Python Fundamentals for Machine Learning

About

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

To apply for the course, you must have passed a Bachelor's degree in Business Economics, Business Administration, or an equivalent degree.

Fee info