I have sorted them in alphabetical order. Slicing is one of the most basic skills of a Python developer. This is THE most comprehensive book on slicing in existence. Another great book about computer games from Al Sweigart.
The book specifically addresses kids but is also interesting for adults who like gaming. This book is one of the most comprehensive Python books I have ever seen. Sometimes this can be a bit dry — but the author does a great work in making it interesting for the reader.
This is a very popular book that asks you to perform programming exercises in your terminal. By putting in the actual effort and typing in all the little commands , you learn more thoroughly at the cost of a larger time investment.
The reference moves from explaining how Python works. Apply machine and deep learning to solve some of the challenges in the oil and gas industry. The book begins with a brief discussion of the oil and gas exploration and production life cycle in the context of data flow through the different stages of industry operations.
This leads to. Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases.
Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data. Hydraulic Fracturing in Unconventional Reservoirs: Theories, Operations, and Economic Analysis, Second Edition, presents the latest operations and applications in all facets of fracturing.
Get Interpretable Machine Learning Books now! Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner.
The concepts,. Applications of Artificial Intelligence Techniques in the Petroleum Industry gives engineers a critical resource to help them understand the machine learning that will solve specific engineering challenges. Jun 1, Feb 17, Feb 16, Jan 16, May 6, May 5, Dec 4, Mar 20, Jul 5, Mar 21, Oct 20, Oct 19, Oct 8, Sep 27, Sep 17, Dec 29, Nov 22, Apr 2, Mar 31, Download the file for your platform.
If you're not sure which to choose, learn more about installing packages. Warning Some features may not work without JavaScript.
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