Practical Machine Learning for Streaming Data with Python: Design, Develop, and Validate Online Learning Models (Paperback) (ISBN-13: 9781484268667)

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


Author: Sayan Putatunda
ISBN-10: 1484268660
ISBN-13: 9781484268667
Publisher: Apress
Language: English
Published: 04/09/2021
Pages: 118
Format: Paperback
Weight: 0.44lbs
Size: 9.21h x 6.14w x 0.29d