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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
Anomaly Detection – Finding Outliers in Data

Detecting anomalies in data is a recurring theme in machine learning. In Chapter 10,Imbalanced Learning – Not Even 1% Win the Lottery, we learned how to spot these interesting minorities in our data. Back then, the data was labeled and the classification algorithms from the previous chapters were apt for the problem. Aside from labeled anomaly detection problems, there are cases where data is unlabeled.

In this chapter, we are going to learn how to identify outliers in our data, even when no labels are provided. We will use three different algorithms and we will learn about the two branches of unlabeled anomaly detection. Here are the topics that will be covered in this chapter:

  • Unlabeled anomaly detection
  • Detecting anomalies using basic statistics
  • Detecting outliers using EllipticEnvelope
  • Outlier and novelty detection...
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