Book Image

Machine Learning for Streaming Data with Python

By : Joos Korstanje
Book Image

Machine Learning for Streaming Data with Python

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)
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

The main steps of a reinforcement learning model

The actions of the agent are the decisions that it can make. This is a limited set of decisions. As you will understand, the agent is just a piece of code, so all its decisions will need to be programmed controls of its own behavior.

If we think of it as a computer game, then you understand that the actions that you as a player can execute are limited by the buttons that you can press on your game console. All of the combinations together still allow for a very wide range of options, but they are limited in some way.

The same is true for our human baby learning to walk. They only have control over their own body, so they would not be able to execute any actions beyond this. This gives a huge number of things that can be done by humans, but still, it is a fixed set of actions.

Making the decisions

Now, as your reinforcement agent is receiving information about its environment (the state), it will need to convert this information...