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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

You're reading from  Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

Product type Book
Published in Jul 2020
Publisher Packt
ISBN-13 9781838826048
Pages 384 pages
Edition 1st Edition
Languages
Author (1):
Tarek Amr Tarek Amr
Profile icon Tarek Amr
Toc

Table of Contents (18) Chapters close

Preface 1. Section 1: Supervised Learning
2. Introduction to Machine Learning 3. Making Decisions with Trees 4. Making Decisions with Linear Equations 5. Preparing Your Data 6. Image Processing with Nearest Neighbors 7. Classifying Text Using Naive Bayes 8. Section 2: Advanced Supervised Learning
9. Neural Networks – Here Comes Deep Learning 10. Ensembles – When One Model Is Not Enough 11. The Y is as Important as the X 12. Imbalanced Learning – Not Even 1% Win the Lottery 13. Section 3: Unsupervised Learning and More
14. Clustering – Making Sense of Unlabeled Data 15. Anomaly Detection – Finding Outliers in Data 16. Recommender System – Getting to Know Their Taste 17. Other Books You May Enjoy

Understanding linear models

To be able to explain linear models well, I would like to start with an example where the solution can be found using a system of linear equations—a technique we all learned in school when we were around 12 years old. We will then see why this technique doesn't always work with real-life problems, and so a linear regression model is needed. Then, we will apply the regression model to a real-life regression problem and learn how to improve our solution along the way.

Linear equations

"Mathematics is the most beautiful and most powerful creation of the human spirit."
– Stefan Banach

In this example, we have five passengers who have taken a taxi trip. Here, we have a record of the distance each taxi covered in kilometers and the fair displayed on its meter at the end of each trip:

We know that taxi meters usually start with a certain amount and then they...

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