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 DNNs

In this section, we'll learn what DNNs are, and we'll understand which regression and classification use cases can be managed with advanced machine learning algorithms.

Artificial Neural Networks (ANNs) are artificial systems that try to reproduce the human brain. They're inspired by biological neural networks and are composed of neurons and synapses that connect the neurons. Each neuron of the artificial network is a component that applies a specific mathematical activation function to the input and returns an output that is passed through a synapse to the next neuron. In ANNs, the neurons are usually organized in layers between the input and the output.

Different from linear models, ANNs are designed to model non-linear relationships between the input and the output variables.

DNNs are ANNs composed of multiple layers between the input and the output, usually two or more. Each layer of neurons is called a hidden layer and its function is to...