Machine learning is currently in demand in a tremendous number of spheres. Thanks to the fast processing of large amounts of data and finding solutions to a lot of problems, this branch of artificial intelligence works very effectively for the benefit of thousands of companies. Thus, business owners reduce their expenses for more employees, while the team of full-time workers is busy with more important things.
The Java programming language is currently one of the most used in the world, especially for working with machine learning. It has attracted many developers with its simplicity, efficiency and ability to handle different projects of any complexity. That is why, if developers want to implement machine learning in a project, they do it with Java. Below we present you the top Java machine learning tools and libraries that can help in the development process.
KNIME is one of the most popular and powerful tools that allows you to analyze large amounts of data and work with machine learning. A big advantage and relief for developers is that this tool allows visual programming. Also, the active KNIME community is constantly introducing updates and additions that can come in handy in your work.
MOA is an open source instrument. It can be used for intelligent analysis of large amounts of data, algorithm building (this includes classification, clustering, failure detection algorithms, outlier detection, and so on), and machine learning experiments aimed at solving a variety of problems. This tool is well suited for large-scale machine learning projects, as well as for projects where real-time data flow analysis is needed.
If you are a beginner in programming, or simply looking for something worthwhile and understandable enough to work with machine learning, consider Java-ML. It is one of the largest and most popular Java machine learning libraries. In addition to open source code and a simple interface, you have access to tutorials, as well as many algorithms for working with ML and analyzing data streams. So, this library will be useful for both beginners and experienced developers.
WEKA is a great resource that allows you to work with data analysis and build machine learning models for effective problem solving. It is always ideal for projects that are developed in the Java language, as it is written in Java itself and is used only for it. The library is absolutely free and offers users not just open source code that can be modified to suit their needs, but also rich functionality for working with ML. Also, here developers will find tutorials, examples of documentation and an active community that is ready to help at any time.
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Apache Mahout is a library developed with Java and Scala. Its main categories of users are data analysts, mathematicians, statisticians, and developers who work with machine learning. Among the advantages of the library are its scalability and the ability to run on entire clusters of servers using Apache Hadoop and Apache Spark support. This library is extremely easy for understanding. Moreover, it has a rich set of features and supports various data formats for processing.
JSAT is a lightweight yet tool-rich library for work with machine learning algorithms and statistical data analysis. Due to its flexibility and lightness, it is understandable for every user. Also, this library is written in pure Java and therefore it is ideal for developers who work in this environment. In general, JSAT is perfect for research purposes or for medium-sized business tasks.
If you are researching or working with deep learning and neural networks, Deeplearning4j is one of the best libraries for you. It allows you to control and monitor the learning process on different types of data (such as text, sound, pictures, numbers) using visualization. In addition, you can use one model to train others. This is a good source that also seamlessly integrates with other java libraries.
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Want to create a business-oriented solution using machine learning? RapidMiner is a platform that can help you with that. The main advantages are that this tool works well with different Java libraries and can automate most of the processes. That is, you can automate the data analysis and training processes, thus getting enough time to evaluate the performance of the models.
This library is designed for simultaneous processing of large amounts of data on clusters. In this way you can easily scale your projects and ensure good performance. Also, this source will allow you training models on streaming data, which is highly advantageous and not time-consuming.
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Environment for Developing KDD-Applications Supported by Index Structures is an excellent tool written entirely in Java that is used for research or large industrial problems. The distinctive feature is the flexibility and customizability of algorithms and functions for data analysis. That is, thanks to modular structure, you can choose what you need and eliminate dependencies.
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