Is Java good for machine learning?

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Machine learning and data science are the recent technologies taking the computing and programming world by storm. Typically, these advancements have boosted automation and business performance to new higher levels.

Many programming languages can handle machine learning and data science applications. Of course, Python and R are the most acclaimed; thus, there are favorites for creating these applications. But Java is equally versatile, though not as popular as the other two. Java can, however, meet the demands of many organizations when it comes to app development – navigation systems, enterprise-level business solutions, etc.

Without a doubt, Java is helpful, thanks to its speedy and reliable nature. Programmers have put it to various tasks, including data mining, data analysis, and building Machine Learning applications. That makes it a powerful tool in handling data science and artificial intelligence tasks.

Java has a rich history in enterprise development. While old may mean outdated in the development and technology world, with Java, age means maturity, and many companies work with a significant part of the language even without being aware of it.
Java powers many things, including infrastructure, applications, software, and other critical working parts of a company’s tech, which can simplify integration and minimize compatibility issues.

Data science and big data go arm in arm, and the most popular frameworks applied in Big data are written in Java, e.g., Fink and Hive. Besides, Java is used in data science and data analysis processes – data import and export, cleaning data, deep learning, Natural Language Processing (NLP), statistical analysis, and data visualization.
But why is Java not as popular as Python?
While Java is faster than Python and has lots of libraries for machine learning and data science, it isn’t very easy to learn and use like Python, especially for beginners.

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Is Java good for machine learning
Is Java good for machine learning

Which is better for machine learning, Java or Python?

Artificial intelligence is a new technology that uses different programming languages. While there is no perfect language for artificial intelligence, some languages perform better than others—Python and Java stand out as some of the best programming languages. 

Python is a well-known high-level programming language that can handle complex scenarios. But it’s also a general-purpose language used across various domains—Developers favor Python for its general simplicity and a less complex syntax. Apart from being open-source and platform-independent, Python has an extensive library.

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Java, like Python, is object-oriented and multi-paradigm. But unlike Python, Java has a somewhat complex syntax, but the speed is much greater. 

Java for machine learning

Besides machine learning solutions, Java supports neural networks, search algorithms, and multi-robot systems. Java tools can create a connection between AI and algorithms; thus, creating appealing graphics and interfaces. Java supports machine learning due to the features like easy to debug, easy-to-code algorithms and high performance, intelligent product development, and simplified work and large-scale projects. Besides, Java bytecode is versatile, transparent, and easy to maintain

Python for ML

Python is termed as a complete language since a programmer at any level can comfortably use it. The notable flexibility of Python makes it a programming language of choice for many. However, the most appealing part is its readability and simple syntax. Implementing artificial intelligence projects is easier in Python because developers don’t need code algorithms. Typically, Python minimizes the burden of code testing. 

The numerous prebuilt libraries in Python are perhaps the most standout feature. These libraries enable efficient implementation of various deep learning and machine learning algorithms. 

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So, which is better for machine learning, Java or Python? Python is better suited for machine learning and artificial intelligence for its accessibility, ease of use, and simplicity. Java performs better than Python; it needs more code than Python and won’t compile bugs, unlike Python. 

Is Java good for AI?

Java is a powerful programming language for developing artificial intelligence solutions. Most programmers agree that Java comes second to Python. But as earlier said, there is no single language that was curated for artificial intelligence. 

Java, as technology, proves beneficial in machine learning. Did you know that algorithms are an essential ingredient in machine learning and data science? Java has many resources that developers can use in various algorithms or even develop if developers use them efficiently. 

