Predictive Analytics with Microsoft Azure Machine Learning, 2nd Edition

Автор: literator от 19-01-2019, 04:29, Коментариев: 0

Категория: КНИГИ » ПРОГРАММИРОВАНИЕ

Название: Predictive Analytics with Microsoft Azure Machine Learning, 2nd Edition
Автор: Roger Barga, Valentine Fontama, Wee Hyong Tok
Издательство: Apress
ISBN: 1484212010
Год: 2015
Страниц: 303
Язык: английский
Формат: True PDF
Размер: 18.7 MB

Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book provides a thorough overview of the Microsoft Azure Machine Learning service with practical guidance for building recommenders, propensity models, and churn and predictive maintenance models.

The authors use task oriented descriptions and concrete end-to-end examples to ensure that the reader can immediately begin using this new service. The book describes all aspects of the service from data ingress to applying machine learning, evaluating the models, and deploying them as web services.

Learn how you can quickly build and deploy sophisticated predictive models with the new Azure Machine Learning from Microsoft.

The end-to-end engineering and maintenance of a production Machine Learning (ML) application requires a highly skilled team that few organizations can build and sustain. Microsoft Azure ML was designed to solve these problems.

- It’s a fully managed cloud service with no software to install, no hardware to manage, and no OS versions or development environments to grapple with.
- Armed with nothing but a browser, data scientists can log on to Azure and start developing ML models from any location, from any device. They can host a practically unlimited number of files on Azure storage.
- A collection of best-of-breed algorithms developed by Microsoft Research is built in, as is support for custom R code. Over 350 open source R packages can be used securely within Azure ML...

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