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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 2. Predicting Categories with K-Nearest Neighbors FREE CHAPTER 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

In this chapter, you learned about how the linear regression algorithm works internally, through key concepts such as residuals and ordinary least squares. You also learned how to visualize a simple linear regression model in two dimensions.

We also covered implementing the linear regression model to predict the amount of a mobile transaction, along with scaling your data in an effective pipeline, to bring potential improvements to your performance.

Finally, you learned how to optimize your model by using the concept of regularization, in the form of ridge and lasso regression.

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