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Python Machine Learning Cookbook

You're reading from   Python Machine Learning Cookbook Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789808452
Length 642 pages
Edition 2nd Edition
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Authors (2):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
Prateek Joshi Prateek Joshi
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Prateek Joshi
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Toc

Table of Contents (18) Chapters Close

Preface 1. The Realm of Supervised Learning FREE CHAPTER 2. Constructing a Classifier 3. Predictive Modeling 4. Clustering with Unsupervised Learning 5. Visualizing Data 6. Building Recommendation Engines 7. Analyzing Text Data 8. Speech Recognition 9. Dissecting Time Series and Sequential Data 10. Analyzing Image Content 11. Biometric Face Recognition 12. Reinforcement Learning Techniques 13. Deep Neural Networks 14. Unsupervised Representation Learning 15. Automated Machine Learning and Transfer Learning 16. Unlocking Production Issues 17. Other Books You May Enjoy

Building a simple classifier

A classifier is a system with some characteristics that allow you to identify the class of the sample examined. In different classification methods, groups are called classes. The goal of a classifier is to establish the classification criterion to maximize performance. The performance of a classifier is measured by evaluating the capacity for generalization. Generalization means attributing the correct class to each new experimental observation. The way in which these classes are identified discriminates between the different methods that are available.

Getting ready

Classifiers identify the class of a new objective, based on knowledge that's been extracted from a series of samples (a dataset...

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