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Practical machine learning for strea...

Putatunda, Sayan.

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Practical machine learning for streaming data with Python = design, develop, and validate online learning models /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Practical machine learning for streaming data with Python/ by Sayan Putatunda.
Reminder of title:	design, develop, and validate online learning models /
Author:	Putatunda, Sayan.
Published:	Berkeley, CA :Apress : : 2021.,
Description:	xvi, 118 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: An Introduction to Streaming Data -- Chapter 2: Concept Drift Detection in Data Streams -- Chapter 3: Supervised Learning for Streaming Data -- Chapter 4: Unsupervised Learning and Other Tools for Data Stream Mining.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-1-4842-6867-4
ISBN:	9781484268674

Practical machine learning for streaming data with Python = design, develop, and validate online learning models /
Putatunda, Sayan.

Practical machine learning for streaming data with Pythondesign, develop, and validate online learning models /[electronic resource] :by Sayan Putatunda. - Berkeley, CA :Apress :2021. - xvi, 118 p. :ill., digital ;24 cm.

Chapter 1: An Introduction to Streaming Data -- Chapter 2: Concept Drift Detection in Data Streams -- Chapter 3: Supervised Learning for Streaming Data -- Chapter 4: Unsupervised Learning and Other Tools for Data Stream Mining.

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. You will: 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.

ISBN: 9781484268674

Standard No.: 10.1007/978-1-4842-6867-4doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .P883 2021

Dewey Class. No.: 006.31

Practical machine learning for streaming data with Python = design, develop, and validate online learning models /
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520 $a 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. You will: 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.
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