This is the code repository for Machine Learning in Java - Second Edition, published by Packt.
Helpful techniques to design, build, and deploy powerful machine learning applications in Java
As the amount of data 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.
This book covers the following exciting features: Discover key Java machine learning libraries, what each library brings to the table, and what kind of problems each is able to solve Learn how to implement classification, regression, and clustering Develop a sustainable strategy for customer retention 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, algorithms, and the toolbox for deep learning Write your own activity recognition model for eHealth applications using mobile sensors
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
data.defineSingleOutputOthersInput(outputColumn);
EncogModel model = new EncogModel(data);
model.selectMethod(data, MLMethodFactory.TYPE_FEEDFORWARD);
model.setReport(new ConsoleStatusReportable());
data.normalize();
Following is what you need for this book: 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.
With the following software and hardware list you can run all code files present in the book (Chapter 1-11).
| Chapter | Software required | OS required |
|---|---|---|
| All | Java version 8 and above | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
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Natural Language Processing with Java - Second Edition [Packt] [Amazon]
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Deep Learning Projects with PyTorch [Video] [Packt] [Amazon]
AshishSingh Bhatia 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 recently he has been exploring R. He is mostly involved in web and mobile developments in various capacity. He always likes to explore new technologies and share his views and thoughts through various online medium and magazines. He believes in sharing his experience with the new generation and do take an active part in training and teaching also.
Bostjan Kaluza Bostjan Kaluza, PhD, is a researcher in artificial intelligence and machine learning. Bostjan is the chief data scientist at Evolven, a leading IT operations analytics company, focusing on configuration and change management. He works with machine learning, predictive analytics, pattern mining, and anomaly detection to turn data into understandable relevant information and actionable insight. Prior to Evolven, Bostjan served as a senior researcher in the department of intelligent systems at the Jozef Stefan Institute, a leading Slovenian scientific research institution, and led research projects involving pattern and anomaly detection, ubiquitous computing, and multi-agent systems. Bostjan was also a visiting researcher at the University of Southern California, where he studied suspicious and anomalous agent behavior in the context of security applications. Bostjan has extensive experience in Java and Python, and he also lectures on Weka in the classroom. Focusing on machine learning and data science, Bostjan has published numerous articles in professional journals, delivered conference papers, and authored or contributed to a number of patents. In 2013, Bostjan published his first book on data science, Instant Weka How-to, Packt Publishing, exploring how to leverage machine learning using Weka.
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