Support Vector Regression: Kernel Tricks

Explores kernel methods in machine learning, emphasizing their application in regression tasks and the prevention of overfitting.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.

Covers Support Vector Regression principles, optimization, and hyperparameters' influence on the fit.

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.