Students will also explore critical concepts such as regularization and overfitting, understanding how to prevent models from becoming too complex and failing to generalize well to new data. The curriculum includes an in-depth look at model evaluation metrics, providing the tools to assess model performance accurately. Towards the end of the month, students will begin working on their final projects, collaborating in groups to apply their learning to real-world problems. This hands-on experience will be crucial in consolidating their knowledge and preparing them for more advanced topics in the final month.

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