This course offers a hands-on, practical introduction to classification and regression techniques, including how to build, train, and evaluate models using Python and Scikit-learn.

What you'll learn:

  • The differences between supervised, semi-supervised, unsupervised, and reinforcement learning

  • How to apply classification and regression techniques using real datasets

  • How to train models using gradient descent and optimize pipelines

  • How to prevent overfitting with bias-variance decomposition, regularization (LASSO, Ridge), and more

  • How to interpret model performance using ROC curves, confusion matrices, and dummy classifiers

  • How to build and tune a complete machine learning pipeline using Scikit-learn


Note: This course is hosted on an external platform (TU Delft via edX). You will be redirected to their website to access the content.

The ILIAD Academy is not affiliated with TU Delft or edX, and any accounts, progress tracking, or certifications will be managed solely through their platform.


Click here to learn more and access the course ➜