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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

Issues with mining unstructured data


Humans can read, parse, and understand unstructured text/documents more easily than computer-based programs. Some of the reasons why text mining is more complicated than general supervised or unsupervised learning are given here:

  • Ambiguity in terms and phrases. The word bank has multiple meanings, which a human reader can correctly associate based on context, yet this requires preprocessing steps such as POS tagging and word sense disambiguation, as we have seen. According to the Oxford English Dictionary, the word run has no fewer than 645 different uses in the verb form alone and we can see that such words can indeed present problems in resolving the meaning intended (between them, the words run, put, set, and take have more than a thousand meanings).

  • Context and background knowledge associated with the text. Consider a sentence that uses a neologism with the suffix gate to signify a political scandal, as in, With cries for impeachment and popularity...

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