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Introduction to machine learning wit...

Müller, Andreas C.

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Introduction to machine learning with Python : = a guide for data scientists /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Introduction to machine learning with Python :/ Andreas C. Müller and Sarah Guido.
其他題名:	a guide for data scientists /
其他題名:	Machine learning with Python
作者:	Müller, Andreas C.
其他作者:	Guido, Sarah.
出版者:	Sebastopol, CA :O'Reilly Media, : c2017.,
面頁冊數:	xii, 378 p. :ill. ;24 cm.
附註:	Includes index.
標題:	Python (Computer program language) -
ISBN:	9781449369415

Introduction to machine learning with Python : = a guide for data scientists /
Müller, Andreas C.

Introduction to machine learning with Python :a guide for data scientists /Machine learning with PythonAndreas C. Müller and Sarah Guido. - 1st ed. - Sebastopol, CA :O'Reilly Media,c2017. - xii, 378 p. :ill. ;24 cm.

Includes index.

Introduction --

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. --

ISBN: 9781449369415UK39.99

LCCN: 2017394288Subjects--Topical Terms:

729789
Python (Computer program language)

LC Class. No.: QA76.73.P98 / .M85 2017

Dewey Class. No.: 005.133

Introduction to machine learning with Python : = a guide for data scientists /
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500 $a Includes index.
505 0 # $t Introduction -- $t Supervised learning -- $t Unsupervised learning and preprocessing -- $t Representing data and engineering features -- $t Model evaluation and improvement -- $t Algorithm chains and pipelines -- $t Working with text data -- $t Wrapping up.
520 # $a 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. -- $c Provided by publisher.
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650 # 0 $a Data mining. $3 562972
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