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Related lectures (31)

Page 3 of 4

Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Covers kernel methods, kernel regression, RBF kernel, and SVM for classification.

Covers overfitting, model selection, validation methods, kernel functions, and SVM concepts.

Covers feature selection, kernel regression, and neural networks through exercises.

Introduces k-Nearest Neighbors for classification and feature expansion to handle nonlinear data through transformed inputs.

Covers nonparametric regression using kernel-based estimation techniques to model complex relationships between variables.

Introduces k-Nearest Neighbors method and feature expansion for nonlinear machine learning through polynomial transformations.

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.