The new trend of machine learning for all developers of Java applications


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Java is still the most popular programming language these days. It is continuously evolving to meet the changing requirements of people and business organizations anywhere in the world.

Why is Java still very popular? Java application development is still on top when it comes to developing software apps and solutions. There are good reasons why the programming language is still the number one choice, including:
1. Practicality. It is described as a ‘blue collar’ programming language, designed to enable developers to get their job done with less fuss, while still enabling them to pick up someone else’s code later and understand what it is supposed to do. With good coding conventions, it’s more readable compared to other languages.

2. Performance/Scalability/Reliability. With more than twenty years and hundreds of thousands of development, Java is a rock-solid platform, which performs on a level that could match or even exceed native code. For scalability and performance, Java is the obvious choice.

3. Freshness. The JDK 8 was a huge change for developers because of the introduction of Lambda expressions and streams API. Suddenly, developers can do things in a more functional manner without the need to learn a whole new language, such as Scala.

Face detection software, self-driving cars and voice controlled speakers are built on machine learning frameworks and technologies. These are only the first wave. In the next decade, a new generation of products would change the world, initiating new approaches to developing software and the products and applications to be created and used. A Java developer who wants to give the best Java application development services, he or she wants to get ahead of the curve now, when tech companies are starting to invest seriously in machine learning.

Machine learning evolved from the artificial intelligence field, which seeks to create machines that are capable of mimicking human intelligence. While machine learning is an emerging trend in computer science, AI is not a new scientific field. A lot of state-of-the-art machine learning approaches are based on old concepts. What changed in the past decade is that computers have now the processing power needed for machine learning algorithms. Majority of algorithms demand a great number of matrix multiplications as well as other mathematical operations to process. Machine learning allows programs to execute quality enhancement processes as well as extend their capabilities with no human involvement. A program that’s built with machine learning could update or extend its own code.

Companies today are scrambling to look for enough programmers and developers that are capable of doing Java based web applications as well as coding for ML and deep learning. The following are top machine learning libraries for Java.

1. WEKA. It is the number one choice for the best Java machine learning library. It’s a fully Java-based workbench that’s used bets for machine learning algorithms. It’s primarily used for data mining, analysis and predictive modelling. It’s portable, free and easy to use with its graphical interface. Its strength lies in classifications, thus apps that need automatic data classification could take advantage from it. Its collection of machine learning algorithms could be directly applied to a set of data or called from one’s own Java code.

2. MOA (Massive Online Analysis). It is an open source software that’s particularly used for machine learning and mining data on data streams in real time. Developed in java, it could be used easily with Weka while scaling to more demanding concerns. Its collection of machine learning tools and algorithms for evaluation are valuable for classification, regression, outlier detection, recommender systems, clustering and concept drift detection. It could be useful for big evolving sets of data and data streams as well as data created by the IoT devices.

3. DEEPLEARNING4. Is one of the most innovative contributors of the ecosystem of Java. It is a commercial grade, open-source distributed deep-learning in Java. Its mission is to bring deep neural networks as well as deep reinforcement learning, together with business scenarios. It is super useful in identifying and sentiment in speech, text and sound. Furthermore, it could be used to detect anomalies in time series data such as financial transactions.

4. MALLET. It is an open source Java machine learning toolkit for language to text. The Java-based package supports statistical natural clustering, language processing, information extraction, document classification, topic modelling as well as other machine learning apps to text. Its specialty includes sophisticated tools for classification of document, like efficient routines for text conversion.

Machine learning is extremely beneficial to developers of Java applications. The benefits could far outweigh the drawbacks with a capable team of developers as well as having a strong system. ML could be used to predict disease outbreaks, predict traffic patterns and many more. All these could help an organization plan and react accordingly.

Dhrumit Shukla
Dhrumit Shukla has been working as Business Development Manager in a software development company named TatvaSoft since 5 years. He is profoundly skilled and well experienced in providing software development services on various technologies ranging from Microsoft .NET to JAVA, SalesForce, BizTalk, SharePoint, PHP, Open Source, iOS, Android, Pentaho and the list goes on.


  1. The task of selecting machine learning tools for big data can be difficult. The available tools have advantages and drawbacks, and many have overlapping uses. The world’s data is growing rapidly, and traditional tools for machine learning are becoming insufficient .


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