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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

Different IR algorithms you will learn

Information retrieval is sometimes referred to as IR. You will learn several algorithms in this chapter that are:

  • Distance based: Two documents are matched based on their proximity, calculated by several distance metrics on the vector representation of the document
  • Set based: Two documents are matched based on their proximity, calculated by several set based/fuzzy set based metrics based on the bag of words (BoW) model of the document

Don't worry if some of the phrases in this section don't make sense right now. By the end of this chapter, you will have a thorough understanding of these techniques and how to use them.

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