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 the XGBoost Boosted Tree classification model

In this section, we'll learn what the XGBoost Boosted Trees classification model is, and we'll understand which classification use cases can be tackled with this ML algorithm.

XGBoost is an open source library that provides a portable gradient boosting framework for different languages. The XGBoost library is available for different programming languages such as C++, Java, Python, R, and Scala, and can work on different operating systems. XGBoost is used to deal with supervised learning use cases, where we use labeled training data to predict target variables.

XGBoost's popularity has grown in the ML community over the years because it has often been the choice of many winning teams during ML competitions, such as the Kaggle - High Energy Physics meets Machine Learning award in 2016.

The classification capabilities of XGBoost Boosted Trees are based on the usage of multiple decision trees that classify...