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)
1
Section 1: Introduction and Environment Setup
5
Section 2: Deep Learning Networks
9
Section 3: Advanced Models with BigQuery ML
15
Section 4: Further Extending Your ML Capabilities with GCP

Chapter 5: Predicting Boolean Values Using Binary Logistic Regression

Binary logistic regression is one of the most widely used Machine Learning (ML) algorithms to predict the classification of future events and behaviors. It's used in different industries and contexts. Some variables that can be predicted with this technique are the propensity to buy a product and the probability of getting positive or negative feedback from customers for a specific service.

Most digital native companies offer their services in subscription mode. In streaming video services, telco operators, and pay TVs, the binary logistic regression technique is widely used to predict the probability of churn of a customer. Predicting this kind of information is fundamental to target marketing campaigns and special offers to customers with the highest propensity to buy and increase revenue.

In this chapter, we'll see all the stages necessary to implement a binary logistic regression model leveraging...