I’ve lost count of the times I’ve bookmarked an AI course, told myself I’d start it “next week,” and then never opened it again. If that sounds like you, this one’s for you. I just found a post from an AI startup founder that turns that whole habit upside down, and it’s one of the most useful lists I’ve come across all year.
The author starts with an idea that’s simple and a bit uncomfortable: every AI skill worth learning is free right now. Most people assume real AI expertise costs money. They see the $2,000 cohorts and the paid masterclasses, so they wait. They bookmark it for later.
Later never comes.
Meanwhile the actual foundations are sitting there, free, untouched.
The Paywall Myth
The original poster is honest about how they got here. They used to pay for every AI course they could find. Half of them turned out to be repackaged YouTube videos with a price tag on top.
Their takeaway stuck with me: the best material was never behind a paywall. It was just harder to find, and nobody bothered to organise it. So the author did it themselves.
Now they run an AI startup, and they see the same pattern with every new hire. The people who move fastest didn’t buy a $2,000 program. They picked two or three free courses and actually finished them.
Consistency beats the price tag every time.
That line is the whole post in six words. Spending money feels like commitment, but finishing is what counts. Here’s the full list of 20 free courses the expert put together, no fluff and no upsell, with a bit of context from me on who each one suits.
Start Here: Non-Technical Foundations
If you don’t write code, start with these. They build intuition without math anxiety.
- Elements of AI: A non-technical intro to ethics and ML basics. Perfect if you want to understand what AI actually is before touching any tools.
- AI For Everyone: Andrew Ng’s jargon-free course for non-engineers. Great for managers and marketers who need to talk about AI with confidence.
- Google AI Essentials: Practical AI skills for daily work. Think emails, research, and planning with AI as your sidekick.
- Generative AI Basics: How gen AI differs from traditional ML. It clears up the difference between a model that predicts and one that creates.
- Large Language Models: How LLMs actually work. Once you get this, your prompts get much sharper.
Structured Beginner Curriculums
Ready for something with more structure? These read like a full syllabus.
- AI For Beginners: Microsoft’s 24-lesson open source curriculum. A solid, step-by-step path you can follow week by week.
- Generative AI For Beginners: 18 lessons on prompting and responsible AI. Hands-on and very practical for anyone building with AI tools.
- IBM AI Fundamentals: Foundations plus a digital badge. Nice if you want something to show on your profile.
The Responsibility Layer
This is the section most people skip, and the creator clearly thinks that’s a mistake. I agree.
- Responsible AI Basics: Fairness, bias, and accountability. Essential if your AI work touches real customers.
- AI Ethics Basics: The societal impact nobody talks about. It helps you spot problems before they become headlines.
Machine Learning for Builders
Now we get technical. These are for people who want to build models, not just use them.
- ML Crash Course: Google’s 15-hour fundamentals with TensorFlow. A fast, focused way to learn the core concepts.
- ML Specialization: Andrew Ng on supervised, unsupervised, and reinforcement learning. The classic path, updated and beginner-friendly.
- Intro To Machine Learning: The core supervised workflow with Python. Short lessons, real code, quick wins.
- Intro To Deep Learning: Neural networks, activation functions, and dropout. This is where the “magic” behind modern AI starts to make sense.
- Computer Vision: Convolutional networks and transfer learning. Ideal if you’re curious how machines “see” images.
- ML Zoomcamp: ML basics all the way to production. It covers the part most courses ignore: actually shipping a model.
- Google Responsible AI: Turning principles into real deployment decisions. Ethics, but applied to real engineering choices.
The Deep End
These are the heavy hitters. Save them for when the basics feel comfortable.
- DL Specialization: Andrew Ng’s five-course deep learning flagship. Thorough, demanding, and very rewarding.
- Neural Networks Zero To Hero: Build GPT from scratch in Python. Honestly, this is the one I’m most excited about. Few things teach you more than building the thing yourself.
- MIT Artificial Intelligence: The classic full course, lectures and problem sets. University-level depth for free.
20 courses.
Zero cost.
Every one of them worth finishing.
How to Actually Finish One
A list this good can turn into another bookmark graveyard, so here’s how I’d put the author’s advice into practice:
- Pick two or three, not twenty: That’s exactly what the fastest-moving hires did. One foundation course plus one hands-on course is a great combo.
- Match the track to your role: Non-technical folks can start with items 1 to 5. Developers can jump straight to 11 to 16.
- Schedule it like a meeting: Block 30 minutes a day. Small, steady sessions beat weekend binges.
- Apply something each week: Use what you learned in a real task right away, even a small one.
The author has already saved this list for their own team, and that tells you a lot about how much they trust it. They also suggest sending it to someone who keeps saying they’ll learn AI “eventually.” We all know at least one person like that (sometimes it’s us).
The original poster wants to know which course you’re starting with, so head over to the full LinkedIn post, see the complete list, and share your pick in the comments.