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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

Product type Paperback

Published in Jul 2022

Publisher Packt

ISBN-13 9781803248363

Length 258 pages

Edition 1st Edition

Languages

Python

Tools

River

Concepts

Machine Learning

Author (1):

Joos Korstanje

View More author details

Table of Contents (17) Chapters

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

Further reading

KNNRegressor: https://riverml.xyz/latest/api/neighbors/KNNRegressor/
LinearRegression: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html
DecisionTree: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html
Tree_plot: https://scikit-learn.org/stable/modules/generated/sklearn.tree.plot_tree.html

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Authors (1)

Joos Korstanje

Joos Korstanje

Joos Korstanje, with his master's degrees in both environmental sciences and data science, has been working on statistics and data science for almost ten years. Through his work in different companies including Disney, Axa, and others, he has closely followed the developments in data science and related fields. This experience in the business world allows him to write about data science from an applied point of view (through his books, Medium, Towards Data Science, LinkedIn, and more).

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