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

Implementing BigQuery ML models within notebooks

In this section, we'll leverage the notebook instance that we configured in the Configuring the first notebook section to run BigQuery SQL statements and develop the BigQuery ML machine learning model.

To learn how a notebook can be used, we'll reuse some of the code blocks that we built in Chapter 4, Predicting Numerical Values with Linear Regression. It's important to remember that the goal of the use case was to predict the rental time of each ride for the New York City bike sharing service. To achieve this goal, we've trained a simple linear regression machine learning model. In this section, we'll use the same technique; that is, we'll be embedding the code into an AI Platform notebook.

Compiling the AI notebook

In this section, we'll compile the notebook using Code cells to embed the SQL queries and Markdown cells to create titles and descriptions. Let's start compiling our notebook...