By Michael Jones
Generative AI is everywhere, so I figured I’d make myself useful to people trying to break into the field but unsure where to begin. As a side project, I started scraping through Reddit and Quora to see what others think and noticed there are a lot of great advice and courses being mentioned. Long story short, I made a list, picked out the top-rated courses based on user reviews, and signed up for them.
Behind the Process
There’s a huge amount of AI-related online courses floating around. Some are free, some are paid. Some can be found on Udemy and similar reputable websites, some can be found for free on Youtube, while others come from no-name places. It’s both good and bad news for people looking to break into the field.
I quickly realized that asking ChatGPT to “give me the best AI courses” wasn’t going to cut it. So I decided to put in some actual elbow grease. I started by scraping hundreds of Reddit and Quora posts where people discussed AI courses. Real opinions, real experiences. I pulled everything into a single Excel sheet and flagged the courses that kept coming up the most. But just knowing which names were popular wasn’t enough. I wanted context. I didn’t know much about most of them beyond the occasional mention, so I dug deeper. I began categorizing them by difficulty. Beginner, intermediate, and advanced. This allowed me to better understand where each course fits and who it’s really for. Next step was actually signing up to them. But then I realized I’ll have to rank them by several factors. First, user ratings and the syllabus obviously, but also how tolerable the instructors were, whether the video and audio didn’t sound like they were recorded in a hallway, how deep the material actually went beyond buzzwords, and whether the assignments made you think or just tick boxes. Community engagement was part of it, too. Some courses have active forums, others might as well be voicemail boxes. Let’s get going.
Prerequisites
Most AI courses start with an unspoken expectation. You should already know probability, linear algebra, calculus, and how to code. This isn’t always made explicit, but it’s logical. Without that background, things can get complicated for you. But no need to despair. You could start with an AI course, and when gaps appear, you loop back to the prerequisite material, dig through a textbook, or skim a YouTube video.
A deep dive into the math isn’t mandatory. You might think it is, but plenty of people skip it and still manage to build things that work. Python, on the other hand, helps. The libraries are there. The docs are long but readable. Stack Overflow fills in the rest. If you can code, you’re already most of the way in.
Best AI Online Courses for Beginners
Best advice for beginners? Don’t jump into diffusion models or whatever’s trending on Hacker News. Start with the basics. Unfortunately yes, that means the boring stuff – neural networks. Without the fundamentals, you’re not learning anything. Neural networks are one of those things that punish guesswork. So here’s what you can do first.
AI for Everyone from Deeplearning.ai
This is one is largely non-technical, 4-module course that unpacks common AI terminology like neural networks and machine learning. You’ll learn what AI can actually do and what it can’t do, but more importantly, where to even use it in your work. You’ll see what building machine learning projects feel like, and how to work with AI teams to build an AI strategy for your company. Who is this for? Everyone who wants to cover all aspects of AI, from basic terms to ethics. Just note its focus is not on engineering. Don’t expect you’ll be launching your own AI companion service or landing a job in OpenAI. But it’s a great start.
AI Nanodegree from Udacity.com
If you’re looking for something beginner-friendly, but slightly more advanced and geared for engineers, then Artificial Intelligence Nanodegree by Sebastian Thrun and Thad Starner is probably the best thing you’ll find online right now. It starts with search algorithms, symbolic logic, planning systems. Stuff you’ve probably seen referenced in passing, now unpacked properly. You’ll be building agents that can plan, optimize, and make decisions without hand-holding. There’s a practical side to it too: algorithms for problem-solving, automated planning, things that quietly run in the background of robotics labs or warehouse management systems. At some point, you’ll stop thinking in “if-then” and start modeling decisions like a machine would. The only downside is that some people might find Udacity a bit on the expensive side. But in my opinion, it’s worth it because the course is really excellent.
