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nexusstc/Introduction to Machine Learning with Python: A Guide for Data Scientists/a96753d10dced45dc8f956244c9212a9.epub

Introduction to Machine Learning with Python : A Guide for Data Scientists Andreas C. Müller, Sarah Guido O'Reilly Media; O'Reilly Media, Inc., 1, FR, 2016

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 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

English [en] · EPUB · 30.4MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/Andreas C. Müller & Sarah Guido - Introduction to Machine Learning with Python: A Guide for Data Scientists (2016, O'Reilly Media).mobi

Introduction to Machine Learning with Python : A Guide for Data Scientists Andreas C. Mueller, Sarah Guido O'Reilly Media; O'Reilly Media, Inc., First edition, Sebastopol, CA, 2016

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

English [en] · MOBI · 7.4MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

ia/introductiontoma0000mull.pdf

Introduction to Machine Learning with Python : A Guide for Data Scientists Andreas C. Müller; Sarah Guido O'Reilly Media; O'Reilly Media, Inc., O'Reilly Media, Sebastopol, CA, 2017

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

English [en] · PDF · 19.6MB · 2017 · 📗 Book (unknown) · 🚀/ia · Save

upload/bibliotik/I/Introduction to Machine Learning with Python - Andreas C. Muller, Sarah Guido.mobi

Introduction to Machine Learning with Python : A Guide for Data Scientists Andreas C. Müller and Sarah Guido O'Reilly Media; O'Reilly Media, Inc., 1, FR, 2016

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 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

English [en] · MOBI · 80.0MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

upload/trantor/en/Guido, Sarah/Introduction to Machine Learning with Python.epub

Introduction to Machine Learning with Python : A Guide for Data Scientists Guido, Sarah & Müller, Andreas C. O'Reilly Media; O'Reilly Media, Inc., 2015

Machine learning has become an integral part of many commercial applicationsand research projects, but this field is not exclusive to large companies withextensive research teams. If you use Python, even as a beginner, this bookwill teach you practical ways to build your own machine learning solutions.With all the data available today, machine learning applications are limitedonly by your imagination.You'll learn the steps necessary to create a successful machine-learningapplication with Python and the scikit-learn library. Authors Andreas Mullerand Sarah Guido focus on the practical aspects of using machine learningalgorithms, rather than the math behind them. Familiarity with the NumPy andmatplotlib 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 dataaspects 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 processingtechniques Suggestions for improving your machine learning and data science skillswords : 104747

English [en] · EPUB · 29.0MB · 2015 · 📗 Book (unknown) · 🚀/upload/zlib · Save

nexusstc/Machine Learning Guide for Oil and Gas Using Python/84bb0bcf926cd79dfa24aaffa8e553c8.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

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.<ul> <li>Helps readers understand how open-source Python can be utilized in practical oil and gas challenges </li> <li>Covers the most commonly used algorithms for both supervised and unsupervised learning</li> <li>Presents a balanced approach of both theory and practicality while progressing from introductory to advanced analytical techniques </li></ul>

English [en] · PDF · 12.4MB · 2021 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/r:\!fiction\0day\1\Machine Learning for OpenCV 4, 2nd ed. - Beyeler,Sharma,Shrimali (Packt Publishing;2019;9781789536300;eng).epub

Machine Learning for OpenCV 4 : Intelligent Algorithms for Building Image Processing Apps Using OpenCV 4, Python, and Scikit-learn, 2nd Edition Beyeler, Michael;Sharma, Aditya;Vishwesh Ravi Shrimali Packt Publishing, Limited; Packt Publishing, 2nd ed, Place of publication not identified, 2019

