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F# for Machine Learning Essentials

You're reading from  F# for Machine Learning Essentials

Product type Book
Published in Feb 2016
Publisher
ISBN-13 9781783989348
Pages 194 pages
Edition 1st Edition
Languages
Author (1):
Sudipta Mukherjee Sudipta Mukherjee
Profile icon Sudipta Mukherjee

Table of Contents (16) Chapters

F# for Machine Learning Essentials
Credits
Foreword
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Introduction to Machine Learning Linear Regression Classification Techniques Information Retrieval Collaborative Filtering Sentiment Analysis Anomaly Detection Index

Summary


In this chapter, you learned about several linear regression models. I hope you will find this information useful to solve some of your own practical problems. For example, you can predict your next electrical bill by doing a historical survey of your old bills. When not sure, start with a single predictor and gradually add more predictors to find a suitable model. Also, you can ask domain experts to locate predictor variables. Although there can be a temptation to use linear regression for prediction, don't give in. Linear regression can't work that way.

In the next chapter, you will learn about several supervised learning algorithms for classification. I hope you have enjoyed reading this chapter.

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