There are an increasing number of tools for Machine Learning in Java. This course will teach you how to choose the appropriate tool for your machine learning task, as well as how to get started with the tool and how to use it.
Choosing the right tool for a machine learning problem among the myriad options is not easy. In this course, Exploring Java Machine Learning Environments, you’ll learn to assess, identify, and use the right tool for the job. First, you’ll explore several characteristics of the available tools for machine learning in Java. Next, you’ll discover the pros and cons of each tool depending on multiple scenarios. Finally, you’ll learn how to get started with each of the tools, consuming data, training a model, evaluating and visualizing the performance in different environments and at different scales. When you’re finished with this course, you’ll have the skills and knowledge of the Machine Learning Java Environment needed to effectively implement industry-grade pipelines.
Course Overview Hi. My name is Nicolae Caprarescu, and I'd like to welcome you to my course, Exploring Java Machine Learning Environments. I'm a freelance software developer, and I've been writing mission‑critical code in a variety of companies and industries. Java dominates the enterprise software world, and many of the biggest companies worldwide use it. In a Java environment, it is very often preferred to use machine learning solutions also written in Java in order to avoid the difficulty of integrating heterogenous parts and to also benefit from the strong scalability provided by Java out of the box. This course will teach you how to choose the appropriate Java tool for your machine learning tasks, as well as how to get started with the tool and how to use it. Some of the major topics that we will cover include clustering species of animals based on their satellite data coordinates using Weka, both programmatically and using the Weka GUI, implementing a Twitter sentiment classifier using DL4J, and implementing a food image classifications solution using Spark MLlib. By the end of this course, you'll have the skills and knowledge of the machine learning Java environment needed to effectively implement industry‑grade pipelines. Before beginning this course, you should be familiar with Java programming and machine learning. I hope you'll join me on this journey to learn how to build in‑demand, scalable Java machine learning solutions with the Exploring Java Machine Learning Environments course at Pluralsight.