Download or read book Practical Java Machine Learning written by Mark Wickham and published by Apress. This book was released on 2018-10-23 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. What You Will LearnIdentify, 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 solutionsWho This Book Is For Experienced Java developers who have not implemented machine learning techniques before.