---

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 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 Who This Book Is For
Experienced Java developers who have not implemented machine learning techniques before.

---

1. Introduction IDE Setup - Eclipse IDE Setup - Android Studio Java Setup Machine Learning Performance with Java Importance of Analytics Initiatives Corporate ML Objectives Business Case for Deploying ML Machine Learning Concerns Developing an ML Methodology State of the Art: Monitoring Research Papers
2. Data: The Fuel for Machine Learning Think Like a Data Scientist Data Pre-Processing JSON and NoSQL Databases ARFF and CSV Files Finding Public Data Creating your Own Data Data Visualization with Java + Javascript Project: DataViz
3. Leveraging Cloud Platforms Google Cloud Platform Amazon AWS Using Machine Learning API´s Project: GCP API Leveraging Cloud Platforms to Create Models
4. Algorithms: The Brains of Machine Learning Overview of Algorithms Supervised Learning Unsupervised Learning Linear Models for Prediction and Classification Naive Bayes for Document Classification Clustering Decision Trees Choosing the Right Algorithm Creating Your Competitve Advantage
5. Java Machine Learning Environments Overview Choosing a Java Environment Deep dive: The Weka Workbench Weka Capabilities Weka Add-ons Rapidminer Overview Project: Document Classification with Weka
6. Integrating Models

---