| 书目名称 | Practical Machine Learning for Streaming Data with Python | | 副标题 | Design, Develop, and | | 编辑 | Sayan Putatunda | | 视频video | | | 概述 | Explains the latest Scikit-Multiflow framework in detail.Explains Supervised and Unsupervised Learning for streaming data.One of the first books in the market on machine learning models for streaming | | 图书封面 |  | | 描述 | 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..What You‘ll Learn.Understand machine learning with str | | 出版日期 | Book 2021 | | 关键词 | Machine Learning; Python; Artificial Intelligence; Streaming data; Concept Drift; Online Learning; Real Ti | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4842-6867-4 | | isbn_softcover | 978-1-4842-6866-7 | | isbn_ebook | 978-1-4842-6867-4 | | copyright | Sayan Putatunda 2021 |
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发表于 2025-3-21 17:59:55
