Practical Machine Learning for Streaming Data with Python: Design Develop and Validate Online Learning Models

Practical Machine Learning for Streaming Data with Python: Design, Develop, and Validate Online Learning Models Sayan Putatunda
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 streaming data.
Who This Book Is For
Machine learning engineers and data science professionals
Categories:
Computers – Computer Science
Year:
2021
Edition:
1
Publisher:
Apress
Language:
english
Pages:
135
ISBN 10:
1484268679
ISBN 13:
9781484268674
File:
10 MB