Machine Learning with R. Supervised Learning: Regression

This book develops supervised learning techniques commonly used in Predictive Artificial Intelligence and Data Science applications. The techniques are illustrated with fully solved examples using the appropriate software. The R language and its libraries related to supervised learning, ideal for working in this field, will be used. The course will go into predictive algorithms such as Multiple Linear Regression, Ridge Regression, PLS Regression, LARS Regression, LASSO Regression, Elastic Net Regression, Generalized Linear Model, Robust Regression, Support Vector Regression (SVR), Kernel Ridge Regression (Kernel Ridge Regression), Kernel Ridge Regression (Kernel Ridge Regression) and Kernel Ridge Regression (Kernel Ridge Regression), Kernel Ridge Regression (KRR), Stochastic Gradient Descendent Regression (SGD), Hubert Regression, Poisson Regression, Negative Binomial Regression, Logit and Probit Models, Count Models and Neural Network Models (LSTM, RNN, NARX, NNAR and GRU).

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Machine Learning with R. Supervised Learning: Regression

This book develops supervised learning techniques commonly used in Predictive Artificial Intelligence and Data Science applications. The techniques are illustrated with fully solved examples using the appropriate software. The R language and its libraries related to supervised learning, ideal for working in this field, will be used. The course will go into predictive algorithms such as Multiple Linear Regression, Ridge Regression, PLS Regression, LARS Regression, LASSO Regression, Elastic Net Regression, Generalized Linear Model, Robust Regression, Support Vector Regression (SVR), Kernel Ridge Regression (Kernel Ridge Regression), Kernel Ridge Regression (Kernel Ridge Regression) and Kernel Ridge Regression (Kernel Ridge Regression), Kernel Ridge Regression (KRR), Stochastic Gradient Descendent Regression (SGD), Hubert Regression, Poisson Regression, Negative Binomial Regression, Logit and Probit Models, Count Models and Neural Network Models (LSTM, RNN, NARX, NNAR and GRU).

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Machine Learning with R. Supervised Learning: Regression

Machine Learning with R. Supervised Learning: Regression

by César Pérez López
Machine Learning with R. Supervised Learning: Regression
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Machine Learning with R. Supervised Learning: Regression

Machine Learning with R. Supervised Learning: Regression

by César Pérez López

Overview

This book develops supervised learning techniques commonly used in Predictive Artificial Intelligence and Data Science applications. The techniques are illustrated with fully solved examples using the appropriate software. The R language and its libraries related to supervised learning, ideal for working in this field, will be used. The course will go into predictive algorithms such as Multiple Linear Regression, Ridge Regression, PLS Regression, LARS Regression, LASSO Regression, Elastic Net Regression, Generalized Linear Model, Robust Regression, Support Vector Regression (SVR), Kernel Ridge Regression (Kernel Ridge Regression), Kernel Ridge Regression (Kernel Ridge Regression) and Kernel Ridge Regression (Kernel Ridge Regression), Kernel Ridge Regression (KRR), Stochastic Gradient Descendent Regression (SGD), Hubert Regression, Poisson Regression, Negative Binomial Regression, Logit and Probit Models, Count Models and Neural Network Models (LSTM, RNN, NARX, NNAR and GRU).

Product Details

BN ID: 2940181611788
Publisher: Scientific Books
Publication date: 10/24/2025
Series: MACHINE LEARNING
Sold by: Draft2Digital
Format: eBook
File size: 13 MB
Note: This product may take a few minutes to download.

About the Author

PhD. Mathematician, Economist and Government Statistician. Professor at the Complutense University of Madrid in the Department of Statistics and Data Science. Author of more than 100 books and articles on Mathematics, Statistics, Economics, and Computer Science.

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