johnmoses/coursera-ai-4-medicine-specialization

Coursera Artificial Intelligence for Medicine Specialization

coursera-ai-4-medicine-specialization

Coursera Artificial Intelligence for Medicine Specialization (offered by deeplearning.ai)

Scores Earned

• Course 1 - AI for Medical Diagnosis: 100%
• Course 2 - AI for Medical Prognosis: 100%
• Course 3 - AI For Medical Treatment: 100%

Skills Gained

• Deep Learning
• Machine Learning
• Time-to-event modeling
• Random Forest
• Model tuning
• Model interpretation
• Image Segmentation
• Natural language extraction

Course Structure

Each course is spread out in weeks, and are made up of lab sessions, quizzes, assignments, related course materials, code and data

Course 1: AI for Medical Diagnosis

Week 1

Labs

• Data Exploration & Image Pre-Processing
• Counting Labels & Weighted Loss Function
• Densenet
• Patient Overlap and Data Leakage

Assignments

• Chest X-Ray Medical Diagnosis with Deep Learning

Week 2

Labs

• ROC Curve and Threshold

Assignments

• Evaluation of Diagnostic Models

Week 3

Labs

• Explore MRI Data and Labels
• Extract a Sub Section
• U-Net Model

Assignments

• Brain Tumor Auto-Segmentation for Magnetic Resonance Imaging (MRI)

Course 2: AI for Medical Prognosis

Week 1

Labs

• Create a Linear Model
• Risk Scores, Pandas and Numpy
• Combine Features
• Concordance Index

Assignments

• Build and Evaluate a Linear Risk model

Week 2

Labs

• Decision Tree Classifier
• Missing Data and Applying a Mask
• Imputation

Assignments

• Risk Models Using Tree-based Models

Week 3

Labs

• Count Patients
• Kaplan-Meier

Assignments

• Survival Estimates that Vary with Time

Week 4

Labs

• Categorical Variables
• Hazard Function
• Permissible Pairs with Censoring and Time

Assignments

• Cox Proportional Hazards and Random Survival Forests

Course 3: AI For Medical Treatment

Week 1

Labs

• Pandas for a Medical Dataset
• Model Training/Tuning Basics with Sklearn
• Logistic Regression Model Interpretation

Assignments

• Estimating Treatment Effect Using Machine Learning

Week 2

Labs

• Cleaning Text
• Using BioC format and the NegBio Library
• Preparing Input for Text Classification

Assignments

• Natural Language Entity Extraction

Week 3

Labs

• Permutation Method
• Introduction To GradCAM (Part 1)
• GradCAM: Continuation (Part 2)

Assignments

• Model Interpretation Methods

Disclaimer

The solutions presented are intended to serve as reference for other learners who enroll in this course.