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

Introducing the business scenario

Imagine being one of the business analysts that works for a large company in New York City. Your company manages the nation's largest bike sharing program. It leverages 10,000 bikes and has realized 600 stations across different New York areas: Manhattan, Brooklyn, Queens, and Jersey City.

The following is a picture from a bike sharing station in New York City:

Figure 4.1 – New York City bike sharing service by Citi Bike

Thanks to the vision and spirit of innovation of the company, huge amounts of data has been collected since 2013 and is still updated every day. This data contains a lot of information about the usage of the service and can be used to extract very interesting statistics.

At the time of writing, the bike sharing service is available for all the customers that have signed up for a weekly, monthly, or yearly subscription. However, this is not accessible to and convenient for people that only...