## Syllabus
Modern machine learning heavily relies heavily on programming, and Python is one of the languages of choice for ML and datascience.
The objective of this course is to:
- make the students Python-ready for the other classes: master packages such as numpy, pandas, matplotlib, scikit-learn, etc
- learn how to use collaborative programming tools: Git, Github
- take a Machine learning challenge (Kaggle) as project validation
The class is taught using a live coding framework: students are given tasks to perform in ~ 5 minutes, then the teachers do it live.
The afternoons are devoted to practical sessions.
## Evaluation
- **Labs**. 3 Labs with Jupyter in groups of 2, 1 Lab on git.
- **Project**. Kaggle competition in groups of 2: ranking in the competition + 4 pages report and 10 minutes presentation in December.
