Explores model evaluation with K-Nearest Neighbor, covering optimal k selection, similarity metrics, and performance metrics for classification models.
Explores data collection, feature selection, model building, and performance evaluation in machine learning, emphasizing feature engineering and model selection.
Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.
