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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

Feature analysis and dimensionality reduction

Among the first tools to master are the different feature analysis and dimensionality reduction techniques. As in supervised learning, the need for reducing dimensionality arises from numerous reasons similar to those discussed earlier for feature selection and reduction.

A smaller number of discriminating dimensions makes visualization of data and clusters much easier. In many applications, unsupervised dimensionality reduction techniques are used for compression, which can then be used for transmission or storage of data. This is particularly useful when the larger data has an overhead. Moreover, applying dimensionality reduction techniques can improve the scalability in terms of memory and computation speeds of many algorithms.

Notation

We will use similar notation to what was used in the chapter on supervised learning. The examples are in d dimensions and are represented as vector:

x = (x1,x2,…xd )T

The entire dataset containing n examples...

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