Review of the course Machine Learning
Back to the courseMachine Learning on Coursera
You may remember that in June, I finished a Udacity online course and decided to take a Coursera course in the near future. I've just finished taking Andrew Ng's Machine Learning course, and would like to wrap up my thoughts about it, as I did for CS262. This post will mainly be about evaluating ML on it's own merits, and then I'll follow it up with another post comparing Udacity to Coursera later on.
The basic premise and structure of the Machine Learning course is pretty simple. Over a 10 week period, a range of topics are covered through short video lectures, and then followed up with review questions and programming exercises. Topics covered include logistic regression, neural networks, clustering and recommender systems. You'll normally cover one or two topics in a single week, totaling up to two or three hours of lectures. Review questions are a simple multiple-choice affair, and will only take 5 minutes each, while you can expect to spent a good couple of hours on each of the eight programming exercises. For me, this totalled up to around 5 hours of work per week, which is pretty close to what Coursera suggests on the course page.
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