Blog
Practical advice to make an impact with Machine Learning
Key learnings from Strata Barcelona 2014
Immediately after PAPIs.io '14 I spent a couple of days at Strata in Barcelona. Strata has several tracks and I ended up going mostly to “business” sessions, but this synthesis of things I heard at the conference will be of interest to technical people as well. Actually there was one business session that had more code in it than another data science session I went to!
Here is my selection of key take-away messages, from sessions I attended (Strata is a huge conference so this is just a very partial view of it):
Qucit launches world’s first bikeshare predictive API
Big data startup Qucit released this month the world’s first bikeshare predictive API, tightly integrated in the popular mobile app for Bordeaux bikes. This is an inspiring example of Machine Learning usage in the real world. One of the value propositions of Predictive is better resource management. Here, in the "smart city" context, it impacts our everyday lives.
Import.io webinar: everyone can do data science
Last week I visited the Import.io offices in London and did a webinar with them in which I showed:
- how to use their tool to easily scrape real estate data from the web
- how to clean that data with the Pandas library in Python
- how to build a real-estate pricing model by sending the clean data to BigML.
Calling All Predictive APIs & Apps Makers
I will be chairing the PAPIs.io conference taking place on 17-18 November 2014 in Barcelona, right before Strata. It will be the first ever international conference dedicated to Predictive APIs and Predictive Apps. If you're interested in presenting your work in this space, a Call for Proposals is open until 8 October 2014.
6 questions you should ask about prediction APIs
As machine learning and predictive analytics services become more widely embraced in the business world, predictive APIs are starting to open up. When evaluating this class of API, it is useful to have a common set of questions — the answers to which will help determine whether prediction APIs are a good fit for your needs and to steer you toward the best product for your organization.
How to predict abstention rates with open data
Open data is a way to increase transparency into what happens in our society. When coupled with predictive modelling, it becomes a way to interpret why things happened. Even though it sounds complex, these techniques have become accessible to the masses. Let's see how this works with elections data.
Co-authored with Alexandre Vallette
Training, interview and books to win with HumanCoders
I am proud to announce that I have teamed up with HumanCoders to set up a groundbreaking Machine Learning training program which is based on Prediction APIs and brings you up to speed in 3 days. At this occasion, they interviewed me and I gave them 3 copies of my book, Bootstrapping Machine Learning, to give away — here's your chance to snatch one of them!
How to Improve Your Subscription-Based Business by Predicting Churn
Churn prediction is one of the most popular Big Data use cases in business. It consists of detecting customers who are likely to cancel a subscription to a service. Although originally a telco giant thing, this concerns businesses of all sizes, including startups. The problem of churn prediction can be tackled with machine learning techniques. Now, thanks to prediction services and APIs, this sort of predictive analytics is no longer exclusive to big players that can afford to hire teams of data scientists.
Automating the Data Scientist
What is a Data Scientist? What is it that they do that is now being automated? What are the new solutions out there that are bringing Data Science directly to domain experts? What is it changing?