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

Chapter 6: Online Classification

In the previous two chapters, you were introduced to some basic notions of classification. You first saw a use case in which online classification models in River were used to build a model that can identify an iris species based on a number of characteristics of a plant. This iris dataset is one of the best-known datasets in the world and is a very common starting point for classification.

After that, you looked at anomaly detection. We discussed how classification models can be used for anomaly detection for those cases where we can label anomalies as one class and non-anomalies as another class. Specific anomaly detection models are often better at the task, as they strive to understand only the non-anomalies. Classification models will strive to understand each of the classes.

In this chapter, you'll go much deeper into classification. The chapter will start by posing definitions of what classification is and what it can be used for....