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Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.
Explores linear models for classification, including parametric models, regression, and logistic regression, along with model evaluation metrics and maximum margin classifiers.
Explores data collection, feature selection, model building, and performance evaluation in machine learning, emphasizing feature engineering and model selection.
