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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

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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