|I'll spend all my time next week studying machine learning. However, I know that it's easy to get lost in overly theoretical material when learning ML.|
So I'm interested in some advice on how to best spend my time, given that I'd like to get as much practical knowledge as possible.
Here is the level I'm at now:
I'm currently halfways though Andrew Ng's ML course on Coursera, and will probably finish this within the week. I love the mix of theory an practice this course is based around.
I've also done the Udacity - Intro to Machine Learning, but found it too theoretical.
I kind of understand the basic principles of linear & logistic regression, cost functions and gradient descent & the normal equation.
By the end of the week, I hope to be able to do linear regression using gradient descent on an actual dataset. If so, the week has been very well spent!
My preferred language is Python.
All tips and suggestions are highly appreciated :)