Stanford Machine Learning Class


For the past 5 weeks I have been doing the Stanford Machine Learning class offered by Coursera. In the past I have started and stopped taking the class but this time around I am fully committed and now into week 5. I’ve learned so much so far. I have always used functions like logistic regression in R and understood a little about how it worked down to the sigmoid function however this class has really given me an appreciation down to the linear algebra that is happening under the hood. The neural networks section has also been revealing. I had always assumed neural networks to be unapproachable however so far the class is doing a great job of breaking it down. I have now applied a neural network from scratch using linear algebra in Octave.

This is definitely a great value for the material and quality of the videos and exercises. I decided to do the certificate version because to me this is a core curriculum class in my MOOC journey. I still have quite a few more weeks in this class however I’m already looking to the next one which will probably be the Caltech EdX Learning from Data Class.

While I am used to python, matlab, and R my initial reaction to Octave has been a little mixed. While it was incredibly easy to grasp the syntax the submission portion of all the classes has been plagued with coding errors. I had to search stack overflow until I found the correct fix. Also I had to install version 4.2.0 and not all versions of Octave worked. I understand the reasoning for choosing Octave though as R does have some data type issues for those new to it and the focus is really on the math not the coding syntax. Also matlab isn’t really an open source nor cheap option for most. Overall I’ve been very happy with this course. I think the presentation and exercises complement the John Hopkin’s Data Science courses and should be taken after the fundamentals of those classes are complete.


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