Introduction to Supervised Learning with Python

The course is designed as an introductory course to get a foundational understanding of some basic supervised machine learning techniques. These techniques are accompanied by Python code demonstrations and by means of the scikit-learn package to try some of these data pipelines yourself. The goal of the course is to equip students with the essential knowledge and skills required to dive deeper into the world of more advanced machine learning techniques.

The following topics will be covered:

• Linear models

• Cross-Validation
• Regularization
• KNN models
• Tree-Based Models
• SVM models
• Interpretability

Participants need to have a basic knowledge of the Python programming language, in particular importing and wrangling DataFrames with the pandas package and visualizing data with the matplotlib/seaborn package.