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Explores optimization methods like gradient descent and subgradients for training machine learning models, including advanced techniques like Adam optimization.
Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of model complexity and different cross-validation methods.
Covers the basics of linear regression in machine learning, exploring its applications in predicting outcomes like birth weight and analyzing relationships between variables.
