Introduction To Machine Learning With Python : A Guide For Data Scientists Paperback English by Sarah Guido - 21 October 2016

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Product Overview

Publisher	O'Reilly Media, Inc, USA
ISBN 10	1449369413
Book Format	Paperback
Book Subtitle	A Guide For Data Scientists
About the Author	In the last four years, he has been maintainer and one of the core contributor of scikit-learn, a machine learning toolkit widely used in industry and academia, and author and contributor to several other widely used machine learning packages. His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms. Sarah is a data scientist who has spent a lot of time working in start-ups. She loves Python, machine learning, large quantities of data, and the tech world. She is an accomplished conference speaker, currently resides in New York City, and attended the University of Michigan for grad school.
Number of Pages	392

ISBN 13	9781449369415
Author	Sarah Guido, Andreas C. Mueller
Language	English
Book Description	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 Muller 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. With this book, you'll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills
Publication Date	21 October 2016

Cart Total  374.00

Introduction To Machine Learning With Python : A Guide For Data Scientists Paperback English by Sarah Guido - 21 October 2016

374.00

 374