Recommendation Systems on Google Cloud

Overview

6-week cohort with live MIT Faculty sessions. Learn to scale AI beyond the pilot stage.

Recommendation Systems on Google Cloud teaches you how to design and deploy recommendation engines using machine learning techniques on Google Cloud.

As the fifth and final course in the Advanced Machine Learning (ML) on Google Cloud series, it brings together classification models and embeddings to help you construct end-to-end pipelines for real-world recommendation systems.

This course explores the key fundamentals of recommendation systems, including:

Core approaches such as content-based, collaborative filtering, and hybrid models using user and content embeddings
Reinforcement learning techniques for building context-aware recommendations
Designing end-to-end pipelines to support recommendation systems at scale

You’ll work through how different recommendation approaches function in practice and how to combine them within a unified machine learning pipeline. The course also introduces reinforcement learning methods to enhance personalization based on user context and behavior.

Syllabus

Devise a content-based recommendation engine.
Implement a collaborative filtering recommendation engine.
Build a hybrid recommendation engine with user and content embeddings.
Use reinforcement learning techniques for contextual bandits in the context of recommendations.