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 recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.
Este curso forma parte de Programa especializado: Sistemas de recomendación
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Programa especializado: Sistemas de recomendaciónUniversidad de MinnesotaAcerca de este Curso
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Resultados profesionales del estudiante
50%
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Curso 4 de 5 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Aprox. 16 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)
Resultados profesionales del estudiante
50%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 4 de 5 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Aprox. 16 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)
ofrecido por
Universidad de Minnesota
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.
Programa - Qué aprenderás en este curso
4 minutos para completar
Preface
4 minutos para completar
1 video (Total 4 minutos)
1 hora para completar
Matrix Factorization (Part 1)
This is a two-part, two-week module on matrix factorization recommender techniques. It includes an assignment and quiz (both due in the second week), and an honors assignment (also due in the second week). Please pace yourself carefully -- it will be difficult to finish in two weeks unless you start the assignments during the first week.
1 hora para completar
5 videos (Total 70 minutos), 1 lectura
6 horas para completar
Matrix Factorization (Part 2)
6 horas para completar
2 videos (Total 15 minutos), 2 lecturas, 6 cuestionarios
2 horas para completar
Hybrid Recommenders
This is a three-part, two-week module on hybrid and machine learning recommendaton algorithms and advanced recommender techniques. It includes a quiz (due in the second week), and an honors assignment (also due in the second week). Please pace yourself carefully -- it will be difficult to finish the honors track in two weeks unless you start the assignments during the first week.
2 horas para completar
6 videos (Total 96 minutos)
Reseñas
Principales reseñas sobre MATRIX FACTORIZATION AND ADVANCED TECHNIQUES
por HL2 de ene. de 2021
Really enjoyed the course! One suggestion I have is to blend in even more advanced techniques such as using neural networks (e.g. NCF)
por DD9 de ene. de 2021
Very good. Per closing comments, it probably needs an update (since 2016) as this is active, progressive area.
por NL23 de abr. de 2020
The content is really good, but overall the interviews with experts in the field are the best of this course.
por AG9 de jun. de 2018
Programming Assignments are not clear enough and the quiz for the last one seems to be a bit off.
Acerca de Programa especializado: Sistemas de recomendación
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.
Preguntas Frecuentes
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