Name: Practical Machine Learning for Streaming Data With Python: Design, Develop, and Validate Online Learning Models (en Inglés)
Price: 41420.00 CLP
Availability: InStock
Author: Putatunda, Sayan
ISBN: 9781484268667

portada Practical Machine Learning for Streaming Data With Python: Design, Develop, and Validate Online Learning Models (en Inglés)

Practical Machine Learning for Streaming Data With Python: Design, Develop, and Validate Online Learning Models (en Inglés)

Formato

Libro Físico

Autor

Sayan Putatunda

Editorial

Apress

Año

2021

Idioma

Inglés

N° páginas

118

Encuadernación

Tapa Blanda

Dimensiones

23.4 x 15.6 x 0.7 cm

Peso

0.20 kg.

ISBN13

9781484268667

N° edición

Categorías

Inteligencia artificial

Practical Machine Learning for Streaming Data With Python: Design, Develop, and Validate Online Learning Models (en Inglés)

Sayan Putatunda (Autor) · Apress · Tapa Blanda

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Reseña del libro "Practical Machine Learning for Streaming Data With Python: Design, Develop, and Validate Online Learning Models (en Inglés)"

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 LearnUnderstand machine learning with streaming data conceptsReview incremental and online learningDevelop models for detecting concept driftExplore techniques for classification, regression, and ensemble learning in streaming data contextsApply best practices for debugging and validating machine learning models in streaming data contextGet introduced to other open-source frameworks for handling streaming data.Who This Book Is ForMachine learning engineers and data science professionals