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Machine learning for Java developers

Gregor Roth | Sept. 18, 2017
Set up a machine learning algorithm and develop your first prediction function in Java.

robot hands
Credit: NYC Media Lab

Self-driving cars, face detection software, and voice controlled speakers all are built on machine learning technologies and frameworks--and these are just the first wave. Over the next decade, a new generation of products will transform our world, initiating new approaches to software development and the applications and products that we create and use.

As a Java developer, you want to get ahead of this curve now--when tech companies are beginning to seriously invest in machine learning. What you learn today, you can build on over the next five years, but you have to start somewhere.

This article will get you started. You will begin with a first impression of how machine learning works, followed by a short guide to implementing and training a machine learning algorithm. After studying the internals of the learning algorithm and features that you can use to train, score, and select the best-fitting prediction function, you'll get an overview of using a JVM framework, Weka, to build machine learning solutions. This article focuses on supervised machine learning, which is the most common approach to developing intelligent applications.

 

Machine learning and artificial intelligence

Machine learning has evolved from the field of artificial intelligence, which seeks to produce machines capable of mimicking human intelligence. Although machine learning is an emerging trend in computer science, artificial intelligence is not a new scientific field. The Turing test, developed by Alan Turing in the early 1950s, was one of the first tests created to determine whether a computer could have real intelligence. According to the Turing test, a computer could prove human intelligence by tricking a human into believing it was also human.

Many state-of-the-art machine learning approaches are based on decades-old concepts. What has changed over the past decade is that computers (and distributed computing platforms) now have the processing power required for machine learning algorithms. Most machine learning algorithms demand a huge number of matrix multiplications and other mathematical operations to process. The computational technology to manage these calculations didn't exist even two decades ago, but it does today.

Machine learning enables programs to execute quality improvement processes and extend their capabilities without human involvement. A program built with machine learning is capable of updating or extending its own code.

 

Supervised learning vs. unsupervised learning

Supervised learning and unsupervised learning are the most popular approaches to machine learning. Both require feeding the machine a massive number of data records to correlate and learn from. Such collected data records are commonly known as a feature vectors. In the case of an individual house, a feature vector might consist of features such as overall house size, number of rooms, and the age of the house.

 

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