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.].
Publisher: Sebastopol, CA : O'Reilly Media, Inc., ©2017Edition: First edition.Description: 384 pages ; illustrations ; 24 cm.Content type: text | still image Media type: unmediated Carrier type: volumeISBN: 9781449369415.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 LernenGenre/Form: Print books.| Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|
| On Shelf | QA76.73 .P98 M85 2017 (Browse shelf) | Available | AU00000000014461 |
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| QA76.73.P98 M544 2021 Python programming in context / | QA76.73.P98 M55 2020 Python projects for beginners : a ten-week bootcamp approach to Python programming / | QA76.73.P98 M839 2018 Beginning programming with Python for dummies / | QA76.73 .P98 M85 2017 Introduction to machine learning with Python : a guide for data scientists / | QA76.73.P98 O75 2020 Math for programmers : 3D graphics, machine learning and simulations with Python / | QA76.73.P98 O94 2018 Python without fear : a beginner's guide that makes you feel smart / | QA76.73.P98 O94 2019 Supercharged python : take your code to the next level / |
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. --