Nouveauté
Machine Learning with R. Supervised Learning: Regression. MACHINE LEARNING
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- FormatePub
- ISBN8231098477
- EAN9798231098477
- Date de parution24/10/2025
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurWalzone Press
Résumé
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).
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).
