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

Exploring and understanding the dataset

As we've already done in the previous use cases, before diving into the development of the ML model, it's necessary to analyze the data that can be used to solve our use case.

We'll start with the analysis of the table structure to have a clear understanding of the data that can be used for our business scenario.

Understanding the data

In this section, we'll look take a look at the data to understand its structure and how it can be used to build our ML model.

To start exploring the data, we need to do the following:

Log in to the Google Cloud Console and access the BigQuery user interface from the navigation menu.
Create a new dataset under the project that we created in Chapter 2, Setting Up Your GCP and BigQuery Environment. For this use case, we'll create the dataset 06_nyc_trees with the default options.
Open the GCP project bigquery-public-data, which hosts all the BigQuery public datasets...