Welcome to Central Library, SUST
Amazon cover image
Image from Amazon.com
Image from Google Jackets

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

By: Contributor(s): Material type: TextTextPublisher: Sebastopol, CA : O'Reilly Media, Inc., 2019, ©2017Edition: First editionDescription: xii, 376 pages : illustrations ; 24 cmISBN:
  • 9789352134571
Other title:
  • Machine learning with Python
Subject(s): DDC classification:
  • 23 005.133
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.
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Call number Copy number Status Date due Barcode
Books Books Seminar Library, Department of Industrial & Production Engineering General Stacks 005.133 MUI (Browse shelf(Opens below)) 1 Available 0076440

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.

There are no comments on this title.

to post a comment.