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Machine Learning for Streaming Data with Python

You're reading from   Machine Learning for Streaming Data with Python Rapidly build practical online machine learning solutions using River and other top key frameworks

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Product type Paperback
Published in Jul 2022
Publisher Packt
ISBN-13 9781803248363
Length 258 pages
Edition 1st Edition
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Author (1):
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Joos Korstanje Joos Korstanje
Author Profile Icon Joos Korstanje
Joos Korstanje
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Table of Contents (17) Chapters Close

Preface 1. Part 1: Introduction and Core Concepts of Streaming Data
2. Chapter 1: An Introduction to Streaming Data FREE CHAPTER 3. Chapter 2: Architectures for Streaming and Real-Time Machine Learning 4. Chapter 3: Data Analysis on Streaming Data 5. Part 2: Exploring Use Cases for Data Streaming
6. Chapter 4: Online Learning with River 7. Chapter 5: Online Anomaly Detection 8. Chapter 6: Online Classification 9. Chapter 7: Online Regression 10. Chapter 8: Reinforcement Learning 11. Part 3: Advanced Concepts and Best Practices around Streaming Data
12. Chapter 9: Drift and Drift Detection 13. Chapter 10: Feature Transformation and Scaling 14. Chapter 11: Catastrophic Forgetting 15. Chapter 12: Conclusion and Best Practices 16. Other Books You May Enjoy

Identifying use cases of classification

The use cases of classification are huge; it is a very commonly used method in many projects. Still, let's see some examples to get a better idea of the different types of use cases that can benefit from classification methods.

Use case 1 – email spam classification

The first use case that is generally built on classification is spam detection in email. Spam emails have been around for a long time. The business model of sending fake emails to generally steal people's money is a big problem, and receiving many spam emails can negatively impact your emailing experience.

Email service providers have come a long way in detecting spam emails automatically and sending them to your spam/junk box. Nowadays, this is all done automatically and relies heavily on machine learning.

If you compare this to our super-small classification example, you could imagine that the decision tree (or any other model) can take several information...

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