Description
- Introduction and business scenario
- Logistic Regression
- Bagging, Random Forest and GBM
- Ensemble methods
- Support Vector Machines
- Neural Networks
- Deep Learning
- Outlier Detection
- Advanced performance criteria
- ROC plots – Comparison between Models
- Feature Selection: Forward Selection, Backward Elimination
- Feature engineering
- Performance (Cost) Model Optimization
- Model Deployment & monitoring
