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  • Book Overview & Buying Mastering Java Machine Learning
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Mastering Java Machine Learning

Mastering Java Machine Learning

By : Uday Kamath, Krishna Choppella
3.4 (9)
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Mastering Java Machine Learning

Mastering Java Machine Learning

3.4 (9)
By: Uday 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

Concept drift and drift detection


As discussed in the introduction of the chapter, the dynamic nature of infinite streams stands in direct opposition to the basic principles of stationary learning; that is, that the distribution of the data or patterns remain constant. Although there can be changes that are swift or abrupt, the discussion here is around slow, gradual changes. These slow, gradual changes are fairly hard to detect and separating the changes from the noise becomes tougher still:

Figure 1 Concept drift illustrated by the gradual change in color from yellow to blue in the bottom panel. Sampled data reflects underlying change in data distribution, which must be detected and a new model learned.

There have been several techniques described in various studies in the last two decades that can be categorized as shown in the following figure:

Figure 2 Categories of drift detection techniques

Data management

The main idea is to manage a model in memory that is consistent with the dynamic...

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Tech Concepts
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Programming languages
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Mastering Java Machine Learning
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