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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

Chapter 2. Practical Approach to Real-World Supervised Learning

The ability to learn from observations accompanied by marked targets or labels, usually in order to make predictions about unseen data, is known as supervised machine learning. If the targets are categories, the problem is one of classification and if they are numeric values, it is called regression. In effect, what is being attempted is to infer the function that maps the data to the target. Supervised machine learning is used extensively in a wide variety of machine learning applications, whenever labeled data is available or the labels can be added manually.

The core assumption of supervised machine learning is that the patterns that are learned from the data used in training will manifest themselves in yet unseen data.

In this chapter, we will discuss the steps used to explore, analyze, and pre-process the data before proceeding to training models. We will then introduce different modeling techniques ranging from...

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