Machine learning (ML) is one of the most dynamic and exciting areas in modern data-driven research. Built upon inspiration from fields as different as statistics, computer science, neurosciences and physics, it allows for automatic learning from complex large-scale datasets. The lectures aim at introducing the core concepts and algorithms of ML in a way easily understood by physicists, both in the setting of supervised learning (linear and logistic regression, ensemble methods, deep neural networks…) and unsupervised learning (dimensional reduction, clustering, generative modelling…). The course is accompanied by Python Jupiter Notebooks, which allow for testing the main algorithms presented in the lectures, and introduces some highly used ML Python packages to the students.