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 linear regression

Linear regression is one of the simplest techniques that we can apply when we have a continuous numerical value to predict. It is a well-known algorithm that was initially introduced in statistics to analyze the correlation between different variables.

Important note

In statistics, the term regression means that two variables are correlated. This term describes a cause and effect relationship. The cause is called the independent variable, while the effect is called the dependent variable. The dependent variable can be calculated as a function of the independent variable.

The following diagram shows the graphical representation of a simple linear relationship between two variables:

Figure 4.2 – Representation of simple linear regression

A linear regression model tries to predict the label by finding the best linear relationship between the label and its features. If the machine learning model uses only one input...