Things got a bit busy and I feel off the wagon posting, but here we are back for the ninth edition of the data science things roundup. If you haven't seen previous editions, it's basically just 3 data science or python related articles or packages that I've stumbled across recently and thought were interesting. This time we have a great paper and 2 python packages, so dig in.
A few useful things to know about machine learning
In this paper, Pedro Domginos out of University of Washington, who you may know from a bunch of books, papers and other great work in AI and machine learning lays out in very simple terms his positions on the field of machine learning for practitioners. Feature engineering is key, more data beats better algorithms, generalization is critical, and ensembles tend to be a good idea. This is absolutely worth the read, and worth printing out and keeping nearby. Check it out here.
Did you previously use R for data science work and switch to python? Did you grow fond of shiny for putting together quick web-based visualizations of things? Do you miss that now that you're in python? Well,Adam Hajari at Pandora sure did, so he made shiny-for-python, called Spyre. Check it out here.
Google's Deepmind group made waves this year with their go-playing ML system, AlphaGo. Inspired by that, Max Pumperia built BetaGo, a framework for building, training, and evaluating go-playing bots. Check it out here.