Java for Machine Learning – 10 Powerful Libraries

In this article, we will discuss machine learning using Java. We will go through the importance of Java for machine learning operations and the various tools through which you can implement your own algorithms.

In the current scenario, the two most popular languages for Machine Learning are Python and R. Apart from these two languages, there is another language – Julia that is highly utilized for scientific computing. But how does Java fare against these languages that are the first choice for many machine learning engineers? We will look at how Java is relevant for Machine Learning and the several libraries that users can utilize to implement Machine Learning in Java.

Why use Java for Machine Learning?

Not just Java, but also its closely related languages like Scala, Clojure, and Cotlin for undertaking various machine learning solutions.

Apart from Machine Learning, Java is the most widely used programming language mainly for software development and for the development of Big Data ecosystems. Large scale enterprises both in the public and private sector have a colossal Java code base that make use of JVM as their primary computing environment. This usually includes Hadoop for the development of big data distributed systems. Apache Spark as a platform for run-time distributed processing. Apache Kafka as a medium for messaging queues and many more. All these platforms make use of Java as their core language to deal with big data. Java has been the primary choice for the development of such data systems due to its scalability, security and reliability.

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Accessing data is the first step towards building much greater machine learning solutions. Therefore, machine learning tools should be able to interface with these technologies really well. Data collection is the first part of the much greater machine learning process. Therefore, we require the ideal machine learning tools for integrating with such data environments.

With the help of the right tools, we can solve many data integration problems. One of the most challenging problems is when the data science project fails to integrate with the production environment. Therefore, a smooth integration will accelerate digital transformation across many businesses and organizations.

By accelerating the digital transformation, we mean by choosing the right machine learning tool for the production of more accurate predictions about the data while maintaining the current technology stack. With the help of these predictions, your business will be able to profit through careful decisions. Therefore, Java provides several tools that will help you to have a proper interface to the production stack.

Best Java Machine Learning Libraries

The following are the top Java Libraries for Machine Learning –

1. DL4J – Deep Learning

DL4J or Eclipse DeepLearning4j is a commercial grade and Eclipse Deeplearning4j is the first commercial-grade, open-source, distributed deep learning library for Java and Scala. It is integrated with Hadoop and Spark providing AI to business using GPUs and CPUs.

Using the Deeplearning4j framework, one can implement the Restricted Boltzmann Machines (RBM), Deep Belief Networks, Deep Autoencoders, stacked denoising autoencoder, word2vec, GloVe etc.

2. ADAMS

ADAMS stands for Advanced Data Mining and Machine Learning System. It provides flexibility at building and maintaining a data-driven, reactive workflow that can be easily integrated.

It provides a wide array of operators, otherwise known as actors that can perform information retrieval, processing, data mining, and visualization. Actors are able to connect implicitly in a tree structure instead of getting placed on a canvas. ADAMS is released under the GPLv3 license.

3. JavaML

Java Machine Learning Library or Java ML comprises of several machine learning algorithms that have a common interface for several algorithms of the same type. It features the Java API which is geared towards addressing software engineers and programmers. Using Java-ML one can use data preprocessing, feature selection, classification, clustering, etc. It also provides several algorithms to the WEKA data mining suite through its API.

Java ML is a general-purpose machine learning library. Implementation of the JavaML algorithms are written clearly that have proper documentation and can be used for future references.

4. Mahout

With the help of this ML framework, one can work with the built-in algorithms. Using Apache Mahout, one can avail the distributed linear algebra framework that allows mathematicians and statisticians to implement their custom made algorithms. Using the scalable ML libraries, you can use a rich set of components using which you can construct a customized recommendation system.

Mahout offers high performance, flexibility, and scalability. The developers designed it as an ML library for enterprise purposes. All of the machine learning operations are implemented on top of the Hadoop’s Map/Reduce paradigm.

5. Neuroph

Neuroph is an Object-Oriented Artificial Neural Network (ANN) that is written in Java. One can easily create and train several neural networks with the help of Java. Furthermore, creating neural networks is possible using the GUI tool – easyNeurons.

The latest release of Neuroph 2.96 contains several API improvements, features as well as examples that can be used for standard machine learning tasks.

6. RapidMiner

RapidMiner is a comprehensive software platform providing an environment for data preparation, machine learning, deep learning, predictive analytics, and text mining. For business applications as well as education, training, rapid prototyping as well as application development, we make use of rapidminer.

RapidMiner provides an easy to construct and maintain machine learning workflow. It provides extensive data loading, feature selection as well as data cleaning with an interfaceable GUI and Java API for developing your applications.

7. Weka

Weka stands for Waikato Environment for Knowledge Analysis. It is a machine learning software written in Java. Weka consists of various machine learning algorithms for data mining. They consist of several machine learning tools that are required for classification, clustering, regression, visualization as well as data mining.

With the help of this GUI suite, you can implement the machine learning algorithms using an interactable platform. It is ideal for beginners who want to understand the know-how of machine learning as they can simply do so without writing a line of code.

8. JSTAT

In order to get a quick grasp over the machine learning problems, the Java Statistical Analysis Tool Library or JSTAT is an ideal tool.

This library is for use under the GPL3 license. A part of this library is mostly for self-education. Most of the code is complete having no external dependencies. It has the largest collection of machine learning algorithms that is available in any framework. It provides high performance and flexibility making it much faster than the other Java libraries. All of these algorithms can be independently applied with the help of an object-oriented framework. It is highly popular in the research and academic areas.

9. ELKI

ELKI is a Java-based data mining framework for the development of KDD applications. The focus of ELKI is algorithm research that provides a unique emphasis outlier detection as well as cluster analysis that are parts of unsupervised learning methods. In order to achieve performance gains, ELKI provides data index structures like R*- tree.

The aim of ELKI is to provide a large collection of highly parameterized algorithms to assist the user in a fair assessment as well as benchmarking of the algorithms. It is most popular among students and researchers who want to gain insights from data.

10. Stanford CoreNLP

Stanford CoreNLP is a set of human language technology tools provided by Stanford University. This is a Java-based annotation framework pipeline through which one can perform various NLP related tasks. It is one of the most used NLP pipelines that can provide base form of words, tokenization, parts of speech, identify text and analysis of syntactic dependencies.

Some of the features of Stanford CoreNLP toolkit are as follows –

• It provides an integrated NLP toolkit with a wide range of grammatical analysis tools.
• It provides a fast and efficient text annotator for pipeline production.
• Stanford CoreNLP is a modern package that is well maintained and regularly updated delivering text analytics of the highest order.
• Another important feature is its support for multiple human languages like Arabic, Chinese, English, etc.
• Apart from Java as its primary tool, Stanford CoreNLP also provides APIs for most major programming languages of the world.
• It can also be used as a simple web-service.

With the help of Stanford’s CoreNLP software, one can easily apply linguistic analytical tools to textual information. One can easily perform textual processing using only two lines of code. It is highly flexible as well as extensible language.

Summary

In this article, we went through several machine learning libraries in Java. We went through how Java is important and how we can avail the various java tools for carrying out machine learning operations. We went through Mahout, JavaML, DL4J and many more. Now, what next? Don’t worry, DataFlair is here to guide you on your learning path. Next, you should explore top Data Science Tools, this will help you to become a master in the technology. Have a look.

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