Linear Models for Classification: Part 3

Related lectures (31)

Page 2 of 4

Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.

Covers the basics of support vector machines, logistic regression, decision boundaries, and the k-Nearest Neighbors algorithm.

Explores linear models, logistic regression, classification metrics, SVM, and their practical use in data science methods.

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

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

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

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

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

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

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.