1. Difference Between Supervised, Unsupervised, and Reinforcement Learning Supervised Learning: Uses labeled data to learn a direct input-output mapping (e.g., image classification). Unsupervised Learning: Finds hidden patterns in unlabeled data (e.g., clustering, anomaly detection). Reinforcement Learning: Learns by interacting with an environment and receiving rewards (e.g., game playing, robotics). 2. Handling Imbalanced Datasets Use oversampling (e.g., SMOTE) or undersampling to balance data. Apply class-weighted loss functions or ensemble methods to improve learning. Use precision, recall, F1-score, or AUC-ROC instead of accuracy for evaluation. 3. Bias-Variance Tradeoff High bias (underfitting): Model is too simple, missing important patterns. High variance (overfitting): Model is too complex, capturing noise instead of general patterns. Solution: Find a balance using regularization, cross-validation, and proper model selection.