Practical Java Machine Learning: Projects with Google Cloud Platform and Amazon Web Services

Practical Java Machine Learning teaches you how to build machine learning solutions for Java development, focusing on data organization and architecture, cloud deployment, algorithm selection, and Java ML solutions on Android mobile devices, sensor data, streaming, and more.

Format: Paperback / softback
Length: 392 pages
Publication date: 24 October 2018
Publisher: APress

This book is for experienced Java developers who want to learn how to build machine learning (ML) solutions for their applications. It covers the basics of ML, including data preparation, model training, and evaluation, and provides practical examples and projects to help you get started.
Machine learning (ML) is a powerful tool for developing Java applications. However, designing ML apps requires careful consideration of data throughout the project life cycle. Practical Java Machine Learning is a comprehensive guide that helps you understand the importance of data and how to organize it for use within your ML project.

The book begins by introducing you to the tools and techniques used in ML, including JSON, visualization, NoSQL databases, and cloud platforms such as Google Cloud Platform and Amazon Web Services. You will learn how to identify and manage your data, including how to use JSON to store and transfer data, how to use visualization to understand your data, and how to use NoSQL databases to store large amounts of data.

Practical Java Machine Learning includes multiple projects that demonstrate the capabilities of the Google Cloud Platform machine learning API. These projects include data visualization for Java, document classification using the Weka ML environment, audio file classification for Android using ML with spectrogram voice data, and machine learning using device sensor data.

Each project includes step-by-step instructions and code examples that you can follow to build your own ML solutions. The book also includes case study examples and projects that you can take away as templates for re-use and exploration for your own machine learning programming projects with Java.

One of the key benefits of Practical Java Machine Learning is that it provides a practical approach to ML. The book focuses on real-world applications and provides you with the skills and knowledge you need to build ML solutions that can be deployed in production environments.

Experienced Java developers who want to learn how to build machine learning solutions for their applications will find Practical Java Machine Learning to be a valuable resource. The book covers the basics of ML, including data preparation, model training, and evaluation, and provides practical examples and projects to help you get started.

In conclusion, Practical Java Machine Learning is a comprehensive guide that helps you understand the importance of data and how to organize it for use within your ML project. It includes multiple projects that demonstrate the capabilities of the Google Cloud Platform machine learning API and provides practical examples and projects to help you get started. Whether you are a beginner or an experienced Java developer, this book is a valuable resource that will help you build machine learning solutions for your applications.

Weight: 822g
Dimension: 179 x 253 x 31 (mm)
ISBN-13: 9781484239506
Edition number: 1st ed.