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.
Название: Machine Learning Applications Using Python ISBN: 1484237862 ISBN-13(EAN): 9781484237861 Издательство: Springer Рейтинг: Цена: 69870.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Part 1: HealthcareChapter 1. Overview of machine learning in healthcare.Chapter 2. Key technological advancements in healthcare.Chapter 3. How to implement machine learning in healthcare.Chapter 4. Case studies on how organizations are changing the game in the market.Chapter 5. Pitfalls to avoid while implementing machine learning in healthcare.Chapter 6. Healthcare specific innovative Ideas for monetizing machine learning. Part 2: Retail Chapter 7. Overview of machine learning in Retail.Chapter 8. Key technological advancements in Retail.Chapter 9. How to implement machine learning in Retail.Chapter 10. Case studies on how organizations are changing the game in the market. c. One discussion based case study. d. One practical case study with Python code.Chapter 11. Pitfalls to avoid while implementing machine learning in retail.Chapter 12. Retail specific innovative Ideas for monetizing machine learning. Part 3: Finance Chapter 13. Overview of machine learning in Finance.Chapter 14. Key technological advancements in Finance.Chapter 15. How to implement machine learning in Finance.Chapter 16. Case studies on how organizations are changing the game in the market. e. One discussion based case study. f. One practical case study with Python code.Chapter 17. Pitfalls to avoid while implementing machine learning in Finance.Chapter 18. Finance specific innovative Ideas for monetizing machine learning.
Do you Want to learn more about Python Machine Learning ?.... then read on.
Machine learning stems from this question: Can a computer go beyond anything we can order to do and learn by itself to do a specific task? Can a laptop surprise us? Instead of having programmers carefully and manually writing a set of data processing rules, can a computer automatically learn these rules by merely looking at the data?
This question paves the way for a new programming paradigm. In classical programming, on which symbolic artificial intelligence is based, human beings insert rules (the program) and the data to be processed according to these rules and obtain answers. Humans enter data and expected responses based on that data with machine learning, and the computer identifies the practices. These rules can then be applied to other data to produce different, original answers.
A machine learning system is trained and not programmed. He is presented with numerous examples relevant to a given task. In these examples, he finds a statistical structure that ultimately allows him to produce the rules for the task's automation. For example, to automate tagging vacation photographs, many examples of images already tagged by humans could be presented to a machine learning system. The system would be tasked with learning the statistical rules based on associating individual images with specific tags.
Machine learning is closely related to statistics, but it differs from them in many important ways. Unlike statistics, machine learning tends to operate with large and complex datasets (such as a dataset of millions of images, each consisting of tens of thousands of pixels) for which classical statistical analysis such as Bayesian analysis would not be usable. . As a result, machine learning, and especially deep learning, exhibits somewhat limited mathematical theory - sometimes too much - and is more technical than mathematical. It is a practical discipline in which ideas often prove more empirically than theoretical.
In this Book you will learning:
What is Data Science and Deep Learning?
Data Science and Applications
Probability - Fundamental - Statistics
Understanding the Fundamentals of iMachine Learning
Types of MachineiLearning
What is iPython? SettingiUp the Environment in Python
K - Nearest Neighbor Algorithms
Means Clustering
Neural Networks - Linear Classifiers
While most books focus on advanced predictive models, this book begins to explain the basic concepts and how to correctly implement Data Science and Machine Learning, with practical examples and simple coding scripts.
This guide provides the necessary knowledge in a practical way. You will learn the steps of Machine Learning, how to implement them in Python, and the most important applications in the real world.
Would you like to know more?
Download the Book, Python Machine Learning.
Scroll to the top of the page and click the "Buy now" button to get your copy now.
Take your Python machine learning ideas and create serverless web applications accessible by anyone with an Internet connection. Some of the most popular serverless cloud providers are covered in this book--Amazon, Microsoft, Google, and PythonAnywhere.
You will work through a series of common Python data science problems in an increasing order of complexity. The practical projects presented in this book are simple, clear, and can be used as templates to jump-start many other types of projects. You will learn to create a web application around numerical or categorical predictions, understand the analysis of text, create powerful and interactive presentations, serve restricted access to data, and leverage web plugins to accept credit card payments and donations. You will get your projects into the hands of the world in no time.
Each chapter follows three steps: modeling the right way, designing and developing a local web application, and deploying onto a popular and reliable serverless cloud provider. You can easily jump to or skip particular topics in the book. You also will have access to Jupyter notebooks and code repositories for complete versions of the code covered in the book.
