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9 Best Libraries for Implementing Machine Learning in Java

Artificial intelligence advancements are at the heart of virtual personal assistants. As well as other cutting-edge technology. Natural language processing, machine learning, and deep learning are the most prominent AI areas. They find use in big corporations. Examples of their use are online ad targeting and self-driving vehicles.

This post isn’t only for web developers that use Java. Business owners will also benefit from this article. They want to know if a programmer can efficiently create machine learning apps. This involves the knowledge of how to use libraries for Java machine learning tools. 

Furthermore, knowing the background is helpful. Especially if you have a voice in tech stack discussions in your organization.

This article will discuss the top 9 libraries for implementing machine learning in Java.


ADAMS stands for Advanced Data Mining and Machine Learning System. It adheres to the “less is more” concept. ADAMS is a unique and adaptable workflow engine. It aims to easily construct and manage real-world processes without complication.

Users usually drag and drop operators or “actors” onto a canvas and then manually connect input and output. ADAMS controls data flow in the workflow using a tree-like structure. This indicates that there aren’t any connections that require explicit mentioning.


ELKI (Environment for Developing KDD-Applications) is an open-source data-mining program. Built-in Java, Index-structure supports it. It has a huge number of highly adjustable algorithm settings and caters to academics and students. Graduate students who are trying to make sense of their datasets frequently utilize them.

It is a knowledge discovery in databases (KDD) software framework. ELKI was created for use in research and education. Its goal is creating, testing, and implementing ML algorithms. As well as their interactions with database index architecture. A variety of data types, file formats, distance, and similarity metrics bear support from ELKI.


The Java Statistical Analysis Tool is a machine learning library for Java. JSAT allows you to get started fast with ML issues. It is available under the GPL3. There are no external dependencies in the code. Also, it contains one of the most comprehensive algorithms libraries of any framework.

So, naturally many regards it as being quicker than other Java libraries. It offers great speed and versatility. Almost all of the algorithms are implemented in Java individually by using an object-oriented framework. It’s mostly useable for research and other purposes.

4. Deeplearning4j

This Java programming toolkit provides a computing environment that includes extensive support for deep learning techniques. It’s an open-source deep-learning library. The goal is to connect deep neural networks and deep reinforcement learning together. Especially for commercial purposes. 

Deeplearning4j is one of the most creative additions to the Java ecosystem. It’s typically used as a JAVA DIY tool. It performs nearly endless concurrent operations.

It is great at recognizing patterns and emotions. Especially in voice, music, and text. Deeplearning4j may also apply in discovering abnormalities. Real-time data is where you can use this feature. Others use-cases are financial transactions, demonstrating that it is intended for use in commercial settings. That is in a Java web application development company rather than as a research tool.

5. JavaML

It’s a Java API that contains a set of Java-reliant machine learning and data mining techniques. With an intention for both software developers and research scientists to utilize it easily. 

The user interfaces for each algorithm are kept basic and straightforward. There is no graphical user interface. But there are clear interfaces for each sort of algorithm.

It is simple in comparison to other clustering algorithms. Plus, it enables the easy development of new methods. Most of the time, developers document the implementation of algorithms very well. Therefore they may be an excellent guide. The library is developed in Java programming language.

6. RapidMiner

RapidMiner is a suite of tools from the Technical University of Dortmund in Germany. It allows data analysts to create new data mining algorithms, set up predictive analysis, and more. 

RapidMiner consists of machine learning frameworks and algorithms. Luckily, it provides a machine learning process that is straightforward. Making it simple to create and comprehend. It has a GUI and a Java API for personal app creation. In addition, data loading, feature selection, and cleaning are possible. It uses machine learning methods to handle data, visualize it, and model it.

7. Massive Online Analysis (MOA)

MOA is an open-source program applicable in real-time machine learning and data mining. Java is the language of use and its combination with Weka is easy. The set of Java application development services with machine learning algorithms and tools serves several purposes. Particularly in the data science community. 

These include regression, clustering, classification, and recommender systems. Of course, there are other things it makes doable! It’s suitable for big datasets, such as data generated by IoT devices. It is made up of a huge number of machine learning algorithms whose creations help it cope with ML on a large scale.

8. Weka (Waikato Environment for Knowledge Analysis)

Weka is the most popular JAVA machine learning library for data mining jobs. Including algorithms that apply directly to a dataset or from Java code. It holds tools for classification, regression, and clustering among other things. 

Clustering, time series prediction, feature selection, anomaly detection, and more. These are all possible with this free, scalable, and simple-to-use library. It’s a set of tools and methods for data analysis and predictive modeling. Also, as graphical user interfaces.


MALLET stands for Machine Learning for Language Toolkit. It’s a part of Java software development services. They find a use for statistical NLP, cluster analysis, topic modeling, document classification, and other ML applications to text. It is a Java ML toolbox for documented texts.  

Andrew McCallum and students from UMASS and UPenn created it. Also, it supports a broad range of algorithms like maximum entropy and decision trees.

Algorithm infrastructure and the implementation of neural networks are the most important factors in deciding on a framework. Other elements that influence decision-making include speed, dataset size, and simplicity of use. No doubt, these libraries, and tools are one of the best advantages of Java.

In Conclusion

When it comes to picking a Java machine learning library, the most essential thing to remember is to know your project’s needs and the issues you want to address.

If you want to further discuss the requirements of your project related to implementing machine learning in Java, you can contact our Java development company for more details.

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