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Explores logistic regression, cost functions, gradient descent, and probability modeling using the logistic sigmoid function.

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

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

Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.

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

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

Covers linear classification using logistic regression, regularization, and multiclass classification.

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

Covers linear models, binary and multi-class classification, and logistic regression with practical examples.

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