An overview of how to use scikit-learn.
How can you customise metrics to pick the right model.
How to apply post-processing in scikit-learn.
Can preprocessing suddenly make an algorithm work?
Can we model by using Natural Intelligence?
Is smoking good for you? Or can we lie with statistics?
When is lack of sleep causing damage? When is it significant?
Is the conclusion different if we don't assume all days are equal?
How can we balance risk and reward?
Can you compile towards a GPU by writing code like numpy?
How do you practically solve the nearest neighbor problem.
How comprehensions work in python.
How to fit models with partial_fit.