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Machine Learning with BigQuery ML: Create, execute, and - Paperback / softback N

£44.80
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Last updated on 16 Apr, 2025 18:12:26 BSTView all revisionsView all revisions

Item specifics

Condition
New: A new, unread, unused book in perfect condition with no missing or damaged pages. See the ...
Weight
100grams
Subjects
N/A
Pages
344
Size
23.5 x 19.1 x 1.8 centimeters (0
Topic
Unknown
Book Title
Machine Learning with BigQuery ML: Create, execute, and improve
ISBN
9781800560307
Publication Year
2021
Type
Textbook
Format
Paperback
Language
English
Publication Name
Machine Learning with BigQuery ML: Create, execute, and improve machine learning models in BigQuery using standard SQL queries
Item Height
93 mm
Author
Alessandro Marrandino
Publisher
Packt Publishing
Subject
Computer Science
Item Width
75 mm
Number of Pages
322 Pages

About this product

Product Information

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 an ML model's life cycle 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 machine learning techniques such as linear regression, binary and multiclass logistic regression, k-means, ARIMA time series, 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 GCP components such as AI 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 for building an effective machine learning model * Forecast business KPIs by leveraging various machine learning 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 machine learning 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 machine learning 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.

Product Identifiers

Publisher
Packt Publishing
ISBN-13
9781800560307
eBay Product ID (ePID)
3049060950

Product Key Features

Subject
Computer Science
Publication Year
2021
Number of Pages
322 Pages
Publication Name
Machine Learning with BigQuery ML: Create, execute, and improve machine learning models in BigQuery using standard SQL queries
Language
English
Type
Textbook
Author
Alessandro Marrandino
Format
Paperback

Dimensions

Item Height
93 mm
Item Width
75 mm

Additional Product Features

Country/Region of Manufacture
United Kingdom
Title_Author
Alessandro Marrandino

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