A practical guide to understanding the core machine learning and deep learning algorithms, and implementing them to create intelligent image processing systems using OpenCV 4 Key Features Gain insights into machine learning algorithms, and implement them using OpenCV 4 and scikit-learn Get up to speed with Intel OpenVINO and its integration with OpenCV 4 Implement high-performance machine learning models with helpful tips and best practices Book Description OpenCV is an opensource library for building computer vision apps. The latest release, OpenCV 4, offers a plethora of features and platform improvements that are covered comprehensively in this up-to-date second edition. You'll start by understanding the new features and setting up OpenCV 4 to build your computer vision applications. You will explore the fundamentals of machine learning and even learn to design different algorithms that can be used for image processing. Gradually, the book will take you through supervised and unsupervised machine learning. You will gain hands-on experience using scikit-learn in Python for a variety of machine learning applications. Later chapters will focus on different machine learning algorithms, such as a decision tree, support vector machines (SVM), and Bayesian learning, and how they can be used for object detection computer vision operations. You will then delve into deep learning and ensemble learning, and discover their real-world applications, such as handwritten digit classification and gesture recognition. Finally, you'll get to grips with the latest Intel OpenVINO for building an image processing system. By the end of this book, you will have developed the skills you need to use machine learning for building intelligent computer vision applications with OpenCV 4. What you will learn Understand the core machine learning concepts for image processing Explore the theory behind machine learning and deep learning algorithm design Discover effective techniques to train your deep learning models Evaluate machine learning models to improve the performance of your models Integrate algorithms such as support vector machines and Bayes classifier in your computer vision applications Use OpenVINO with OpenCV 4 to speed up model inference Who this book is for This book is for Computer Vision professionals, machine learning developers, or anyone who wants to learn machine learning algorithms and implement them using OpenCV 4. If you want to build real-world Co...

English [en] · EPUB · 13.3MB · 2019 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/Andreas C. Müller and Sarah Guido - Introduction to Machine Learning with Python (2016, ).pdf

Introduction to Machine Learning with Python : A Guide for Data Scientists Andreas C. Müller and Sarah Guido O'Reilly Media; O'Reilly Media, Inc., 1, FR, 2016

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 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

English [en] · PDF · 33.2MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

lgli/F:\twirpx\_19\_9\1974709\1mueller_andreas_c_guido_sarah_introduction_to_machine_learni.mobi

Introduction to Machine Learning with Python : A Guide for Data Scientists Mueller Andreas C., Guido Sarah. O'Reilly Media; O'Reilly Media, Inc., Early Release, 2016

Early Release - Raw & Unedited. — O'Really Media, 2016 (September, 25). — 340 p. — ISBN: 1449369413, 978-1-491-91721-3. Many Python developers are curious about what machine learning is and how it can be concretely applied to solve issues faced in businesses handling medium to large amount of data. Machine Learning with Python teaches you the basics of machine learning and provides a thorough hands-on understanding of the subject. You’ll learn important machine learning concepts and algorithms, when to use them, and how to use them. The book will cover a machine learning workflow: data preprocessing and working with data, training algorithms, evaluating results, and implementing those algorithms into a production-level system. Table of Contents: Introduction. Supervised Learning. Unsupervised Learning and Preprocessing. Summary of scikit-learn methods and usage. Representing Data and Engineering Features. Model evaluation and improvement. Algorithm Chains and Pipelines. Working with Text Data.

English [en] · MOBI · 90.2MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/I:\it-books_dl\3289\Introduction to Machine Learning with Python.pdf

Introduction to Machine Learning with Python : A Guide for Data Scientists Andreas C. Mueller, Sarah Guido O'Reilly Media; O'Reilly Media, Inc., 1, FR, 2016

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 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

English [en] · PDF · 25.8MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/Müller, Andreas C. ; Guido, Sarah - Introduction to Machine Learning with Python: A Guide for Data Scientists (2016, O'Reilly Media).mobi

Introduction to Machine Learning with Python : A Guide for Data Scientists Müller, Andreas C. ; Guido, Sarah O'Reilly Media; O'Reilly Media, Inc., First edition, Sebastopol, CA, 2016

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

English [en] · MOBI · 5.9MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

nexusstc/Introduction to Machine Learning with Python A Guide for Data Scientists/fc4c80688d9325bd027452edbd314aa3.pdf

Introduction to Machine Learning with Python : A Guide for Data Scientists Andreas C. Mueller, Sarah Guido O'Reilly Media; O'Reilly Media, Inc., First edition, Sebastopol, CA, 2016

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 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

English [en] · PDF · 33.2MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

upload/bibliotik/I/Introduction to Machine Learning with Python - Andreas C. Muller.epub

Introduction to Machine Learning with Python : A Guide for Data Scientists Guido, Sarah;Müller, Andreas Christian O'Reilly Media; O'Reilly Media, Inc., First edition, fourth release, 2018;2016

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 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

English [en] · EPUB · 30.4MB · 2017 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

nexusstc/Introduction to Machine Learning with Python: A Guide for Data Scientists/a179856ae785ce182520bbfe7ad85b03.pdf

