Monthly Archives: November 2013

Machine Learning – Linear Regression (Part 2)

Continuing from where we left last post, we were discussing the batch gradient algorithm. The iterative formula is The most obvious way to do this in normal Python would be to run a double nested loop, one from 1 to m to sum up the partial derivative term, and the other from 1 to n. […]

Machine Learning – Linear Regression (Part 1)

Hi, before I start writing anything, I would say sorry if this article (and the forthcoming articles hopefully) seems plagiarised. It mainly is from different sources, that I’ve written in my own words, mostly for my own reference, I would really love to see someone else benefit from this too, so here we go. Lets […]

Machine Learning – A few basic concepts

Hi, I had taken this informal Stanford machine learning course last year, and since my GSoC finally got over I thought I would take it a bit more seriously. The Machine Learning course was in octave, and I started implementing a few algorithms using Python and Numpy. Here are a few must now concepts that […]