Practical Machine Learning for Streaming Data with Python : Design, Develop, ...

Practical Machine Learning for Streaming Data with Python : Design, Develop, and Validate Online Learning Models, Paperback by Putatunda, Sayan, ISBN 1484268660, ISBN-13 9781484268667, Brand New, Free shipping in the US<div>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.&nbsp;<br></div><div><p>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.</p><p>Introduction to the various supervised and unsupervised algorithms for streaming data, and their implementation on various datasets using Python are also covered. Th concludes by briefly covering other open-source tools available for streaming data such as Spark, MOA (Massive Online Analysis), Kafka, and more.</p></div><div><div><br></div></div><div><b>What You'll Learn</b></div><div><ul><li>Understand machine learning with streaming data concepts</li><li>Review incremental and online learning</li><li>Develop models for detecting concept drift</li><li>Explore techniques for classification, regression, and ensemble learning in streaming data contexts</li><li>Apply best practices for debugging and validating machine learning models in streaming data context</li><li>Get introduced to other open-source frameworks for handling streaming&nbsp;data.</li></ul></div><div><div><b>Who This Book Is For</b></div><div><b><br></b></div><div>Machine learning engineers and data science professionals</div></div><div><br></div>