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  • Book Overview & Buying Machine Learning for Streaming Data with Python
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Machine Learning for Streaming Data with Python

Machine Learning for Streaming Data with Python

By : Joos Korstanje
4.2 (9)
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Machine Learning for Streaming Data with Python

Machine Learning for Streaming Data with Python

4.2 (9)
By: Joos Korstanje

Overview of this book

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.
Table of Contents (17 chapters)
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1
Part 1: Introduction and Core Concepts of Streaming Data
5
Part 2: Exploring Use Cases for Data Streaming
11
Part 3: Advanced Concepts and Best Practices around Streaming Data
15
Chapter 12: Conclusion and Best Practices

Chapter 4: Online Learning with River

In this and the coming three chapters, you will learn how to work with a library for online machine learning called River. Online machine learning is a part of machine learning in which models are designed in such a way that they can update their learned model on the reception of any new data point.

Online machine learning is the opposite of offline machine learning, which is the regular machine learning that you are probably already aware of. In general, in machine learning, a model will try to learn a mathematical rule that can perform a certain task. This task is learned on the basis of a number of data points. The mathematics behind these tasks is based on statistics and algorithmics.

In this chapter, you will discover how to work with online machine learning, and you will discover multiple types of online machine learning. You will go more in depth into the differences between online and offline machine learning. You will also see how...

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Machine Learning for Streaming Data with Python
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