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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
English [en] · PDF · 25.8MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save
description
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
Alternative filename
lgrsnf/I:\it-books_dl\3289\Introduction to Machine Learning with Python.pdf
Alternative filename
nexusstc/Introduction to Machine Learning with Python: A Guide for Data Scientists/ffc742b37523f423a530f0f02df3bd84.pdf
Alternative filename
zlib/Computers/Artificial Intelligence (AI)/Andreas C. Müller, Sarah Guido/Introduction to Machine Learning with Python: A Guide for Data Scientists_2735809.pdf
Alternative title
Введение в машинное обучение с помощью Python: руководство для специалистов по работе с данными: [полноцветное издание]
Alternative author
Андреас Мюллер, Сара Гвидо; [перевод с английского и редакция А. В. Груздева]
Alternative author
Andreas C. Müller, Sarah Guido
Alternative author
Müller, Andreas, Guido, Sarah
Alternative author
Мюллер, Андреас
Alternative publisher
O'Reilly Media, Incorporated
Alternative publisher
Диалектика
Alternative edition
First edition, third release, Sebastopol, CA, 2017
Alternative edition
United States, United States of America
Alternative edition
O'Reilly Media, Sebastopol, CA, 2017
Alternative edition
First edition, Sebastopol, CA, 2016
Alternative edition
First edition, Beijing, 2016
Alternative edition
Москва [и др.], Russia, 2017
Alternative edition
September 25, 2016
metadata comments
lg1526951
metadata comments
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metadata comments
Предм. указ.: с. 465-472
Пер.: Müller, Andreas C. Introduction to machine leaning with Python Beijing [etc.] : O'Reilly, cop. 2017 978-1-449-36941-5
metadata comments
РГБ
metadata comments
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Alternative description
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.
You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.
With this book, you'll learn:
Fundamental concepts and applications of machine learning
Advantages and shortcomings of widely used machine learning algorithms
How to represent data processed by machine learning, including which data aspects to focus on
Advanced methods for model evaluation and parameter tuning
The concept of pipelines for chaining models and encapsulating your workflow
Methods for working with text data, including text-specific processing techniques
Suggestions for improving your machine learning and data science skills
Alternative description
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. -- Provided by publisher
Alternative description
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. Introduction to 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.
date open sourced
2016-06-29
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