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

ISBN-13: 9781484239506 / Angielski / Miękka / 2018 / 392 str.

Mark Wickham

Recenzje

"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)

Spis treści

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

O autorze

Mark Wickham is an active developer and has been a developer for many years, mostly in Java.  He is passionate about exploring advances in artificial intelligence and machine learning using Java. New software approaches, applied to the ever expanding volume of data we now have available to us, enables us to create Java solutions which were not before conceivable. He is a frequent speaker at developer conferences. His popular classes cover practical topics such as connectivity, push messaging, and audio/video.  Mark has led software development teams for Motorola, delivering infrastructure solutions to global telecommunications customers. While at Motorola, Mark also led product management and product marketing teams in the Asia Pacific region. Mark has been involved in software and technology for more than 30 years and began to focus on the Android platform in 2009, creating private cloud and tablet based solutions for the enterprise. Mark majored in Computer Science and Physics at Creighton University, and later obtained an MBA from the University of Washington and the Hong Kong University of Science and Technology. Mark is also active as a freelance video producer, photographer, and enjoys recording live music.  Previously Mark wrote Practical Android (Apress, 2018).

Blurb

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