Название: Fundamentals of Supervised Machine Learning: With Applications in Python, R, and Stata
Автор: Giоvаnni Сеrulli
Издательство: Springer
Год: 2023
Страниц: 416
Язык: английский
Формат: pdf (true), epub
Размер: 63.8 MB

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.

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.

The Chapter 1 offers a general introduction to supervised Machine Learning (ML) and constitutes the basics to get through the next and subsequent chapters of this book. It starts by providing an introduction to the basics of Machine Learning, discussing its definition, rationale, and usefulness. We point out that Machine Learning is a transformative technology within the field of artificial intelligence that enables computers to learn and make predictions or decisions without explicit programming. The rationale behind Machine Learning is the need to extract meaningful insights from the vast amount of available data. Traditional analytical approaches are often inadequate for handling such data, while Machine Learning algorithms excel in identifying patterns and relationships within them.

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.