Book Image

Machine Learning: End-to-End guide for Java developers

By : Boštjan Kaluža, Jennifer L. Reese, Krishna Choppella, Richard M. Reese, Uday Kamath
Book Image

Machine Learning: End-to-End guide for Java developers

By: Boštjan Kaluža, Jennifer L. Reese, Krishna Choppella, Richard M. Reese, Uday Kamath

Overview of this book

Machine Learning is one of the core area of Artificial Intelligence where computers are trained to self-learn, grow, change, and develop on their own without being explicitly programmed. In this course, we cover how Java is employed to build powerful machine learning models to address the problems being faced in the world of Data Science. The course demonstrates complex data extraction and statistical analysis techniques supported by Java, applying various machine learning methods, exploring machine learning sub-domains, and exploring real-world use cases such as recommendation systems, fraud detection, natural language processing, and more, using Java programming. The course begins with an introduction to data science and basic data science tasks such as data collection, data cleaning, data analysis, and data visualization. The next section has a detailed overview of statistical techniques, covering machine learning, neural networks, and deep learning. The next couple of sections cover applying machine learning methods using Java to a variety of chores including classifying, predicting, forecasting, market basket analysis, clustering stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, and deep learning. The last section highlights real-world test cases such as performing activity recognition, developing image recognition, text classification, and anomaly detection. The course includes premium content from three of our most popular books: [*]Java for Data Science [*]Machine Learning in Java [*]Mastering Java Machine Learning On completion of this course, you will understand various machine learning techniques, different machine learning java algorithms you can use to gain data insights, building data models to analyze larger complex data sets, and incubating applications using Java and machine learning algorithms in the field of artificial intelligence.
Table of Contents (5 chapters)

Chapter 2. Java Libraries and Platforms for Machine Learning

Implementing machine learning algorithms by yourself is probably the best way to learn machine learning, but you can progress much faster if you step on the shoulders of the giants and leverage one of the existing open source libraries.

This chapter reviews various libraries and platforms for machine learning in Java. The goal is to understand what each library brings to the table and what kind of problems is it able to solve?

In this chapter, we will cover the following topics:

  • The requirement of Java to implement a machine learning app
  • Weka, a general purpose machine learning platform
  • Java machine learning library, a collection of machine learning algorithms
  • Apache Mahout, a scalable machine learning platform
  • Apache Spark, a distributed machine learning library
  • Deeplearning4j, a deep learning library
  • MALLET, a text mining library

We'll also discuss how to architect the complete machine learning app stack for both single...