a guide for data scientists Müller, Andreas C. creator author. Guido, Sarah author. text First edition. monographic eng

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xii, 376 pages : illustrations ; 24 cm 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. Andreas C. Müller and Sarah Guido. Includes index. Python (Computer program language) Programming languages (Electronic computers) Data mining Data mining Programming languages (Electronic computers) Python (Computer program language) Maschinelles Lernen QA76.73.P98 M85 2016 9781449369415 2017394288 260313 20260313074442.0 19777557 eng