Fundamentals of Supervised Machine Learning
With Applications in Python, R, and Stata
Springer
ISBN 978-3-031-41339-1
Standardpreis
Bibliografische Daten
Fachbuch
Buch. Softcover
2024
45 s/w-Abbildungen, 99 Farbabbildungen.
In englischer Sprache
Umfang: xxix, 391 S.
Format (B x L): 15,5 x 23,5 cm
Verlag: Springer
ISBN: 978-3-031-41339-1
Weiterführende bibliografische Daten
Das Werk ist Teil der Reihe: Statistics and Computing
Produktbeschreibung
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 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.
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Kundeninformationen
Presents the fundamental notions of supervised machine learning Provides a balance between the theory and applications of machine learning using Python, R, and Stata Fosters an understanding and awareness of machine learning methods over different software platforms
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