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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
English [en] · MOBI · 80.0MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/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
lgli/Z:\Bibliotik_\24\I\Introduction to Machine Learning with Python - Andreas C. Muller, Sarah Guido.mobi
Alternative filename
lgrsnf/Z:\Bibliotik_\24\I\Introduction to Machine Learning with Python - Andreas C. Muller, Sarah Guido.mobi
Alternative filename
nexusstc/Introduction to Machine Learning with Python: A Guide for Data Scientists/dfb1369b173ce8e92f2f60f9ef8cb32b.mobi
Alternative filename
zlib/Computers/Computer Science/Andreas C. Müller and Sarah Guido/Introduction to machine learning with Python a guide for data scientists_5668209.mobi
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. Mueller, 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
lg2581355
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
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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
date open sourced
2020-07-26
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