The processeses and the methods followed in Academia for evaluating a Machine Learning Model are different from the approaches used by the Industry. Why? [4min read]
Despite selection and information bias it is possible to do inference from non-randomized experiments?. The good ol' statistics comes to help us with its strong theoretical framework. [6min read]
Working with machine learning is not what it used to be. Let's face it. Now, there is much less time for hacking and much more time for deployment. The situation is not new and certainly not bad at all. That is why you should be prepared for the new roles and positions offered by the market. [5min read] (updated 04/09/2021)
Feature selection is a topic any machine learning practicioner should master. There are plenty strategies for performing feature selection. Some more useful than others. Some with more limitation than benefits. Here, I mention the most common approaches for feature selection using information collected from articles, books and research papers. [5 min read]
