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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 A practical guide to implementing supervised and unsupervised machine learning algorithms in Python

Product type Paperback

Published in Jul 2020

Publisher Packt

ISBN-13 9781838826048

Length 384 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1):

Tarek Amr

View More author details

Table of Contents (18) Chapters

Preface

1. Section 1: Supervised Learning

2. Introduction to Machine Learning FREE CHAPTER

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

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Further reading

For more information on the relative topics of this chapter, please refer to the following links:

Learn Python Programming – Second Edition, by Fabrizio Romano: https://www.packtpub.com/application-development/learn-python-programming-second-edition

Hands-On Data Analysis with Pandas, by Stefanie Molin: https://www.packtpub.com/big-data-and-business-intelligence/hands-data-analysis-pandas

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

Published in: Jul 2020

Publisher: Packt

ISBN-13: 9781838826048

© 2020 Packt Publishing Limited All Rights Reserved

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Authors (1)

Tarek Amr

Tarek Amr

Tarek Amr has 8 years of experience in data science and machine learning. After finishing his postgraduate degree at the University of East Anglia, he worked in a number of startups and scale-up companies in Egypt and the Netherlands. This is his second data-related book. His previous book covered data visualization using D3.js. He enjoys giving talks and writing about different computer science and business concepts and explaining them to a wider audience. He can be reached on Twitter at @gr33ndata. He is happy to respond to all questions related to this book. Feel free to get in touch with him if any parts of the book need clarification or if you would like to discuss any of the concepts here in more detail.

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