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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

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781838826048
Length 384 pages
Edition 1st Edition
Languages
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Author (1):
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Tarek Amr Tarek Amr
Author Profile Icon Tarek Amr
Tarek Amr
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Table of Contents (18) Chapters Close

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

Summary

This chapter marks the end of this book. I hope all the concepts discussed here are clear by now. I also hope the mixture of the theoretical background of each algorithm and its practical use paved the way for you to adapt the solutions offered here for the different problems you meet in practice in real life. Obviously, no book can be conclusive, and new algorithms and tools will be available to you in the future. Nevertheless, Pedro Domingos groups the machine learning algorithms into five tribes. Except for the evolutionary algorithms, we have met algorithms that belong to four out of Domingos' five tribes. Thus, I hope the various algorithms discussed here, each with their own approach, will serve as a good foundation when dealing with any new machine learning solutions in the future.

All books are a work in progress. Their value is not only in their content but goes beyond that to include the value that comes from the future discussions they spark. Be...

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