Transformations of Input or Output

Related lectures (31)

Page 3 of 4

Explores linear regression in high dimensions and practical house price prediction from a dataset.

Explores Ridge and SVR regression, emphasizing kernel tricks for non-linear regression.

Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.

Covers supervised learning through likelihood maximization to find optimal parameters.

Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.

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

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

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

Covers the basics of machine learning, including supervised and unsupervised techniques, linear regression, and model training.

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.