1.Introduction
- usage
- different methods
- steps of ML projects
- cost function
- parameters, hyperparameters
- performance metrics
2. Data and project ideas
3. Data preparation
4. regular ML methods I - regression
5. regular ML methods I - classification
- logistic / multinomial regression; SVM; Decision tree
6. ensemble methods
- Stacking
- Random Forest
- XGBoost
7. Neural nets I – structure
8. Neural nets II – tuning