Free Coursera AI Courses: 5 Picks From Google and IBM
A list of 5 free Coursera AI courses from Google and IBM is doing the rounds. I opened every course page. Here are the real hours, ratings and what "free" actually means.
8 min read · Last updated: October 24, 2026
The five free Coursera AI courses getting shared this week are IBM's Introduction to Artificial Intelligence, Google's Introduction to AI, Andrew Ng's AI For Everyone, the Machine Learning Specialization, and the IBM AI Developer Professional Certificate. All five let you enroll for free. Certificates cost money. Only three come from Google or IBM.
Came here from Instagram, LinkedIn, X or YouTube? Here is the full source list you came for. Everything below is built from these, nothing else.
The list that started it
The course pages (checked Oct 24 2026)
Key takeaways
- The five courses range from 1 hour (Google's Introduction to AI) to 146 hours of listed module time (IBM AI Developer certificate).
- Only 3 of the 5 are made by Google or IBM. The other two are from DeepLearning.AI, and one of those is co-offered by Stanford Online.
- AI For Everyone is the most taken of the five, with 2,655,772 enrolled and a 4.8 rating from 53,450 reviews.
- The Machine Learning Specialization has the highest rating, 4.9 from 39,426 reviews, and is the only one that needs you to code from day one.
- "Free" means free to enroll or audit. Every page says the shareable certificate needs a paid purchase, and the Specialization page lists Coursera at $49 a month.
- If you plan to take the IBM certificate, skip IBM's standalone intro course. It is already course 2 inside the certificate.
What happened?
On October 24, 2026, Rohit Malhotra published a list in the Medium publication Dev Resources titled "5 Best Free Coursera Courses to Learn AI in 2026–27 (from Google and IBM)". It recommends five courses in order, from a no-code introduction to a full professional certificate, and says they can be audited for free while certificates cost extra.
Lists like this get reshared a lot, and the details drift with every share. So I opened every course page on Coursera on the same day and wrote down the hours, ratings, enrollment counts and the actual free terms. That is what this article is built on.
What are the 5 free Coursera AI courses?
The five courses are two beginner explainers from IBM and Google, a non-technical course by Andrew Ng, Andrew Ng's three-course Machine Learning Specialization, and IBM's ten-course AI Developer certificate. Together they run from a one-hour overview to roughly six months of part-time study. Here are the numbers straight from each course page.
| # | Course | Made by | Time (per Coursera) | Rating | Enrolled |
|---|---|---|---|---|---|
| 1 | Introduction to Artificial Intelligence (AI) | IBM | About 14 hours, 4 modules | 4.7 (23,450 reviews) | 970,987 |
| 2 | Introduction to AI | 1 hour, 4 modules | 4.8 (13,549 reviews) | 974,122 | |
| 3 | AI For Everyone | DeepLearning.AI (Andrew Ng) | 7 hours, 4 modules | 4.8 (53,450 reviews) | 2,655,772 |
| 4 | Machine Learning Specialization | DeepLearning.AI and Stanford Online | 3 courses, 95 hours of listed course time | 4.9 (39,426 reviews) | 853,289 |
| 5 | IBM AI Developer Professional Certificate | IBM | 10 courses, 6 months at 4 hours a week | 4.6 (83,271 reviews) | 262,287 |
All figures were read from the Coursera pages on October 24, 2026. They change daily.
One correction to the headline. The list is billed as "from Google and IBM", but AI For Everyone and the Machine Learning Specialization are DeepLearning.AI courses taught by Andrew Ng. They are still on the list for a good reason: they are the two highest rated of the five.
Why does this matter to a developer?
It matters because the five courses are not interchangeable. Three of them teach you to talk about AI, and two teach you to build with it. If you are a developer, the 1-hour and 7-hour courses are context, not skills. The Specialization and the IBM certificate are where the code is.
Here is how each one splits on that line.
IBM's Introduction to Artificial Intelligence covers AI concepts, application domains, career impact and ethics. Its four modules are 4, 3, 4 and 3 hours, and the page says no experience is required. Skills listed include Retrieval-Augmented Generation (RAG) and natural language processing, but at a concept level.
Google's Introduction to AI is the first course in the Google AI Essentials Specialization. It is 1 hour, with 3 assignments. You can finish it over lunch.
AI For Everyone is Andrew Ng's non-technical course. Its four modules are What is AI?, Building AI Projects, Building AI In Your Company, and AI and Society. It is useful if you need to explain to a manager why an AI project will or will not work.
The Machine Learning Specialization is the technical one. Its three courses are:
- Supervised Machine Learning: Regression and Classification (33 hours)
- Advanced Learning Algorithms (34 hours)
- Unsupervised Learning, Recommenders, Reinforcement Learning (28 hours)
The tools listed are NumPy, scikit-learn and TensorFlow, and the course descriptions say you build models in Python. The stated prerequisites are basic coding (loops, functions, if/else) and high-school math. If you want a preview of what course 1 covers, our notes on supervised machine learning walk through the core idea.
The IBM AI Developer Professional Certificate is the longest. Its ten courses cover software engineering basics, generative AI, prompt engineering, HTML, CSS and JavaScript, Python, Flask, building generative AI apps with Python, and interview prep. Tools listed are Python, LangChain, IBM Cloud and prompt engineering. The FAQ says courses 1 to 5 need no programming, while the final two need programming and software engineering skills.
