Archive of month: June 2013

Visualising True Positives and False Positives against Features with scikit-learn

Here I’m starting to look into the errors caused in the social media brand disambiguator project. Below I look at true and false positives (correct and mistaken is-a-brand classifications) and plot them against the number of features that two different classifiers can use to calculate their class membership probabilities. First I’m using the default LogisticRegression […]

Visualising the internals of Logistic Regression on a Text Matrix

Below I have some plots that visualise the term matrix (as a binary matrix and as a TF-IDF matrix) for the brand disambiguation project followed by a visualisation of the coefficients used in scikit-learn’s LogisticRegression classifier using l1 and l2 penalties. Using a CountVectorizer with binary=True we can mark the absence or presence of a […]

Open Sourcing “The Screencasting Handbook”

Back in 2010 I released the finished version of my first commercial eBook The Screencasting Handbook. It was 129 pages of distilled knowledge for the budding screencaster, written in part to introduce my (then) screencasting company ProCasts to the world (which I sold years back) and based on experience teaching through ShowMeDo. Today I release […]

Social Media Brand Disambiguator first steps

As noted a few days back I’m spending June working on a social-media focused brand disambiguator using Python, NLTK and scikit-learn. This project has grown out of frustrations using existing Named Entity Recognition tools (like OpenCalais and DBPediaSpotlight) to recognise brands in social media messages. These tools are generally trained to work on long-form clean […]