Can Machine Learning Be Done in Java?

Machine learning is one of the most popular domains in computer science and engineering right now. The preferred languages for data science and machine learning currently include the likes of Python and R, but can machine learning be done in another popular language, Java?

Machine learning can be done in Java. In fact, there are plenty of advantages to using Java for machine learning, especially when it comes to dealing with large scale data sets. Python is usually recommended because of its ease and because it was adopted earlier by the data science community.

In this article, we will be discussing this subject in detail. We will look at the advantages of using Java in building machine learning applications. And we will also be looking at some of the most popular and useful machine learning tools and libraries for Java.  

Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!

Machine Learning in Java

It is absolutely possible to do machine learning in Java. Other programming languages such as Python & R are generally favored over Java. But that doesn’t necessarily mean one can’t do machine learning with Java. In fact, there are even some advantages to doing machine learning in Java. We will be exploring them in a later section.

While we’ve clearly established that it is possible to do machine learning with Java, we also have to acknowledge the fact that it is not the preferred choice. Nor is it recommended to students or beginners in machine learning. The reason behind this is because of the evolution of the tools and methods within the data science community.

Since machine learning is a computationally expensive task, the code often needs to be translated to faster lower-level languages such as C or C++. Also, there are myriads of other recurring tasks that are a part of machine learning. This is where the libraries come in. These are pre-bundled codes that can simply be imported into your environment and used instead of having to code the whole thing yourself.

There are some great machine learning tools and libraries available for Java. We have dedicated a whole section to this matter later in the article.

Why Is Python Chosen Over Java for Machine Learning?

There is a very simple reason why Python is currently considered the gold standard in the machine learning domain.

First of all, anyone who has coded in multiple languages, including Python, knows that it is one of the simplest programming languages to learn. That doesn’t necessarily mean it isn’t powerful. It is. And incredibly so. This is the main reason why a lot of researchers prefer to work in this language.

Now one thing you should know about machine learning is that it is a solution-oriented task. Owing to Python’s simplicity, the programmer simply has to worry much less about the code and dedicate the extra time towards finding the actual solution to the problem. This is the reason why early researchers and developers preferred this language for machine learning.

This early adoption led to a lot of additional machine learning tools and libraries being developed for Python. And this is the reason why this language is the preferred language for machine learning today. The early adoption meant that people started building libraries and tools for machine learning in Python earlier than they did for languages like Java. This has given the Python ecosystem a slight edge on the matter.

The better and faster-evolving community apart, the main reason why Python is chosen over Java for machine learning is still because of its simplicity.

Machine learning is a computationally, expensive task. So the final implementation often has to be done in a fast lower-level language such as C or C++. Of course, you could build the whole thing in C or C++, but that will take longer. If you want to build a functional model in a short time and test it, it makes more sense to build one in Python (a vastly simpler language) and then translate the code to C or C++.

Reasons to Choose Java for Machine Learning

In the previous section, we discussed why Python is currently preferred over Java in the machine learning community. But we clearly specified that machine learning could be done in Java as well. Well, believe it or not, there are actually some advantages to choosing Java over other languages.

Here are the six main reasons why you might want to choose Java for machine learning:

It Is Ubiquitous

The first and primary reason you might want to choose Java for machine learning is simply the ubiquitousness of the language at the enterprise level. Java is one of the oldest and most popular languages for enterprise development.

This means that many companies and organizations demanding machine learning assistance could already be using plenty of software and tools built using Java. If so, this will mean easier integration and lesser compatibility issues when adding Java coded machine learning to the loop.

It Is Strongly Typed

Java is a strongly typed programming language. This means that Java developers must be a lot more explicit about the data types and variables they use in their programs compared to Python developers. A lot of people confuse strong typing with static typing, but this simply isn’t true.

This mode of programming may feel inconvenient when working with small scale data. But when working with larger data sets, strongly typed programs make it easier to manage the data and maintain the overall codebase. Furthermore, it also saves developers from the extra effort needed to write and conduct unit tests.

It Has Plenty of Frameworks for Big Data

Big data, simply put, refers to very large scale data sets. Since we live in the era of large scale data collection, big data is a big part of the modern machine learning domain. Like we mentioned earlier, a strongly-typed Java code is much more convenient when dealing with large data sets. But there are also plenty of popular big data frameworks that have been written in Java. Hadoop, Hive, Spark, and Fink are some examples.

