Know The Top Java Machine Learning Tools and Libraries In 2022

Know The Top Java Machine Learning Tools and Libraries In 2022

Machine learning (ML) and deep learning skills are one of the emerging trends in the technological space in recent times. And companies are continually looking to hire Java developers with sound knowledge of ML. ML has brought about significant transformations in various domains of both industry and academia. The two most widely used languages for ML are Python and R. But how does Java fit as the first choice for ML engineers?

The IT industry has evolved at a fleeting rate. In recent years, software engineers are diligently working towards developing new tools and techniques, which results in launching new trends and technologies. One such trending field is Java Machine learning Tools and Libraries.  

Machine Learning is defined as the subset field of Artificial Intelligence and has been a crucial part of the research for decades. However, in the current scenario, the majority of the new technologies rely on Machine Learning, thanks to its great potential to develop highly prominent applications of ML. From virtual personal assistants and online advertising targets to self-driving cars and disaster prediction, almost every technology requires Machine Learning.  

With the introduction of these hi-tech applications, there has been a noticeable increase in machine learning work. Researchers, firms, and development organizations are ready to hire Java developers, well-versed with Machine Learning skills, which are rare to find. Therefore, Java India offers new tools and libraries using Machine learning algorithms to be used in Java. These libraries support existing java developers while promoting them to try their hands on Machine learning as well.

How do Java libraries in Machine Learning Applications help?

The skills required in machine learning tally with Java. Java, being the most popular programming language after Python, has become a norm for implementing ML algorithms. The major advantages of learning Java include acceptance by the ML community, marketability, easy maintenance, readability, and more.  

Major Machine learning libraries for Java technology

Weka

Weka refers to the most popular Java machine learning libraries. It is an open-source Java-based workbench used for a wide range of Machine learning applications including data mining, data analysis, and predictive modelling. Weka is mostly used to implement machine learning algorithms directly to a dataset via a Java program. This library features a well-designed GUI along with a command-line interface to offer great control over the app. Also, Weka is preferred for developing new machine learning schemes, as well.

Massive Online Analysis (MOA)

Massive Online Analysis (MOA) refers to an open-source Java tool primarily used for machine learning on data streams in real-time. It offers a rich collection of machine learning algorithms for features like regression, classification, outlier detection, clustering, recommender systems, concept drift detection, and more.  

Deeplearning4j

Deeplearning4j is one of the most innovative contributors to the Java ecosystem. It is an open-source, commercial-grade, distributed deep-learning library of Java. This library is also considered a DIY tool for Java developers who plan on implementing machine learning algorithms with Hadoop. It is also capable of writing programs for pattern recognition and goal-oriented machine learning applications by detecting the patterns in speech, sound, and text formats.  

Also Read – Importance Of Java Programming In Machine Learning And Data Science

Mallet

Mallet is another machine learning toolkit used by Java developers. This Java-based library supports various machine learning applications including statistical natural language processing, clustering, and topic modelling. It also supports a wide array of algorithms like Decision Trees, Naïve Bayes, maximum entropy, and codes to evaluate classifier performance. MALLET offers tools required for sequence tagging and topic modelling.  

Java-ML (Java Machine Learning Library)

Java-ML is another open-source Java framework/Java API that primarily targets data scientists planning to work on Java. It offers a huge collection of Machine Learning algorithms including data pre-processing, feature selection, classification, clustering, and more. However, this library does not offer a GUI, yet you can find similar algorithms with a very clear common interface that helps Java coders in implementing any new algorithm.  

The Java-ML library offers a well-documented source code and numerous code samples and tutorials to make it easy for Java developers to get started with their web application development.  

JSAT

Java Statistical Analysis Tool refers to an open-source Machine Learning tool that offers one of the largest collections of the machine learning algorithm. Built on core Java, this library does not extend any external dependencies. It was primarily designed for self-education; therefore, all of its code is self-contained. JSAT is also the most suited library for Java developers working on a small or medium-sized Machine Learning project.

Encog Machine Learning Framework

Encog refers to a Java Machine Learning framework that supports a variety of advanced algorithms. It is open-source software and does not require any license or activation fees. Also, it offers a GUI-based workbench to support numerous classes to normalize and process data. One of the major features of this library includes multi-threaded and scalable training algorithms.

