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AI theory, architecture, models
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Module 2
Core Machine Learning Concepts
1
Implementing Gradient Descent Variants
Implement gradient descent, stochastic gradient descent (SGD), and mini-batch gradient descent
2
Adaptive Learning Rates: AdaGrad, RMSprop, and Adam
Apply adaptive learning rate methods including AdaGrad, RMSprop, and Adam
3
Splitting Data for Machine Learning
Properly split data into training, validation, and test sets
4
K-Fold Cross-Validation for Model Assessment
Implement K-fold cross-validation to assess model generalization
5
Overfitting and Underfitting: Diagnosis and Mitigation
Diagnose and mitigate overfitting and underfitting
6
Understanding the Bias-Variance Tradeoff
Analyze the bias-variance tradeoff in model complexity
7
Classification Model Evaluation Metrics
Evaluate classification models using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC
8
Evaluating Regression Models
Evaluate regression models using metrics like MSE, MAE, and R-squared
Previous module
Mathematical and Statistical Foundations for AI
Next module
Classical and Ensemble Learning Algorithms