How should you start to learn machine learning using Java?

How should you start to learn machine learning using Java?

• Admin
• Nov 03, 2019
• 10 comments

When you talk about the domain of AI (Artificial Intelligence) and ML (Machine Learning), most experts would suggest you learn Python and R programming languages. Java is seldom talked about and yet, you can use it for AI, ML, etc. According to some 2017 studies, it’s the front-end web developers who leverage their familiarity with JavaScript to machine learning. It was found that 16% prioritized Java for the purpose, while 8% were found to avoid the cumbersome C/C++. It was noticed that front-end desktop application developers prioritized Java more than others (21%), which was in line with Java’s frequent use in enterprise-focused applications. The studies found that enterprise developers tend to use Java in all projects, which included machine learning as well. Though Python and R have their own advantages, you can also use Java for machine learning, AI, and other areas of data science if you’re already adept in it.

1- Machine learning in Java

There’s a misconception that without learning Python or R, you can’t succeed in machine learning. However, the truth is that if you’ve got a Java development background, you can do without learning these popular programming languages. You should remember that Java gives support for development in any field you want, and data science is no different. By using third-party open source libraries, you can leverage your expertise as a Java developer to implement a data science algorithm and get things done. Though there’s no denying that Python or R come with their own set of advantages, you won’t need to learn them specifically to execute machine learning- or data science-related algorithms.

2- Leading machine learning libraries for Java

If you’re looking for some of the best machine learning libraries for Java, you’ll find Weka to be the most popular choice. Weka is suitable for data mining tasks, where algorithms can either be called from your own Java code or applied directly to a dataset. Weka contains tools for functions like clustering, classification, regression, association rules, and visualization.

Apache Mahout is another machine learning library for Java, which is designed to be enterprise-ready. This scalable and flexible ML framework comes with in-built algorithms to help you create your own algorithm implementations. Mahout’s distributed linear algebra framework allows statisticians, mathematicians, analytics professionals, and data scientists to implement their own algorithms.

ADAMS (Advanced Data mining And Machine learning System) is a flexible workflow engine that uses a tree-like structure to manage how data flows in the workflow. This means there exist no explicit connections that are essential. Using ADAMS, you can quickly build and maintain real-world workflows that are generally complex in nature.

Some other machine learning libraries for Java are ELKI (Environment for Developing KDD-Applications Supported by Index Structures), Deeplearning4j, JavaML, MALLET (MAchine Learning for LanguagE Toolkit), JSAT (Java Statistical Analysis Tool), and RapidMiner, to name a few.

If you’re a Java programmer or are adept in Java, the fastest route to a career in machine learning is enrolling in a machine learning bootcamp. Taught by industry experts and having ample hands-on training, such a bootcamp will help you fast-track your machine learning career dreams.

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To learn more about machine learning, click here and read our another article.