TY  - BOOK
AU  - Putatunda, Sayan
TI  - Practical machine learning for streaming data with python: design, develop, and validate online learning models
SN  - 9781484268667
U1  - 006.31 
PY  - 2021///
CY  - New York
PB  - Apress Media
KW  - Machine learning
KW  - Python
KW  - Data stream
KW  - Computer program language
N1  - 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.


N2  - 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

ER  -