{"product_id":"9781484268667","title":"Practical Machine Learning for Streaming Data with Python: Design, Develop, and Validate Online Learning Models","description":"\u003ch1\u003ePractical Machine Learning for Streaming Data with Python: Design, Develop, and Validate Online Learning Models\u003c\/h1\u003e \u003ch2\u003ePutatunda, Sayan\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cdiv\u003eDesign, 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. \u003cbr\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003cp\u003eYou'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.\u003c\/p\u003e\n\u003cp\u003eIntroduction 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.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003e\u003c\/div\u003e\u003cdiv\u003e\u003cb\u003eWhat You'll Learn\u003c\/b\u003e\u003c\/div\u003e\u003cdiv\u003e\u003cul\u003e\n\u003cli\u003eUnderstand machine learning with streaming data concepts\u003c\/li\u003e\n\u003cli\u003eReview incremental and online learning\u003c\/li\u003e\n\u003cli\u003eDevelop models for detecting concept drift\u003c\/li\u003e\n\u003cli\u003eExplore techniques for classification, regression, and ensemble learning in streaming data contexts\u003c\/li\u003e\n\u003cli\u003eApply best practices for debugging and validating machine learning models in streaming data context\u003c\/li\u003e\n\u003cli\u003eGet introduced to other open-source frameworks for handling streaming data.\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003cdiv\u003e\u003cb\u003eWho This Book Is For\u003c\/b\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cb\u003e\u003cbr\u003e\u003c\/b\u003e\u003c\/div\u003e\n\u003cdiv\u003eMachine learning engineers and data science professionals\u003c\/div\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Apress\u003c\/p\u003e \u003cp\u003ePublication Date: 2021-04-09\u003c\/p\u003e \u003cp\u003eFormat: Paperback\u003c\/p\u003e \u003cp\u003eISBN-13: 9781484268667\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-1-4842-6867-4\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 118\u003c\/p\u003e ","brand":"Apress","offers":[{"title":"Default Title","offer_id":45385565339788,"sku":"9781484268667","price":58.49,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9781484268667.jpg?v=1776861093","url":"https:\/\/lateknightbooks.com\/products\/9781484268667","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}