What You'll Learn
Extend your machine learning models using simple techniques to create compelling and interactive web dashboards
Leverage the Flask web framework for rapid prototyping of your Python models and ideas
Create dynamic content powered by regression coefficients, logistic regressions, gradient boosting machines, Bayesian classifications, and more
Harness the power of TensorFlow by exporting saved models into web applications
Create rich web dashboards to handle complex real-time user input with JavaScript and Ajax to yield interactive and tailored content
Create dashboards with paywalls to offer subscription-based access
Access API data such as Google Maps, OpenWeather, etc.
Apply different approaches to make sense of text data and return customized intelligence
Build an intuitive and useful recommendation site to add value to users and entice them to keep coming back
Utilize the freemium offerings of Google Analytics and analyze the results
Take your ideas all the way to your customer's plate using the top serverless cloud providers
Who This Book Is For
Those with some programming experience with Python, code editing, and access to an interpreter in working order. The book is geared toward entrepreneurs who want to get their ideas onto the web without breaking the bank, small companies without an IT staff, students wanting exposure and training, and for all data science professionals ready to take things to the next level.
Do you Want to learn more about Python Machine Learning ?.... then read on.
Machine learning stems from this question: Can a computer go beyond anything we can order to do and learn by itself to do a specific task? Can a laptop surprise us? Instead of having programmers carefully and manually writing a set of data processing rules, can a computer automatically learn these rules by merely looking at the data?
This question paves the way for a new programming paradigm. In classical programming, on which symbolic artificial intelligence is based, human beings insert rules (the program) and the data to be processed according to these rules and obtain answers. Humans enter data and expected responses based on that data with machine learning, and the computer identifies the practices. These rules can then be applied to other data to produce different, original answers.
A machine learning system is trained and not programmed. He is presented with numerous examples relevant to a given task. In these examples, he finds a statistical structure that ultimately allows him to produce the rules for the task's automation. For example, to automate tagging vacation photographs, many examples of images already tagged by humans could be presented to a machine learning system. The system would be tasked with learning the statistical rules based on associating individual images with specific tags.
Machine learning is closely related to statistics, but it differs from them in many important ways. Unlike statistics, machine learning tends to operate with large and complex datasets (such as a dataset of millions of images, each consisting of tens of thousands of pixels) for which classical statistical analysis such as Bayesian analysis would not be usable. . As a result, machine learning, and especially deep learning, exhibits somewhat limited mathematical theory - sometimes too much - and is more technical than mathematical. It is a practical discipline in which ideas often prove more empirically than theoretical.
In this Book you will learning:
What is Data Science and Deep Learning?
Data Science and Applications
Probability - Fundamental - Statistics
Understanding the Fundamentals of iMachine Learning
Types of MachineiLearning
What is iPython? SettingiUp the Environment in Python
K - Nearest Neighbor Algorithms
Means Clustering
Neural Networks - Linear Classifiers
While most books focus on advanced predictive models, this book begins to explain the basic concepts and how to correctly implement Data Science and Machine Learning, with practical examples and simple coding scripts.
This guide provides the necessary knowledge in a practical way. You will learn the steps of Machine Learning, how to implement them in Python, and the most important applications in the real world.
Would you like to know more?
Download the Book, Python Machine Learning.
Scroll to the top of the page and click the "Buy now" button to get your copy now.
Автор: Kulkarni, Shrirang Ambaji , Gurupur, Varadrah P. Название: Introduction to IoT with Machine Learning and Image Processing using Raspberry Pi ISBN: 1138543527 ISBN-13(EAN): 9781138543522 Издательство: Taylor&Francis Рейтинг: Цена: 163330.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book introduces Raspberry Pi, using real world applications in computer vision, machine learning, and deep learning. It provides a detailed, step-by-step, approach to application development for users without any prior programming knowledge.
Автор: Kar Krishnendu Название: Mastering Computer Vision with TensorFlow 2.x: Build advanced computer vision applications using machine learning and deep learning techniques ISBN: 1838827064 ISBN-13(EAN): 9781838827069 Издательство: Неизвестно Рейтинг: Цена: 60070.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: You will learn the principles of computer vision and deep learning, and understand various models and architectures with their pros and cons. You will learn how to use TensorFlow 2.x to build your own neural network model and apply it to various computer vision tasks such as image acquiring, processing, and analyzing.
Do you want to start programming with python, but you're worried it will be too difficult to understand? Looking to learn your first coding language, or add another one to your list? Then this book is for you.
