Practical Machine Learning for Streaming Data with Python

Design, Develop, and Validate Online Learning Models

Sayan Putatunda

• Publisher: Apress
• Year: 2021
• Print ISBN: 9781484268667
• eBook ISBN: 9781484268674
• Topic: Mathematics
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Mathematics

Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This book is a quick start guide for data scientists and machine learning engineers looking to implement machine learning models for streaming data with Python to generate real-time insights.

You'll start with an introduction to streaming data, the various challenges associated with it, some of its real-world business applications, and various windowing techniques. You'll then examine incremental and online learning algorithms, and the concept of model evaluation with streaming data and get introduced to the Scikit-Multiflow framework in Python. This is followed by a review of the various change detection/concept drift detection algorithms and the implementation of various datasets using Scikit-Multiflow.

Introduction to the various supervised and unsupervised algorithms for streaming data, and their implementation on various datasets using Python are also covered. The book concludes by briefly covering other open-source tools available for streaming data such as Spark, MOA (Massive Online Analysis), Kafka, and more.

What You'll Learn

• Understand machine learning with streaming data concepts
• Review incremental and online learning
• Develop models for detecting concept drift
• Explore techniques for classification, regression, and ensemble learning in streaming data contexts
• Apply best practices for debugging and validating machine learning models in streaming data context
• Get introduced to other open-source frameworks for handling streamingdata.

Who This Book Is For
Machine learning engineers and data science professionals

Table of contents

• Cover
• Front Matter
• 1. An Introduction to Streaming Data
• 2. Concept Drift Detection in Data Streams
• 3. Supervised Learning for Streaming Data
• 4. Unsupervised Learning and Other Tools for Data Stream Mining
• Back Matter