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Fundamentals of supervised machine l...

Cerulli, Giovanni.

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Fundamentals of supervised machine learning = with applications in Python, R, and Stata /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Fundamentals of supervised machine learning/ by Giovanni Cerulli.
Reminder of title:	with applications in Python, R, and Stata /
Author:	Cerulli, Giovanni.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	xxix, 391 p. :illustrations (some col.), digital ;24 cm.
[NT 15003449]:	Preface -- The Ontology of Machine Learning -- The Statistics of Machine Learning -- Model Selection and Regularization -- Discriminant Analysis, Nearest Neighbor and Support Vector Machines -- Tree Modelling -- Artificial Neural Networks -- Deep Learning -- Sentiment Analysis -- Index.
Contained By:	Springer Nature eBook
Subject:	Supervised learning (Machine learning) -
Online resource:	https://doi.org/10.1007/978-3-031-41337-7
ISBN:	9783031413377

Fundamentals of supervised machine learning = with applications in Python, R, and Stata /
Cerulli, Giovanni.

Fundamentals of supervised machine learningwith applications in Python, R, and Stata /[electronic resource] :by Giovanni Cerulli. - Cham :Springer International Publishing :2023. - xxix, 391 p. :illustrations (some col.), digital ;24 cm. - Statistics and computing,2197-1706. - Statistics and computing..

Preface -- The Ontology of Machine Learning -- The Statistics of Machine Learning -- Model Selection and Regularization -- Discriminant Analysis, Nearest Neighbor and Support Vector Machines -- Tree Modelling -- Artificial Neural Networks -- Deep Learning -- Sentiment Analysis -- Index.

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 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.

ISBN: 9783031413377

Standard No.: 10.1007/978-3-031-41337-7doiSubjects--Topical Terms:

731226
Supervised learning (Machine learning)

LC Class. No.: Q325.75

Dewey Class. No.: 006.31

Fundamentals of supervised machine learning = with applications in Python, R, and Stata /
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520 $a 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 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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