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F# for Machine Learning Essentials

You're reading from   F# for Machine Learning Essentials Get up and running with machine learning with F# in a fun and functional way

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Product type Paperback
Published in Feb 2016
Publisher
ISBN-13 9781783989348
Length 194 pages
Edition 1st Edition
Languages
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Author (1):
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Sudipta Mukherjee Sudipta Mukherjee
Author Profile Icon Sudipta Mukherjee
Sudipta Mukherjee
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Table of Contents (9) Chapters Close

Preface 1. Introduction to Machine Learning FREE CHAPTER 2. Linear Regression 3. Classification Techniques 4. Information Retrieval 5. Collaborative Filtering 6. Sentiment Analysis 7. Anomaly Detection Index

Chapter 7. Anomaly Detection

"Find the odd one out automatically."

Anomaly Detection is the art of finding the odd one out in a bunch of data automatically. As the name suggests, it is the science of finding the data that is anomalous compared to the other. However, there are several kinds of anomalies; sometimes the normal (non-anomalous) data can be line anomalous data. Anomaly detection is mostly an unsupervised learning problem because it is very difficult, if not impossible, to get a labeled training dataset that is anomalous. Sometimes, anomalies are referred to as "outliers."

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