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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

Topics in text mining


As we saw in the first section, the area of text mining and performing Machine Learning on text spans a wide range of topics. Each topic discussed has some customizations to the mainstream algorithms, or there are specific algorithms that have been developed to perform the task called for in that area. We have chosen four broad topics, namely, text categorization, topic modeling, text clustering, and named entity recognition, and will discuss each in some detail.

Text categorization/classification

The text classification problem manifests itself in different applications, such as document filtering and organization, information retrieval, opinion and sentiment mining, e-mail spam filtering, and so on. Similar to the classification problem discussed in Chapter 2, Practical Approach to Real-World Supervised Learning, the general idea is to train on the training data with labels and to predict the labels of unseen documents.

As discussed in the previous section, the preprocessing...

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