Java is scalable with most frameworks and libraries. But also, there are extensive ai libraries in Java. The reason why Java is suitable for artificial intelligence include:

  • Execution speed: Java is the fastest programming language, meaning that AI technologies run faster. Java owes its faster execution speed to statically typing and compilation.
  • Coding: Indentation is not compulsory in Java, making it easier than Python or R. besides, coding requires fewer lines. If you are well accustomed to writing codes, Java is valuable for AI.
  • Varied Libraries: many AI libraries that support artificial intelligence in Java exist- for instance, ADAMS, Mahaut, JavaML, WEKA, and Deeplearning4j. 
  • Learning Curve: While in many places coders need to put in a lot of effort, Java is generally much easier and more comfortable than other allied languages. Knowing the language better places you at a better position to carrying out operations. 
  • Portability: There is less rework involved when transferring Java from one operating system to another. This is an attractive feature in Java as you can use it on different CPUs and Operating Systems.
  • Coding in different algorithms: being object-oriented and time scalable, Java has all it needs to power AI solutions. Its search algorithms are tightly connected to artificial intelligence, making it easy to code in different algorithms. 

Java deletes useless data automatically, making it a good choice for large-scale projects, thanks to the built-in garbage collector. 

Is Java used in ML?

Java is the oldest open source programming language but still has a lot to offer when it comes to machine learning. The most obvious thing is its extensive libraries related to machine learning, which you can easily download. These libraries provide a collection of machine learning algorithms implemented in Java. 

If you have background knowledge in Java coding, performing AI and ML operations is easy – you won’t need to go via Ruby or Python. Java offers support for development in any domain, meaning it accommodates data science perfectly. 

According to developers, the Java Virtual Machine (JVM) is one of the best platforms for machine learning and data science. Ideally, Java Virtual Machine enables developers to write similar code across multiple platforms. Additionally, it allows developers to develop custom tools at a much faster speed. Perhaps the most interesting thing is that it has many IDEs that help to improve overall productivity levels.

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Java 8 came with Lambda expressions. These expressions allow developers to manage the enormous Java capabilities, thus simplifying large enterprise or data science projects. 

Since it is a strongly typed language, Java ensures that coders are specific and explicit about variables and data types they deal with. Strong typing makes it easy for developers to manage large data applications alongside simplifying codebase maintenance. Also, it eliminates the need to write unit tests. 

Scaling an application in Java is very easy for data scientists and programmers, making it an excellent choice for developing complex machine learning and artificial intelligence applications, more so, when building from scratch. Developers considering scaling their applications should consider Java as a helpful language. 

So, is Java used in ML? Java is reliable for machine learning as it features entire packed libraries and tools specifically for ML. Besides, its speed, cross-platform capabilities, and scalability are critical.  

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Machine learning with Java tutorial

Machine learning is a complex field, and tutorials offer basic and advanced concepts on machine learning. Working professionals and students can learn machine language, considering it is a growing technology that allows computers to learn automatically from past data. 

Machine learning uses a wide range of algorithms for creating mathematical models and making predictions using historical information. ML has applications including image recognition, recommender system, email filtering, speech recognition, and Facebook auto-tagging.

Machine Learning is a subtype of artificial intelligence and is mainly concerned with the development of algorithms. With the help of historical data, i.e., training data, ML algorithms build mathematical models to help in prediction and decision making without implicit programming. ML brings together statistics and computer science. 

How does ML work?

An ML system uses historical data to build prediction models whenever it receives new data. The output accuracy depends on the amount of data – larger data amounts help to develop better models, which predict output more accurately. 

Features of ML:

  • ML uses data to detect patterns in a given dataset.
  • ML systems improve automatically after learning from past data.
  • ML is data-driven technology.

Classification of ML

Machine learning is broadly placed into three groups: 

Supervised learning: in this method, a sample labeled data is provided to the ML system to train it to predict the output. The system creates a model using the data to comprehend and learn about each data set once training is complete. 

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Unsupervised learning: in this method, the ML system learns on its own. Training is offered with unlabelled data, and the system works on data without supervision. 

Reinforcement learning: this is a feedback-based learning method – the learning agent gets a reward for the right action and a penalty for the wrong one. The agent learns automatically with this feedback and improves its performance. 

Why Java is not suitable for machine learning

Pretty much up to now, you know that Java is ideal for machine learning but not the best. As earlier stated, there is no single programming language designed only for machine learning, except for Julia; some languages offer better performance than others.

