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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

Some interesting things you can do


You can use machine learning to differentiate between dogs and cats from photographs. Then surprisingly enough, you may tweak the same algorithm to detect the cancerous cells from the normal cells in breast cancer patients. You may use decision trees to predict whether there will be a traffic jam on a given date and time. There can be several other parameters that can be helpful while predicting a traffic jam. The intention is that after reading this chapter you will be able to use these classification techniques to address some of the problems you are facing yourself.

Binary classification using k-NN

In this example, you will solve the kaggle cat and dog identification challenge (https://www.kaggle.com/c/dogs-vs-cats). The challenge is to identify dogs and cats from photographs. Following are a couple of example photographs:

  • Image 1

  • Image 2

How might we decide if an image contains a cat or a dog? What are the visual differences between cats and dogs? Some of...

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