Best Intermediate and Advanced AI Online Courses
I get that some of you might be engineers, data analysts, or software developers looking to transition into AI. With that in mind, I’ve made sure to include a mix of intermediate and advanced courses alongside the beginner-friendly ones. But here’s something I’ve learned the hard way: skipping the basics can come back to bite you later. Even if you have a computer science degree or a solid programming background, I still recommend going through the foundational AI courses. Not because they’ll teach you how to code, but because they’ll help you understand how machine learning models actually work, where their blind spots are, and what kinds of problems they’re best suited to solve. AI isn’t just another API you plug in. It’s a different way of thinking about data and systems, and the early lessons are where that mindset starts to take shape. Plus, revisiting the fundamentals can expose assumptions or gaps in your knowledge you didn’t realize were there. And once you’ve filled those in, stepping into more advanced material becomes a lot smoother. Courses on deep learning, model optimization, and AI deployment will make much more sense, and you’ll be able to apply them with far more confidence. Beyond that, completing a well-rounded set of courses not only helps you build competence, it signals commitment. On a CV or portfolio, it shows that you’re not just dabbling, but investing real time in understanding the field.
Deep Learning Specialization from Deeplearning.com
This program is a no-brainer. It’s one of the best rated AI programs out there, with over 1 million people already joining. I’m not really surprised, because Andre Ng is one of the pioneers of machine learning. Back to the course. It’s laid out in a way that doesn’t fight you. It’s modular, clean, and lets you move at your own speed. The main plus point for me is that it’s all about practicality. Stuff you can actually apply to your AI side project. The lectures take those intimidating AI concepts and slice them into something digestible, with assignments that don’t feel like busywork. The content keeps up with the breakneck pace of machine learning, and the course holds a 4.9 rating from over 120K learners on Coursera.
Artificial Intelligence Course from MIT
Now, this one is a real gem. Despite originating from 2010, it’s still as relevant today, and even teaches neural networks. It will introduce you to basic knowledge representation on AI and learning methods of AI. It’s completely free because it is a part of MIT OpenCourseWare, and if you prefer an academic approach to artificial intelligence, then you’ll love it. The only downside here is that it’s not hosted on platforms like Udemy or Coursera so you don’t have any tools to help you out.
CS224N: Natural Language Processing with Deep Learning
Stanford has made its natural language processing course, CS224N, available for free online, mirroring MIT’s earlier move with OpenCourseWare. It’s the same material students get on campus, but served as a YouTube playlist. You start the course with word vectors. Specifically, the kind that turn arithmetic into something semantic. For example, (King – Man) + Woman = Queen. It feels like a trick. It isn’t. This kind of transformation was one of the first signs that language could be reduced to math without losing its shape. Course is presented by professor Christopher Manning who teaches Machine Learning, Linguistics, and Computer Science at Stanford. He also runs the Artificial Intelligence Laboratory. His work laid the groundwork for many of the deep learning algorithms used in natural language processing today. Since 2000, he’s been teaching Stanford’s NLP with Deep Learning course. You can follow along online, if you want. The downside is there’s no certificate at the end. No forum. No group to ask questions. Just the material and you.
What Next?
You’ve finished the courses. That’s no small thing. Congratulations. Now, before the momentum fades, pick up Andrew Ng’s How to Build Your Career in AI. It’s positioned as a launchpad, useful whether you’re just stepping into the job market or pivoting from somewhere else entirely. Still, books only take you so far. YouTube can be just as valuable, provided you know where to look. Andrej Karpathy’s channel is a great example. He explains complex AI concepts with precision, but without dumbing things down. Perfect if you’re an engineer or aiming to become one. And if credentials matter to you, Karpathy co-founded OpenAI and led AI at Tesla. His lectures are as insightful as they are unpretentious.
From there, it’s time to leave your silo. Start connecting. The AI field is evolving fast, and staying plugged in is just as important as learning the theory. Reddit is full of people who are on the same path. Some just starting out, others already working in the field. Whether you’re looking for feedback, job leads, or just a place to ask questions without judgment, the community is there.
Learning AI is not just about what you know. It’s about who you know, how you apply it, and how quickly you can adapt. You’ve already taken the first big step. Keep going.
Disclaimer: This article contains sponsored marketing content. It is intended for promotional purposes and should not be considered as an endorsement or recommendation by our website. Readers are encouraged to conduct their own research and exercise their own judgment before making any decisions based on the information provided in this article.
