[abhgh]

In the specific case of ML courses - many of which I have TA-ed or attended classes of, this reason does not ring true at all. Libraries for most standard algorithms are available in some form with a Python interface (or for the more statistical stuff: R). Its almost always the inertia from the initial design of the course.

It is also not true today that not knowing Matlab harms your industry productivity in ML. This might have been true around a decade ago, but most teams outside academia also have moved to non-Matlab resources. And if anything, this has been further reinforced by Deep Learning libraries, the current crop of MLOps tools and cloud-based frameworks.

Matlab might be good for specific areas, but ML has not been a stronghold for a while. It is also important to remember that in the context of numerical accuracy or computation speed, Python is almost always the user-facing layer. You might (correctly) argue that the Python language is slower/faster than X, but this is not a useful metric for comparing libraries and frameworks, where the compute heavy code is probably in C/C++: numpy, tensorflow, pytorch are good examples of this.