In case you missed my free webinar on “Model-Based Machine Learning“,  here is the recording.

Apologies for the poor quality of the video. Domino Data Lab’s webinar platform suffered a service degradation while recording the event. The webinar slides may be found below.

If you have any questions, please do not hesitate to contact me. Finally, I would like to thank Daniel Enthoven and Daniel Chalef from Domino Data Lab for setting up this webinar.

A couple of months ago, I announced the ggplot2-extensions website which tracks and lists extensions built on top of the popular R visualization package ggplot2.

Now, I wanted to make it even easier for R users to filter and search for these extensions and so I have added a Gallery page. You can now search packages based on a filter like: if it’s on CRAN; or if  it’s for a particular task e.g. time series, networks, tech, etc.


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I am glad to announce that I shall be presenting a live webinar with Domino Data Labs on July 20, 2016 from 11:00 – 11:30 AM PST on Model-Based Machine Learning and Probabilistic Programming using RStan. If you are interested in adopting machine learning but are overwhelmed by the vast amount of learning algorithms, this webinar will show how to overcome that challenge. This blog post describes most of the material we will cover in the webinar. Here is the abstract for the webinar:
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I am excited to be invited by the United Nations Global Pulse lab to speak at the 2nd Data Science Africa Workshop scheduled to take place in Kampala, Uganda from 30th June to 1st July. The theme of this workshop is “Using data science to monitor and achieve the global goals (UNDP goals) in Africa“. I will be speaking particularly on “Data Science for Sustainable Cities”. My talk is titled: “Sustainable Urban Transport Planning using Big Data from Mobile Phones“; which is the work I am doing as part of my PhD research. Particularly, I will talk about how developing countries can leverage low-cost, readily available and massive amounts of mobile phone data to improve their Transportation Planning policies.

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