I’ve lost count of how many “learn AI” courses I’ve bookmarked and never opened. The price tag made them feel important, so I kept saving them for a better week. That week never showed up.
Then I ran into a post from the founder of an AI startup who admits to doing the same thing, except they actually paid. The author bought every AI course they could find. Half of them turned out to be repackaged YouTube videos with a checkout page bolted on. Their takeaway flipped how I think about this whole topic, so let me walk through the myths they take apart one by one.
Myth 1: Real AI expertise costs money
This is the big one. People see the $2000 cohorts and the paid masterclasses and assume that’s where the serious knowledge lives. The author’s experience says otherwise. The best material was never behind a paywall. It was just scattered across a dozen sites, and nobody bothered to organize it. So the original poster did the organizing.
Myth 2: Waiting for the “right” course is smart
Bookmarking feels productive. It isn’t. The post puts it bluntly: “Later never comes.” Meanwhile the actual foundations sit there, free and untouched. The cost of waiting isn’t the $2000 you save. It’s the months of skill you never build.
Myth 3: A bigger price means faster results
Running a startup, this industry pro watches the same pattern with every new hire. The people who ramp up fastest didn’t buy a premium program. They picked two or three free courses and finished them. Consistency beats the price tag every time. That’s the whole secret, and it’s a little annoying how simple it is.
The 20 free courses the creator recommends
Here’s the full list from the post, with the author’s one-line note on each. No fluff, no upsell:
- Elements of AI: non-technical intro to ethics and ML basics
- AI For Everyone: Andrew Ng’s jargon-free course for non-engineers
- Google AI Essentials: practical AI skills for daily work
- Generative AI Basics: how gen AI differs from traditional ML
- Large Language Models: how LLMs actually work
- AI For Beginners: Microsoft’s 24-lesson open source curriculum
- Generative AI For Beginners: 18 lessons on prompting and responsible AI
- IBM AI Fundamentals: foundations plus a digital badge
- Responsible AI Basics: fairness, bias, and accountability
- AI Ethics Basics: the societal impact nobody talks about
- ML Crash Course: Google’s 15 hour fundamentals with TensorFlow
- ML Specialization: Andrew Ng on supervised, unsupervised, reinforcement learning
- Intro To Machine Learning: the core supervised workflow with Python
- Intro To Deep Learning: neural networks, activation functions, dropout
- Computer Vision: convolutional networks and transfer learning
- ML Zoomcamp: ML basics all the way to production
- Google Responsible AI: turning principles into real deployment decisions
- DL Specialization: Andrew Ng’s five course deep learning flagship
- Neural Networks Zero To Hero: build GPT from scratch in Python
- MIT Artificial Intelligence: the classic full course, lectures and problem sets
20 courses. Zero cost. Every one of them worth finishing.
How I’d actually use this list
Don’t try to do all 20. That’s just the paid-course trap in a free costume. Pick based on where you are right now:
- Complete beginner or non-technical: start with Elements of AI or AI For Everyone, then Google AI Essentials for daily-work skills.
- Want to know what’s under the hood: Large Language Models, then Generative AI For Beginners.
- Ready to code: ML Crash Course, then Intro To Machine Learning, then Neural Networks Zero To Hero if you want to build GPT from scratch.
- Going deep: the ML and DL Specializations from Andrew Ng, plus the MIT course for the full academic foundation.
The truth worth acting on
The barrier to learning AI was never money. It was finishing. Pick two courses, block the time on your calendar, and actually complete them.
The author says they’ve already saved this list for their own team, and honestly, I’d do the same. If you know someone who keeps saying they’ll learn AI “eventually,” this is the nudge to send them.
Head over to the full LinkedIn post to read the original write-up and tell the creator which course you’re starting with.