Understand the role and real-world applications of AI in bioinformatics and healthcare.

Explore common biomedical datasets like MIMIC-III, NIH Chest X-ray, and PhysioNet.

Learn Python basics and data science libraries for biomedical data analysis.

Clean, transform, and engineer features from biomedical data (e.g., diagnosis codes, imaging).

Apply ML algorithms like Logistic Regression, SVM, and Random Forest to clinical problems.

Evaluate models using metrics such as Accuracy, AUC, and F1 Score.

Build CNN models for disease detection from X-ray and retinal images.

Use RNN/LSTM for time-series biomedical signals like ECG and EEG.

Implement and train models using TensorFlow or PyTorch.

Develop a full AI project (e.g., heart disease or pneumonia prediction) using real datasets.

Learn about explainability tools (SHAP, LIME) and ethical challenges in healthcare AI.

Deploy your trained models using Flask or Streamlit for basic healthcare applications.