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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 types of linear regression algorithms


Based on the approach used and the number of input parameters, there are several types of linear regression algorithms to determine the real value of the target variable. In this chapter, you will learn how to implement the following algorithms using F#:

  • Simple Least Square Linear Regression

  • Multiple Linear Regression

  • Weighted Linear regression

  • Ridge Regression

  • Multivariate Multiple Linear Regression

These algorithms will be implemented using a robust industry standard open source .NET mathematics API called Math.NET. Math.NET has an F# friendly wrapper.

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