Is Java good for machine learning? Let's Find Out

Machine Learning

Machine learning and data science are the recent technologies that are 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 less popular than 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 While old may mean outdated in the development and technology world, with Java, age means maturity, and many companies work with a significant part of the language even without being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.

How does Machine Learning work?

A Machine learning 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 Machine Learning  

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

Classification of Machine Learning    

Machine learning is broadly placed into three groups, namely: 

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.

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. 

Java and Machine Learning

Java is the oldest open-source programming language, but it 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.

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 features like easy debugging, easy-to-code algorithms, high performance, intelligent product development, simplified work, and large-scale projects. Besides, Java bytecode is versatile, transparent, and easy to maintain

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.

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 the 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 who are considering scaling their applications should consider Java as a helpful language.

Can Java be used for machine learning? Java is still reliable for machine learning as its features are entirely packed with libraries and tools specifically for ML. Besides, its speed, cross-platform capabilities, and scalability are critical.

The Best languages 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. 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 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.

On the other hand, unlabelled data features algorithms such as neural networks and association algorithms. Knowing about machine learning and real-world applications 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 Machine Learning, 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, and TensorFlow.jl, MLBase.jl and ScikitLearn.jl.     

JavaScript    

JavaScript  is an excellent language for web development, but it has also found its way into Machine Learning 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.     

The Best language For Both Machine Learning and Artificial Intelligence    

Machine learning is fast-spreading, and every computing enthusiast is trying to get the most out of the technology. Besides, programming languages are being developed to contribute to carrying out ML tasks.

Currently, many languages claim to support the creation of artificial intelligence applications. 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 to be immature – a small community and few libraries and tools.

The best languages for both machine learning and Artificial intelligence are:

Python    

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    

Java boasts many open-source libraries; it is user-friendly and provides an independent platform, making it great for developing AI. The flexibility it 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++    

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.    

Conclusion    

Machine learning is a trend to admire – as a method currently, 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. While Java 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 with the highest popularity and efficiency.

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