Machine Learning in Java: Helpful techniques to design, build, and deploy powerful machine learning applications in Java, 2nd Edition

Leverage the power of Java and its associated machine learning libraries to build powerful predictive models

Key Features

• Solve predictive modeling problems using the most popular machine learning Java libraries
• Explore data processing, machine learning, and NLP concepts using JavaML, WEKA, MALLET libraries
• Practical examples, tips, and tricks to help you understand applied machine learning in Java

Book Description

As the amount of data in the world continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and Data Science. The main challenge is how to transform data into actionable knowledge.

Machine Learning in Java will provide you with the techniques and tools you need. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. The code in this book works for JDK 8 and above, the code is tested on JDK 11.

Moving on, you will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text analysis. By the end of the book, you will have explored related web resources and technologies that will help you take your learning to the next level.

By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.

What you will learn

• Discover key Java machine learning libraries
• Implement concepts such as classification, regression, and clustering
• Develop a customer retention strategy by predicting likely churn candidates
• Build a scalable recommendation engine with Apache Mahout
• Apply machine learning to fraud, anomaly, and outlier detection
• Experiment with deep learning concepts and algorithms
• Write your own activity recognition model for eHealth applications

Who this book is for

If you want to learn how to use Java's machine learning libraries to gain insight from your data, this book is for you. It will get you up and running quickly and provide you with the skills you need to successfully create, customize, and deploy machine learning applications with ease. You should be familiar with Java programming and some basic data mining concepts to make the most of this book, but no prior experience with machine learning is required.

Table of Contents

1. Applied Machine Learning Quick Start
2. Java Libraries and Platforms for Machine Learning
3. Basic Algorithms - Classification, Regression, and Clustering
4. Customer Relationship Prediction with Ensembles
5. Affinity Analysis
6. Recommendation Engine with Apache Mahout
7. Fraud and Anomaly Detection
8. Image Recognition with Deeplearning4j
9. Activity Recognition with Mobile Phone Sensors
10. Text Mining with Mallet - Topic Modeling and Spam Detection
11. What is Next?

AshishSingh Bhatia is a reader and learner at his core. He has more than 11 years of rich experience in different IT sectors, encompassing training, development, and management. He has worked in many domains, such as software development, ERP, banking, and training. He is passionate about Python and Java and has recently been exploring R. He is mostly involved in web and mobile development in various capacities. He likes to explore new technologies and share his views and thoughts through various online media and magazines. He believes in sharing his experience with the new generation and also takes part in training and teaching.

Bostjan Kaluza is a researcher in artificial intelligence and machine learning with extensive experience in Java and Python. Bostjan is the chief data scientist at Evolven, a leading IT operations analytics company. He works with machine learning, predictive analytics, pattern mining, and anomaly detection to turn data into relevant information. Prior to Evolven, Bostjan served as a senior researcher in the department of intelligent systems at the Jozef Stefan Institute and led research projects involving pattern and anomaly detection, ubiquitous computing, and multi-agent systems. In 2013, Bostjan published his first book, Instant Weka How-To, published by Packt Publishing, exploring how to leverage machine learning using Weka.