What is this talk about?

For a specific (and simple) use case, we will go through the steps needed to learn from data by training and evaluating a Machine Learning model. In doing so, we will touch on: - Problem setting and terminology - Learning and predicting - Feature selection and importance, Hyperparameters, Cross Validation

What is this talk NOT about? - Deep Learning - Different Machine Learning models - In-depth scikit-learn ... - AI and PowerPoint ;)

Disclaimer As an introductory level talk, no prerequisites are required. Familiarity with Python syntax may be advantageous but is not mandatory.