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

By: Müller, Andreas C [author.].
Contributor(s): Guido, Sarah [author.].
Edition: First edition.Description: xii, 376 pages : illustrations ; 24 cm.ISBN: 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 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. --
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Item type Current location Call number Status Date due Barcode Item holds
E-Book SKOLKOVO Library
Shelves 		QA76.73.P98 M85 2016 (Browse shelf) Available
Book Skoltech library
Shelves 		QA76.73.P98 M85 2016 (Browse shelf) Checked out 27/06/2024 2000007915
Total holds: 0
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QA279.5 .C36 Bayesian methods for data analysis / QA76.2.A2 The innovators : QA76.2.A2 I87 The innovators : QA76.73.P98 M85 2016 Introduction to machine learning with Python : QA76.76.A65 S255 2011 CUDA by example : QA76.77 .T359 2024 Modern operating systems. QA76.9.C66 S87 The future of the professions :

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 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. --

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