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Machine and Deep Learning
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Module 1
Foundations of Supervised Learning
1
Supervised Learning: Function Approximation
Formalize supervised learning as a function approximation problem with training and test distributions.
2
Bias-Variance Tradeoff Explained
Explain the bias-variance tradeoff and its implications for model selection.
3
Learning Curves for Diagnosing Overfitting and Underfitting
Diagnose overfitting and underfitting using learning curves.
4
Regression Model Evaluation Metrics
Select and compute evaluation metrics for regression tasks (MSE, MAE, R²).
5
Evaluating Classification Models with Confusion Matrices
Compute accuracy, precision, recall, and F1-score using a confusion matrix.
6
Understanding ROC Curves and AUC
Plot a Receiver Operating Characteristic (ROC) curve and interpret the Area Under the Curve (AUC).
7
K-Fold Cross-Validation for Model Generalization
Implement k-fold cross-validation to estimate model generalization performance.
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