Regression: Exercises

Related lectures (32)

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Covers the concept of kernel regression and K-nearest neighbors for making data linearly separable.

Covers supervised learning, classification, regression, decision boundaries, overfitting, Perceptron, SVM, and logistic regression.

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

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

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Covers random features in neural networks and kernel regression equivalence.

Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

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