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.
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.
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.
Описание: This book will get you to grips with the Spark Python API. You`ll explore how Python can be used with Spark to build scalable and reliable data-intensive applications.
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.
Описание: Is it a great idea to try to use JavaScript for computer programming?Also, would you like to control switch, LED, and so on by simply programming them with a single board, even without changing the board itself when something goes wrong?
Arduino is a fascinating platform used to build electronic projects. It is preferred by a lot of experts just starting out electronic projects.The Arduino board is processed to use simple chips called Microcontrollers.
While JavaScript is among the widely computer programming languages in the world. JavaScript is used by various software companies in the world, so much that many computer operations will not work without it.
Both JavaScript and Arduino are pretty simple to use.
The most exciting feature of JavaScript is that it allows you to program using English-based commands, not numeric codes and that makes it a whole lot easier for humans to program. This means programmers can read and easily understand the computer language.
Coding with an Arduino program can make it pretty easy to control your electronics because you don't have to change the whole board when something goes wrong, each faulty microchip can be easily replaced.
However there are vital skills you need in order to navigate JavaScript and Arduino as an expert. If you are a beginner who hasn't even run the JavaScript or Arduino application by accident or you have some raw idea, the book attached below can help you explore easily.
"Programming For Beginners Volume 1: 2 Books in 1: Arduino Programming for Beginners and Javascript for Beginners" by Matthew Python.
Here's what you'll learn:
-What is Arduino?
-What is JavaScript? (JavaScript in XHTML documents, in browsers, etc.)
-Why JavaScript? (Types of JavaScript execution, how to create one, Python Vs Java and so on)
-Explanation of Arduino Components
-Getting started with Arduino
-Basic and advanced Programming Structure for JavaScript
Programming language has rendered itself as the language of choice for coding beginners and advanced software programmers alike.
This book is written to help you master the basic concepts of Python, SQL and Arduino coding and how you can utilize your coding skills to analyze a large volume of data and uncover valuable information that can otherwise be easily lost in the volume. Python was designed primarily to emphasize readability of the programming code, and its syntax enables programmers to convey ideas using fewer lines of code. The Structured Query Language (SQL) is the language of choice define, manipulate, control and query the data within a RDBMS. Starting Arduino Programming allows you to rapidly and intuitively develop your programming abilities through sketching in code. This book provides you with an understanding of the standard structure for developing Arduino code, including the functions, syntax, structure, and libraries needed to produce future tasks.
This book will provide you overarching guidance on how you can use Python to develop machine learning models using Scikit-Learn, most widely used Python-based machine learning library. Python programming, data analysis, and machine learning are widely credited in the birth of the powerhouse, which is the "Silicon Valley." To become a machine learning expert, a sound and in-depth understanding of every nuance of this area is critical. On the other hand, you can efficiently and effectively learn and write SQL statements or queries to retrieve from and update data on relational databases such as MySQL. You will be able to install the free and open MySQL user interface with the instructions provided in this book. This book will help you comprehend the basic principles of Arduino, its advantages, benefits and applications in numerous markets and platforms.
Some of the highlights of the book include:
●Step by step instructions on how to install Python on your operating systems (Windows, Mac, and Linux).
●Concept of Python data types is presented in exquisite detail with various examples of each data type.
●Learn how to create Python variables and assign desired data type to them.
●A variety of SQL data types that are a pre-requisite for learning SQL have been explained in an easy to understand language.
●Learn how to create an effective database on the MySQL server as well as to create data tables and insert data into those tables.
●Learn CREATE VIEW, MERGE, TEMPTABLE, UNDEFINED, Updatable SQL Views, ALTER VIEW and CREATE OR REPLACE VIEW statements.
●What is Arduino Programming.
●Arduino - Program Structure
●Arduino - String Item
Finally, as an added bonus you will learn some Python, SQL and Arduino tips and tricks to take your machine learning programming game to the next level. Don't you think it can be that easy? If you really want to have proof of all this, don't waste any more time
Get the book today and start right now .
Автор: Arduino Matthew Название: Python for Data Science ISBN: 1801257574 ISBN-13(EAN): 9781801257572 Издательство: Неизвестно Рейтинг: Цена: 3304.00 р. Наличие на складе: Нет в наличии.
Описание:
The truth is: Python is one of the best languages, suited for programming, especially when it comes to a data scientist.
You must be knowing that Python is one of the most common programming languages. But, why should you learn this language? Also, why should you learn this language, if you want to pursue a career in data science? Read On to know more.
Python is a very flexible language, besides being powerful and free. It is also an open-source language, besides being simple and coming with easy to read syntaxes. With the help of python, data manipulation, data visualization, as well as, data analysis becomes very simple. Python also has all the libraries, that are vital for every application of machine learning and scientific processing of data. Though Python is a high-level language, it is also quite easy to learn and is procedure-oriented besides being object-oriented.
With data science gaining more popularity, the programmers need to have deep knowledge in Python, one of the languages required in data science. Python is widely used by data scientists because of the presence of a large number of libraries. It can help in performing multiple tasks like web development, database, data mining, image processing, graphical user interface and a lot more. As Python can combine improvement, along with the ability to interface with algorithms of high performance, it is a very popular language among the data scientists.
DOWNLOAD: Python for Data Science: how to learn basic contents to work with data with this programming language with this beginner's guide. Machine learning tools, concepts, and data analysis crash course.
Data Science is all about ETL or extraction-transformation-loading process, which makes Python, very much suited for the purpose. Python has shown a sharp rise, as high as 51%, in its popularity, as a top data science tool. This is because Python can integrate very well with most cloud and platform-as-a-service providers. It also supports multiprocessing, which is helpful for parallel computing. The language ensures large-scale performance in both machine learning and data science. Most importantly, it can be extended with the modules written in other languages like C and C++.
The goal of the eBook is simple: The eBook is a comprehensive resource to help us understand the importance of Python for data science.
You will also learn:
- Fundamentals of Python for Data Science
- History of python; how to install python; What is Data Science; history of Data Science; data science and artificial intelligence; data science tips and tricks.
- Basic fundamentals of Python
- The Python keywords; How to name an identifier; the Python statements; the Comments; bringing in the Python Variables; the Operators; the Python Functions; The Python Classes; Control Flow.
Описание: This practical book provides data scientists and developers with blueprints for best practice solutions to common tasks in text analytics and natural language processing. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler provide real-world case studies and detailed code examples in Python to help you get started quickly.
Название: Machine Learning Applications Using Python ISBN: 1484237862 ISBN-13(EAN): 9781484237861 Издательство: Springer Рейтинг: Цена: 10480.00 р. 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.
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.
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