Exploratory Data Analysis (24%)
- Feature engineering and selection
- Statistical analysis techniques
- Data visualization approaches
- Handling missing data and outliers
ML Implementation and Operations (20%)
- Model deployment and inference
- MLOps pipelines and automation
- Model monitoring and drift detection
- Security and compliance for ML
