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
Practical Machine Learning for Streaming Data with Python
RRP:
$129.00
Description
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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