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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 3: Data Analysis on Streaming Data

Now that you have seen an introduction to streaming data and streaming use cases, as well as an introduction to streaming architecture, it is time to enter into the core of this book: analytics and machine learning.

As you probably know, descriptive statistics and data analysis are the entry points into machine learning, but they are also often used as a standalone use case. In this chapter, you will first discover descriptive statistics from a traditional statistics viewpoint. Some parts of traditional statistics focus on making correct estimations of descriptive statistics when only part of the data is available.

In streaming, you will encounter such problems in an even more impacting manner than in batch data. Through a continuous data collection process, your descriptive statistics will continue changing on every new data point. This chapter will propose some solutions for dealing with this.

You will also build a data visualization...

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Machine Learning for Streaming Data with Python
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