Fundamentals of Supervised Machine Learning

<p>This book presents the fundamental theoretical notions of supervised machine learning along with a wide range of applications using Python R and Stata. It provides a balance between theory and applications and fosters an understanding and awareness of the availability of machine learning methods over different software platforms.</p><p>After introducing the machine learning basics the focus turns to a broad spectrum of topics: model selection and regularization discriminant analysis nearest neighbors support vector machines tree modeling artificial neural networks deep learning and sentiment analysis. Each chapter is self-contained and comprises an initial theoretical part where the basics of the methodologies are explained followed by an applicative part where the methods are applied to real-world datasets. Numerous examples are included and for ease of reproducibility the Python R and Stata codes used in the text along with the related datasets are available online.</p><p>The intended audience is PhD students researchers and practitioners from various disciplines including economics and other social sciences medicine and epidemiology who have a good understanding of basic statistics and a working knowledge of statistical software and who want to apply machine learning methods in their work.</p><br>