• 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

• 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

Target Audience

• 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.

About the Instructor

This course is instructed by Ali M. Shabestari and Dr. Motahare Shabestari.

Preprocessing:

• Session 01: Prerequisites
• Session 02: Variables, Input/Output, Data Types, Strings, and Operators
• Session 03: Control Flow (if / else), For loop, and While loop
• Session 04: Functions, Anonymous Functions (lambda) and Exception Handling
• Session 05: Reading & Writing in Pandas, Indexing, Selecting & Assigning and Summary Functions & Map
• Session 06: Grouping & Aggregation, Merging & Combining, and Data Types & Missing Values
• Session 07: Dataset Introduction, Outlier Detection & Handling, Missing Data Imputation, and Data Type Preprocessing
• Session 08: Introduction to EDA, Primary Steps (review of data preprocessing), Univariate Analysis, and Bivariate Analysis
• Session 09: Introduction to NumPy & Arrays, Array Operations & Indexing, and Statistical Analysis with NumPy

Machine Learning

• Session 10: Classification & Regression,  Baseline Model (Logistic Regression), Prediction & Probability Threshold, Evaluation Metrics and Extra session (Notebook)
• Session 11: Decision Tree, Bagging (Random Forest), Boosting (XGBoost), and Overfitting & Model Selection
• Session 12: Hyperparameters, Hyperparameter Tuning, and Retraining
• Session 13: Importance of Interpretability, Global Feature Importance, and SHAP
• Session 14: Feature Engineering, Feature Creation, Feature Selection, and Dimensionality Reduction
• Session 15: Saving & Packaging the Model, Environment Reproducibility, Introduction to Model Deployment, and Conclusion & Next Steps
• Session 16: Project