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Machine Learning with scikit-learn Quick Start Guide

You're reading from  Machine Learning with scikit-learn Quick Start Guide

Product type Book
Published in Oct 2018
Publisher Packt
ISBN-13 9781789343700
Pages 172 pages
Edition 1st Edition
Languages
Author (1):
Kevin Jolly Kevin Jolly
Profile icon Kevin Jolly
Toc

Table of Contents (10) Chapters close

Preface 1. Introducing Machine Learning with scikit-learn 2. Predicting Categories with K-Nearest Neighbors 3. Predicting Categories with Logistic Regression 4. Predicting Categories with Naive Bayes and SVMs 5. Predicting Numeric Outcomes with Linear Regression 6. Classification and Regression with Trees 7. Clustering Data with Unsupervised Machine Learning 8. Performance Evaluation Methods 9. Other Books You May Enjoy

Technical requirements

You will be required to have Python 3.6 or greater, Pandas ≥ 0.23.4, Scikit-learn ≥ 0.20.0, NumPy ≥ 1.15.1, Matplotlib ≥ 3.0.0, Pydotplus ≥ 2.0.2, Image ≥ 3.1.2, Seaborn ≥ 0.9.0, and SciPy ≥ 1.1.0 installed on your system.

The code files of this chapter can be found on GitHub:
https://github.com/PacktPublishing/Machine-Learning-with-scikit-learn-Quick-Start-Guide/blob/master/Chapter_07.ipynb.

Check out the following video to see the code in action:

http://bit.ly/2qeEJpI

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