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Google Cloud

Recommendation Systems on Google Cloud

Google Cloud via Coursera

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Overview

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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 476 ratings

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