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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 8: Reinforcement Learning

The reinforcement learning paradigm is very different than standard machine learning and even the online machine learning methods that we have covered in earlier chapters. Although reinforcement learning will not always be a better choice than "regular" learning for many use cases, it is a powerful tool for tackling re-learning and the adaptation of models.

In reinforcement learning, we give the model a lot of decisive power to do its re-learning and to update the rules of its decision-making process. Rather than letting the model make a prediction and hardcode the action to take for this prediction, the model will directly decide on the action to take.

For automated machine learning pipelines in which actions are effectively automated, this can be a great choice. Of course, this must be complemented with different types of logging, monitoring, and more. For cases in which we need a prediction rather than an action, reinforcement learning...

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Machine Learning for Streaming Data with Python
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