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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

Chapter 3: Data Analysis on Streaming Data

Now that you have seen an introduction to streaming data and streaming use cases, as well as an introduction to streaming architecture, it is time to enter into the core of this book: analytics and machine learning.

As you probably know, descriptive statistics and data analysis are the entry points into machine learning, but they are also often used as a standalone use case. In this chapter, you will first discover descriptive statistics from a traditional statistics viewpoint. Some parts of traditional statistics focus on making correct estimations of descriptive statistics when only part of the data is available.

In streaming, you will encounter such problems in an even more impacting manner than in batch data. Through a continuous data collection process, your descriptive statistics will continue changing on every new data point. This chapter will propose some solutions for dealing with this.

You will also build a data visualization...

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