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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 4. Information Retrieval

"Ways to find a needle in haystack"

Information overload is almost a passé term; however, it is still valid. Information retrieval is a big arena and most of it is far from being solved. However, that being said, we have come a long way and the results produced by some of the state-of-the-art information retrieval algorithms are really impressive. You may not know that you are using information retrieval but whenever you search for some documents on your PC or on the internet, you are actually using the product of an information retrieval algorithm in the background. So as the metaphor goes, finding the needle (read information/insight) in a haystack (read your data archive on your PC or on the web) is the key to successful business. This chapter is dedicated to showing you how you can code up some of the popular and useful information retrieval algorithms using F# and then use them in your problem domain.

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