Python is an incredible programming language, beloved by developers and programmers the world over. Now, you can join in and start programming in no time Designed for the beginner, this book dives into the fundamental behind Python in an easy-to-follow way, giving you all the tools you need to start coding.
Covering everything from the basics of Python code to NumPy, Scikit-Learn and the libraries that work with Python, you'll discover:
How to Install, Run, and Understand Python on Any Operating System
A Comprehensive Introduction to Python
Python Basics and Writing Code
Python as an OOP Language
Writing Loops, Conditional Statements, Exceptions and More
Python Expressions and The Beauty of Inheritances
The Libraries that Work With Python - NumPy, Scikit-Learn, Matpotlib and SciPy
And Much More
Even if you've never worked with code before, Python is the best language for the beginner - and this book is your ticket to understanding it. Now you can start programming with ease, and understand why Python is such a powerful language
So what are you waiting for? Buy now to begin your journey with Python Programming today
Get to grips with essential concepts and step-by-step explanations to apply TDD practices to your Python projects while keeping your test suite under control
Key Description:
Build robust Python applications using TDD and BDD methodologies
Test Python web applications using WebTest and web frameworks
Leverage PyTest to implement stringent testing mechanisms to ensure fault-tolerant applications
Book Description:
Test-driven development (TDD) is a set of best practices that helps developers to build more scalable software and is used to increase the robustness of software by using automatic tests. This book shows you how to apply TDD practices effectively in Python projects.
You'll begin by learning about built-in unit tests and Mocks before covering rich frameworks like PyTest and web-based libraries such as WebTest and Robot Framework, discovering how Python allows you to embrace all modern testing practices with ease. Moving on, you'll find out how to design tests and balance them with new feature development and learn how to create a complete test suite with PyTest. The book helps you adopt a hands-on approach to implementing TDD and associated methodologies that will have you up and running and make you more productive in no time. With the help of step-by-step explanations of essential concepts and practical examples, you'll explore automatic tests and TDD best practices and get to grips with the methodologies and tools available in Python for creating effective and robust applications.
By the end of this Python book, you will be able to write reliable test suites in Python to ensure the long-term resilience of your application using the range of libraries offered by Python for testing and development.
What You Will Learn:
Find out how tests can make your life easier as a developer and discover related best practices
Explore PyTest, the most widespread testing framework for Python
Get to grips with the most common PyTest plugins, including coverage, flaky, xdist, and picked
Write functional tests for WSGI web applications with WebTest
Run end-to-end tests for web applications using Robot Framework
Understand what test-driven development means and why it is important
Discover how to use the range of tools available in Python
Build reliable and robust applications
Who this book is for:
This book is for Python developers looking to get started with test-driven development and developers who want to learn about the testing tools available in Python. Developers who want to create web applications with Python and plan to implement TDD methodology with PyTest will find this book useful. Basic knowledge of Python programming is required.
Discover how you can become a Python pro in no time with this comprehensive beginner's guide
Do you want to start programming with python, but you're worried it will be too difficult to understand? Looking to learn your first coding language, or add another one to your list? Then this book is for you.
Python is an incredible programming language, beloved by developers and programmers the world over. Now, you can join in and start programming in no time Designed for the beginner, this book dives into the fundamental behind Python in an easy-to-follow way, giving you all the tools you need to start coding.
Covering everything from the basics of Python code to NumPy, Scikit-Learn and the libraries that work with Python, you'll discover:
How to Install, Run, and Understand Python on Any Operating System
A Comprehensive Introduction to Python
Python Basics and Writing Code
Python as an OOP Language
Writing Loops, Conditional Statements, Exceptions and More
Python Expressions and The Beauty of Inheritances
The Libraries that Work With Python - NumPy, Scikit-Learn, Matpotlib and SciPy
And Much More
Even if you've never worked with code before, Python is the best language for the beginner - and this book is your ticket to understanding it. Now you can start programming with ease, and understand why Python is such a powerful language
So what are you waiting for? Buy now to begin your journey with Python Programming today
Scroll Up and Click the Buy now Button
Автор: Beningo Jacob Название: MicroPython Projects: A do-it-yourself guide for embedded developers to build a range of applications using Python ISBN: 1789958032 ISBN-13(EAN): 9781789958034 Издательство: Неизвестно Рейтинг: Цена: 55170.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: MicroPython Projects is a project-based guide that provides you with a wide range of projects along the lines of electronic applications, Android Applications, GPS, automation devices, and so on. With this pragmatic approach, you will be confident enough to design complex projects on MicroPython spanning altogether new areas of the technology.
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