Java is a good language for many things as it is considered a mature language. Being around for close to 30 years means that it has won the hearts of many companies and has a massive codebase. Much of the open stack for processing big data is written in Java. This implies that Java-based machine learning projects are most likely to be easier to integrate with existing repositories. 

Again, there are lots of third-party libraries for machine learning – for instance, Deeplearning4j allows the creation of any neural network. Besides, it furnishes support for popular algorithms such as linear regression and k-nearest neighbors. If this is complicated for you, you can opt for the Neuroph, an alternative Java library for neural networks. 

And if you want the machine learning algorithms to run exceptionally well in a process called Hyperparameter tuning, consider using the Arbiter Java library. 

But why is Java not good for machine learning? Java is good for machine learning; the only shortcomings are that other languages perform better. Machine learning is performance, and the standard Java doesn’t have hardware acceleration libraries. Besides, its garbage collector slows Java down and prevents optimizing memory access to prevent page faults. On the contrary, non-standard Java uses hardware acceleration libraries like BLAS.

Remember, Big data stacks are built using Java-based technology exclusively, making them appropriate for large amounts of data. However, if you are performing ML on small data, smaller scalable platforms might be right. The bottom line is knowing how to use Java can be a massive advantage for a programmer intending to use it.

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C++ or Java for machine learning?

Most programmers prefer coding in C++ to Java, and there are various reasons to explain this phenomenon. However, to be great at machine learning, a programmer needs to understand the statistics and mathematics behind the data science and machine learning algorithms alongside the details of the algorithms. Sadly, neither C++ nor Java has excellent support for releasing the algorithms from the computer like Python does. Note that algorithms are the most critical parts of machine learning than the programming language itself. 

Nonetheless, C/C++ is second to Python but not as close as JAVA is to C++/C. With C++ and Java at second and third positions, respectively, it means that these two languages have a following regarding machine learning. Choosing between C++ and Java shouldn’t be a real issue because the primary decisive factor is the area of application. 

For instance, Java is a darling to many developers if they work on cyber-attacks and fraud detection systems or network security. These algorithms are used mainly by large organizations, especially in financial institutions – internal development teams prefer using Java. 

On the contrary, if you intend to work on artificial intelligence in games and robot locomotion, C++ is favored among developers. C++ offers a higher level of control, higher performance, and efficiency. Remember, C++ is a low-level language featuring a highly complex AI language, making it a natural choice. 

Another critical point is your professional background. Electronic engineers prefer C++ for front-end web development, while front-end desktop application developers prefer Java. Besides, enterprise developers use Java in all projects, including machine learning.

If you want to enhance your existing app/projects, C++ is a better language to use, but it isn’t suitable for building apps from scratch. Additionally, C++ is excellent for engineering tasks. So, choosing between C++ and Python should be guided by your interest. 

Best language for machine learning and AI

Machine learning is fast speeding, and every computing enthusiast is trying to get the most out of the technology. Besides, there is the development of programming languages to aid in carrying out the ML tasks. 

Currently, many languages claim to support artificial intelligence application creation. Nonetheless, only a few have earned a place in the heart of hearts of programmers. Languages like Julia were explicitly created to support data science. That means their entire structure is built on statistical and mathematical connotations. This should put it right as the correct language for machine learning – but it isn’t. Why? It’s a relatively newer language, and many programmers consider it as not mature – a small community and few libraries and tools. 

So, what’s the best language for machine learning and AI? 


According to the latest statistics, Python boasts over 8.2 million developers worldwide, making it the most preferred language. Python is a choice language for data analytics, machine learning, and AI because of less complexity, platform independence, and better readability. 

R Programming Language

The R language is written for statistics, but data miners and data analysts can use it. This language is suitable for ML as it can handle large numbers quickly. Besides, it carries ML’s statistical principles, making it easy to apply to big data.


Java boasts many open-source libraries; it is user-friendly and provides an independent platform, making it great for developing AI. The flexibility its offers – ease of debugging codes, scalability, the ability to support large-scale enterprises, and graphical representation of data alongside its Virtual Machine Technology enable AI language development on different platforms.