Are these Coursera AI courses really free?
Partly. Every one of the five pages shows an "Enroll for free" button and offers financial aid. But every page also says that earning the shareable certificate requires buying the certificate experience. So the learning can be free, the proof of learning is not, and the exact free access varies by course.
The pages are not consistent about how much you get without paying. This is what each one actually says:
| Course | What the page says about free access |
|---|---|
| IBM Introduction to AI | Enroll for free, financial aid available, certificate needs purchase |
| Google Introduction to AI | Enroll for free, free trial for eligible learners, certificate needs purchase |
| AI For Everyone | Free trial for eligible learners, some courses offer "Full Course, No Certificate" |
| Machine Learning Specialization | Auditing is free but gives no certificate, subscription is $49 a month |
| IBM AI Developer certificate | Enroll for free, financial aid available, included with Coursera Plus |
The Machine Learning Specialization page is the only one that states plainly that auditing is free. Its FAQ also says you cannot take the course for free, which means you cannot get the graded certificate track for free. Certificates there also require finishing the programming assignments.
My reading: treat "free" as "free to watch and learn". If you need a certificate for a CV, budget for it or apply for financial aid, which all five pages mention.
Which free AI course should you take first?
If you already code, start with the Machine Learning Specialization, because it is the only one of the five that teaches you to build models from the first week. If you do not code yet, start with Google's 1-hour Introduction to AI, then AI For Everyone, then decide whether you want the technical path.
A simple way to choose:
| You are... | Take this | Time |
|---|---|---|
| A non-developer who wants the vocabulary | Google Introduction to AI, then AI For Everyone | 8 hours |
| A developer who wants to build ML models | Machine Learning Specialization | 95 hours of course time |
| A beginner who wants to become an AI app developer | IBM AI Developer Professional Certificate | About 6 months part time |
| Someone who needs a broad, no-code overview with ethics | IBM Introduction to AI | About 14 hours |
The Specialization assumes Python-style code. If you are still choosing a first language, read Python vs JavaScript: which to learn first before you start. For AI and ML work, the courses here lean on Python.
What changes in practice?
The practical change is that you can plan a full beginner-to-builder path on one platform without paying anything until you want a certificate. But the five courses overlap, and taking them in the listed order wastes time. Here is what to watch for.
IBM's intro course is inside the IBM certificate. Course 2 of the IBM AI Developer Professional Certificate is Introduction to Artificial Intelligence (AI), listed at 13 hours. That is course #1 on the list. If you plan to do the certificate, do not take the intro separately.
The certificate's listed time and its module hours disagree. The page says 6 months at 4 hours a week, which is about 104 hours. The ten courses add up to 146 hours of listed time. Plan for the bigger number.
The Specialization's timeline also disagrees with itself. The page says 2 months at 10 hours a week. Its FAQ estimates 5 hours a week. The three courses list 95 hours combined. At 10 hours a week, that is closer to 10 weeks than 8.
The two Andrew Ng courses do different jobs. AI For Everyone has no code. The Machine Learning Specialization is all code. Taking the first does not prepare you for the second in any technical sense.
What to do this week
Pick one path and finish its first block in seven days. Do not enroll in all five.
- Decide your track today. Developer who wants to build models: Machine Learning Specialization. Beginner who wants a job building AI apps: IBM AI Developer certificate. Non-technical: Google, then AI For Everyone.
- Enroll on the course page and look for the free option. Check the enrollment dialog for an audit or "Full Course, No Certificate" choice before you start a free trial.
- If you need the certificate, apply for financial aid now. All five pages say it is available, and it is better to apply before you are halfway through.
- Block 5 to 10 hours on your calendar. That is enough for the Google course plus AI For Everyone, or the first two weeks of Supervised Machine Learning.
- Build one small thing by Sunday. Even a single regression notebook in NumPy beats a finished video playlist.
If you want to speed up the reading part, our guide on using AI to learn programming faster shows how to use a chatbot as a tutor without letting it do the work for you.
FAQ
Are Google and IBM AI courses on Coursera free?
You can enroll in Google's Introduction to AI and IBM's Introduction to Artificial Intelligence and the IBM AI Developer certificate for free, according to their course pages. The shareable certificate requires a paid purchase on all of them. Financial aid is listed as available.
What is the best free Coursera course to learn AI as a developer?
The Machine Learning Specialization by DeepLearning.AI and Stanford Online. It has the highest rating of the five at 4.9 from 39,426 reviews, and it teaches supervised learning, neural networks and unsupervised methods in Python with NumPy, scikit-learn and TensorFlow.
How long does it take to finish the IBM AI Developer Professional Certificate?
Coursera lists it at 6 months at 4 hours a week. The ten courses add up to 146 hours of listed time, so expect closer to 8 or 9 months at that weekly pace. Courses 1 to 5 need no programming.
Do I need to know coding for AI For Everyone?
No. The course page says no prior experience is required. It is a 7-hour, non-technical course in four modules about what AI can do, how AI projects get built, and how AI affects companies and society.
Pick one track and finish the first 10 hours this week. While you study, the free tools on Art of Code can handle the small chores, and our AI-first programming roadmap shows what to learn after these courses.
Written by Adithya Guttha, Founder of Art of Code.
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