Machine Learning for Streaming Data with Python: Rapidly build practical online machine learning solutions using River and other top key frameworks
Language: English
Published by Packt Publishing 2022-07-15, 2022
- Softcover
- New
Seller: Chiron Media, Wallingford, United KingdomChiron Media
AbeBooks seller since August 2, 2010
Condition: New
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Add to basketSeller Inventory # 6666-IUK-9781803248363
- Title
- Machine Learning for Streaming Data with Python: Rapidly build practical online machine learning solutions using River and other top key frameworks
- Author
- Joos Korstanje
- Publisher
- Packt Publishing 2022-07-15
- Publication year
- 2022
- Condition
- New
- Binding
- Paperback
- Language
- English
- ISBN 10
- 180324836X
- ISBN 13
- 9781803248363
Apply machine learning to streaming data with the help of practical examples, and deal with challenges that surround streaming
Key Features
- Work on streaming use cases that are not taught in most data science courses
- Gain experience with state-of-the-art tools for streaming data
- Mitigate various challenges while handling streaming data
Book Description
Streaming data is the new top technology to watch out for in the field of data science and machine learning. As business needs become more demanding, many use cases require real-time analysis as well as real-time machine learning. This book will help you to get up to speed with data analytics for streaming data and focus strongly on adapting machine learning and other analytics to the case of streaming data.
You will first learn about the architecture for streaming and real-time machine learning. Next, you will look at the state-of-the-art frameworks for streaming data like River. Later chapters will focus on various industrial use cases for streaming data like Online Anomaly Detection and others. As you progress, you will discover various challenges and learn how to mitigate them. In addition to this, you will learn best practices that will help you use streaming data to generate real-time insights.
By the end of this book, you will have gained the confidence you need to stream data in your machine learning models.
What you will learn
- Understand the challenges and advantages of working with streaming data
- Develop real-time insights from streaming data
- Understand the implementation of streaming data with various use cases to boost your knowledge
- Develop a PCA alternative that can work on real-time data
- Explore best practices for handling streaming data that you absolutely need to remember
- Develop an API for real-time machine learning inference
Who this book is for
This book is for data scientists and machine learning engineers who have a background in machine learning, are practice and technology-oriented, and want to learn how to apply machine learning to streaming data through practical examples with modern technologies. Although an understanding of basic Python and machine learning concepts is a must, no prior knowledge of streaming is required.
Table of Contents
- An Introduction to Streaming Data
- Architectures for Streaming and Real-Time Machine Learning
- Data Analysis on Streaming Data
- Online Learning with River
- Online Anomaly Detection
- Online Classification
- Online Regression
- Reinforcement Learning
- Drift and Drift Detection
- Feature Transformation and Scaling
- Catastrophic Forgetting
- Conclusion and Best Practices
"Synopsis" may belong to another edition of this title.
About the Author
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).
"About the title" may belong to another edition of this title.
Chiron Media
Wallingford, United Kingdom
AbeBooks seller since August 2, 2010
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