Книга Machine Learning for Business Analytics: Concepts,  Techniques and Applications with JMP Pro  (R), 2nd E dition Shmueli

Machine Learning for Business Analytics: Concepts, Techniques and Applications with JMP Pro (R), 2nd E dition

Автор: Shmueli
Език: Английски език
Корици: С твърди корици
Издател: John Wiley and Sons Ltd
Наличност: Външен склад в ограничено количество
Изпращаме след 11-15 дни
138.50 270.88 лв
MACHINE LEARNING FOR BUSINESS ANALYTICS An up-to-date introduction to a market-leading platform for...

Информация за книгата

Автор
Език
Английски език
Корици
Книга - С твърди корици
Издадена
2023
страници
624
EAN
9781119903833
Enbook ID
41479655
Издател
Теглоt
666

Пълно описание

MACHINE LEARNING FOR BUSINESS ANALYTICS
 
An up-to-date introduction to a market-leading platform for data analysis and machine learning
 
Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro(r), 2nd ed. offers an accessible and engaging introduction to machine learning. It provides concrete examples and case studies to educate new users and deepen existing users' understanding of their data and their business. Fully updated to incorporate new topics and instructional material, this remains the only comprehensive introduction to this crucial set of analytical tools specifically tailored to the needs of businesses.
 
Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro(r), 2nd ed. readers will also find:
* Updated material which improves the book's usefulness as a reference for professionals beyond the classroom
* Four new chapters, covering topics including Text Mining and Responsible Data Science
* An updated companion website with data sets and other instructor resources: www.jmp.com/dataminingbook
* A guide to JMP Pro(r)'s new features and enhanced functionality
 
Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro(r), 2nd ed. is ideal for students and instructors of business analytics and data mining classes, as well as data science practitioners and professionals in data-driven industries.

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