This post contains my answers from a Quora session I did on machine learning and artificial intelligence. Each section contains a link to the original Quora question, the overall session can be found here.

Every survival analysis method I’ve talked about so far in this series has had one thing in common: we’ve only looked at one event in a customer lifetime (churn). In many cases, that’s a perfectly fine way to go about things… we want our customers to stick with us, so churn is the event of interest. So why would we ever need to think about competing risks?

My last blog wasn’t so sexy, what with all the data cleansing, and no predictive modelling. But in this blog I do something really cool – I train a machine learning model to find the left ventricle of the heart in an MRI image. And I couldn’t have done it without all of that boring data cleansing. #kaggle @kaggle

In Part 1 of this blog series, I described how to fix the brightness and contrast of the MRI images. In this blog we finish cleaning up the input data.

I’m currently competing in the Second Annual Data Science Bowl at Kaggle. This is by far the most difficult competition that I have entered to date. At the time of writing I am placed 62nd out of 755 entries, with only a day remaining to lock down my methodology. There’s a lot more I’d like to do to improve my model, but alas, I don’t have the time!

Together with other scikit-learn developers we’ve created an umbrella organization for scikit-learn-related projects named scikit-learn-contrib. The idea is for this organization to host projects that are deemed too specific or too experimental to be included in the scikit-learn codebase but still offer an API which is compatible with scikit-learn and would like to benefit of the visibility of being labeled as scikit-learn-compatible.

In this post I’ll show you some animations of tiny neural nets learning. Finding the right neural net for a given problem needs experience and experimentation. I’ll show you the steps and missteps it took me to find a good net to predict a noisy sine function.

The best way to learn an algorithm is to watch it in action. This is why I created the simplest possible neural network in Keras. It’s just a single neuron. We will train it on the simplest nonlinear example.

It seems it may only be a matter of time before the best Go player on the planet is a computer. AlphaGo beat the European champion in Go and was driven by machine learning, a technology that has underpinned the recent major advances in artificial intelligence in computer vision, speech recognition and language translation.

I recently had a chat with Benjamin Bengfort, a data scientist finishing his PhD at the University of Maryland, and Jenny Kim, a software engineer at Cloudera, about their forthcoming O’Reilly Media book (now in Early Access), Data Analytics with Hadoop: An Introduction for Data Scientists.