C++ is suitable for building neural networks. Its greatest benefit is speed since AI development has complex computations, and a higher rate makes calculations faster.

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Best language for machine learning

Machine learning tries to mimic the human brain – by searching for patterns within data to make predictions. Ideally, it looks at a wide range of things, including numbers, words, and images. So machine learning systems power search engines, people detection, self-driving cars, medicine and health, and content recommendation systems, among others.

The data set should reflect the real-life predictions, and this involves classifying data into the set—besides, the amount of data and whether it’s labeled or not is a matter of concern. In labeled data, you are bound to find algorithms like regression algorithms, decision trees, and instance-based algorithms. 

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On the other hand, unlabelled data features algorithms such as neural networks and association algorithms. 

Knowing about machine learning and real-world application is one thing. However, knowing how to perform it is another critical issue. So, understanding the use of programming languages in machine learning is a crucial place to begin. Among data scientists, some languages are preferred by others due to their robust and versatile features. The most preferred languages include:

Python:  Python is described as a language for the masses, as many people currently use it due to its simple syntax. For ML, Python features lots of libraries – for example, TensorFlow (neural network), NumPy (matrices, linear algebra), SciPy, Pandas (visualization), and Scikit learn (statistical modeling). 

Julia: Julia was created to match R’s, MATLAB’s, and Python’s functionality and the execution speed of C++ and Java, though it is still far from achieving it. At its core, Julia was built for machine learning as it focuses on the scientific computing domain. Its notable libraries include Flux, Knet, TensorFlow.jl, MLBase.jl and ScikitLearn.jl. 

JavaScript:  though an excellent language for web development, JavaScript has found its way into ML via TensorFlow.js, an open-source library created by Google, and it uses ML modes to develop in the browser. 

Other popular options include C/C++, Scala, Java, and Lisp. 

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Machine learning is a trend to currently admire – as a method, it analyses data and uses the results to automate the analytical models it builds. Machine language is based on the idea that systems can learn from data by identifying patterns and making decisions just like humans without the intervention of humans. 

Modern machine learning is more advanced than previously used as it is based on artificial intelligence, i.e., computers can learn without being instructed. ML is interesting because when models are exposed to new data, they independently adapt by learning from previous computations to produce not only reliable but also repeatable results and decisions – this is a new science but with significant momentum. 

The ability to automatically use complex mathematical calculations to big data severally and much faster is recent. Common examples of such machine learning systems are Self-driving cars; Online recommendation offers like those on Amazon and Netflix, and fraud detection. 

The most tricky part of machine learning is knowing the best part to begin, which involves finding the best language to use. WhileJava has enough libraries and speed to handle machine learning applications, Python stands out as the most favorite language. 

If you are just getting started on machine learning, using a beginner-friendly programming language is the right way to go. However, if you already know machine learning systems, other factors like your background training, tasks, and hand and work requirements play a critical role. 

Java, C, and C++ have excellent speed, which is an admirable feature for handling complex tasks. However, they are pretty hard for beginners to pick up. On the other hand, Julia was created specifically for data science, so its libraries are tailored for data analysis, but it isn’t widespread yet. Python remains the only language highest popularity and efficiency. 

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Luis Gillman
Luis Gillman

Hi, I Am Luis Gillman CA (SA), ACMA
I am a Chartered Accountant (SA) and CIMA (SA) and author of Due Diligence: A strategic and Financial Approach.

The book was published by Lexis Nexis on 2001. In 2010, I wrote the second edition. Much of this website is derived from these two books.

In addition I have published an article entitled the Link Between Due Diligence and Valautions.

Disclaimer: Whilst every effort has been made to ensure that the information published on this website is accurate, the author and owners of this website take no responsibility  for any loss or damage suffered as a result of relience upon the information contained therein.  Furthermore the bulk of the information is derived from information in 2018 and use therefore is at your on risk. In addition you should consult professional advice if required.