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nexusstc/Machine Learning Guide for Oil and Gas Using Python/8256d83a9e2adeaba9a5692af8565a02.pdf
Machine learning guide for oil and gas using Python : ǂa ǂstep-by-step breakdown with data, algorithms, codes, and applications 🔍
Hoss Belyadi , Alireza Haghighat Elsevier Science & Technology; Gulf Professional Publishing, Elsevier Ltd., Cambridge, MA, 2021
英语 [en] · PDF · 13.0MB · 2021 · 📘 非小说类图书 · 🚀/lgli/lgrs/nexusstc/zlib · Save
描述
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
备用文件名
lgli/Hoss Belyadi, Alireza Haghighat - Machine Learning Guide for Oil and Gas Using Python_ A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications-Gulf Professional Publishing (2021).pdf
备用文件名
lgrsnf/Hoss Belyadi, Alireza Haghighat - Machine Learning Guide for Oil and Gas Using Python_ A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications-Gulf Professional Publishing (2021).pdf
备用文件名
zlib/no-category/Hoss Belyadi, Alireza Haghighat/Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications_25279256.pdf
备选标题
Введение в машинное обучение с помощью Python: руководство для специалистов по работе с данными: [полноцветное издание]
备选标题
Machine learning with Python cookbook : practical solutions from preprocessing to deep learning
备选标题
Introduction to Machine Learning with Python : A Guide for Data Scientists
备选标题
Машинное обучение с использованием Python. Сборник рецептов
备选作者
Андреас Мюллер, Сара Гвидо; [перевод с английского и редакция А. В. Груздева]
备选作者
Крис Элбон; перевод с английского А. Логунова
备选作者
Belyadi, Hoss, Haghighat, Alireza
备选作者
Andreas C. Mueller, Sarah Guido
备选作者
Andreas C. Müller; Sarah Guido
备选作者
Müller, Andreas, Guido, Sarah
备选作者
Мюллер, Андреас
备选作者
Albon, Chris
备选作者
Chris Albon
备选作者
Элбон, Крис
备用出版商
Gulf Professional Publishing, an imprint of Elsevier
备用出版商
O'Reilly Media; O'Reilly Media, Inc.
备用出版商
Elsevier Science & Technology Books
备用出版商
Academic Press, Incorporated
备用出版商
O'Reilly Media, Incorporated
备用出版商
Morgan Kaufmann Publishers
备用出版商
БХВ-Петербург
备用出版商
Brooks/Cole
备用出版商
Диалектика
备用版本
First edition, Beijing Boston Farnham Sebastopol Tokyo, 2018
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First edition, third release, Sebastopol, CA, 2017
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Kidlington ; Cambridge (Mass.), cop. 2021
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United States, United States of America
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O'Reilly Media, Sebastopol, CA, 2017
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First edition, Beijing, [China, 2018
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First edition, Sebastopol, CA, 2016
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First edition, Sebastopol, CA, 2018
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Санкт-Петербург, Russia, 2022
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First edition, Beijing, 2016
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Москва [и др.], Russia, 2017
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September 25, 2016
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Apr 01, 2018
备用版本
1, FR, 2016
备用版本
1, PS, 2018
备用版本
1, PS, 2021
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备用描述
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.
You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.
With this book, you'll learn:
Fundamental concepts and applications of machine learning
Advantages and shortcomings of widely used machine learning algorithms
How to represent data processed by machine learning, including which data aspects to focus on
Advanced methods for model evaluation and parameter tuning
The concept of pipelines for chaining models and encapsulating your workflow
Methods for working with text data, including text-specific processing techniques
Suggestions for improving your machine learning and data science skills
备用描述
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills
备用描述
This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. You'll find recipes for: Vectors, matrices, and arrays Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Support vector machines (SVM), naïve Bayes, clustering, and neural networks Saving and loading trained models
备用描述
With Early Release ebooks, you get books in their earliest form--the author's raw and unedited content as he or she writes--so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. The Python programming language and its libraries, including pandas and scikit-learn, provide a production-grade environment to help you accomplish a broad range of machine-learning tasks. With this comprehensive cookbook, data scientists and software engineers familiar with Python will benefit from almost 200 practical recipes for building a comprehensive machine-learning pipeline--everything from data preprocessing and feature engineering to model evaluation and deep learning. Learn from author Chris Albon, a data scientist who has written more than 500 tutorials on Python, data science, and machine learning. Each recipe in this practical cookbook includes code solutions that you can put to work right away, along with a discussion of how and why they work--making it ideal as a learning tool and reference book. -- Provided by Publisher
备用描述
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 Is Utilization In Various Oil And Gas Scenarios, Such As Well Testing, Shale Reservoirs And Production Optimization. While Similar Resources Are Often Too Mathematical, This Book Balances Theory With Applications, Including Use Cases That Help Solve Different 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
备用描述
Книга содержит около 200 рецептов решения практических задач машинного обучения, таких как загрузка и обработка текстовых или числовых данных, отбор модели, уменьшение размерности и многие другие. Рассмотрена работа с языком Python и его библиотеками, в том числе pandas и scikit-learn. Решения всех задач сопровождаются подробными объяснениями. Каждый рецепт содержит работающий программный код, который можно вставлять, объединять и адаптировать, создавая собственное приложение. Приведены рецепты решений с использованием: векторов, матриц и массивов; обработки данных, текста, изображений, дат и времени; уменьшения размерности и методов выделения или отбора признаков; оценивания и отбора моделей; линейной и логистической регрессии, деревьев, лесов и к ближайших соседей; опорно-векторных машин (SVM), наивных байесовых классификаторов, кластеризации и нейронных сетей; сохранения и загрузки натренированных моделей
备用描述
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. -- Provided by publisher
备用描述
Vectors, Matrices, And Arrays -- Loading Data -- Data Wrangling -- Handling Numerical Data -- Handling Categorical Data -- Handling Text -- Handling Dates And Times -- Handling Images -- Dimensionalit Reduction Using Feature Extraction -- Dimensionality Reduction Using Feature Selection -- Model Evaluation -- Model Selection -- Linear Regression -- Trees And Forests -- K-nearest Neighbors -- Logistic Regression -- Support Vector Machines -- Naive Bayes -- Clustering -- Neural Networks -- Saving And Loading Trained Models. Chris Albon. Includes Index.
开源日期
2023-06-21
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