Introduction to machine learning with Python a guide for data scientists
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 solu...
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| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Beijing, Boston, Farnham, Sebastopol, Tokyo
O'Reilly
October 2016
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| Ausgabe: | First edition |
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| Online Zugang: | Cover Inhaltsverzeichnis Full Text |
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| Zusammenfassung: | 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. -- 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 |
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| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | xii, 384 Seiten Illustrationen, Diagramme |
| ISBN: | 9781449369415 978-1-4493-6941-5 |