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

Exploring and understanding the dataset

As we learned in Chapter 4, Predicting Numerical Values with Linear Regression, before diving into the ML implementation, it's necessary to analyze the data available for our use case. We need to begin by having a clear understanding of the data that can be used for our business scenario.

Understanding the data

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

Log in to Google Cloud Console and access the BigQuery user interface from the navigation menu.
Create a new dataset in the project that we created in Chapter 2, Setting Up Your GCP and BigQuery Environment. For this use case, we'll create the 05_chicago_taxi dataset with the default options.
Open the bigquery-public-data GCP project that hosts all the BigQuery public datasets and browse the items until you find the chicago_taxi_trips dataset. In this public dataset, we can see only one BigQuery table: taxi_trips. This table contains all the information...