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 using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations.
Acerca de este Curso
Habilidades que obtendrás
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.
- 5 stars60,45 %
- 4 stars29,65 %
- 3 stars6,32 %
- 2 stars1,78 %
- 1 star1,78 %
Principales reseñas sobre INTRODUCTION TO RECOMMENDER SYSTEMS: NON-PERSONALIZED AND CONTENT-BASED
Great intro to recommendation systems, the course is well structured and engaging to all students of different backgrounds.
Great introduction to Recommender systems. Really got me thinking about how I could apply them.
Great course. I have already been able to apply what I have learned to me job. Looking forward to the next one.
One of the best courses I have taken on Coursera. Choosing Java for the lab exercises makes them inaccessible for many data scientists. Consider providing a Python version.
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.
¿Cuándo podré acceder a las lecciones y tareas?
¿Qué recibiré si me suscribo a este Programa especializado?
¿Hay ayuda económica disponible?
How does this course relate to the prior versions of "Introduction to Recommender Systems"?
¿Tienes más preguntas? Visita el Centro de Ayuda al Alumno.