Applied Machine Learning for Health Professionals​

Part I: Python Programming & Data Preprocessing

Objectives:

• Introduce Python fundamentals, from variables and control flow to functions and modules.
• Master NumPy & pandas libraries for cleaning, transforming, and organizing tabular health datasets.
• Learn best practices for handling outlier values, categorical encoding, normalization, and feature engineering in medical data
• Apply techniques directly to sample datasets drawn from cohort studies and electronic health records

Part II: Machine-Learning Models & Clinical Implementation

Objectives:

• Explore core supervised-learning algorithms: classifiers (e.g., logistic regression, decision trees) and regressors (e.g., linear regression, random forest)
• Understand model assumptions, strengths, and ideal use cases in health fields.
• Develop skills for training, hyperparameter tuning, and cross-validation on tabular medical datasets.
• Learn rigorous evaluation metrics to assess clinical applicability.

Capstone Project

In the final module, participants will apply their new skills to a real medical dataset. Guided through the end-to-end ML workflow, they will:

1. Prepare and preprocess raw clinical data
2. Select and train appropriate models
3. Tune hyperparameters for optimal performance
4. Evaluate and interpret results with an eye toward clinical deployment

Who Should Enroll?

• Physicians, nurses, and allied health professionals seeking to leverage data science in their practice
• Clinical researchers aiming to incorporate predictive analytics into their studies
• Data analysts in healthcare settings who want a structured, medically-focused ML curriculum

By the end of this course, you will be able to independently develop and evaluate machine-learning models that address real-world challenges in various medical domains, empowering you to research and drive innovation in patient care.