Three talks about Machine Learning, sponsored by Ab Initio and Bullhorn.
Nelle Varoquaux: Working on a machine learning challenge with sklearn
Analysing data is hard. You have to normalize the data, extract the features, and choose and (sometimes) implement the right algorithm to perform the correct analysis. More and more, companies outsource this problem to data analysists (and geeks) in the form of challenges. In this talk, I will present tips and tricks to solve such a challenge using the machine learning toolbox scikit-learn. I will give concrete examples from several bioinformatics challenges.
Michael Selik: Why Big Data?
Many articles assert that Big Data will create value, but few if any explain why. Michael Selik will discuss different characteristics of data (volume, velocity, and variety), when each is valuable, and how to extract that value even if you're not a machine learning expert.
Vik Paruchuri: edX Ease and Discern
EdX runs MOOCs (Massive Open Online Courses), and uses machine learning to grade student essays. All our code is open-source, including the grading code, and we have open APIs for you to use our ML engine. I'll describe how it works, and how you can use it for your own machine learning tasks.
Pizza is sponsored by Ab Initio, with drinks at Meadhall afterward sponsored by Bullhorn.
We'll also be live-streaming the evening at http://youtube.com/bostonpython.