Frameworks may not be relevant to small scale data science projects. But when you’re looking to build a full-fledged learning system, something that, say, a large enterprise will use on a day to day basis, frameworks are absolutely crucial. So, it simply makes more sense for a big data project to be written in Java.

It Is the King of Scalability

When you’re building large scale applications from scratch, scalability is a big factor that requires consideration. Simply put, an application’s scalability is the ease with which it can be distributed and used by an increasing user base. Java is a language that was built with scalability in mind. Twitter, for example, switched from Ruby to Java in order to scale better.

So when building large scale machine learning applications, Java can be very useful in terms of scalability.

It Is Faster Than Other Popular Machine Learning Languages

Another huge consideration for choosing Java for machine learning is its speed of execution. It is simply unmatched by the other more popular languages used in machine learning. So when speed is a critical factor, it pays to make some extra effort to build your machine learning applications in Java. 

For consideration, you should know that companies like Facebook, Twitter, and LinkedIn use Java for their data engineering tasks.

It Has Plenty of Machine Learning Libraries of Its Own

Like we’ve mentioned plenty of times in the previous sections, languages like Python and R are the preferred choices for machine learning. This is because their ecosystem started working with machine learning earlier and, as such, have developed more robust and faster-evolving libraries and tools.

But you should know that Java has plenty of great tools and libraries for machine learning and data science purposes. We will be discussing this in detail in the following section.

Tools & Libraries for Machine Learning in Java

Here are some of the most popular and powerful tools and libraries for machine learning in Java:


Weka is one of the most popular machine learning libraries for Java. It is an open-source library meaning it is free. It is also an easy-to-use library, featuring an extensive graphical user interface in addition to a command-line interface.

You can use Weka for machine learning in two ways. You can either call it from your Java code using the Java API that comes with the library. The API can be useful when you’re building an evolving application that improves itself based on the data it collects. Alternatively, you could simply use the GUI.

All the fundamental machine learning tools like classification, regression, clustering, feature selection, anomaly detection, visualization, association rules mining, etc. are included. 


MALLET stands for Machine Learning for Language Toolkit. It is an open-source machine learning library for Java that focuses on Natural Language Processing.

You can either use this library by calling it in your Java code using the Java API or use its command-line interface. The Java API is available for decision trees, maximum-entropy, hidden Markov models, conditional random fields, and Naïve Bayes, among others. 

You can use the MALLET library for a number of different NLP related machine learning applications like information extraction, document classification, clustering, cluster analysis, topic modeling, etc. An advanced add-on package called GRMM is also available, which can be used for the training of CRFs with graphical structure.


Deeplearning4j, or DL4j in short, is one of the most popular machine learning libraries for Java out there. It is a commercial-grade open-source library, meaning it can be used in large scale commercial machine learning applications. It is integrated with two popular big data frameworks like Hadoop and Spark.

Deep learning for Java is a particularly well-fitting example in the context of this article. Its primary goal is to make deep reinforcement learning and deep neural net useful in business contexts rather than research. Java being the popular enterprise-level language that it is, can make the most of the DL4j library in developing business goal-oriented ML applications.

DL4j is also compatible with other JVM based languages such as Scala or Kotlin.


Java Machine Learning Library (Java-ML for short) is another popular open-source machine learning library for Java. It is a framework/API that comes with a vast array of ML algorithms and tools suitable for scientists, developers, and engineers alike. Although it lacks a GUI, all algorithms have a recognizable interface.

One of the greatest things about Java-ML is that it is incredibly straightforward compared to some of the other libraries out there. It comes with an extensively documented source code and a healthy amount of tutorials and sample codes. It can work with pretty much any file format as long as the data set is limited to one data per line and is separated by commas or semicolons.


Apache Mahout is another great library that finds particular usage in building scalable ML applications. It is a distributed linear algebra framework and a mathematically expressive Scala DSL that is suitable for statisticians, data scientists, and data analysts. 

When applied in business-level applications, Mahout can be particularly useful in three different contexts:

  • When you’re building a recommendation system.
  • When you’re clustering related items like documents together.
  • When you’re trying to classify a set of unlabeled items like documents.

