Machine Learning with Scikit-Learn

Frank Evans will be speaking to us about machine learning with scikit-learn!

Machine Learning is often seen as a magic black box that is difficult to use in a project. The open-source scikit-learn library in Python makes integrating a very large number of powerful machine learning algorithms into your project very easy and straightforward. For this reason, it is one of the most popular machine learning frameworks, both within Python as well as in general Data Science use.

In this talk, Frank will walk through the structure of how scikit-learn works, an overview of the types of things it is capable of doing, how to structure your data to work with scikit-learn, and how to integrate the results elsewhere in your project; all with code samples illustrating the way.

Frank is a Data Scientist that works heavily with big data systems. His work spans financial analysis and behavioral analytics to sports analytics. His primary interest is in machine learning and feature engineering on very large scales. You can check out his work here: https://github.com/frankdevans and http://www.slideshare.net/frankdevans.

We'll start the meetup at 11:30, but come a few minutes early to meet other Python developers, dabblers, hobbyists, and learners! Between meetups, you can chat with us in the #pythonista channel in Techlahoma's Slack. Get more information at https://www.techlahoma.org/spaces and sign up at http://slack.techlahoma.org/

Techlahoma provides pizza and drinks for our meetups. If you have any dietary restrictions, please notify the group organizers, and we'll do our best to accommodate them!

StarSpace46 parking:
https://static1.squarespace.com/static/57422f09ab48dec01e5e20c7/t/588fb33ee3df2828e8ceb5ab/1485814862865/ss46parking
You can park in the lot on the southwest corner of Sheridan and Klein. Enter through the wooden gate on the west side of the building, then go across the patio to the Techlahoma Event Space door.