{"product_id":"practical-machine-learning-for-streaming-data-with-python-design-develop-and-validate-online-learning-models-von-sayan-putatunda","title":"Practical Machine Learning for Streaming Data with Python","description":"\n                                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. \n                \n                \u003cbr\u003e\n                                \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                                \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                                \n                \u003cbr\u003e\n                                \n                \u003cb\u003eWhat You'll Learn\u003c\/b\u003e\n                                \n                \u003cul\u003e\n                                        \n                    \u003cli\u003eUnderstand machine learning with streaming data concepts\u003c\/li\u003e\n                                        \n                    \u003cli\u003eReview incremental and online learning\u003c\/li\u003e\n                                        \n                    \u003cli\u003eDevelop models for detecting concept drift\u003c\/li\u003e\n                                        \n                    \u003cli\u003eExplore techniques for classification, regression, and ensemble learning in streaming data contexts\u003c\/li\u003e\n                                        \n                    \u003cli\u003eApply best practices for debugging and validating machine learning models in streaming data context\u003c\/li\u003e\n                                        \n                    \u003cli\u003eGet introduced to other open-source frameworks for handling streaming data.\u003c\/li\u003e\n                                    \n                \u003c\/ul\u003e\n                                \n                \u003cb\u003eWho This Book Is For\u003c\/b\u003e\n                                \n                \u003cb\u003e\n                                        \n                    \u003cbr\u003e\n                                    \n                \u003c\/b\u003e\n                                Machine learning engineers and data science professionals\n                \n                \u003cbr\u003e\n                            \n            \u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9781484268667\"\u003e\u003ch3\u003eDesign, Develop, and Validate Online Learning Models\u003c\/h3\u003e\u003c\/div\u003e","brand":"Libri","offers":[{"title":"Softcover - 9781484268667","offer_id":39417490276445,"sku":"9781484268667","price":64.19,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/68577e5e-951e-4be7-8ac8-4fed5456547a.jpg?v=1775707772","url":"https:\/\/shop.autorenwelt.de\/en\/products\/practical-machine-learning-for-streaming-data-with-python-design-develop-and-validate-online-learning-models-von-sayan-putatunda","provider":"Autorenwelt Shop","version":"1.0","type":"link"}