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Machine Learning with scikit-learn Quick Start Guide

You're reading from   Machine Learning with scikit-learn Quick Start Guide Classification, regression, and clustering techniques in Python

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781789343700
Length 172 pages
Edition 1st Edition
Languages
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Author (1):
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Kevin Jolly Kevin Jolly
Author Profile Icon Kevin Jolly
Kevin Jolly
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Table of Contents (10) Chapters Close

Preface 1. Introducing Machine Learning with scikit-learn FREE CHAPTER 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

Summary

This chapter has given you a brief introduction into what machine learning is for those of you who are just beginning your journey into the world of machine learning. You have learned about how scikit-learn fits into the context of machine learning and how you can go about installing the necessary software.

Finally, you had a brief glimpse at all the algorithms that you will learn to implement as you progress through this book, as well as its associated applications in the real world.

In the next chapter, you will learn how to implement your first algorithm the K-Nearest Neighbors algorithm!

You have been reading a chapter from
Machine Learning with scikit-learn Quick Start Guide
Published in: Oct 2018
Publisher: Packt
ISBN-13: 9781789343700
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