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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

Algorithms for detecting anomalies in River

In this chapter, you will again use River for online machine learning algorithms. There are other libraries out there, but River is a very promising candidate for being the go-to Python package for online learning (except for reinforcement learning).

You will see two of the online machine learning algorithms for anomaly detection that River currently (version 0.9.0) contains, as follows:

  • OneClassSVM: An online adaptation of the offline version of One-Class SVM
  • HalfSpaceTrees: An online adaptation of Isolation Forests

You will also see how to work with the constant thresholder and the quantile thresholder.

The use of thresholders in River anomaly detection

Let's first look at the use of thresholders, as they will be wrapped around the actual anomaly detection algorithms.

Anomaly detection algorithms will generally return a score between 0 and 1 to indicate to the model to what extent the observation is...

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