basic notions of supervised machine learning,
loading a dataset to expose it to scikit-learn and exploratory data analysis,
training a model from purely numerical data,
processing of categorical data,
assembly of a scikit-learn pipeline to chain data shaping and learning,
evaluation of models, notably by cross-validation,
model selection with hyper-parameters adjustment.