Multiclass Classification

Related lectures (31)

Page 2 of 4

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

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

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

Explores logistic regression for binary classification, covering probability modeling, optimization methods, and regularization techniques.

Covers logistic regression, probabilistic interpretation, and feature engineering techniques.

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

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

Explores generalized linear regression, logistic regression, and multiclass classification in machine learning.

Covers linear models, logistic regression, decision boundaries, k-NN, and practical applications in authorship attribution and image data analysis.