Linear Models for Classification

Related lectures (31)

Page 1 of 4

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Explores linear models for classification, including binary classification, logistic regression, decision boundaries, and support vector machines.

Explores linear models for classification, including logistic regression, decision boundaries, and support vector machines.

Explores the extension of linear classifiers to handle multiclass problems and compares their performance on various datasets.

Covers linear models for classification, focusing on logistic regression and support vector machines.

Explores linear models for classification, logistic regression, and gradient descent in machine learning.

Covers linear models, binary classification, logistic regression, and model evaluation metrics.

Covers linear models for classification, logistic regression training, evaluation metrics, and decision boundaries.

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

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.