Machine Learning : A Concise Introduction

model selection, and dealing with biased data samples and software limitations essential elements of most applied projects. Written by an expert in the field, this important resource: Illustrates many classification methods with a single, running example, highlighting similarities and differences between methods Presents side-by-side Python and R source code which shows how to apply and interpret many of the techniques covered Includes many thoughtful exercises as an integral part of the text, with an appendix of selected solutions Contains useful information for effectively communicating with clients on both technical and ethical topics Details classification techniques including likelihood methods, prototype methods, neural networks, classification trees, and support vector machines A volume in the popular Wiley Series in Probability and Statistics, Machine Learning offers the practical information needed for an understanding of the methods and application of machine learning for advanced undergraduate and

beginner graduate students, data science and machine learning practitioners, and other technical professionals in adjacent fields. [Publisher's Text]

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