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Machine Learning with BigQuery ML

You're reading from   Machine Learning with BigQuery ML Create, execute, and improve machine learning models in BigQuery using standard SQL queries

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Product type Paperback
Published in Jun 2021
Publisher Packt
ISBN-13 9781800560307
Length 344 pages
Edition 1st Edition
Languages
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Author (1):
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Alessandro Marrandino Alessandro Marrandino
Author Profile Icon Alessandro Marrandino
Alessandro Marrandino
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Toc

Table of Contents (20) Chapters Close

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

Preparing the dataset

Before starting the ML implementation, it's necessary to analyze and prepare the data for our use case. Since the dataset has been already used in Chapter 4, Predicting Numerical Values with Linear Regression, we will not start the analysis from the beginning, but we will focus exclusively on the queries relevant for our use case.

To start the preparation of our data, we need to do the following:

  1. Log into Google Cloud Console and access the BigQuery user interface from the navigation menu.
  2. 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 11_nyc_bike_sharing_dnn with the default options.
  3. Now we're ready to create the table that will contain the training dataset. Let's execute the following SQL statement:
    CREATE OR REPLACE TABLE `11_nyc_bike_sharing_dnn.training_table` AS
            ...
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