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Statistical Learning

by Trevor Hastie , Robert Tibshirani
  • 0.0
9 Weeks
The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. The pdf for this book is available for free on the book website. Linear model selection and regularization...
$149
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