Machine Learning Guide for Oil and Gas Using Python A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications
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Form:Einzelkauf Download
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Sprache:Englisch
Fr. 125.90
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB 3
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
09.04.2021
Verlag
Elsevier Science & Techn.Seitenzahl
476 (Printausgabe)
Dateigröße
37830 KB
Sprache
Englisch
EAN
9780128219300
Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The reference moves from explaining how Python works to step-by-step examples of utilization in various oil and gas scenarios, such as well testing, shale reservoirs and production optimization. Petroleum engineers are quickly applying machine learning techniques to their data challenges, but there is a lack of references beyond the math or heavy theory of machine learning. Machine Learning Guide for Oil and Gas Using Python details the open-source tool Python by explaining how it works at an introductory level then bridging into how to apply the algorithms into different oil and gas scenarios. While similar resources are often too mathematical, this book balances theory with applications, including use cases that help solve different oil and gas data challenges.
- Helps readers understand how open-source Python can be utilized in practical oil and gas challenges
- Covers the most commonly used algorithms for both supervised and unsupervised learning
- Presents a balanced approach of both theory and practicality while progressing from introductory to advanced analytical techniques
- Helps readers understand how open-source Python can be utilized in practical oil and gas challenges
- Covers the most commonly used algorithms for both supervised and unsupervised learning
- Presents a balanced approach of both theory and practicality while progressing from introductory to advanced analytical techniques
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