Can you easily apply machine learning to new problems? Can you easily extract insights from these models? Do you know common pitfall, trade-offs and optimization tennicques. In other words, is Machine Learning just another tool in your tool box? or is it more of an exotic audacious project? In this talk and discussion, Emil Pedersen, will walk through a down to earth practical approach to machine learning with an emphasis on

1) Interpretable models (SHAP features)

2) Modelling tips and reasoning

3) Practical approach to becoming more familiar with Machine Learning