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

By: Contributor(s): Material type: TextPublisher: Sebastopol, CA : O'Reilly Media, Inc., 2016Copyright date: ©2017Edition: First editionDescription: xii, 376 pages : illustrations ; 24 cmContent type:
  • text
  • still image
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781449369415
Other title:
  • Machine learning with Python
Subject(s): LOC classification:
  • QA76.73.P98 M85 2016
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. -- Provided by publisher.
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Non-Fiction Kapasa Makasa University Open Access Kapasa Makasa University Non-fiction QA76.73.P98 Mul (Browse shelf(Opens below)) Available 301425
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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. -- Provided by publisher.

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