Machine Learning for Streaming Data with Python

Rapidly build practical online machine learning solutions using River and other top key frameworks

Key Features

• Work on streaming use cases that are not taught in most data science courses
• Gain experience with state-of-the-art tools for streaming data
• Mitigate various challenges while handling streaming data

Book Description

Streaming data is the new top technology to watch in the field of data science and machine learning. With business needs become more demanding, many use cases require real time analysis as well as real time machine learning. This book will allow to get up to speed with data analytics for streaming data and focusses 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. You will then look at the state-of-the-art frameworks for streaming data like River.

You will learn various industrial use cases for streaming data like Online Anomaly Detection and others. Then, you will deep dive into challenges and how you will mitigate them. You will then learn the best practices that will help you use streaming data to generate real-time insights.

Upon completion of the book, you will be confident about using streaming data in your machine learning models.

What you will learn

• Understand the challenges and advantages of working with streaming data
• Develop real-time insights from streaming data
• Learn its implementation with various use cases to boost your knowledge
• Develop a PCA alternative that can work on real-time data
• Best practices that you absolutely need to remember

Who This Book Is For

Data scientists and Machine learning engineers who have a basis in Machine Learning are practice and technology-oriented and want to learn how to apply Machine Learning to streaming data through practical examples with modern technologies. The reader will need to understand basic Python and Machine Learning concepts and Python but will require no prior knowledge of streaming.

Table of Contents

1. Introduction to Streaming data
2. Architectures for Streaming and Real Time Machine Learning
3. Data Analysis on Streaming data
4. Online Learning with River
5. Online Anomaly Detection
6. Online Classification
7. Online Regression
8. Reinforcement Learning
9. Drift and Drift Detection
10. Feature Transformation and Scaling
11. Catastrophic Forgetting
12. Conclusion and Best Practices