The second month focuses on building upon the foundational knowledge acquired in the first month, diving deeper into the tools and techniques essential for developing sophisticated AI models. Students will be introduced to TensorFlow and PyTorch, two of the most widely used frameworks in the AI community. They will learn the key features of each framework and how to perform basic operations and model building. This month emphasizes the importance of proper model training, covering the steps involved, the significance of splitting data, and the algorithms used for training models effectively.

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.