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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: TextPublication details: Mumbai: Shroff Publishers and Distributors Pvt.Ltd., 2017Edition: First editionDescription: xii, 376 pages : illustrations ; 24 cmISBN:
  • 9789352134571
Other title:
  • Machine learning with Python
Subject(s): DDC classification:
  • 005.133 23 M9581i
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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Holdings
Item type Current library Call number Vol info Copy number Status Barcode
Books Library, Independent University, Bangladesh (IUB) Reference Stacks 005.133 M9581i (Browse shelf(Opens below)) 2017 01 Not For Loan 029609
Books Library, Independent University, Bangladesh (IUB) General Stacks 005.133 M9581i (Browse shelf(Opens below)) 2017 02 Available 029610
Books Library, Independent University, Bangladesh (IUB) General Stacks 005.133 M9581i (Browse shelf(Opens below)) 2017 03 Available 029611
Total holds: 0

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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