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 AI Platform Notebooks

In this section, we'll learn what AI Platform Notebooks is and the advantages that it can provide.

AI Platform Notebooks is a fully managed service that allows data engineers and data scientists to use a JupyterLab development environment. This service allows us to develop, evaluate, test, and deploy machine learning models.

JupyterLab is a web tool for data scientists that is developed and maintained by Project Jupyter (https://jupyter.org/about). The goal of the project is to develop open source software and standards that offer a unique interface across different programming languages.

Using AI Platform Notebooks with JupyterLab can bring several advantages:

We can easily set up our pre-configured machine learning environments with the most important and useful ML libraries, such as TensorFlow, Keras, PyTorch, and others.
We can leverage the scalability of the cloud by increasing the size of the hardware resources according...