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.
What you'll learn
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.
Table of contents
Welcome to Recommendation Systems on Google Cloud
9mins
Recommendation Systems Overview
21mins
Content-Based Recommendation Systems
30mins
- Content-Based Recommendation Systems 2m
- Similarity Measures 3m
- Building a User Vector 4m
- Making Recommendations Using a User Vector 2m
- Making Recommendations for Many Users 7m
- Lab intro: Create a Content-Based Recommendation System 0m
- Pluralsight: Getting Started with GCP and Qwiklabs 4m
- Lab: Content-Based Filtering by Hand 0m
- Using Neural Networks for Content-Based Recommendation Systems 4m
- Lab Intro: Create a Content-Based Recommendation System Using a Neural Network 1m
- Lab: Content-Based Filtering using Neural Networks 0m
- Summary 3m
- Readings: Content-Based Recommendation Systems 0m
Collaborative Filtering Recommendations Systems
98mins
- Types of User Feedback Data 7m
- Embedding Users and Items 13m
- Factorization Approaches 7m
- The ALS Algorithm 5m
- Preparing Input Data for ALS 6m
- Creating Sparse Tensors For Efficient WALS Input 3m
- Instantiating a WALS Estimator: From Input to Estimator 5m
- Instantiating a WAL Estimator: Decoding TFRecords 4m
- Instantiating a WALS Estimator: Recovering Keys 13m
- Instantiating a WALS Estimator: Training and Prediction 6m
- Lab Intro: Collaborative Filtering with Google Analytics Data 1m
- Lab: Collaborative Filtering on Google Analytics data 0m
- Issues with Collaborative Filtering 22m
- Cold Starts 6m
- Readings: Collaborative Filtering Recommendations Systems 0m
Neural Networks for Recommendation Systems
62mins
- Hybrid Recommendation Systems 15m
- Lab Intro: Designing a Hybrid Recommendation System 6m
- Lab Intro: Designing a Hybrid Collaborative Filtering Recommendation System 4m
- Lab Intro: Designing a Hybrid Knowledge-based Recommendation System 5m
- Lab Intro: Building a Neural Network Hybrid Recommendation System 0m
- Lab: ML on GCP: Hybrid Recommendations with the MovieLens Dataset 0m
- Context-Aware Recommendation Systems 8m
- Context-Aware Algorithms 5m
- Contextual Postfiltering 3m
- Modeling Using Context-Aware Algorithms 5m
- YouTube Recommendation System Case Study: Overview 3m
- YouTube Recommendation System Case Study: Candidate Generation 3m
- YouTube Recommendation System Case Study: Ranking 3m
- Summary 2m
- Readings: Neural Networks for Recommendation Systems 0m
Reinforcement Learning
68mins
- Introduction to module 2m
- Introduction to Reinforcement Learning 11m
- The reinforcement learning framework and workflow 17m
- Model-based and model-free reinforcement learning 5m
- Value-based reinforcement learning 12m
- Policy-based reinforcement learning 5m
- Contextual bandits 7m
- Applications of reinforcement learning 8m
- Readings: Reinforcement Learning 0m
- Lab Intro 1m
- Lab: Applying Contextual Bandits for Recommendations with Tensorflow and TF-Agents 0m
- Lab Review 0m
Summary
8mins