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Practical Machine Learning for Streaming Data with Python - Design, Develop, and Validate Online Learning Models (Paperback, 1st ed.)
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Practical Machine Learning for Streaming Data with Python - Design, Develop, and Validate Online Learning Models (Paperback, 1st ed.)
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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
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