Explores sources of unfairness in machine learning, the importance of fairness metrics, and evaluating model predictions using various fairness metrics.
Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.
Explores data collection, feature selection, model building, and performance evaluation in machine learning, emphasizing feature engineering and model selection.
