Einführung in Machine Learning mit Python: Praxiswissen Data Science

Müller, Andreas C., Guido, Sarah

ISBN 10: 3960090498 / ISBN 13: 9783960090496

Published by Dpunkt.Verlag GmbH, 2017

About this Item

Ausreichend/Acceptable: Exemplar mit vollständigem Text und sämtlichen Abbildungen oder Karten. Schmutztitel oder Vorsatz können fehlen. Einband bzw. Schutzumschlag weisen unter Umständen starke Gebrauchsspuren auf. / Describes a book or dust jacket that has the complete text pages (including those with maps or plates) but may lack endpapers, half-title, etc. (which must be noted). Binding, dust jacket (if any), etc may also be worn.

Seller Inventory # M03960090498-B

Bibliographic Details

• Title: Einführung in Machine Learning mit Python: Praxiswissen Data Science
• Publisher: Dpunkt.Verlag GmbH
• Publication Date: 2017
• Language: German
• ISBN 10: 3960090498
• ISBN 13: 9783960090496
• Binding: Soft cover
• Condition: acceptable

About this title

Synopsis

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
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