The Course Bait-and-Switch
Photo by David Clode @davidclode on Unsplash
You signed up for an AI course to build the future. Instead, you got a statistics textbook and a headache. Since Machine learning remains one of the hottest topics for software developers in 2025, you might wonder if the course you signed up for will make the intended impact.
It’s no wonder that the interest level is high, given the dream of self-driving cars. The excitement of AI-powered medical diagnosis, and the thrill of seeing AI generated art. It’s all real, it’s all current and exciting technology.
So, you want to build neural networks that outthink humans or create the next ChatGPT? Naturally, you enroll in an online course.
Part one is likely an easy introduction. They show machine vision, and the possibilities pique your excitement.
Part two is where the linear regression starts. It doesn’t end there.
The Disappointment
You have probably dreamt of deep learning. Convolutional networks. Robots taking over the world.
You didn’t think the core of AI was plotting little blue dots on a graph and fitting a straight line over them. The reality sinks in, this is it.
The problem isn’t that machine learning courses teach linear regression. It’s that they don’t tell you that’s most of what you’ll be doing for a long, long time. You sign up thinking you’re part of the future, only to be hit with pages of calculus, gradient descent problems, and a sudden realization. AI is boring at its heart. You wanted to train a model to play chess like Magnus Carlsen, but first, you need to prove you understand matrix multiplication.
There’s nothing wrong with understanding the fundamentals. In fact, they’re crucial and helpful in understanding the core of AI. But this isn’t what people signed up for when they were shown a thumbnail of a self-driving Tesla recognizing pedestrians. I’ve looked at the numbers of people completing the Andrew Ng course, and the drop-off rate is huge after the first and second sessions of the course (in terms of ratings it drops 27k, to 7k, to 4k).
Where the Magic Really Happens
When you grind through the weeks (or months) of statistics, probability, loss functions you get to training a neural network.
Then you get the truth. AI isn’t magic, it’s simply math.
That’s why AI research is hard. You need to know the basics before you can move to understand AI on a basic level. Here’s the thing, those basics are boring.
This is a Good Thing
It’s completely understandable that AI courses don’t sell themselves as logistic regression and debugging gradient descent. There wouldn’t be many enrolled students, and for those people, the machine learning journey would never start.
Yet the opportunity is there for people who push through. It’s those people, the ones who embrace the mathematical grunt work, are the ones who actually make it in AI and get to the interesting stuff.
There’s a reason the job market isn’t flooded with self-proclaimed AI engineers, despite every tech influencer claiming, “AI is the future.” The reality is that most people quit when they realize AI isn’t just cool demos. The journey is a slog through algebra, probability distributions, and endless tweaking of hyperparameters.
If you’ve started this journey you’ll know there is a pot of gold at the end, you just need to work through it and you’ll get there in the end!
Conclusion
If you’re slogging through an AI course and wondering when it gets exciting, congratulations.
You’re on the right path. That’s because AI actually isn’t about futuristic technology and the magic of alien minds at all.
Like algorithmic programming, it’s about your own battle. It’s about your own patience, persistence and focus. It’s about being willing to fight through the boring stuff to get to the breakthroughs.
If you still think AI is magic, I’ve got news for you. Enjoy your linear regression, if not, you’ll never make it to part 3 of the course.