Best Java Machine Learning Libraries

Artificial Intelligence and machine learning are some of the most disruptive yet over-influential technologies that seem to have penetrated way deeper than expected in every industry globally. Web development is not an exception. Machine learning is a subset of artificial intelligence that has the potential to imitate human behavior to a great extent and perform some of the most complicated tasks, as it has never done before. 

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Coming to the development realm, there is no denying the fact that Java is one of the most popular, object-oriented programming languages. It provides a wide range of uncanny benefits and advantages such as simple-to-code robust, highly scalable, reusable coding, unmatchable libraries, flexibility, and security. I can go on and on with Java development but you can find numerous well-written posts on the technology explaining how Java’s framework ensures hardcore convenience.  

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No wonder, java has been thoroughly favoring this particular technology and as time passes by it continues to do so. The global market size of machine learning is about to reach USD 209.91 billion by 2029 from USD 21.17 billion in 2022 at a compound annual growth rate (CAGR) of 38.8%. The following post focuses on top Java machine libraries to take into account. 

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A Quick Overview

Machine learning is not a new concept but makes the most of algorithms and data to mimic right in a manner a human would perform the task or maybe in a better way. Translating to recommendation engines, spam filtering to autonomous vehicles, everything works around ML. So in other words, this is an algorithm software that offers the most accurate and insightful predictions with the help of tons and tons of data gathered and stored to solve relevant issues. Now there are four ways in which the concept of machine learning can be successfully classified. 

• Supervised Learning - This learning model features all the inputs and expected outputs. Here it is possible to find a relevant connection in between. A wide range of datasets is successfully trained in such a manner that they enable the machine learning model to successfully classify data and predict accurate results like never before. 
• Unsupervised learning - As the name implies, this model is left without label datasets. Here it is possible to look out for proper patterns in its input to categorize data into different and proper groups.  
• Semi-supervised learning - The next interesting approach is a rare combination of both supervised and unsupervised algorithms where a large amount of data is given with the least amount of labelled data.  
• Reinforcement learning - The MI model seamlessly interacts with a dynamic environment. Here different programs are required to perform different tasks to receive rewards and punishments just so that you can improvise as much as possible in terms of accuracy. 

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Now without any further delay, it’s time to consider the top-most machine-learning libraries and tools in Java. 

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Top 10 Java Machine Learning Libraries

1. Weka

Weka is highly recommended for its amazing GUI. In addition, this one is an open-source, machine-learning algorithm that is highly considered to perform vivid tasks such as data mining.  Weka is an acronym for Waikato Environment for Knowledge Analysis. Here you will come across a variety of tools that can assist in proper data classification, preparation, regression, rules mining, clustering, visualization and so more. This Java machine-learning library successfully enters the following list since it features built-in help and a beautiful comprehensive manual for successful data mining. The library can be accessed via Java API, standard terminal applications, or GUI. 

2. DeepLearning4J

Our next contender that needs to be taken into account when it comes to the top Java machine learning library is DeepLearning4J also known as Eclipse DeepLearning4J. So what is this? It is a full-fledged set of tools that needs to be used for JVM to successfully support deep learning algorithms. DeepLearning4J is also a framework that is used to train Java models at the same time it can successfully interoperate with Python. Here I would like to mention the top submodels to consider.

• Nd4j
• Samediff
• Python 4J
• Libnd4J
• Datavec
• Apache Spark Integration

3. ADAMs

Another interesting short form for Advanced Data Analytics and Machine Learning systems. The popular Java machine learning library is mainly used to develop as well as maintain reactive, data-driven workflows easily. 

4. ELKI

Environment for Loping KDD-Applications Index-Structures short for ELKI is another interesting data mining open-source framework mainly used for Java. Here the tech focuses on algorithm research. Here you will also come across data index structures that seamlessly ensure performance-based benefits like none other. So whether it is data management tools or data mining tasks, it is possible to independently evaluate.  

5. JAVAML

The next alluring Java machine learning library is that it offers a unique collection of data mining and machine learning algorithms. JAVAML is a readily available API that is also pretty extensible and can be widely used by research scientists as well as software developers. A well-documented source code is what you are about to explore featuring numerous code examples and tutorials. 

6. MOA

Our next Java machine learning library is MOA short for massive online analysis. This one acts as a real treasure for those who are working in the context of data streaming. The framework is successfully designed for both online as well as real-time analysis when it comes to evolving data. As the name implies, MOA is highly capable of handling tons and tons of data streams ensuring the option for the development of apps which require real-time predictions, like stock market analysis, financial markets or IoT sensor data monitoring.

Some of its core aspects include:

• Expertise in mining big data streams and evolving data
• A wide range of machine learning algorithms and tools for evaluation.
• Efficient and scalable for real-time analytics.

7. MALLET

And our next Java machine learning library turns out to be MALLET. This is Machine Learning for Language Toolkit and works wonders when we talk about natural language processing. Whether it's document classification or clustering topic modelling, and more, MALLET works wonders in every possible manner. Certain tasks such as efficiently implementing the Latent Dirichlet Allocation (LDA) or offering full-fledged support to complex machine learning applications such as topic modelling can be well taken care of. No wonder this library is recommended for text analytics and natural language processing and used in social media analysis, sentiment analysis, and topic discovery. 

8. Apache Jena

If you are looking for a semantic web framework that is specially designed for Java then Apache Jena is where your search ends. The library is used to develop Linked Data and Semantic Web Applications. It features RDF API which is used to create as well as read RDF graphs. Here you are bound to receive a programming environment for RDFS, OWL, and SPARQL. Apache Jena supports RDG graph serialization to RDF/XML, relational database, Turtle, TriG, JSON-LD, and Notation 3.

9. PowerLoom

PowerLoom is a knowledge-representation system that is used to create intelligent, knowledge-based apps. Wondering how? Here basically the use of logic-based, expressive language and a natural deduction interface engine is pretty immense so you are able to see the best possible outcome. PowerLoom works in Stella which can be translated into Java, C++, or LISP. 

10. Neuroph

Last but certainly not the least is Neuoph. This one is an object-oriented artificial neural network Java framework used to train machine learning neural networks. Here you are bound to access unmatchable GUI tools and a Java class library which are supposed to train these neural networks.  

Conclusion

So that’s all for now! These are some of the most amazing Java machine-learning libraries to take into account. Each one is of a kind, so picking up any random library won’t work at all, you need to focus on your core needs and requirements see which one would easily fit, and then pick one wisely.  I hope you find the following post worth reading. If so, it would be a great help if you could share this and help us in reaching more and more readers. Good luck with your upcoming Java development project and keep watching the space for more information and updates.