Recommendation Systems on Google Cloud

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.

• Welcome to Recommendation Systems on Google Cloud
• Welcome to Recommendation Systems on Google Cloud
• Recommendation Systems Overview
• Introduction
• Types of Recommendation Systems
• Content-Based or Collaborative
• Recommendation System Pitfalls
• Discussion
• Reading: Recommendation Systems Overview
• Recommendation Systems Overview
• Content-Based Recommendation Systems
• Content-Based Recommendation Systems
• Similarity Measures
• Building a User Vector
• Making Recommendations Using a User Vector
• Making Recommendations for Many Users
• Lab intro: Create a Content-Based Recommendation System
• Implementing a Content-Based Filtering using Low Level TensorFlow Operations
• Using Neural Networks for Content-Based Recommendation Systems
• Lab Intro: Create a Content-Based Recommendation System Using a Neural Network
• Summary
• Readings: Content-Based Recommendation Systems
• Content-Based Recommendation Systems Quiz
• Collaborative Filtering Recommendations Systems
• Types of User Feedback Data
• Embedding Users and Items
• Factorization Approaches
• The ALS Algorithm
• Preparing Input Data for ALS
• Creating Sparse Tensors For Efficient WALS Input
• Instantiating a WALS Estimator: From Input to Estimator
• Instantiating a WAL Estimator: Decoding TFRecords
• Instantiating a WALS Estimator: Recovering Keys
• Instantiating a WALS Estimator: Training and Prediction
• Lab Intro: Collaborative Filtering with Google Analytics Data
• Issues with Collaborative Filtering
• Cold Starts
• Readings: Collaborative Filtering Recommendations Systems
• Collaborative Filtering Recommendations Systems
• Neural Networks for Recommendation Systems
• Hybrid Recommendation Systems
• Lab Intro: Designing a Hybrid Recommendation System
• Lab Intro: Designing a Hybrid Collaborative Filtering Recommendation System
• Lab Intro: Designing a Hybrid Knowledge-based Recommendation System
• Lab Intro: Building a Neural Network Hybrid Recommendation System
• ML on GCP: Hybrid Recommendations with the MovieLens Dataset
• Context-Aware Recommendation Systems
• Context-Aware Algorithms
• Contextual Postfiltering
• Modeling Using Context-Aware Algorithms
• YouTube Recommendation System Case Study: Overview
• YouTube Recommendation System Case Study: Candidate Generation
• YouTube Recommendation System Case Study: Ranking
• Summary
• Readings: Neural Networks for Recommendation Systems
• Neural Networks for Recommendations
• Reinforcement Learning
• Introduction to module
• Introduction to Reinforcement Learning
• The reinforcement learning framework and workflow
• Model-based and model-free reinforcement learning
• Value-based reinforcement learning
• Policy-based reinforcement learning
• Contextual bandits
• Applications of reinforcement learning
• Reinforcement Learning
• Readings: Reinforcement Learning
• Lab Intro
• Applying Contextual Bandits for Recommendations with Tensorflow and TF-Agents
• Lab Review
• Summary
• Course Summary
• Specialization Summary
• All Course Readings
• Your Next Steps
• Course Badge