Sign up to Coursera courses for free
In this course, you'll apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.
• Devise a content-based recommendation engine
• Implement a collaborative filtering recommendation engine
• Build a hybrid recommendation engine with user and content embeddings
>>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<
Recommendation Systems Overview
-In this module, we review the scope and plan for the course, define what recommendation systems are, review the different types of recommendation systems and discuss common problems that arise when developing recommendation systems.
Content-Based Recommendation Systems
-In this module, we demonstrate how to build a recommendation system using characteristics of the users and items.
COLLABORATIVE FILTERING RECOMMENDATION SYSTEMS
-In this module, we show how the data of the interactions between users and items from many different users can be combined to improve the quality of predictions.
Neural Networks for Recommendation Systems
-In this module we show how various recommendation systems can be combined as part of a hybrid approach.
Building an End-to-End Recommendation System
-In this module we put all the pieces together to build a smart end-to-end workflow for your newly built WALS recommendation model for news articles.
-In this final module, we review what you have learnt so far about recommendation systems and the specialization more broadly.