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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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Toc

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

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "There is no predict_many function here, so it is necessary to do a loop with predict_one repeatedly."

A block of code is set as follows:

def self_made_decision_tree(observation): 
    if observation.can_speak: 
        if not observation.has_feathers: 
            return 'human'     
    return 'not human'  
for i,row in data.iterrows(): 
    print(self_made_decision_tree(row)) 

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

from sklearn.datasets import make_blobs 
X,y=make_blobs(shuffle=True,centers=2,n_samples=2000) 

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: "Select System info from the Administration panel."

Tips or important notes

Appear like this.

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