The rapid growth of artificial intelligence and data science has made scikit-learn one of the most popular Python libraries. The tutorial will present the main components of scikit-learn, covering aspects such as standard classifiers and regressors, cross-validation, or pipeline construction, with examples from various fields of application. Hands-on sessions will focus on medical applications, such as classification for computer-aided diagnosis or regression for the prediction of clinical scores.

Learning outcomes :

Ability to solve a real-world machine learning problem with scikit-learn

Prerequisites :

• Basic knowledge of Python (pandas, numpy)
• Notions of machine learning
• No prior medical knowledge is required

Link toward the registration page