Explores data collection, feature selection, model building, and performance evaluation in machine learning, emphasizing feature engineering and model selection.
Explores model evaluation with K-Nearest Neighbor, covering optimal k selection, similarity metrics, and performance metrics for classification models.
Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.
