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Machine Learning For The Oil And Gas Industry Using Python

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Machine Learning Guide for Oil and Gas Using Python

Machine Learning Guide for Oil and Gas Using Python Book
Author : Hoss Belyadi,Alireza Haghighat
Publisher : Gulf Professional Publishing
Release : 2021-04-09
ISBN : 0128219300
Language : En, Es, Fr & De

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Book Description :

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

Machine Learning in the Oil and Gas Industry

Machine Learning in the Oil and Gas Industry Book
Author : Yogendra Narayan Pandey,Ayush Rastogi,Sribharath Kainkaryam,Srimoyee Bhattacharya,Luigi Saputelli
Publisher : Apress
Release : 2020-11-03
ISBN : 9781484260937
Language : En, Es, Fr & De

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Book Description :

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 a survey of some interesting problems, which are good candidates for applying machine and deep learning approaches. The initial chapters provide a primer on the Python programming language used for implementing the algorithms; this is followed by an overview of supervised and unsupervised machine learning concepts. The authors provide industry examples using open source data sets along with practical explanations of the algorithms, without diving too deep into the theoretical aspects of the algorithms employed. Machine Learning in the Oil and Gas Industry covers problems encompassing diverse industry topics, including geophysics (seismic interpretation), geological modeling, reservoir engineering, and production engineering. Throughout the book, the emphasis is on providing a practical approach with step-by-step explanations and code examples for implementing machine and deep learning algorithms for solving real-life problems in the oil and gas industry. What You Will Learn Understanding the end-to-end industry life cycle and flow of data in the industrial operations of the oil and gas industry Get the basic concepts of computer programming and machine and deep learning required for implementing the algorithms used Study interesting industry problems that are good candidates for being solved by machine and deep learning Discover the practical considerations and challenges for executing machine and deep learning projects in the oil and gas industry Who This Book Is For Professionals in the oil and gas industry who can benefit from a practical understanding of the machine and deep learning approach to solving real-life problems.

Machine Learning and Data Science in the Oil and Gas Industry

Machine Learning and Data Science in the Oil and Gas Industry Book
Author : Patrick Bangert
Publisher : Elsevier
Release : 2021-03-15
ISBN : 0128207140
Language : En, Es, Fr & De

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Book Description :

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 stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value. Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful Gain practical understanding of machine learning used in oil and gas operations through contributed case studies Learn change management skills that will help gain confidence in pursuing the technology Understand the workflow of a full-scale project and where machine learning benefits (and where it does not)

Building Machine Learning Systems Using Python

Building Machine Learning Systems Using Python Book
Author : Dr Deepti Chopra
Publisher : BPB Publications
Release : 2021-05-07
ISBN : 9389423619
Language : En, Es, Fr & De

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Book Description :

Explore Machine Learning Techniques, Different Predictive Models, and its Applications KEY FEATURES ● Extensive coverage of real examples on implementation and working of ML models. ● Includes different strategies used in Machine Learning by leading data scientists. ● Focuses on Machine Learning concepts and their evolution to algorithms. DESCRIPTION This book covers basic concepts of Machine Learning, various learning paradigms, different architectures and algorithms used in these paradigms. You will learn the power of ML models by exploring different predictive modeling techniques such as Regression, Clustering, and Classification. You will also get hands-on experience on methods and techniques such as Overfitting, Underfitting, Random Forest, Decision Trees, PCA, and Support Vector Machines. In this book real life examples with fully working of Python implementations are discussed in detail. At the end of the book you will learn about the unsupervised learning covering Hierarchical Clustering, K-means Clustering, Dimensionality Reduction, Anomaly detection, Principal Component Analysis. WHAT YOU WILL LEARN ● Learn to perform data engineering and analysis. ● Build prototype ML models and production ML models from scratch. ● Develop strong proficiency in using scikit-learn and Python. ● Get hands-on experience with Random Forest, Logistic Regression, SVM, PCA, and Neural Networks. WHO THIS BOOK IS FOR This book is meant for beginners who want to gain knowledge about Machine Learning in detail. This book can also be used by Machine Learning users for a quick reference for fundamentals in Machine Learning. Readers should have basic knowledge of Python and Scikit-Learn before reading the book. TABLE OF CONTENTS 1. Introduction to Machine Learning 2. Linear Regression 3. Classification Using Logistic Regression 4. Overfitting and Regularization 5. Feasibility of Learning 6. Support Vector Machine 7. Neural Network 8. Decision Trees 9. Unsupervised Learning 10. Theory of Generalization 11. Bias and Fairness in ML

