Machine Learning: Recommender Systems & Dimensionality Reduction

Provided by:
0/10 stars
based on  0 reviews
Provided by:
Cost FREE
Start Date On demand
Machine Learning: Recommender Systems & Dimensionality Reduction

Course Details

Cost

FREE

Upcoming Schedule

  • On demand

Course Provider

Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

Provider Subject Specialization
Humanities
Sciences & Technology
4679 reviews

Course Description

Case Study: Recommending Products How does Amazon recommend products you might be interested in purchasing? How does Netflix decide which movies or TV shows you might want to watch? What if you are a new user, should Netflix just recommend the most popular movies? Who might you form a new link with on Facebook or LinkedIn? These questions are endemic to most service-based industries, and underlie the notion of collaborative filtering and the recommender systems deployed to solve these problems. In this fourth case study, you will explore these ideas in the context of recommending products based on customer reviews. In this course, you will explore dimensionality reduction techniques for modeling high-dimensional data. In the case of recommender systems, your data is represented as user-product relationships, with potentially millions of users and hundred of thousands of products. You will implement matrix factorization and... Case Study: Recommending Products How does Amazon recommend products you might be interested in purchasing? How does Netflix decide which movies or TV shows you might want to watch? What if you are a new user, should Netflix just recommend the most popular movies? Who might you form a new link with on Facebook or LinkedIn? These questions are endemic to most service-based industries, and underlie the notion of collaborative filtering and the recommender systems deployed to solve these problems. In this fourth case study, you will explore these ideas in the context of recommending products based on customer reviews. In this course, you will explore dimensionality reduction techniques for modeling high-dimensional data. In the case of recommender systems, your data is represented as user-product relationships, with potentially millions of users and hundred of thousands of products. You will implement matrix factorization and latent factor models for the task of predicting new user-product relationships. You will also use side information about products and users to improve predictions. Learning Outcomes: By the end of this course, you will be able to: -Create a collaborative filtering system. -Reduce dimensionality of data using SVD, PCA, and random projections. -Perform matrix factorization using coordinate descent. -Deploy latent factor models as a recommender system. -Handle the cold start problem using side information. -Examine a product recommendation application. -Implement these techniques in Python.
Machine Learning: Recommender Systems & Dimensionality Reduction course image
Reviews 0/10 stars
0 Reviews for Machine Learning: Recommender Systems & Dimensionality Reduction

Ratings details

  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars

Rankings are based on a provider's overall CourseTalk score, which takes into account both average rating and number of ratings. Stars round to the nearest half.

No reviews yet. Be the first!

Rating Details


  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars

Rankings are based on a provider's overall CourseTalk score, which takes into account both average rating and number of ratings. Stars round to the nearest half.