Fundamentals of Machine Learning

This website contains the practicals for the Fundamentals of Machine Learning training course.

Librairies

These notebooks have been created with Python 3.13.2 and the following libraries:

• matplotlib 3.10.1
• numpy 2.2.4
• pandas 2.2.3
• scikit-learn 1.6.1

To use the same libraries, you can make use of the package_list.yml file:

• With Anaconda, follows the “Importing an environment” tutorial.
• With the command line, using conda or mamba (a fast replacement of conda with no licensing issues)

   mamba env create -f package_list.yml -n introml
   mamba activate introml
   pip install torch --index-url https://download.pytorch.org/whl/cpu

Content

• Practical 1: getting started with scikit-learn - or
• Practical 2: model selection, regularization - or
• Practical 3: dimension reduction and clustering - or
• Practical 4: trees and ensemble models - or
• Practical 5: training neural networks

Advanced use (git and nbstripout)

To facilitate version control with jupyter, please use nbstripout to clean your notebooks before a commit,

nbstripout <notebook_name>.ipynb

git diff <notebook_name>.ipynb will help you check the changes made.