Hydraulic Fracturing in Unconventional Reservoirs

Hydraulic Fracturing in Unconventional Reservoirs Book
Author : Hoss Belyadi,Ebrahim Fathi,Fatemeh Belyadi
Publisher : Gulf Professional Publishing
Release : 2019-06-18
ISBN : 0128176660
Language : En, Es, Fr & De

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Book Description :

Hydraulic Fracturing in Unconventional Reservoirs: Theories, Operations, and Economic Analysis, Second Edition, presents the latest operations and applications in all facets of fracturing. Enhanced to include today’s newest technologies, such as machine learning and the monitoring of field performance using pressure and rate transient analysis, this reference gives engineers the full spectrum of information needed to run unconventional field developments. Covering key aspects, including fracture clean-up, expanded material on refracturing, and a discussion on economic analysis in unconventional reservoirs, this book keeps today's petroleum engineers updated on the critical aspects of unconventional activity. Helps readers understand drilling and production technology and operations in shale gas through real-field examples Covers various topics on fractured wells and the exploitation of unconventional hydrocarbons in one complete reference Presents the latest operations and applications in all facets of fracturing

Introduction to Machine Learning

Introduction to Machine Learning Book
Author : Jeeva Jose
Publisher : KHANNA PUBLISHING HOUSE
Release : 2021-12-04
ISBN : 9389139066
Language : En, Es, Fr & De

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Book Description :

AICTE recommended book for Indian Universities and Autonomous colleges. This book can be used as a self-study material or for instructor assisted teaching. Frequent questions for interviews and examinations are provided.

Machine Learning for Cybersecurity Cookbook

Machine Learning for Cybersecurity Cookbook Book
Author : Emmanuel Tsukerman
Publisher : Packt Publishing Ltd
Release : 2019-11-25
ISBN : 1838556346
Language : En, Es, Fr & De

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Book Description :

Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection Key Features Manage data of varying complexity to protect your system using the Python ecosystem Apply ML to pentesting, malware, data privacy, intrusion detection system(IDS) and social engineering Automate your daily workflow by addressing various security challenges using the recipes covered in the book Book Description Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you'll learn how to use Python libraries such as TensorFlow and scikit-learn to implement the latest artificial intelligence (AI) techniques and handle challenges faced by cybersecurity researchers. You'll begin by exploring various machine learning (ML) techniques and tips for setting up a secure lab environment. Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for malware, which you'll train and test on real samples. As you progress, you'll build self-learning, reliant systems to handle cybersecurity tasks such as identifying malicious URLs, spam email detection, intrusion detection, network protection, and tracking user and process behavior. Later, you'll apply generative adversarial networks (GANs) and autoencoders to advanced security tasks. Finally, you'll delve into secure and private AI to protect the privacy rights of consumers using your ML models. By the end of this book, you'll have the skills you need to tackle real-world problems faced in the cybersecurity domain using a recipe-based approach. What you will learn Learn how to build malware classifiers to detect suspicious activities Apply ML to generate custom malware to pentest your security Use ML algorithms with complex datasets to implement cybersecurity concepts Create neural networks to identify fake videos and images Secure your organization from one of the most popular threats – insider threats Defend against zero-day threats by constructing an anomaly detection system Detect web vulnerabilities effectively by combining Metasploit and ML Understand how to train a model without exposing the training data Who this book is for This book is for cybersecurity professionals and security researchers who are looking to implement the latest machine learning techniques to boost computer security, and gain insights into securing an organization using red and blue team ML. This recipe-based book will also be useful for data scientists and machine learning developers who want to experiment with smart techniques in the cybersecurity domain. Working knowledge of Python programming and familiarity with cybersecurity fundamentals will help you get the most out of this book.

Machine Learning for Time Series Forecasting with Python

Machine Learning for Time Series Forecasting with Python Book
Author : Francesca Lazzeri
Publisher : John Wiley & Sons
Release : 2020-12-15
ISBN : 1119682363
Language : En, Es, Fr & De

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Book Description :

Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting. Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to: Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models’ performance and accuracy Understand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.

