Practical Java Machine Learning
Projects with Google Cloud Platform and Amazon Web Services
Buch in deiner Nähe kaufen
Beschreibung
Build machine learning (ML) solutions for Java development. This book shows you that when designing ML apps, data is the key driver and must be considered throughout all phases of the project life cycle. Practical Java Machine Learning helps you understand the importance of data and how to organize it for use within your ML project. You will be introduced to tools which can help you identify and manage your data including JSON, visualization, NoSQL databases, and cloud platforms including Google Cloud Platform and Amazon Web Services.
Practical Java Machine Learning includes multiple projects, with particular focus on the Android mobile platform and features such as sensors, camera, and connectivity, each of which produce data that can power unique machine learning solutions. You will learn to build a variety of applications that demonstrate the capabilities of the Google Cloud Platform machine learning API, including 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.
After reading this book, you will come away with 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.
You will:
- Identify, organize, and architect the data required for ML projects
- Deploy ML solutions in conjunction with cloud providers such as Google and Amazon
- Determine which algorithm is the most appropriate for a specific ML problem
- Implement Java ML solutions on Android mobile devices
- Create Java ML solutions to work with sensor data
- Build Java streaming based solutions
Build machine learning (ML) solutions for Java development. This book shows you that when designing ML apps, data is the key driver and must be considered throughout all phases of the project life cycle. Practical Java Machine Learning helps you understand the importance of data and how to organize it for use within your ML project. You will be introduced to tools which can help you identify and manage your data including JSON, visualization, NoSQL databases, and cloud platforms including Google Cloud Platform and Amazon Web Services.
Practical Java Machine Learning includes multiple projects, with particular focus on the Android mobile platform and features such as sensors, camera, and connectivity, each of which produce data that can power unique machine learning solutions. You will learn to build a variety of applications that demonstrate the capabilities of the Google Cloud Platform machine learning API, including data visualizationfor 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.
After reading this book, you will come away with 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.
What You Will Learn
- Identify, organize, and architect the data required for ML projects
- Deploy ML solutions in conjunction with cloud providers such as Google and Amazon
- Determine which algorithm is the most appropriate for a specific ML problem
- Implement Java ML solutions on Android mobile devices
- Create Java ML solutions to work with sensor data
- Build Java streaming based solutions
Experienced Java developers who have not implemented machine learning techniques before.
A practical, hands-on book covering the latest machine learning and cloud techniques that Java developers can use Explains why machine learning techniques can yield new functionality which was not previously possible using traditional development approaches Includes several case study examples and projects
Autor*in
Mark Wickham
Themen in »Practical Java Machine Learning«
Java ML machine learning AI artificial intelligence data visualization visualization big data algorithms data science supervised learning unsupervised learning cloud mobile android
Stimmen zu »Practical Java Machine Learning«
“The book is focused on readers who have some background in Java development and want to learn how to use Java frameworks for machine learning. … The book does a good job of explaining these topics to beginners by briefly describing the different kinds of algorithms and their application. … Java developers could use this book as a first approach to machine learning algorithms.” (Santiago Vidal, Computing Reviews, October 11, 2019)
()