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Covers regression analysis for disentangling data using linear regression modeling, transformations, interpretations of coefficients, and generalized linear models.
Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.
Explores linear regression, least squares, residuals, and confidence intervals in regression models.
Covers linear regression, weighted regression, locally weighted regression, support vector regression, noise handling, and eye mapping using SVR.
Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.
Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
