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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

Text processing components and transformations


In this section, we will discuss some common preprocessing and transformation steps that are done in most text mining processes. The general concept is to convert the documents into structured datasets with features or attributes that most Machine Learning algorithms can use to perform different kinds of learning.

We will briefly describe some of the most used techniques in the next section. Different applications of text mining might use different pieces or variations of the components shown in the following figure:

Figure 10: Text Processing components and the flow

Document collection and standardization

One of the first steps in most text mining applications is the collection of data in the form of a body of documents—often referred to as a corpus in the text mining world. These documents can have predefined categorization associated with them or it can simply be an unlabeled corpus. The documents can be of heterogeneous formats or standardized...

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