Fast Company came to our office to film a short interview with me. If you’ve been following bitly closely there won’t be any surprises for you here, but I did get to talk about some of the exciting projects that we’re working on.
The best part about filming the interview was getting back to my computer after, where my clever co-workers had been silently answering the questions along with me in our chat room — with a lot more snark.
I’m really excited that An Introduction to Machine Learning with Web Data is now available for purchase!
This is a 2 hour and 43 minute instructional video that walks you through basic machine learning algorithms, first theoretically and mathematically, and then with Python example code (which is available here).
This video is an instructional take and builds on the material I covered in my Strange Loop 2010 keynote Machine Learning: A Love Story and the Data Bootcamp I did with Joe Adler, Drew Conway, and Jake Hofman at the Strata Conference in February.
I’d also like to acknowledge the many collaborators, colleagues, and friends who have made definite contributions to my thinking about this material and how best to present it, particularly Chris Wiggins who co-authored A Taxonomy of Data Science and Andrew, Dennis, Jan, Jesse, and Julie, the members of the studio audience for the class (who were amazing).
If you like it, please leave it a good review! As always, questions and comments are welcome here or by e-mail.
Special thanks to the always awesome Adam (from MakerBot) for helping us breeze through the final configuration and calibration steps.
The video from my keynote at Strange Loop 2010 is up!
You can watch the video here: Machine Learning: A Love Story
The original abstract:
Machine learning has come a long way in recent years — from a long-marginalized field so old it still has the word “machine” in the name, to the last, best hope for making sense of our massive flows of data.
The art of ‘data science’ is asking the right questions; the answers are generally trivial or impossible. This talk will focus more on questions than on answers. I’ll give a brief history of the field with a focus on the fundamental math and algorithmic tools that we use to address these kinds of problems, then walk through several descriptive and predictive scenarios.
Finally, I’ll show one example system using bit.ly data in-depth, from the backend infrastructure through the algorithms and data processing layer to show a functioning product.
Attendees should expect to hear some good stories of data gone right and data gone awry, and walk away with a few new clever tricks.
The presentation was calibrated for the audience in the room, but I’ll be happy to answer any questions in the comments below!