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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
英语 [en] · PDF · 25.6MB · 2016 · 📘 非小说类图书 · 🚀/lgli/lgrs/nexusstc/zlib · Save
描述
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
备用文件名
lgli/Introduction to Machine Learning with Python (early access): A Guide for Data Scientists - Andreas C. Mueller, Sarah Guido (2016).pdf
备用文件名
lgrsnf/Introduction to Machine Learning with Python (early access): A Guide for Data Scientists - Andreas C. Mueller, Sarah Guido (2016).pdf
备用文件名
zlib/Computers/Artificial Intelligence (AI)/Andreas C. Müller, Sarah Guido/Introduction to Machine Learning with Python: A Guide for Data Scientists_5150505.pdf
备选标题
Введение в машинное обучение с помощью Python: руководство для специалистов по работе с данными: [полноцветное издание]
备选作者
Андреас Мюллер, Сара Гвидо; [перевод с английского и редакция А. В. Груздева]
备选作者
Andreas C. Müller, Sarah Guido
备选作者
Müller, Andreas, Guido, Sarah
备选作者
Мюллер, Андреас
备用出版商
O'Reilly Media, Incorporated
备用出版商
Диалектика
备用版本
First edition, third release, Sebastopol, CA, 2017
备用版本
United States, United States of America
备用版本
O'Reilly Media, Sebastopol, CA, 2017
备用版本
First edition, Sebastopol, CA, 2016
备用版本
First edition, Beijing, 2016
备用版本
Москва [и др.], Russia, 2017
备用版本
September 25, 2016
备用版本
1, FR, 2016
元数据中的注释
This version doesn’t have the anoying it--ebooks watermark
元数据中的注释
0
元数据中的注释
lg1541778
元数据中的注释
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元数据中的注释
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
元数据中的注释
РГБ
元数据中的注释
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备用描述
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
备用描述
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
备用描述
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.
开源日期
2016-08-14
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