Master recommender systems.. Learn to design, build, and evaluate recommender systems for commerce and content.

4.4

estrellas

651 calificaciones

Joseph A Konstan +1 instructores más

15.304 ya inscrito

ofrecido por

Programa especializado: Sistemas de recomendaciónUniversidad de Minnesota

Qué aprenderás

Build recommendation systems

Implement collaborative filtering

Master spreadsheet based tools

Use project-association recommenders

Habilidades que obtendrás

Collaborative Filtering
Recommender Systems
Evaluation
LensKit
Matrix Factorization
Summary Statistics
Term Frequency Inverse Document Frequency (TF-IDF)
Microsoft Excel

Acerca de este Programa Especializado

6454 vistas recientes

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.

Certificado para compartir

Obtén un certificado al finalizar

Cursos 100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Cronograma flexible

Establece y mantén fechas de entrega flexibles.

Nivel intermedio

Se requiere cierto nivel de experiencia relacionada.

Aproximadamente 5 meses para completarse

Ritmo sugerido de 3 horas/semana

Inglés (English)

Subtítulos: Inglés (English), Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Alemán (German), Ruso (Russian), Español (Spanish)

Certificado para compartir

Obtén un certificado al finalizar

Cursos 100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Cronograma flexible

Establece y mantén fechas de entrega flexibles.

Nivel intermedio

Se requiere cierto nivel de experiencia relacionada.

Aproximadamente 5 meses para completarse

Ritmo sugerido de 3 horas/semana

Inglés (English)

Subtítulos: Inglés (English), Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Alemán (German), Ruso (Russian), Español (Spanish)

Cómo funciona el programa especializado

Toma cursos

Un programa especializado de Coursera es un conjunto de cursos que te ayudan a dominar una aptitud. Para comenzar, inscríbete en el programa especializado directamente o échale un vistazo a sus cursos y elige uno con el que te gustaría comenzar. Al suscribirte a un curso que forme parte de un programa especializado, quedarás suscrito de manera automática al programa especializado completo. Puedes completar solo un curso: puedes pausar tu aprendizaje o cancelar tu suscripción en cualquier momento. Visita el panel principal del estudiante para realizar un seguimiento de tus inscripciones a cursos y tu progreso.

Proyecto práctico

Cada programa especializado incluye un proyecto práctico. Necesitarás completar correctamente el proyecto para completar el programa especializado y obtener tu certificado. Si el programa especializado incluye un curso separado para el proyecto práctico, necesitarás completar cada uno de los otros cursos antes de poder comenzarlo.

Obtén un certificado

Cuando completes todos los cursos y el proyecto práctico, obtendrás un Certificado que puedes compartir con posibles empleadores y tu red profesional.

Hay 5 cursos en este Programa Especializado

Curso1

Curso 1

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

4.5

estrellas

601 calificaciones

•

125 reseña

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.

After completing this course, you will be able to compute a variety of recommendations from datasets using basic spreadsheet tools, and if you complete the honors track you will also have programmed these recommendations using the open source LensKit recommender toolkit. In addition to detailed lectures and interactive exercises, this course features interviews with several leaders in research and practice on advanced topics and current directions in recommender systems.

Curso2

Curso 2

Nearest Neighbor Collaborative Filtering

4.3

estrellas

294 calificaciones

•

66 reseña

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 similar tastes to a target user and combines their ratings to make recommendations for that user. You will explore and implement variations of the user-user algorithm, and will explore the benefits and drawbacks of the general approach. Then you will learn the widely-practiced item-item collaborative filtering algorithm, which identifies global product associations from user ratings, but uses these product associations to provide personalized recommendations based on a user's own product ratings.

Curso3

Curso 3

Recommender Systems: Evaluation and Metrics

4.4

estrellas

217 calificaciones

•

30 reseña

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, product coverage, and serendipity. You will learn how different metrics relate to different user goals and business goals. You will also learn how to rigorously conduct offline evaluations (i.e., how to prepare and sample data, and how to aggregate results). And you will learn about online (experimental) evaluation. At the completion of this course you will have the tools you need to compare different recommender system alternatives for a wide variety of uses.

Curso4

Curso 4

Matrix Factorization and Advanced Techniques

4.3

estrellas

178 calificaciones

•

26 reseña

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.

Instructores

Joseph A Konstan

Distinguished McKnight Professor and Distinguished University Teaching Professor

Computer Science and Engineering

173.878 alumnos

11 cursos

Michael D. Ekstrand

Assistant Professor

Dept. of Computer Science, Boise State University

101.083 alumnos

6 cursos

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.

Preguntas Frecuentes

¿Cuál es la política de reembolsos?
Si estás suscrito, obtienes una prueba gratis de 7 días, que podrás cancelar cuando desees sin ningún tipo de penalidad. Una vez transcurrido ese tiempo, no realizamos reembolsos. No obstante, puedes cancelar tu suscripción cuando quieras. Consulta nuestra política completa de reembolsos.
¿Puedo inscribirme en un solo curso?
¡Sí! Para empezar, haz clic en la tarjeta del curso que te interesa e inscríbete. Puedes inscribirte y completar el curso para obtener un certificado que puedes compartir o puedes acceder al curso como oyente para ver los materiales del curso de manera gratuita. Cuando cancelas la suscripción de un curso que forma parte de un programa especializado, se cancela automáticamente la suscripción de todo el programa especializado. Visita el panel del estudiante para realizar un seguimiento de tu progreso.
¿Hay ayuda económica disponible?
Sí, Coursera ofrece ayuda económica a los estudiantes que no pueden pagar la tarifa. Solicítala haciendo clic en el enlace de Ayuda económica que está debajo del botón “Inscribirse” a la izquierda. Se te pedirá que completes una solicitud. Recibirás una notificación en caso de que se apruebe. Deberás completar este paso para cada uno de los cursos que forman parte del Programa especializado, incluido el proyecto final. Obtén más información.
¿Puedo tomar este curso de manera gratuita?
Cuando te inscribes en el curso, tienes acceso a todos los cursos del programa especializado y obtienes un certificado cuando completas el trabajo. Si solo deseas leer y ver el contenido del curso, puedes participar del curso como oyente de manera gratuita. Si no puedes pagar la tarifa, puedes solicitar ayuda económica.
¿Este curso es 100 % en línea? ¿Necesito asistir a alguna clase en persona?
Este curso es completamente en línea, de modo que no necesitas ir a un aula en persona. Puedes acceder a tus lecciones, lecturas y tareas en cualquier momento y cualquier lugar a través de Internet o tu dispositivo móvil.
¿Cuánto tiempo se necesita para completar un programa especializado?
Most learners should be able to complete the specialization in 20-26 weeks.
What background knowledge is necessary?
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.
Do I need to take the courses in a specific order?
While each component can be useful by itself, the courses do build on each other and should be taken in order.
¿Recibiré crédito universitario por completar el programa especializado?
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.
What will I be able to do upon completing the Specialization?
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.
What is the honors track?
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.
How does this Specialization relate to the prior Recommender Systems courses?
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.

¿Tienes más preguntas? Visita el Centro de Ayuda al Alumno.