Manage different business scenarios with the right machine learning technique using Google's highly scalable BigQuery ML
▶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
▶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
▶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.
▶What this book covers
- Chapter 1, Introduction to Google Cloud and BigQuery, provides an overview of the Google Cloud Platform and of the BigQuery analytics database.
- Chapter 2, Setting Up Your GCP and BigQuery Environment, explains the configuration of your first Google Cloud account, project, and BigQuery environment.
- Chapter 3, Introducing BigQuery Syntax, covers the main SQL operations for working on BigQuery.
- Chapter 4, Predicting Numerical Values with Linear Regression, explains the development of a linear regression ML model to predict the trip durations of a bike rental service.
- Chapter 5, Predicting Boolean Values Using Binary Logistic, explains the implementation of a binary logistic regression ML model to predict the behavior of a taxi company's customers.
- Chapter 6, Classifying Trees with Multiclass Logistic Regression, explains the development of a multiclass logistic ML algorithm to automatically classify species of trees according to their natural characteristics.
- Chapter 7, Clustering Using the K-Means Algorithm, covers the implementation of a clustering system to identify the best-performing drivers in a taxi company.
- Chapter 8, Forecasting Using Time Series, outlines the design and implementation of a forecasting tool to predict and present the sales of specific products.
- Chapter 9, Suggesting the Right Product by Using Matrix Factorization, explains how to build a recommendation engine, using the matrix factorization algorithm, that suggests the best product to each customer.
- Chapter 10, Predicting Boolean Values Using XGBoost, covers the implementation of a boosted tree ML model to predict the behavior of a taxi company's customers.
- Chapter 11, Implementing Deep Neural Networks, covers the design and implementation of a Deep Neural Network (DNN) to predict the trip durations of a bike rental service.
- Chapter 12, Using BigQuery ML with AI Notebooks, explains how AI Platform Notebooks can be integrated with BigQuery ML to develop and share ML models.
- Chapter 13, Running TensorFlow Models with BigQuery ML, explains how BigQuery ML and TensorFlow can work together.
- Chapter 14, BigQuery ML Tips and Best Practices, covers ML best practices and tips that can be applied during the development of a BigQuery ML model.
