Now that I’m no longer working at Jukedeck, I happen to have plenty of free time on my hands! I’ve been spending this time travelling, catching up on my reading list, helping out with activities of the Music Tech Community – India and making music among other things. In an effort to satisfy a long-standing curiosity, I signed up for the Recommender Systems specialisation being offered on Coursera by University of Minnesota, and recently completed it. It comprised of four courses:

1. Introduction to Recommender Systems: Non-personalised and Content-based (certificate)
2. Nearest Neighbour Collaborative Filtering (certificate)
3. Recommender Systems: Evaluation and Metrics (certificate)
4. Matrix Factorisation and Advanced Techniques (certificate)

It took me about a month to complete all four courses at a fairly liesurely pace given how much time I had at my disposal while not working. This was a very well-taught specialisation with some of the best-designed Courses I’ve done on Coursera so far. It covered a wide range of topics that offered a comprehensive overview of a vast area of research. Solving the assignments by hand was a new, but very engaging experience that really allowed me to focus on what actually happens at a very basic level under-the-hood in such systems. It was all done by implementing the various formulae for content-based filtering, item-item collaborative filtering, user-user collaborative filtering (including matrix factorisation methods) in spreadsheets. There was an Honours Track in each course that focused on implementing the various types of recommender systems and related concepts that I decided not to pursue, as all the programming was in Java. I decided I would follow the courses up with my own implementation projects in Python as that’s something of greater interest to me. So now I’m looking for little projects to get me going.

I would definitely recommend this specialisation to anyone interested in Recommender Systems. It has left me with a very good understanding of the basics and a fair idea of the various directions in which I can pursue things in more detail. Not to mention, a tonne of references to read up on which I look forward to doing along with implementing some of the algorithms in the coming weeks.