Introduction to Machine Learning with Python : A Guide for Data Scientists Andreas C. Müller, Sarah Guido O'Reilly Media; O'Reilly Media, Inc., Early release, 1, 2016

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 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

English [en] · PDF · 9.2MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/Andreas C. Müller & Sarah Guido - Introduction to Machine Learning with Python: A Guide for Data Scientists (2016, O'Reilly Media).azw3

Introduction to Machine Learning with Python : A Guide for Data Scientists Andreas C. Mueller, Sarah Guido O'Reilly Media; O'Reilly Media, Inc., First edition, Sebastopol, CA, 2016

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

English [en] · AZW3 · 14.8MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

nexusstc/Introduction to Machine Learning with Python: A Guide for Data Scientists/76907dd28aac646353c5b57dd3867c72.mobi

Introduction to Machine Learning with Python : A Guide for Data Scientists Andreas C. Müller, Sarah Guido O'Reilly Media; O'Reilly Media, Inc., 1, FR, 2016

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 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

English [en] · MOBI · 81.5MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/Andreas C. Müller; Sarah Guido - Introduction to Machine Learning with Python: A Guide for Data Scientists (2016, O'Reilly Media).pdf

Introduction to Machine Learning with Python : A Guide for Data Scientists Andreas C. Mueller, Sarah Guido O'Reilly Media; O'Reilly Media, Inc., 1, FR, 2016

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 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

English [en] · PDF · 6.1MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

lgli/Andreas C. Müller & Sarah Guido - Introduction to Machine Learning with Python: A Guide for Data Scientists (2016, O'Reilly Media).azw3

Introduction to Machine Learning with Python : A Guide for Data Scientists Andreas C. Mueller, Sarah Guido O'Reilly Media; O'Reilly Media, Inc., First edition, Sebastopol, CA, 2016

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

English [en] · AZW3 · 7.1MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

lgli/F:\!upload\_books\Introduction to Machine Learning with Python.epub

Introduction to Machine Learning with Python : A Guide for Data Scientists Sarah Guido, Andreas C. Müller O'Reilly Media; O'Reilly Media, Inc., 2018

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 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

English [en] · EPUB · 29.0MB · 2018 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/Andreas C. Müller; Sarah Guido - Introduction to Machine Learning with Python: A Guide for Data Scientists (2016, O'Reilly Media).epub

Introduction to Machine Learning with Python : A Guide for Data Scientists Andreas C. Müller; Sarah Guido O'Reilly Media; O'Reilly Media, Inc., 1, FR, 2016

Introduction to Machine Learning with Python: A Guide for Data Scientists ntroduction to Machine Learning with Python: A Guide for Data ScientistsAndreas C. Müller, Sarah Guido5.0 / 5.0 2 comments 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 skillsCategories:Computers - CyberneticsYear:2016Edition:1Publisher:O’Reilly MediaLanguage:EnglishPages:392ISBN 10:1449369413ISBN 13:9781449369415File:PDF, 31.62 MB

English [en] · EPUB · 3.3MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

nexusstc/Introduction to Machine Learning with Python: A Guide for Data Scientists/e1d80b00f428069531eb54c30a5c6319.pdf

Introduction to Machine Learning with Python : A Guide for Data Scientists Andreas C. Mueller, Sarah Guido O'Reilly Media; O'Reilly Media, Inc., early access, 2016

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 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

English [en] · PDF · 25.6MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

upload/newsarch_ebooks/2019/07/04/1449369413.pdf

Introduction to Machine Learning with Python : A Guide for Data Scientists Andreas C. Müller, Sarah Guido O'Reilly Media; O'Reilly Media, Inc., 1, FR, 2016

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 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

English [en] · PDF · 33.2MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

upload/bibliotik/I/Introduction to Machine Learnin - Andreas C. Muller.azw3

Introduction to Machine Learning with Python : A Guide for Data Scientists Müller, Andreas C.;Guido, Sarah O'Reilly Media; O'Reilly Media, Inc., 1, FR, 2016

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 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

English [en] · AZW3 · 14.3MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

upload/bibliotik/I/Introduction to Machine Learning with Python - Andreas C. Muller.pdf

Introduction to Machine Learning with Python : A Guide for Data Scientists Guido, Sarah;Müller, Andreas Christian O'Reilly Media; O'Reilly Media, Inc., First edition, fourth release, 2018

