You're reading from Machine Learning with BigQuery ML Create, execute, and improve machine learning models in BigQuery using standard SQL queries

Product type Paperback

Published in Jun 2021

Publisher Packt

ISBN-13 9781800560307

Length 344 pages

Edition 1st Edition

Languages

Python

Tools

BigQuery

Concepts

Machine Learning

Author (1):

Alessandro Marrandino

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Table of Contents (20) Chapters

Preface

1. Section 1: Introduction and Environment Setup

2. Chapter 1: Introduction to Google Cloud and BigQuery FREE CHAPTER

3. Chapter 2: Setting Up Your GCP and BigQuery Environment

4. Chapter 3: Introducing BigQuery Syntax

5. Section 2: Deep Learning Networks

6. Chapter 4: Predicting Numerical Values with Linear Regression

7. Chapter 5: Predicting Boolean Values Using Binary Logistic Regression

8. Chapter 6: Classifying Trees with Multiclass Logistic Regression

9. Section 3: Advanced Models with BigQuery ML

10. Chapter 7: Clustering Using the K-Means Algorithm

11. Chapter 8: Forecasting Using Time Series

12. Chapter 9: Suggesting the Right Product by Using Matrix Factorization

13. Chapter 10: Predicting Boolean Values Using XGBoost

14. Chapter 11: Implementing Deep Neural Networks

15. Section 4: Further Extending Your ML Capabilities with GCP

16. Chapter 12: Using BigQuery ML with AI Notebooks

17. Chapter 13: Running TensorFlow Models with BigQuery ML

18. Chapter 14: BigQuery ML Tips and Best Practices

19. Other Books You May Enjoy

Summary

In this chapter, we built a recommendation engine based on the matrix factorization algorithm. After we introduced the business scenario, we discovered what matrix factorization is and the difference between explicit and implicit models. Before diving into data exploration, we enabled BigQuery Flex Slots, which are necessary to train this category of ML algorithms.

Then, we applied some analyses and data preparation steps to the sample data collected by Google from the Google Merchandise e-commerce portal. Here, we've focused on the fields that were actually required to build our BigQuery ML model.

Next, we created our training table, which includes the purchases that were made by each user, along with the related quantity for each product.

After that, we trained our matrix factorization model on the data that we'd prepared. When the model was trained, we evaluated its key performance indicators using SQL code and the BigQuery UI.

Finally, we generated...