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
- 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
- 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
- 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
- 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