The class is meant to introduce one to machine learning. As such, the problems are usually fairly simple and one wouldn't need to cheat unless all one is attempting to do it to solve those without looking at either the leture videos or slides.
(Translating from Python to Octave might, on the other hand, require more effort in comparison to implementing the solutions in Octave.)
Andrew Ng gives you a free introductory course in one of the hottest topics in computing, and in exchange asks you not to do one thing. And you do that one thing. I have my solutions in octave, and it would be really convenient for me to back them up on github, but I keep them on a usb stick for this very reason. I am respecting the man's wishes who was so kind as to teach me about machine learning.
You should take them down if you don't have the explicit permission to share them as the honor code states. You don't have explicit permission, do you?
And just as a practical matter, there are dozens of github repos with literal (as in copy, paste, submit, done) solutions to these problems already available.
You're certainly free to disagree, but I do not view this as violating either the spirit or the intent of the honor code. This content has been out for years and is not in any way novel or unique. It's simply another vector through which the material can be learned, possibly opening it up to an even wider audience. Which is, I believe, what Andrew's goal was all along.
When I took the class earlier this year, the answer was - effectively - "no". I mean, yeah, you could do some trickery with calling Python from Octave using whatever FFI Octave has, or you could possibly reverse engineer the protocol they use to talk from your code to the upstream server... but anybody doing all that would be doing more work that just completing the assignments in Octave to begin with.
https://github.com/mstampfer/Coursera-Stanford-ML-Python is an example.