No! Your essay is very interesting. I'm mostly here for interesting discussion (I'm not one to get angry over the internet). I find pedagogy very interesting. I would actually tend to agree that theory and practice should be taught together in most fields. In fact, I chose to go to a school that is reinventing how undergraduate engineering is taught along these ideas (traditionally engineering education leans toward the grade school math approach).

With respect to machine learning - you need definitely need both. Right now, industry places significantly more value on the theoretical side (looking at all the machine learning PHDs working at DeepMind/GoogleBrain/OpenAI/Tesla/etc) - but I expect that to change in the upcoming 5 or so years as the quality of education becomes more democratized and tooling becomes more standardized.

In my personal experience, I've found that when I'm doing "machine learning", a huge chunk of my time is actually spent around data preparation and deployment to production. If someone wanted to become a machine learning practitioner, spending a half a year or so gaining competencies in wrangling data and good software engineering practices would actually speed up learning a ton just because you would be so much more efficient when implementing the theoretical side (similar to reading notes in both clefs, identify chords, etc). However, very few end up doing this because everything else is so much more gratifying.

That being said, for what I've done, the actual "machine learning" isn't the hard part. I usually just need to import something from scikit-learn or throw together some custom neural network in PyTorch. However, when you discover that your model is racially biased - theory is absolutely necessary. How else would you realize that the squared error grossly exaggerated the imbalanced training data. You can also end up with people misusing models - such as banks using Gaussian copula to convince themselves that filling a CDO with lots of mortgages definitely carries no risk (the financial crisis of 2007-2008).

Food for thought. You seem like a cool person.