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Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications

Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications

Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications Paperback - 2021

by Hoss Belyadi

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Paperback. New. New Book; Fast Shipping from UK; Not signed; Not First Edition; 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 practic
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Details

  • Title Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications
  • Author Hoss Belyadi
  • Binding Paperback
  • Condition New
  • Pages 476
  • Volumes 1
  • Language ENG
  • Publisher Gulf Professional Publishing
  • Publication date 2021-04-13
  • Bookseller's Inventory # ria9780128219294_inp
  • ISBN 9780128219294 / 0128219297
  • Weight 1.4 lbs (0.64 kg)
  • Dimensions 9 x 6 x 0.96 in (22.86 x 15.24 x 2.44 cm)
  • Category Computers - General Information
  • Quantity available 350

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From the publisher

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.

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