertungen
61 Bewertungen
Kurs3

Kurs 3

Recommender Systems: Evaluation and Metrics

4.4
Sterne
190 Bewertungen
29 Bewertungen
Kurs4

Kurs 4

Matrix Factorization and Advanced Techniques

4.3
Sterne
154 Bewertungen
24 Bewertungen

von

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University of Minnesota

Häufig gestellte Fragen

  • 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! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

  • 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.

  • 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. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.

  • This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

  • 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.

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