Learning machine learning can feel like a big commitment. But if you start with the right course—one that explains things simply and doesn’t require a PhD—you’ll find it much easier to stick with. The best part? You don’t have to pay anything. Several universities offer free online courses that cover machine learning from the ground up. No hidden fees, no trial periods, and definitely no need to pay before you know what you’re getting into.
Below are seven standout university-level courses you can take for free. Each one brings something different to the table, so whether you’re looking for short explanations or full-on theory, you’ll find something here.
7 Free University Courses to Learn Machine Learning
Stanford University – Machine Learning by Andrew Ng
This is the course that most individuals begin with—and for a reason. Andrew Ng, Coursera co-founder and one of the more recognizable names in AI, does a great job of explaining machine learning. The course deconstructs things into bite-sized chunks: linear regression, neural networks, and supervised learning, to name a few. You don't need to be a math whiz to keep up. There is a little bit of coding, but you can do it at your speed.
One thing to keep in mind: it's MATLAB/Octave-based, not Python. That catches some people off guard, but the reasoning remains the same. And if you ever do need to port that knowledge over to Python, it's not too bad.
MIT – Introduction to Deep Learning
This one’s a bit more fast-paced. If you already have some idea of how machine learning works and want to get into deep learning quickly, this course is a solid pick. It’s from MIT, and it’s free on their OpenCourseWare site. The lectures are clear, the projects are challenging but doable, and you’ll walk away with a better understanding of convolutional neural networks, reinforcement learning, and transformers.
Plus, it uses TensorFlow and Python, which means the tools you’re learning are actually used in real-life machine learning jobs. So, if you're hoping to build something after the course, you're in a good spot.
Harvard – CS50’s Introduction to Artificial Intelligence with Python
You may have already heard of Harvard’s CS50. It’s one of the most popular computer science courses online. This version dives into artificial intelligence but also covers the machine learning basics clearly. It starts slow, with search and constraint satisfaction problems, then moves into machine learning topics like classification and neural networks.
The exercises are what really make this one stick. You'll build projects using Python, and by the end, you'll have a mini portfolio to show off. Everything is free, and there's an active community, too—so if you get stuck, you're not on your own.
University of Washington – Machine Learning Specialization
This course is split into multiple parts and hosted on Coursera. It focuses on practical skills more than theoretical ones, which works well if you're the kind of person who learns by doing. You’ll work with real datasets, use Jupyter Notebooks, and get to know tools like Scikit-learn and Pandas along the way.
The professors explain things in plain language, and they don’t expect you to know everything upfront. If you have a bit of Python experience and can follow basic math, you’ll do fine. You’ll also start recognizing common ML patterns that pop up in real-world tasks.
Columbia University – Machine Learning by Prof. John Paisley
Columbia’s course is a little more academic. It doesn’t cut corners, but it’s still beginner-friendly if you take your time. You’ll explore supervised learning, clustering, and some foundational math concepts that explain why these models work in the first place.
It’s available through edX, and you can audit it for free. If you’re someone who likes to understand not just how but why things work, this one’s a good choice. You’ll get that deeper understanding without being overwhelmed by theory.
Caltech – Learning From Data
This one’s for people who want a more structured classroom feel. It’s taught by Professor Yaser Abu-Mostafa and is freely available on YouTube. It focuses on the core ideas behind machine learning: what learning means in a statistical sense, how to build models, and what limitations you’ll run into.
It’s not light on math, but the professor explains each idea clearly, and he builds concepts up one at a time. If you like thinking in systems and want to see how ML works from the ground up, this course covers a lot of ground without rushing you through.
University of Helsinki – Elements of AI
This course is a little different from the rest. It’s not exclusively focused on machine learning, but it does have a dedicated section on it. It’s great for someone who’s starting from scratch and wants a gentle introduction. The explanations are simple, the interface is clean, and the whole thing is self-paced.
What makes this course special is how accessible it is. You don’t need any background in coding or math to understand the lessons. It’s more about concepts and less about implementation, which makes it a good warm-up before diving into more technical courses.
What Makes These Courses Worth It?
None of these courses ask for your credit card. That already sets them apart from a lot of other “free” resources. More importantly, these are taught by real professors from top universities—people who know what they’re talking about and care about making the material understandable.
They’re also structured. So you’re not just watching random YouTube tutorials or reading disconnected blog posts. You’re learning step-by-step, which matters a lot when dealing with something as layered as machine learning.
One Last Thing
It’s easy to feel stuck or behind when starting out in machine learning, especially when others seem way ahead. But none of them started as experts. They watched the same videos, got stuck on the same problems, and googled the same questions you’re about to google. The only difference is—they kept going.
With these free courses, you’ve got more than enough to get started. All that’s left is to choose one and hit play.
