Archives of ArtificialIntelligence

Kaggle’s Quora Question Pairs Competition

Kaggle‘s Quora Question Pairs competition has just closed, I’m pleased to say that with 10 days effort I ranked in the top 39th percentile (rank 1346 of 3396 in the private leaderboard). Having just run and spoken at PyDataLondon 2017, taught ML in Romania and worked on several client projects I only freed up time […]

Scikit-learn training in London this April 7-8th

We’re running a 2 day scikit-learn and statsmodels training course through my ModelInsight with Jeff Abrahamson (ex-Google) at the start of April (7-8th) in central London. You should join this course if you’d like to: confidently use scikit-learn to solve machine learning problems strengthen your statistical foundations so you know both what to use and why […]

Data-Science stuff I’m doing this year

2014 was an interesting year, 2015 looks to be even richer. Last year I got to publish my High Performance Python book, help co-organise the rather successful PyDataLondon2014 conference, teach High Performance in public (slides online) and in private, keynote on The Real Unsolved Problems in Data Science and start my ModelInsight AI agency. That […]

Starting Spark 1.2 and PySpark (and ElasticSearch and PyPy)

The latest PySpark (1.2) is feeling genuinely useful, late last year I had a crack at running Apache Spark 1.0 and PySpark and it felt a bit underwhelming (too much fanfare, too many bugs). The media around Spark continues to grow and e.g. today’s hackernews thread on the new DataFrame API has a lot of […]

New Data Science training in April – Machine Learning (scikit-learn and statsmodels) and High Performance Python

In April my ModelInsight data science agency will be running two sets of 2-day training courses in London: Understand Statistics and Big Data using Scikit-Learn and Friends (April 7-8) including scikit-learn and statsmodels with a strong grounding in the necessary everyday statistics to use machine learning effectively High Performance Python (April 9-10) covering profiling (for […]

Annotate.io self-learning text cleaner demo online

A few weeks I posted some notes on a self-learning text cleaning system, to be used by data scientists who didn’t want to invest time cleaning their data by hand. I have a first demo online over at annotate.io (the demo code is here in github). The intuition behind this is that we currently divert […]

A first approach to automatic text data cleaning

In October I gave the opening keynote at PyConIreland on The Real Unsolved Problems in Data Science. One of the topics I covered was poor quality data, by some estimates data cleaning occupies 50-80% of a data scientist’s time. Personally I’ve just spent the better part of last year figuring out ways to convert poorly-represented […]

Why are technical companies not using data science?

Here’s a quick question. How come more technical companies aren’t making use of data science? By “technical” I mean any company with data and the smarts to spot that it has value, by “data science” I mean any technical means to exploit this data for financial gain (e.g. visualisation to guide decisions, machine learning, prediction). […]

PyDataLondon 3rd event

This week we had our 3rd PyDataLondon meetup (@PyDataLondon), this builds on our 2nd event. We’re really happy to see the group grow to over 400 members, co-org Emlyn made a plot (see below) of our linear growth. Our main speakers: Andrew Clegg (chief Data Scientist at Pearson Publishing in London) spoke on his Snake […]

What confusion leads from self driving vehicles and their talking to each other?

This is a light follow-up from my “Do self driving cars make the courier redundant?”  post from January. I’m wondering which first- and second-order effects occur from self-driving cars talking to each other. Let’s assume they can self-drive and self-park and that they have some ability to communicate with each other. Noting their speed and […]