1. Einführung
2. Lineare Regression
3. Logistische Regression
4. Generalized Linear Models
5. Multinomial Logistic Regression
6. Feature Engineering
7. Bewerten und Tunen von Modellen
8. Locally Weighted Regression
9. Gaussian Discriminant Analysis
10. Naive Bayes
11. Support Vector Machines
12. Anomaly Detection
13. Decision Trees & Ensemble Models
14. Clustering
15. Neuronale Netzwerke