Book Image

Machine Learning with BigQuery ML

By : Alessandro Marrandino
Book Image

Machine Learning with BigQuery ML

By: Alessandro Marrandino

Overview of this book

BigQuery ML enables you to easily build machine learning (ML) models with SQL without much coding. This book will help you to accelerate the development and deployment of ML models with BigQuery ML. The book starts with a quick overview of Google Cloud and BigQuery architecture. You'll then learn how to configure a Google Cloud project, understand the architectural components and capabilities of BigQuery, and find out how to build ML models with BigQuery ML. The book teaches you how to use ML using SQL on BigQuery. You'll analyze the key phases of a ML model's lifecycle and get to grips with the SQL statements used to train, evaluate, test, and use a model. As you advance, you'll build a series of use cases by applying different ML techniques such as linear regression, binary and multiclass logistic regression, k-means, ARIMA time series, deep neural networks, and XGBoost using practical use cases. Moving on, you'll cover matrix factorization and deep neural networks using BigQuery ML's capabilities. Finally, you'll explore the integration of BigQuery ML with other Google Cloud Platform components such as AI Platform Notebooks and TensorFlow along with discovering best practices and tips and tricks for hyperparameter tuning and performance enhancement. By the end of this BigQuery book, you'll be able to build and evaluate your own ML models with BigQuery ML.
Table of Contents (20 chapters)
Section 1: Introduction and Environment Setup
Section 2: Deep Learning Networks
Section 3: Advanced Models with BigQuery ML
Section 4: Further Extending Your ML Capabilities with GCP

Discovering binary logistic regression

In this section, we'll learn what binary logistic regression is and we'll understand the use cases that can be tackled with this ML algorithm.

Logistic regression is a classification ML technique that can be used to predict a categorical variable. We can apply binary logistic regression when the variable to predict is binary and can assume only two values, such as true or false, yes or no, or 1 or 0.

In order to predict one of the two labels, this ML algorithm calculates the probability of two different outcomes and allows us to choose a probability threshold to get the final classification of the binary variable.

Since this is an algorithm based on a regression technique, the prediction of the label is based on a set of independent variables called features that are used to predict the dependent variable, called a label.

This ML technique can be used to answer relevant business questions across different industries, such...