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Introduction to machine learning with Python : a guide for data scientists / Andreas C. M�uller and Sarah Guido.

By: M�uller, Andreas C [author.].
Contributor(s): Guido, Sarah [author.].
Material type: materialTypeLabelBookPublisher: Sebastopol, CA : O'Reilly Media, Inc., 2016.Copyright date: �2017Edition: First edition.Description: xii, 376 pages : illustrations ; 24 cm.Content type: text | still image Media type: unmediated Carrier type: volumeISBN: 9781449369415; 1449369413.Other title: Machine learning with Python.Subject(s): Python (Computer program language) | Programming languages (Electronic computers) | Data mining | Data mining | Programming languages (Electronic computers) | Python (Computer program language) | Maschinelles LernenDDC classification: 005.133
Contents:
Introduction -- Supervised learning -- Unsupervised learning and preprocessing -- Representing data and engineering features -- Model evaluation and improvement -- Algorithm chains and pipelines -- Working with text data -- Wrapping up.
Summary: 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 M�uller 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.
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Central Library, Shahjalal University of Science & Technology

 

General Stacks 		005.133 MUI (Browse shelf) 1 Available 0075129

Includes index.

Introduction -- Supervised learning -- Unsupervised learning and preprocessing -- Representing data and engineering features -- Model evaluation and improvement -- Algorithm chains and pipelines -- Working with text data -- Wrapping up.

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 M�uller 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.

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