Math for Programmers

Math for Programmers Book
Author : Paul Orland
Publisher : Manning Publications
Release : 2021-01-12
ISBN : 1617295353
Language : En, Es, Fr & De

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Book Description :

In Math for Programmers you’ll explore important mathematical concepts through hands-on coding. Filled with graphics and more than 300 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today’s hottest fields. As you tackle the basics of linear algebra, calculus, and machine learning, you’ll master the key Python libraries used to turn them into real-world software applications. Summary To score a job in data science, machine learning, computer graphics, and cryptography, you need to bring strong math skills to the party. Math for Programmers teaches the math you need for these hot careers, concentrating on what you need to know as a developer. Filled with lots of helpful graphics and more than 200 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today’s hottest programming fields. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Skip the mathematical jargon: This one-of-a-kind book uses Python to teach the math you need to build games, simulations, 3D graphics, and machine learning algorithms. Discover how algebra and calculus come alive when you see them in code! About the book In Math for Programmers you’ll explore important mathematical concepts through hands-on coding. Filled with graphics and more than 300 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today’s hottest fields. As you tackle the basics of linear algebra, calculus, and machine learning, you’ll master the key Python libraries used to turn them into real-world software applications. What's inside Vector geometry for computer graphics Matrices and linear transformations Core concepts from calculus Simulation and optimization Image and audio processing Machine learning algorithms for regression and classification About the reader For programmers with basic skills in algebra. About the author Paul Orland is a programmer, software entrepreneur, and math enthusiast. He is co-founder of Tachyus, a start-up building predictive analytics software for the energy industry. You can find him online at www.paulor.land. Table of Contents 1 Learning math with code PART I - VECTORS AND GRAPHICS 2 Drawing with 2D vectors 3 Ascending to the 3D world 4 Transforming vectors and graphics 5 Computing transformations with matrices 6 Generalizing to higher dimensions 7 Solving systems of linear equations PART 2 - CALCULUS AND PHYSICAL SIMULATION 8 Understanding rates of change 9 Simulating moving objects 10 Working with symbolic expressions 11 Simulating force fields 12 Optimizing a physical system 13 Analyzing sound waves with a Fourier series PART 3 - MACHINE LEARNING APPLICATIONS 14 Fitting functions to data 15 Classifying data with logistic regression 16 Training neural networks

Transactional Machine Learning with Data Streams and AutoML

Transactional Machine Learning with Data Streams and AutoML Book
Author : Sebastian Maurice
Publisher : Apress
Release : 2021-05-20
ISBN : 9781484270226
Language : En, Es, Fr & De

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Book Description :

Understand how to apply auto machine learning to data streams and create transactional machine learning (TML) solutions that are frictionless (require minimal to no human intervention) and elastic (machine learning solutions that can scale up or down by controlling the number of data streams, algorithms, and users of the insights). This book will strengthen your knowledge of the inner workings of TML solutions using data streams with auto machine learning integrated with Apache Kafka. Transactional Machine Learning with Data Streams and AutoML introduces the industry challenges with applying machine learning to data streams. You will learn the framework that will help you in choosing business problems that are best suited for TML. You will also see how to measure the business value of TML solutions. You will then learn the technical components of TML solutions, including the reference and technical architecture of a TML solution. This book also presents a TML solution template that will make it easy for you to quickly start building your own TML solutions. Specifically, you are given access to a TML Python library and integration technologies for download. You will also learn how TML will evolve in the future, and the growing need by organizations for deeper insights from data streams. By the end of the book, you will have a solid understanding of TML. You will know how to build TML solutions with all the necessary details, and all the resources at your fingertips. What You Will Learn Discover transactional machine learning Measure the business value of TML Choose TML use cases Design technical architecture of TML solutions with Apache Kafka Work with the technologies used to build TML solutions Build transactional machine learning solutions with hands-on code together with Apache Kafka in the cloud Who This Book Is For Data scientists, machine learning engineers and architects, and AI and machine learning business leaders.

