Arthur kordon (114 résultats)

Paperback. Etat : Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

Etat : good. A copy that has been read, remains in good condition. All pages are intact, and the cover is intact. The spine and cover show signs of wear. Pages can include notes and highlighting and show signs of wear, and the copy can include "From the library of" labels or previous owner inscriptions. 100% GUARANTEE! Shipped with delivery confirmation, if you're not satisfied with purchase please return item! Ships via media mail.…

Etat : Very Good. Very Good Condition. Five star seller - Buy with confidence.

Paperback. Etat : Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Etat : Very Good. : Este libro, escrito por Tim Rey, Arthur Kordon y Chip Wells, es una guía esencial para profesionales de la previsión, ingenieros, estadísticos y economistas. La obra detalla enfoques prácticos para la minería de grandes conjuntos de datos de series temporales, explicando cómo seleccionar variables de entrada candidatas útiles para modelos de regresión en entornos con una gran cantidad de datos, además de identificar la estructura de correlación entre dichas variables y la variable de pronóstico.Publicado por SAS Institute, este texto es una herramienta fundamental para quienes buscan comprender los desafíos y las soluciones en la previsión aplicada dentro de un entorno empresarial. Con un enfoque técnico y riguroso, proporciona las bases necesarias para optimizar los procesos de análisis de datos y mejorar la precisión en la toma de decisiones basada en modelos estadísticos. EAN: 9781607646624 Tipo: Libros Categoría: Tecnología|Ciencias Título: Applied Data Mining for Forecasting Using SAS Autor: Tim Rey| Arthur Kordon| Chip Wells Editorial: SAS Institute Idioma: en Páginas: 336 Formato: tapa blanda.…

Paperback. Etat : Very Good. 1st ed. 2020. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

xx, 262 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.

Etat : As New. Unread book in perfect condition.

Etat : New.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

Etat : New.

Etat : New.

Paperback. Etat : New. 2020 ed. This book offers practical guidelines on creating value from the application of data science based on selected artificial intelligence methods. In Part I, the author introduces a problem-driven approach to implementing AI-based data science and offers practical explanations of key technologies: machine learning, deep learning, decision trees and random forests, evolutionary computation, swarm intelligence, and intelligent agents. In Part II, he describes the main steps in creating AI-based data science solutions for business problems, including problem knowledge acquisition, data preparation, data analysis, model development, and model deployment lifecycle. Finally, in Part III the author illustrates the power of AI-based data science with successful applications in manufacturing and business. He also shows how to introduce this technology in a business setting and guides the reader on how to build the appropriate infrastructure and develop the required skillsets. The book is ideal for data scientists who will implement the proposed methodology and techniques in their projects. It is also intended to help business leaders and entrepreneurs who want to create competitive advantage by using AI-based data science, as well as academics and students looking for an industrial view of this discipline.…

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : New. In English.

Etat : New.

Etat : New. In English.

Etat : As New. Unread book in perfect condition.

Paperback. Etat : New.

Etat : As New. Unread book in perfect condition.

Taschenbuch. Etat : Neu. Neuware -This book offers practical guidelines on creating value from the application of data science based on selected artificial intelligence methods. In Part I, the author introduces a problem-driven approach to implementing AI-based data science and offers practical explanations of key technologies: machine learning, deep learning, decision trees and random forests, evolutionary computation, swarm intelligence, and intelligent agents. In Part II, he describes the main steps in creating AI-based data science solutions for business problems, including problem knowledge acquisition, data preparation, data analysis, model development, and model deployment lifecycle. Finally, in Part III the author illustrates the power of AI-based data science with successful applications in manufacturing and business. He also shows how to introduce this technology in a business setting and guides the reader on how to build the appropriate infrastructure and develop the required skillsets. The book is ideal for data scientists who will implement the proposed methodology and techniques in their projects. It is also intended to help business leaders and entrepreneurs who want to create competitive advantage by using AI-based data science, as well as academics and students looking for an industrial view of this discipline.…

Perfect Paperback. Etat : Brand New. 336 pages. 11.00x8.50x0.75 inches. In Stock.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New. In English.

Etat : As New. Unread book in perfect condition.

Taschenbuch. Etat : Neu. Neuware - This book offers practical guidelines on creating value from the application of data science based on selected artificial intelligence methods.In Part I, the author introduces a problem-driven approach to implementing AI-based data science and offers practical explanations of key technologies: machine learning, deep learning, decision trees and random forests, evolutionary computation, swarm intelligence, and intelligent agents. In Part II, he describes the main steps in creating AI-based data science solutions for business problems, including problem knowledge acquisition, data preparation, data analysis, model development, and model deployment lifecycle. Finally, in Part III the author illustrates the power of AI-based data science with successful applications in manufacturing and business. He also shows how to introduce this technology in a business setting and guides the reader on how to build the appropriate infrastructure and develop the required skillsets.The book is ideal for data scientists who will implement the proposed methodology and techniques in their projects. It is also intended to help business leaders and entrepreneurs who want to create competitive advantage by using AI-based data science, as well as academics and students looking for an industrial view of this discipline.…

Etat : New.

Etat : New.