The central goals of the course:

• Programming basics in Python.
• Fundamental tools and techniques in Python to discover, visualise, manipulate and clean data (mainly with the Pandas and Matplotlib libraries).
• Holistic overview of the ML landscape.
• Basic challenges with data for ML (missing values, noise, over- and undersampling).
• Introduction to supervised learning vs unsupervised learning and reinforcement learning.
• Introduction to bias-variance tradeoff and techniques to avoid over- and underfitting.
• Introduction to selected ML algorithms such as linear regression, decision trees, random forests, ensemble learning, etc.
• Classification and regression examples.
• Implementing basic ML models using Python (including feature scaling and test/train split development).
• Evaluating the performance of ML models using performance metrics.
• Hands-on practice of the acquired knowledge.