Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Google Cloud

Recommendation Systems with TensorFlow on GCP

Google Cloud and Google via Coursera

Overview

In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.

This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.

Syllabus

  • Welcome to Recommendation Systems on Google Cloud
    • This module previews the topics covered in the course.
  • Recommendation Systems Overview
    • This module defines what recommendation systems are, reviews the different types of recommendation systems, and discusses common problems that arise when developing recommendation systems.
  • Content-Based Recommendation Systems
    • This module demonstrates how to build a recommendation system using characteristics of the users and items and how to use Qwiklabs to complete each of your labs using Google Cloud.
  • Collaborative Filtering Recommendations Systems
    • This module shows 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
    • This module shows how various recommendation systems can be combined as part of a hybrid approach.
  • Reinforcement Learning
    • This module presents the goals of reinforcement learning and shows where reinforcement learning fits in machine learning.
  • Summary
    • This module reviews the topics explored in this course.

Taught by

Google Cloud Training

Reviews

4.5 rating at Coursera based on 445 ratings

Start your review of Recommendation Systems with TensorFlow on GCP

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.