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 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

English [en] · PDF · 32.1MB · 2018 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

nexusstc/Machine Learning Guide for Oil and Gas Using Python/a38e4691df05f574cbe1ecea5f443023.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

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.<ul> <li>Helps readers understand how open-source Python can be utilized in practical oil and gas challenges </li> <li>Covers the most commonly used algorithms for both supervised and unsupervised learning</li> <li>Presents a balanced approach of both theory and practicality while progressing from introductory to advanced analytical techniques </li></ul>

English [en] · PDF · 47.7MB · 2021 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

nexusstc/Machine Learning Guide for Oil and Gas Using Python/798063aa53b74634616349fabbd79749.epub

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

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.<ul> <li>Helps readers understand how open-source Python can be utilized in practical oil and gas challenges </li> <li>Covers the most commonly used algorithms for both supervised and unsupervised learning</li> <li>Presents a balanced approach of both theory and practicality while progressing from introductory to advanced analytical techniques </li></ul>

English [en] · EPUB · 120.8MB · 2021 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

upload/newsarch_ebooks/2022/02/05/Machine Learning Guide for Oil and Gas Using Python.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

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.<ul> <li>Helps readers understand how open-source Python can be utilized in practical oil and gas challenges </li> <li>Covers the most commonly used algorithms for both supervised and unsupervised learning</li> <li>Presents a balanced approach of both theory and practicality while progressing from introductory to advanced analytical techniques </li></ul>

English [en] · PDF · 46.9MB · 2021 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

nexusstc/Machine Learning Guide for Oil and Gas Using Python/911e7ab57739f2192a2a98b9d3f23871.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

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.<ul> <li>Helps readers understand how open-source Python can be utilized in practical oil and gas challenges </li> <li>Covers the most commonly used algorithms for both supervised and unsupervised learning</li> <li>Presents a balanced approach of both theory and practicality while progressing from introductory to advanced analytical techniques </li></ul>

English [en] · PDF · 47.7MB · 2021 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

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

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.<ul> <li>Helps readers understand how open-source Python can be utilized in practical oil and gas challenges </li> <li>Covers the most commonly used algorithms for both supervised and unsupervised learning</li> <li>Presents a balanced approach of both theory and practicality while progressing from introductory to advanced analytical techniques </li></ul>

English [en] · PDF · 13.0MB · 2021 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/F:\!upload\_books\Machine Learning for OpenCV.epub

Machine learning for OpenCV : advanced methods and deep learning Michael Beyeler Packt Publishing Limited, Бестселлеры O'Reilly, Санкт-Петербург [и др.], Russia, 2018

"A practical introduction to the world of machine learning and image processing using OpenCV and Python. Computer vision is one of today's most exciting application fields of Machine Learning, From self-driving cars to medical diagnosis, computer vision has been widely used in various domains. This course will cover essential concepts such as classifiers and clustering and will also help you get acquainted with neural networks and Deep Learning to address real-world problems. The course will also guide you through creating custom graphs and visualizations, and show you how to go from raw data to beautiful visualizations. By the end of this course, you will be ready to create your own ML system and will also be able to take on your own machine learning problems."--Resource description page. Read more... Abstract: "A practical introduction to the world of machine learning and image processing using OpenCV and Python. Computer vision is one of today's most exciting application fields of Machine Learning, From self-driving cars to medical diagnosis, computer vision has been widely used in various domains. This course will cover essential concepts such as classifiers and clustering and will also help you get acquainted with neural networks and Deep Learning to address real-world problems. The course will also guide you through creating custom graphs and visualizations, and show you how to go from raw data to beautiful visualizations. By the end of this course, you will be ready to create your own ML system and will also be able to take on your own machine learning problems."--Resource description page

English [en] · EPUB · 28.0MB · 2018 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

nexusstc/Введение в машинное обучение с помощью Python. Руководство для специалистов по работе с данными/92d41009bda644918cbef3d5d5973be8.pdf

Введение в машинное обучение с помощью Python: руководство для специалистов по работе с данными: [полноцветное издание] Андреас Мюллер, Сара Гвидо; [перевод с английского и редакция А. В. Груздева] Vilyams, Москва [и др.], Russia, 2017

Russian [ru] · PDF · 8.7MB · 2017 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save