Machine Learning with BigQuery ML: Create, execute, and improve machine learning models in BigQuery using standard SQL queries

Manage different business scenarios with the right machine learning technique using Google's highly scalable BigQuery ML Key Features Gain a clear understanding of AI and machine learning services on GCP, learn when to use these, and find out how to integrate them with BigQuery ML Leverage SQL syntax to train, evaluate, test, and use ML models Discover how BigQuery works and understand the capabilities of BigQuery ML using examples Book Description 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. What you will learn Discover how to prepare datasets to build an effective ML model Forecast business KPIs by leveraging various ML models and BigQuery ML Build and train a recommendation engine to suggest the best products for your customers using BigQuery ML Develop, train, and share a BigQuery ML model from previous parts with AI Platform Notebooks Find out how to invoke a trained TensorFlow model directly from BigQuery Get to grips with BigQuery ML best practices to maximize your ML performance Who this book is for This book is for data scientists, data analysts, data engineers, and anyone looking to get started with Google's BigQuery ML. You'll also find this book useful if you want to accelerate the development of ML models or if you are a business user who wants to apply ML in an easy way using SQL. Basic knowledge of BigQuery and SQL is required. Table of Contents Introduction to Google Cloud and BigQuery Setting Up Your GCP and BigQuery Environment Introducing BigQuery Syntax Predicting Numerical Values with Linear Regression Predicting Boolean Values Using Binary Logistic Regression Classifying Trees with Multiclass Logistic Regression Clustering Using the K-Means Algorithm Forecasting Using Time Series Suggesting the Right Product by Using Matrix Factorization Predicting Boolean Values Using XGBoost Implementing Deep Neural Networks Using BigQuery ML with AI Notebooks Running TensorFlow Models with BigQuery ML BigQuery ML Tips and Best Practices **Manage different business scenarios with the right machine learning technique using Google's highly scalable BigQuery ML** * Gain a clear understanding of AI and machine learning services on GCP, learn when to use these, and find out how to integrate them with BigQuery ML * Leverage SQL syntax to train, evaluate, test, and use ML models * Discover how BigQuery works and understand the capabilities of BigQuery ML using examples 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. * Discover how to prepare datasets to build an effective ML model * Forecast business KPIs by leveraging various ML models and BigQuery ML * Build and train a recommendation engine to suggest the best products for your customers using BigQuery ML * Develop, train, and share a BigQuery ML model from previous parts with AI Platform Notebooks * Find out how to invoke a trained TensorFlow model directly from BigQuery * Get to grips with BigQuery ML best practices to maximize your ML performance This book is for data scientists, data analysts, data engineers, and anyone looking to get started with Google's BigQuery ML. You'll also find this book useful if you want to accelerate the development of ML models or if you are a business user who wants to apply ML in an easy way using SQL. Basic knowledge of BigQuery and SQL is required. 1. Introduction to Google Cloud and BigQuery 2. Setting Up Your GCP and BigQuery Environment 3. Introducing BigQuery Syntax 4. Predicting Numerical Values with Linear Regression 5. Predicting Boolean Values Using Binary Logistic Regression 6. Classifying Trees with Multiclass Logistic Regression 7. Clustering Using the K-Means Algorithm 8. Forecasting Using Time Series 9. Suggesting the Right Product by Using Matrix Factorization 10. Predicting Boolean Values Using XGBoost 11. Implementing Deep Neural Networks 12. Using BigQuery ML with AI Notebooks 13. Running TensorFlow Models with BigQuery ML 14. BigQuery ML Tips and Best Practices