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.
Author Name: Nicolae Caprarescu
Author Description:
Now an independent consultant, Nicolae started his career in 2013 as a software engineer. Over the years, Nicolae worked on systems ranging from high-frequency Java trading engines to various apps for startups. Nicolae’s technical roles have always been full-stack, most often focusing on Java back-ends and web-based front-ends: Java, Spring, JDBC, SQL, Maven, Gradle, TeamCity, Jenkins, TDD, JUnit, mocking, automated testing, JavaScript, Selenium and RESTful. Nicolae’s technical interests include… more

Table of Contents

• Course Overview
  2mins
• Understanding the Java Machine Learning Ecosystem
  19mins
• Implementing a Machine Learning Workflow with Weka
  20mins
• Implementing a Machine Learning Workflow with DL4J
  33mins
• Implementing a Machine Learning Workflow with Spark MLlib
  20mins