Python Machine Learning for Beginners

Python Machine Learning for Beginners Book
Author : Ai Publishing
Publisher : Unknown
Release : 2020-10-23
ISBN : 9781734790153
Language : En, Es, Fr & De

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Book Description :

Python Machine Learning for BeginnersMachine Learning (ML) and Artificial Intelligence (AI) are here to stay. Yes, that's right. Based on a significant amount of data and evidence, it's obvious that ML and AI are here to stay.Consider any industry today. The practical applications of ML are really driving business results. Whether it's healthcare, e-commerce, government, transportation, social media sites, financial services, manufacturing, oil and gas, marketing and salesYou name it. The list goes on. There's no doubt that ML is going to play a decisive role in every domain in the future.But what does a Machine Learning professional do?A Machine Learning specialist develops intelligent algorithms that learn from data and also adapt to the data quickly. Then, these high-end algorithms make accurate predictions. Python Machine Learning for Beginners presents you with a hands-on approach to learn ML fast.How Is This Book Different?AI Publishing strongly believes in learning by doing methodology. With this in mind, we have crafted this book with care. You will find that the emphasis on the theoretical aspects of machine learning is equal to the emphasis on the practical aspects of the subject matter.You'll learn about data analysis and visualization in great detail in the first half of the book. Then, in the second half, you'll learn about machine learning and statistical models for data science.Each chapter presents you with the theoretical framework behind the different data science and machine learning techniques, and practical examples illustrate the working of these techniques.When you buy this book, your learning journey becomes so much easier. The reason is you get instant access to all the related learning material presented with this book--references, PDFs, Python codes, and exercises--on the publisher's website. All this material is available to you at no extra cost. You can download the ML datasets used in this book at runtime, or you can access them via the Resources/Datasets folder.You'll also find the short course on Python programming in the second chapter immensely useful, especially if you are new to Python. Since this book gives you access to all the Python codes and datasets, you only need access to a computer with the internet to get started. The topics covered include: Introduction and Environment Setup Python Crash Course Python NumPy Library for Data Analysis Introduction to Pandas Library for Data Analysis Data Visualization via Matplotlib, Seaborn, and Pandas Libraries Solving Regression Problems in ML Using Sklearn Library Solving Classification Problems in ML Using Sklearn Library Data Clustering with ML Using Sklearn Library Deep Learning with Python TensorFlow 2.0 Dimensionality Reduction with PCA and LDA Using Sklearn Click the BUY NOW button to start your Machine Learning journey.

Machine Learning Solutions

Machine Learning Solutions Book
Author : Jalaj Thanaki
Publisher : Packt Publishing Ltd
Release : 2018-04-27
ISBN : 1788398890
Language : En, Es, Fr & De

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Book Description :

Practical, hands-on solutions in Python to overcome any problem in Machine Learning Key Features Master the advanced concepts, methodologies, and use cases of machine learning Build ML applications for analytics, NLP and computer vision domains Solve the most common problems in building machine learning models Book Description Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job. You’ll encounter a set of simple to complex problems while building ML models, and you'll not only resolve these problems, but you’ll also learn how to build projects based on each problem, with a practical approach and easy-to-follow examples. The book includes a wide range of applications: from analytics and NLP, to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overftting datasets, hyperparameter tuning, and more. Here, you'll also learn to make more timely and accurate predictions. In addition, you'll deal with more advanced use cases, such as building a gaming bot, building an extractive summarization tool for medical documents, and you'll also tackle the problems faced while building an ML model. By the end of this book, you'll be able to fine-tune your models as per your needs to deliver maximum productivity. What you will learn Select the right algorithm to derive the best solution in ML domains Perform predictive analysis effciently using ML algorithms Predict stock prices using the stock index value Perform customer analytics for an e-commerce platform Build recommendation engines for various domains Build NLP applications for the health domain Build language generation applications using different NLP techniques Build computer vision applications such as facial emotion recognition Who this book is for This book is for the intermediate users such as machine learning engineers, data engineers, data scientists, and more, who want to solve simple to complex machine learning problems in their day-to-day work and build powerful and efficient machine learning models. A basic understanding of the machine learning concepts and some experience with Python programming is all you need to get started with this book.

