These days having skills in deep learning and machine learning is one of the most trending things in the tech world right now, and businesses are looking to hire developers who possess good knowledge in machine learning.
In fact, Java has become a usual norm for implementing new machine learning algorithms these days. There are so many benefits of learning Java and is accepted by the people in machine learning community, easy maintenance, marketability, and readability, among others.
If you want to integrate machine learning into your existing Java business applications then you must hire Java developers for the same.
In this post, we will list down some of the best libraries for implementing machine learning in existing Java applications. All these libraries have been compiled by the popularity level from various blogs, websites, and forums.
Here is the list:
This machine learning library is specially designed for Java by providing a computer framework with wide support for deep learning algorithms. It is considered as one of the best contributors for Java when it comes to machine learning, it an open source deep learning library which brings deep neural networks and deep learning reinforcement together for the various business environment. It usually works as a DIY tool for Java and has the ability to handle all the limitless virtual concurrent tasks.
Moreover, this library is useful for identifying sentiment and patterns in a speech, text, and sound. It can also be used for finding out anomalies in time series data like financial data clearly shows that it can be used for business scenarios rather than a research tool.
ELKI stands for Environment for Developing KDD- Applications Supported by Index-structure, is another open source machine learning library designed for data mining in Java. Specially designed for researchers and students, it provides a large number of algorithmic parameters which are highly configurable.
It is mostly used by graduate students who are looking to create some sensible database. It aims to develop and evaluate advanced data mining algorithm and its interaction with database index structures. With ELKI Java developers can use arbitrary data types, file formats, or distance or similar measurements.
It is a Java library with a huge collection of machine learning and data mining algorithms. It is developed to be used by both research scientists and Java developers. There is no GUI for this library but it features clear interface for each type of algorithms. When we compare it with other clustering algorithms it is very straightforward and allows easy implementation of any new algorithm. Most of the times, the implementation of algorithms is clearly written and properly documented, thus it can be used as a reference. The library is developed using Java.
JSAT stands for Java Statistical Analysis Tool, is a machine learning library developed in Java for solving machine learning problems. It is available to use under the GPL3 license. All the source code is self-contained, having no external dependencies. It is having one of the largest collections of machine learning algorithms available within the library. It is considered as one of the fastest Java machine learning libraries, providing high performance and flexibility. All the machine learning algorithms in this library are implemented using object-oriented framework.
MALLET stands for Machine Learning for Language Toolkit, it is an integration collection of Java source code which can be used in areas like statistical NLP, topic modeling, cluster analysis, document classification, and various other machine learning applications to text. It can also be called as Java machine learning toolkit for text. It was developed by students from UMASS and UPenn and provides support for various algorithms like the decision tree, naive bayes, and maximum entropy.
Mahout is a machine learning framework with integrated machine learning algorithms which help developers in creating their own implementations of algorithms. Mahout is an algebraic framework which is designed to allow data scientists, mathematicians, and analytics professionals to execute their own algorithms. This machine learning library is scalable and provides a rich set of components which allows you to develop a customized recommendation system for a wide variety of algorithms. It offers high performance, flexibility, and scalability, this machine learning library is designed to be business ready.
Weka is another popular machine learning library for Java which can be utilized for data mining and analysis task, where one can apply algorithms directly to a dataset or create the new one on their own using Java source code. This library contains many tools like regression, clustering, classification, and visualization. This library is free to use, portable and easy to use which supports feature selection, anomaly detection, time series prediction, and more. Weka stands for Waikato Environment for Knowledge Analysis, which can be defined as a collection of algorithms and tools for predictive modeling and data analysis along with graphical user interface.
For the past few years, there is a new trend around machine learning. The fact is that most of these above-mentioned libraries are open source which means that the abilities and information can be easily grabbed by the developers, and all the developers have to do is to think what can be done using these libraries. Machine learning in Java is going to completely revolutionize the way applications work.
If you are looking to stay ahead of the competition and want to integrate machine learning in your existing Java business application then you should hire Java developers. At ValueCoders, we have a dedicated team of Java developers who have more than 4200 projects to 2500 clients since its inception in the year 2004. Feel free to Contact Us!