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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

So far in this book, we have used supervised learning algorithms to spot anomalous samples. This chapter offered additional solutions when no labels are provided. The solutions explained here stem from different fields of machine learning, such as statistical learning, nearest-neighbor, and tree-based ensembles. Each one of the three tools explained here can excel, but also have disadvantages. We also learned that evaluating machine learning algorithms when no labels are provided is tricky.

This chapter will deal with unlabeled data. In the previous chapter, we learned how to cluster data, and then we learned how to detect the outliers in it here. We still have one more unsupervised learning topic to discuss in this book, though. In the next chapter, we will cover an important topic relating to e-commerce—recommendation engines. Since it is the last chapter of this book, I'd alsolike to go through the possible approaches to machine learning model...

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