Introduction to machine learning with Python : a guide for data scientists / Andreas C. Müller and Sarah Guido.
Tipo de material:
TextoIdioma: Inglés Editor: Sebastopol, CA : Distribuidor: O'Reilly Media, Inc., Fecha de copyright: ©2016Edición: primera ediciónDescripción: xii, 384 páginas : figuras, illustraciones ; 24 x 18 cmTipo de contenido: - texto.
- sin medio.
- volumen.
- 9781449369415
- Machine learning with Python
- QA 76.73.P98 M85 2016
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Biblioteca Antonio Enriquez Savignac | Biblioteca Antonio Enriquez Savignac | Colección General | QA 76.73.P98 M85 2016 (Navegar estantería(Abre debajo)) | Ejem. 2 | Disponible | Ingeniería Logística | 042991 |
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| QA 76 .73 .P98 M15 2013 Python for data analysis / | QA76 .73 .P98 M337 2019 Big data analysis with python : combine spark and python to unlock the powers of parallel computing and machine learning / | QA 76 .73 .P98 M38 2019 Python crash course : a hands-on, project-based introduction to programming / | QA 76.73.P98 M85 2016 Introduction to machine learning with Python : a guide for data scientists / | QA 76 .73 .P98 R373 2019 Python machine learning : machine learning and deep learning with python, scikit-learn, and tensorflow 2 / | QA 76 .73 .P98 Z51 Python programming : an introduction to computer science / | QA 76 .73 .R83 B38 Distributed programming with ruby / |
Incluye index [pág. 375-384].
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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