Apache Mahout features a Japa API for each of these algorithms. In addition, it also features a console interface.


RapidMiner is one of the most popular Java tools for data analytics right now. It is employed by a range of large companies, including Siemens, Samsung, Cisco, Hitachi, etc. This ML tool aptly presents itself as “One Platform, Does Everything.”

The GUI is very intuitive and can be used for most purposes. RapidMiner is particularly built for data analytics teams, who can use it to implement code-free data analysis. In addition, it also features a Java API, which can be useful if you are coding and developing an ML application of your own.

The tool features all the necessary features that a data scientist may need, from machine learning algorithms to visualization tools. This makes it a helpful tool in simplifying data science. It also has a huge community of users, meaning you’re going to find plenty of help settling in. 


Java Statistical Analysis Tool (or JSAT for short) is a great machine learning library for Java. It contains a large collection of algorithms that can work with any framework. 

These include: 

  • Data Transforms (AutoDeskew, Linear Transform, ZeroMeanTransform, PNormNormalization, Nominal To Numeric, etc.) 
  • Predictive Algorithms (Logistic Regression, Logistic Regression DCD, DCDs, Linear Batch, Linear SGD, etc.) 
  • Kernel-Based Algorithms (Platt’s SMO, Kernel SGD, Double Update Online Learning (DUOL), etc.)
  • Tree-Based algorithms (ID3, Decision Tree, Random Tree, Random Forest, Extra Tree, and Extra Random Trees (ERTrees)) 
  • Nearest Neighbor/Vector Quantization based algorithms (Nearest Neighbor, Discriminant Adaptive Nearest Neighbor. DANN, Learning Vector Quantization (LVQ), Self Organizing Map (SOM), etc.)
  • Meta Algorithms (AdaBoostM1, SAMME, Logit Boost, Bagging, Wagging, etc.)


MOA stands for Massive Online Analysis. It is an open-source machine learning framework that can be used for data mining and analysis purposes. It is particularly strong at dealing with large real-time data streams.

MOA features an extendable framework that can be used on large evolving data sets, like those generated by IoT devices that are becoming more ubiquitous by the day. The range of algorithms can be used for classification, regression, clustering, concept drift detection, recommendation systems, and outlier detections. 


ELKI (Environment for Developing KDD-Applications Supported by Index Structures) is another great machine learning tool written in Java. It is an open-source tool and is particularly powerful in unsupervised methods of cluster analysis and outlier detection. ELKI offers data index structures like R*-Tree, which can go a long way in improving any data mining/analysis application’s performance and scalability.

ELKI is particularly known for its modular design and extensibility. As such, it allows arbitrary data types, algorithms, file formats, and evaluation methods. The modular design also allows for the optimization of the algorithms, meaning the overall operation will be faster.

Another unique aspect of ELKI is its separation of the data management tasks and the data mining algorithms. This makes it possible to analyze the two aspects independently.

ELKI comes with extensive documentation, a nice tutorial, and plenty of example codes. It is primarily aimed at researchers rather than business-oriented developers.   

Author’s Recommendations: Top Data Science Resources To Consider

Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.

  • DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
  • MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
  • Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.


Python is usually recommended over Java when it comes to the domain of machine learning. But this is simply because of the relative ease of the language and due to its existing ubiquitousness in the domain. Someone who is already well versed in Java can go about building capable machine learning applications in Java. And there are even advantages to doing so.

Java is particularly useful when it comes to dealing with large scale data sets. This is one of the reasons why companies like Twitter, Facebook, and LinkedIn use Java in their Data Engineering domains.

BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.

  1. Can Java be used for machine learning and data science? (n.d.). KDnuggets.
  2. (n.d.). Deeplearning4j.
  3. EdwardRaff/JSAT. (n.d.). GitHub.
  4. (n.d.). Java Machine Learning Library (Java-ML).

Affiliate Disclosure: We participate in several affiliate programs and may be compensated if you make a purchase using our referral link, at no additional cost to you. You can, however, trust the integrity of our recommendation. Affiliate programs exist even for products that we are not recommending. We only choose to recommend you the products that we actually believe in.


Daisy is the founder of Passionate for the field of Data Science, she shares her learnings and experiences in this domain, with the hope to help other Data Science enthusiasts in their path down this incredible discipline.

Recent Posts