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Mastering Java Machine Learning

Mastering Java Machine Learning

By : Kamath, Krishna Choppella
3.4 (9)
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Mastering Java Machine Learning

Mastering Java Machine Learning

3.4 (9)
By: Kamath, Krishna Choppella

Overview of this book

Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain.
Table of Contents (13 chapters)
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10
A. Linear Algebra
12
Index

What you need for this book

This book assumes you have some experience of programming in Java and a basic understanding of machine learning concepts. If that doesn't apply to you, but you are curious nonetheless and self-motivated, fret not, and read on! For those who do have some background, it means that you are familiar with simple statistical analysis of data and concepts involved in supervised and unsupervised learning. Those who may not have the requisite math or must poke the far reaches of their memory to shake loose the odd formula or funny symbol, do not be disheartened. If you are the sort that loves a challenge, the short primer in the appendices may be all you need to kick-start your engines—a bit of tenacity will see you through the rest! For those who have never been introduced to machine learning, the first chapter was equally written for you as for those needing a refresher—it is your starter-kit to jump in feet first and find out what it's all about. You can augment your basics with any number of online resources. Finally, for those innocent of Java, here's a secret: many of the tools featured in the book have powerful GUIs. Some include wizard-like interfaces, making them quite easy to use, and do not require any knowledge of Java. So if you are new to Java, just skip the examples that need coding and learn to use the GUI-based tools instead!

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