Roman Roznik Data Science Slave) Pitfalls of Machine Learning

Roman Roznik Data Science Slave) Moscow Python Conf 2017 No doubt machine learning is a hot topic in recent years, it seem s everybody can easily become a data scientist and do ML within few lines of code. Reality is much harder. Understanding the problem, preparing right training data, cleaning them, designing features, interpretability, complexity of the model, defining right metrics, looking at false positives, negatives, interpretation of ML results or AB tests those are topics highly tied with data science that are often overlooked and underrate. I d like to emphasize that those are very important and ML itself is just a one small piece of complex data science puzzle. Not a single line of a code in this talk.