Practical machine learning for streaming data with python: design, develop, and validate online learning models

By:

Putatunda, Sayan

Material type: Text

TextPublication details: Apress Media 2021 New YorkDescription: xvi, 118 p. ill. Includes bibliographical references and indexISBN:

9781484268667

Subject(s):

DDC classification:

006.31 P8P7

Summary: 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. https://www.apress.com/gp/book/9781484268667

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Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	Ahmedabad General Stacks	Non-fiction	006.31 P8P7 (Browse shelf(Opens below))	Available		203878

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Table of contents

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.

https://www.apress.com/gp/book/9781484268667

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