Explores machine learning basics, adversarial challenges, biases, distributional shift, and deployment complexities.
Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.
Introduces machine learning basics, including data collection, model evaluation, and feature normalization.
Explores model evaluation with K-Nearest Neighbor, covering optimal k selection, similarity metrics, and performance metrics for classification models.
