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Machine Learning: End-to-End guide for Java developers

You're reading from   Machine Learning: End-to-End guide for Java developers Data Analysis, Machine Learning, and Neural Networks simplified

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Product type Course
Published in Oct 2017
Publisher Packt
ISBN-13 9781788622219
Length 1159 pages
Edition 1st Edition
Languages
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Authors (2):
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Krishna Choppella Krishna Choppella
Author Profile Icon Krishna Choppella
Krishna Choppella
Uday Kamath Uday Kamath
Author Profile Icon Uday Kamath
Uday Kamath
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Toc

Chapter 5. Real-Time Stream Machine Learning

In Chapter 2, Practical Approach to Real-World Supervised Learning, Chapter 3, Unsupervised Machine Learning Techniques, and Chapter 4, Semi-Supervised and Active Learning, we discussed various techniques of classification, clustering, outlier detection, semi-supervised, and active learning. The form of learning done from existing or historic data is traditionally known as batch learning.

All of these algorithms or techniques assume three things, namely:

  • Finite training data is available to build different models.
  • The learned model will be static; that is, patterns won't change.
  • The data distribution also will remain the same.

In many real-world data scenarios, there is either no training data available a priori or the data is dynamic in nature; that is, changes continuously with respect to time. Many real-world applications may also have data which has a transient nature to it and comes in high velocity or volume such as IoT sensor...

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