Methods for Petroleum Well Optimization

Methods for Petroleum Well Optimization Book
Author : Rasool Khosravanian,Bernt S. Aadnoy
Publisher : Gulf Professional Publishing
Release : 2021-09-22
ISBN : 0323902324
Language : En, Es, Fr & De

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Book Description :

Drilling and production wells are becoming more digitalized as oil and gas companies continue to implement machine learning and big data solutions to save money on projects while reducing energy and emissions. Up to now there has not been one cohesive resource that bridges the gap between theory and application, showing how to go from computer modeling to practical use. Methods for Petroleum Well Optimization: Automation and Data Solutions gives today’s engineers and researchers real-time data solutions specific to drilling and production assets. Structured for training, this reference covers key concepts and detailed approaches from mathematical to real-time data solutions through technological advances. Topics include digital well planning and construction, moving teams into Onshore Collaboration Centers, operations with the best machine learning (ML) and metaheuristic algorithms, complex trajectories for wellbore stability, real-time predictive analytics by data mining, optimum decision-making, and case-based reasoning. Supported by practical case studies, and with references including links to open-source code and fit-for-use MATLAB, R, Julia, Python and other standard programming languages, Methods for Petroleum Well Optimization delivers a critical training guide for researchers and oil and gas engineers to take scientifically based approaches to solving real field problems. Bridges the gap between theory and practice (from models to code) with content from the latest research developments supported by practical case study examples and questions at the end of each chapter Enables understanding of real-time data solutions and automation methods available specific to drilling and production wells, such as digital well planning and construction through to automatic systems Promotes the use of open-source code which will help companies, engineers, and researchers develop their prediction and analysis software more quickly; this is especially appropriate in the application of multivariate techniques to the real-world problems of petroleum well optimization

IoT Machine Learning Applications in Telecom Energy and Agriculture

IoT Machine Learning Applications in Telecom  Energy  and Agriculture Book
Author : Puneet Mathur
Publisher : Apress
Release : 2020-05-09
ISBN : 1484255496
Language : En, Es, Fr & De

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Book Description :

Apply machine learning using the Internet of Things (IoT) in the agriculture, telecom, and energy domains with case studies. This book begins by covering how to set up the software and hardware components including the various sensors to implement the case studies in Python. The case study section starts with an examination of call drop with IoT in the telecoms industry, followed by a case study on energy audit and predictive maintenance for an industrial machine, and finally covers techniques to predict cash crop failure in agribusiness. The last section covers pitfalls to avoid while implementing machine learning and IoT in these domains. After reading this book, you will know how IoT and machine learning are used in the example domains and have practical case studies to use and extend. You will be able to create enterprise-scale applications using Raspberry Pi 3 B+ and Arduino Mega 2560 with Python. What You Will Learn Implement machine learning with IoT and solve problems in the telecom, agriculture, and energy sectors with Python Set up and use industrial-grade IoT products, such as Modbus RS485 protocol devices, in practical scenarios Develop solutions for commercial-grade IoT or IIoT projects Implement case studies in machine learning with IoT from scratch Who This Book Is For Raspberry Pi and Arduino enthusiasts and data science and machine learning professionals.

Handbook of Research on Smart Technology Models for Business and Industry

Handbook of Research on Smart Technology Models for Business and Industry Book
Author : Thomas, J. Joshua,Fiore, Ugo,Lechuga, Gilberto Perez,Kharchenko, Valeriy,Vasant, Pandian
Publisher : IGI Global
Release : 2020-06-19
ISBN : 1799836460
Language : En, Es, Fr & De

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Book Description :

Advances in machine learning techniques and ever-increasing computing power has helped create a new generation of hardware and software technologies with practical applications for nearly every industry. As the progress has, in turn, excited the interest of venture investors, technology firms, and a growing number of clients, implementing intelligent automation in both physical and information systems has become a must in business. Handbook of Research on Smart Technology Models for Business and Industry is an essential reference source that discusses relevant abstract frameworks and the latest experimental research findings in theory, mathematical models, software applications, and prototypes in the area of smart technologies. Featuring research on topics such as digital security, renewable energy, and intelligence management, this book is ideally designed for machine learning specialists, industrial experts, data scientists, researchers, academicians, students, and business professionals seeking coverage on current smart technology models.

Numerical Python

Numerical Python Book
Author : Robert Johansson
Publisher : Apress
Release : 2018-12-24
ISBN : 1484242467
Language : En, Es, Fr & De

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Book Description :

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more. Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis. After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. What You'll Learn Work with vectors and matrices using NumPy Plot and visualize data with Matplotlib Perform data analysis tasks with Pandas and SciPy Review statistical modeling and machine learning with statsmodels and scikit-learn Optimize Python code using Numba and Cython Who This Book Is For Developers who want to understand how to use Python and its related ecosystem for numerical computing.