Mahout

Apache Mahout refers to a well-distributed linear algebra framework, written in Java and Scala. This library is best suited for data scientists, analytics professionals, researchers, mathematicians, and statisticians. It offers built-in machine learning algorithms that help the Java developers new in Machine Learning.  

Mahout library also offers a console interface and supports the Java APIs for algorithms like clustering, classification, and collaborative filtering. Above all, it is one of the rare Java frameworks that are completely business-ready and can handle any complex processes with a large amount of data.  

ADAMS

The acronym for Advanced Data Mining and Machine Learning System, it is released under the GPLv3 structure. It offers flexibility in developing and maintaining real-world workflows that are generally complex in nature. ADAMS makes use of a tree-like structure to monitor how data flows in the workflow instead of placing operators, or ‘actors’ on a canvas. It indicates there are no explicit connections that are of necessity.

ELKI

The acronym for Environment for Developing KDD-Applications is supported by Index structures. Written in Java, it is an open-source data mining software. Popularly used by graduate students looking to create sensible datasets, it provides a diverse set of highly configurable algorithm parameters. Designed for use in research and teaching, it is a KDD (Knowledge Discovery in Databases) software framework. The segregation of data mining algorithms and data management tasks for the individual analysis of the two is exclusive among data mining frameworks. ELKI uses R*tree and other data index structures that offer high performance and scalability.

RapidMiner

RapidMiner is an eclectic software channel built for analytics team. It provides an environment for machine learning, deep learning, data preparation, text mining, and predictive analytics. It eases construction and maintenance of machine learning workflows. Automated machine learning accelerates and simplifies data science projects. It is currently used by technological giants such as Samsung, Siemens, Cisco, GE, Hitachi, Salesforce and others.

Final thoughts

Lastly, the current industry trends are highly dynamic and require remarkable performance by businesses to stay ahead in the competition. Our highly-equipped Java experts apply their skills to support new technologies like Machine Learning to help our client’s businesses grow and adapt to the ever-evolving market trends.  

Connect with us today.

FAQs

Q1. How are Artificial Intelligence and Machine Learning related?

Initially, AI started as a subfield of computer science with the primary goal of solving tasks that humans can, but computers can’t. For instance, writing a computer program that implements a set of rules.  

On the other hand, Machine Learning is considered a subfield of AI and associated with the development of algorithms for computers to automatically learn models from data.  

Q2. Which Machine Learning algorithms are considered among the best?

Unfortunately, there’s no one algorithm that best suits your needs. There are many factors like different datasheets, questions, and assumptions made before choosing the right algorithm. We would suggest consulting our expert Java developers for professional assistance.  

Q3. Why are there so many Deep Learning libraries?

The Deep Learning field is highly experimental. Therefore, the libraries are mostly the product of a developer’s research work. Additionally, the algorithms are not very simple compared to the other algorithms. Since every developer has their own idea how what a perfect interface must look like, they end up creating a new library. You can tweak and reuse the library as per your project’s requirements.

Q4. How much does it cost to develop machine learning solutions?

The costs of machine learning development vary depending on your requirements. If you need solutions with basic features, costs are comparatively lower. At Java India, we offer machine learning development services at a competitive price. 

Q5. How can I hire java developers?  

You can hire Java developers easily from staff augmentation and IT outsourcing companies like Java India. We allow you to hire Java developers on hourly/weekly/monthly basis.

Q6. What is natural language processing?

Natural language processing (NLP) is a part of artificial intelligence. It uses computational linguistics that provides parsing and linguistic interpretation of texts. NLP enables systems to learn, analyze, and understand human language.

Q7. What type of enterprise software applications can we create using Java?

Java allows you to develop a comprehensive range of enterprise software, like:

• Customer management solutions

• Supply chain software  

• Inventory solutions  

• Retail and eCommerce solutions  

• Employee management systems, & more.  

So, these are some of the top Java Machine learning tools and libraries that our Java development company have discussed in this article. Have a look at once, if your searching for the same.