How do we build interpretable machine learning models? Or, in other words, how do we build trust in the models we design? This is such a critical question in every machine learning project. We need to find a way to use these powerful ML algorithms and still make them work in business setting. So in this episode #20 of our DataHack Radio podcast, we welcome Christoph Molar, author of the popular book - "Interpretable Machine Learning". Who better to talk about this fundamental and critical topic? Read more here: https://www.analyticsvidhya.com/blog/2019/03/datahack-radio-interpretable-machine-learning-christoph-molnar