The best AI certifications and courses, ranked and explained

Strictly, a certification is a credential you earn by passing a proctored exam set by a vendor such as AWS, Microsoft or Google Cloud, and a course certificate records that you finished a course; Coursera and Udemy issue those, even when the programme is called a “Professional Certificate”. Most people, and the ranking on this page, use “AI certification” for both.

Vendor credentials and Coursera certificates can both be checked through a public link. What differs is whether a proctored exam stands behind them. The ranking on this page is courses, scored on six factors, and the vendor exams have a table of their own, so you can see which kind you are choosing.

For a course, the Google AI Professional Certificate or its shorter sibling, Google AI Essentials: neither asks you to write code. For a proctored exam, AWS Certified AI Practitioner (AIF-C01) is a no-code entry point, because AWS lists developing or coding AI models as out of scope for it.

Microsoft’s fundamentals credential is no longer a no-code option: the AI-900 exam has retired, and Azure AI Fundamentals is now earned through AI-901, which expects basic Python. Microsoft’s no-code exams are now its AI Business Professional (AB-730), for Microsoft 365 Copilot users, and AI Transformation Leader (AB-731), for business decision-makers; AB-730 and Google Cloud’s Generative AI Leader are both compared on our vendor exams page.

They can, and more easily for some than others. AWS and Google Cloud issue their certifications as Credly badges, Microsoft lists a holder’s credentials on a shareable Microsoft Learn transcript, and Coursera certificates carry a public verification link.

How much weight a credential carries still varies by role and employer. A proctored exam tells an employer you passed a test under supervision; a course certificate tells them you finished a course. Either way, the skills behind it, shown in an interview or a portfolio, count for more than the paper.

Some course certificates are free. Elements of AI, from the University of Helsinki, gives the certificate for its Introduction to AI course at no cost, and our free certifications guide covers the other genuinely free routes.

On Coursera, financial aid can lower the price of a course’s certificate: you apply per course, never for a whole Specialization, and Coursera allows up to 16 days for each decision. A free trial does not make the certificate free, because Coursera’s trial screen puts the certificate after the first payment. The proctored vendor exams in the vendor-exam table on this page all charge a fee, although AWS gives certified holders a 50% voucher toward their next exam.

It depends on which kind. The short course tracks take a few evenings to a few weeks, the university specializations take months at a few hours a week, and a vendor exam is one sitting after your own preparation; the vendor-exam table on this page gives each exam’s length where the vendor publishes it.

If you have only a few hours a week, choose something short enough to finish. Completion realism is one of the six factors behind every score here, because an unfinished certificate is worth nothing.

Most developers are hired to ship AI features rather than train models, so start with a tool course you can finish in a week, such as AI For Developers With GitHub Copilot, Cursor AI & ChatGPT on Udemy or DataCamp’s AI for Software Engineering track, then build on the API stack with DataCamp’s Associate AI Engineer for Developers, which covers APIs, embeddings, vector databases and LangChain. Our guide for software engineers sets out the order and the alternatives.

For the exam, match the cloud. Microsoft retired its Azure AI Engineer Associate (AI-102) and replaced it with a different certification, the Azure AI Apps and Agents Developer Associate (exam AI-103); AWS offers the Machine Learning Engineer – Associate and the Generative AI Developer – Professional; Google Cloud’s is the Professional Machine Learning Engineer. All of them are compared on our vendor exams page.

We have not found an independent salary figure tied to any single AI certification, so we do not publish one, and a provider’s own salary claim is marketing until someone else confirms it.

The credentials closest to the best-paid AI work are the advanced exams, Google Cloud’s Professional Machine Learning Engineer and AWS’s Generative AI Developer – Professional, because they test the engineering those roles do. Salary impact is one of the six factors behind every score here, judged by how directly a credential maps to paid roles.

When they close a specific gap: a proctored credential for a job advert that names one, or structured practice you would not otherwise do. They are not a substitute for work you can show.

Pick by what the certificate has to do for you. If a job advert names one, take the vendor exam for the cloud the employer runs; if you need the skill, take the shortest course that teaches it and build something with it. Our guide to whether AI certifications are worth it goes through the trade-offs in detail.

There is no single best one, only the best one for what you need it to do. If a job advert asks for a certification, it means a proctored vendor exam: AWS Certified AI Practitioner (AIF-C01) is a no-code entry point, because AWS lists developing or coding AI models as out of scope for it, and Google Cloud's Professional Machine Learning Engineer is an advanced one. The vendor-exam table on this page gives each exam's fee and format, beside a course that prepares for it.

If you need the skill rather than the credential, take a course. For a beginner who does not code, the Google AI Professional Certificate is where we would start; for someone who writes Python, DeepLearning.AI's courses on retrieval-augmented generation and on agentic AI teach building with language models. The ranking on this page orders courses by our score, the plain average of six factors, so you can see why each one sits where it does.