Linear Classification Models: From Binary to Multiclass

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Linear Models for Classification: Multi-Class Extensions

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

Linear Models: Classification

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

Linear Models for Classification: Part 3

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

Linear Models: Recap and Logistic Regression

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

Linear Models for Classification

Explores linear models for classification, logistic regression, decision boundaries, SVM, multi-class classification, and practical applications.

Linear Models for Classification

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

Linear Models for Classification: Logistic Regression and SVM

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

Linear Models: Continued

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

Generalized Linear Regression: Classification

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

Logistic Regression: Fundamentals and Applications

Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.