Book Image

Machine Learning in Java - Second Edition

By : AshishSingh Bhatia, Bostjan Kaluza
Book Image

Machine Learning in Java - Second Edition

By: AshishSingh Bhatia, Bostjan Kaluza

Overview of this book

As the amount of data in the world continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and Data Science. The main challenge is how to transform data into actionable knowledge. Machine Learning in Java will provide you with the techniques and tools you need. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. The code in this book works for JDK 8 and above, the code is tested on JDK 11. Moving on, you will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text analysis. By the end of the book, you will have explored related web resources and technologies that will help you take your learning to the next level. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.
Table of Contents (13 chapters)

Applied Machine Learning Quick Start

This chapter introduces the basics of machine learning, laying down common themes and concepts and making it easy to follow the logic and familiarize yourself with the topic. The goal is to quickly learn the step-by-step process of applied machine learning and grasp the main machine learning principles. In this chapter, we will cover the following topics:

  • Machine learning and data science
  • Data and problem definition
  • Data collection
  • Data preprocessing
  • Unsupervised learning
  • Supervised learning
  • Generalization and evaluation

If you are already familiar with machine learning and are eager to start coding, then quickly jump to the chapters that follow this one. However, if you need to refresh your memory or clarify some concepts, then it is strongly recommend revisiting the topics presented in this chapter.