Python Fundamentals for Machine Learning

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.

Course leader

Balu Mohandas Menon

Target group

Master's level

Fee info

EUR 691: EU/EEA citizens
EUR 2489: NON-EU/EEA citizens

Scholarships

No scholarships available

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