Mastering Python for Finance

Mastering Python for Finance Book
Author : James Ma Weiming
Publisher : Packt Publishing Ltd
Release : 2019-04-30
ISBN : 1789345278
Language : En, Es, Fr & De

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Book Description :

This book enables you to develop financial applications by harnessing Python’s strengths in data visualization, interactive analytics, and scientific computing. You will be using popular libraries such as TensorFlow, Keras, sklearn, and so on to extend the functionalities of your financial applications by using smart machine learning techniques.

Python Machine Learning for Beginners

Python Machine Learning for Beginners Book
Author : AI OU
Publisher : Unknown
Release : 2021
ISBN : 9781801814805
Language : En, Es, Fr & De

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Book Description :

This course lays the foundations for both a theoretical and practical understanding of machine learning and artificial intelligence, utilizing Python as a beginner-friendly introduction and invitation to further study Key Features A crash course in Python programming Interactive, guided practice through a series of machine learning exercises Instant access to PDFs, Python codes, and exercises from the publisher's website at no extra cost Book Description Machine Learning (ML) and Artificial Intelligence (AI) are here to stay. Yes, that's right. Based on a significant amount of data and evidence, it's obvious that ML and AI are here to stay. Consider any industry today. The practical applications of ML are really driving business results. Whether it's healthcare, e-commerce, government, transportation, social media sites, financial services, manufacturing, oil and gas, marketing, and sales. You name it. The list goes on. There's no doubt that ML is going to play a decisive role in every domain in the future. But what does a machine learning professional do? A machine learning specialist develops intelligent algorithms that learn from data and also adapt to the data quickly. Then, these high-end algorithms make accurate predictions. Python Machine Learning for Beginners presents you with a hands-on approach to learn ML fast. You'll learn about data analysis and visualization in great detail in the first half of the book. Then, in the second half, you'll learn about machine learning and statistical models for data science. Each chapter presents you with the theoretical framework behind the different data science and machine learning techniques, and practical examples illustrate the working of these techniques. By the end of this course, you will have a firm grasp on the theoretical foundations of machine learning and artificial intelligence as well as having explored and practiced various real-world applications through Python. The code bundle for this course is available at https://www.aispublishing.net/nlp-crash-course1603576259757 What you will learn Get up to speed with Python programming Explore Python NumPy and Pandas libraries for data analysis Practice data visualization via Matplotlib, Seaborn, and Pandas libraries Solve regression problems in ML using Sklearn library Solve classification problems in ML using Sklearn library Study data clustering with ML using Sklearn library Cover deep learning with Python TensorFlow 2.0 Perform dimensi...

Handbook of Multiphase Flow Assurance

Handbook of Multiphase Flow Assurance Book
Author : Taras Y. Makogon
Publisher : Gulf Professional Publishing
Release : 2019-06-04
ISBN : 0128130636
Language : En, Es, Fr & De

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Book Description :

Handbook of Multiphase Flow Assurance allows readers to progress in their understanding of basic phenomena and complex operating challenges. The book starts with the fundamentals, but then goes on to discuss phase behavior, fluid sampling, fluid flow properties and fluid characterization. It also covers flow assurance impedance, deliverability, stability and integrity issues, as well as hydraulic, thermal and risk analysis. The inclusion of case studies and references helps provide an industrial focus and practical application that makes the book a novel resource for flow assurance management and an introductory reference for engineers just entering the field of flow assurance. Starts with flow assurance fundamentals, but also includes more complex operating challenges Brings together cross-disciplinary discussions and solutions of flow assurance in a single text Offers case studies and reference guidelines for practical applications

Data Analytics for Drilling Engineering

Data Analytics for Drilling Engineering Book
Author : Qilong Xue
Publisher : Springer Nature
Release : 2019-12-30
ISBN : 303034035X
Language : En, Es, Fr & De

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Book Description :

This book presents the signal processing and data mining challenges encountered in drilling engineering, and describes the methods used to overcome them. In drilling engineering, many signal processing technologies are required to solve practical problems, such as downhole information transmission, spatial attitude of drillstring, drillstring dynamics, seismic activity while drilling, among others. This title attempts to bridge the gap between the signal processing and data mining and oil and gas drilling engineering communities. There is an urgent need to summarize signal processing and data mining issues in drilling engineering so that practitioners in these fields can understand each other in order to enhance oil and gas drilling functions. In summary, this book shows the importance of signal processing and data mining to researchers and professional drilling engineers and open up a new area of application for signal processing and data mining scientists.