Then you might actually want to start in Matlab / Octave with Mchael Ng's course...

criddell · on Sept 17, 2015

I think you are talking about Andrew Ng's course.

I completed it and can't recommend it more highly. It is a really excellent, dense course and Ng is a very good teacher.

https://www.coursera.org/learn/machine-learning

joshz · on Sept 17, 2015

Geoffrey Hinton's archived course is all about neural nets, I think you can enroll in the archived version, no code, just theory.

https://www.coursera.org/course/neuralnets

artmageddon · on Sept 17, 2015

I've completed the course as well - have you used any of the knowledge from it on anything in particular after you completed the course?

zardo · on Sept 17, 2015

I'm taking the Coursera course right now. The course page at Stanford has a lot of student projects. The breadth of applications is pretty huge, definitely worth a check if you're looking for an idea.

http://cs229.stanford.edu

brudgers · on Sept 17, 2015

I am working through Ng's course currently. It is hitting the right tones against my mathsephobia...keeping me constantly in that state of semiunderstanding that is intuition, a term Ng uses often.

His choice of Octave/MatLab simplifies issues of dependencies. In particular the soft ones of documentation and community. This is something a lot of academic contexts get wrong with software: the tools are either to open ended and students wind up manipulating matrices with forloops or there's an inflexible stack of professional tools that require massive effort to learn and an orthogonal community or there is a toy IDE based on a senior thesis.

Octave more or less follows the Unix philosophy of doing one thing and thus can meet many people where they are rather than with a one true way.