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

View More author details

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

Discovering matrix factorization

In this section, we'll learn what matrix factorization is, and how it can be used to build recommendation engines.

Matrix factorization represents a class of algorithms usually used to build recommendation engines. These algorithms are built on matrices that represent the interactions between users and items. In these kinds of matrices, the following occurs:

Each user or customer is represented as a row.
Each item or product corresponds to a column of the matrix.
Each cell of the matrix is filled with a numeric value: the feedback.

This feedback represents a rating that a specific user has given to a specific item.

In the following screenshot, we can see an example of a matrix where the rows are the customers of a video streaming service and the columns are the films offered by the platform. Some of the cells contain a rating that ranges from 1 to 5:

Figure 9.2 – Representation of a recommendation...