Spécialisation Recommender Systems
Master Recommender Systems. Learn to design, build, and evaluate recommender systems for commerce and content.
À propos de cette Spécialisation
This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative techniques. Designed to serve both the data mining expert and the data literate marketing professional, the courses offer interactive, spreadsheet-based exercises to master different algorithms along with an honors track where learners can go into greater depth using the LensKit open source toolkit. A Capstone Project brings together the course material with a realistic recommender design and analysis project.
Créé par :
5 courses
Suivez l'ordre suggéré ou choisissez le vôtre.
Projets
Conçu pour vous aider à vous exercer et à appliquer les compétences que vous avez acquises.
Certificats
Mettez en évidence vos nouvelles compétences sur votre CV ou sur LinkedIn.
Cours
- Intermediate Specialization.
- Some related experience required.
COURS 1
Introduction to Recommender Systems: Non-Personalized and Content-Based
- Engagement
- 4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track.
- Sous-titres
- English
À propos du cours
This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation usingVous pouvez choisir de suivre seulement ce cours. En savoir plus.
COURS 2
Nearest Neighbor Collaborative Filtering
- Sous-titres
- English
À propos du cours
In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similaVous pouvez choisir de suivre seulement ce cours. En savoir plus.
COURS 3
Recommender Systems: Evaluation and Metrics
- Sous-titres
- English
À propos du cours
In this course you will learn how to evaluate recommender systems. You will gain familiarity with several families of metrics, including ones to measure prediction accuracy, rank accuracy, decision-support, and other factors such as diversity,Vous pouvez choisir de suivre seulement ce cours. En savoir plus.
COURS 4
Matrix Factorization and Advanced Techniques
- Sous-titres
- English
À propos du cours
In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recVous pouvez choisir de suivre seulement ce cours. En savoir plus.
COURS 5
Recommender Systems Capstone
Session à venir : Dec 24- Engagement
- 1-3 weeks of study, 3-5 hours per week
- Sous-titres
- English
À propos du Projet Final
This capstone project course for the Recommender Systems Specialization brings together everything you've learned about recommender systems algorithms and evaluation into a comprehensive recommender analysis and design project. You will be given a caVous pouvez choisir de suivre seulement ce cours. En savoir plus.
Créateurs
Joseph A Konstan
Distinguished McKnight Professor and Distinguished University Teaching Professor
Michael D. Ekstrand
Assistant Professor
FAQs
More questions? Visit the Learner Help Center.
