Artificial Intelligence
Courses on machine learning, deep learning, and putting language models to work in everyday practice.
Verified courses first, then the rest of the category
Ninety minutes of AI skills aimed specifically at students, which is a narrower audience than most introductions to the subject. From Canva Design School.
Not independently verified
At three hours the longest item on the Asana Academy index, a badge course on AI Studio. Free with a free account, and substantially deeper than its twenty-minute AI courses.
Not independently verified
A twenty-minute course on using AI features for personal productivity inside Asana. Short and product-specific, free with a free account.
Not independently verified
Building and structuring a content system through the Model Context Protocol rather than by hand. Free from Webflow University, with an account offered to track progress.
Not independently verified
A one-hour beginner session from DeepLearning.AI fronted by Andrew Ng. Short enough that its length is the main thing to know before starting.
Not independently verified
A short course built with Anthropic and published on the DeepLearning.AI platform, running about 1h47m at beginner level. Partner-built courses like this one are taught around a specific vendor’s tools, which is worth knowing before starting.
Not independently verified
A two-hour course on working with Anthropic’s Claude models, taught as interactive exercises on the DataCamp platform. Requires a paid plan, with no price stated on the catalog listing.
Not independently verified
A short course built with Anthropic and published on the DeepLearning.AI platform, running about 2h at intermediate level. Partner-built courses like this one are taught around a specific vendor’s tools, which is worth knowing before starting.
Not independently verified
A community-written course on computer vision machine learning, using the models and libraries in the Hugging Face ecosystem. Free and openly readable, with the community authorship worth knowing before starting.
Not independently verified
A course on engineering context for code agents, one of the newer subjects where structured published training is still scarce. Free and openly readable from Hugging Face.
Not independently verified
Writing instructions for AI agents operating on a Webflow project, a subject barely covered in published training anywhere. Free from Webflow University, with an account offered to track progress.
Not independently verified
A university-level course on building with AI rather than on using one vendor product, which is rarer in open catalogs than it should be. Free from Saylor, readable as a guest without an account, with a free certificate on completion.
Not independently verified
Machine learning taught as fundamentals rather than as a library tour, sitting alongside the statistics and Python courses in the same catalog. Free from Saylor, readable as a guest without an account, with a free certificate on completion.
Not independently verified
The specialization that established DeepLearning.AI, running to over one hundred and twenty-seven hours across its constituent courses. Intermediate level and a serious time commitment rather than an introduction.
Not independently verified
Hugging Face’s course on deep reinforcement learning, taught through the libraries in its own ecosystem. Published free and openly readable, and one of the few structured treatments of the subject available without payment.
Not independently verified
A course on diffusion models and how to use them with the diffusers library. Published free by Hugging Face and openly readable, aimed at people who already have machine learning foundations.
Not independently verified
A twenty-minute course framing AI features as a way to make organisational work legible rather than merely faster. Free with a free account.
Not independently verified
The follow-on to the Introduction to AI course, moving from concepts to the algorithms behind them. Basic Python is recommended rather than required, and the course is free with registration. Produced by the University of Helsinki with MinnaLearn.
Not independently verified
A free introduction to artificial intelligence built for people with no technical background, produced by the University of Helsinki with MinnaLearn. It is one of the most widely taken AI courses published by a university and requires no programming. Registration is required and the course is free.
Not independently verified
IBM on applying generative AI within digital marketing work specifically. Delivered through Coursera; the browse listing publishes no price.
Not independently verified
Andrew Ng’s general audience introduction to generative AI, structured as three weeks at one to two hours a week, with thirty-two video lessons and two code examples. No prior AI or coding knowledge is required. Free to enrol, with graded assignments and the certificate behind a paid membership.
Not independently verified
IBM’s growth marketing specialization built around generative AI tooling. Delivered through Coursera; the browse listing publishes no price.
Not independently verified
IBM on generative AI within HR work, one of the few structured treatments of AI in that function. Delivered through Coursera; the browse listing publishes no price.
Not independently verified
IBM on generative AI for product managers, addressed to what to build rather than how to build it. Delivered through Coursera; the browse listing publishes no price.
Not independently verified
About this subject
Courses on artificial intelligence, spanning three quite different things that share a name: the mathematical and algorithmic foundations, the engineering of building and deploying models, and practical training on using AI tools competently at work. The subject also moves faster than anything else in this directory, which changes how you should read a course listing.
Guidance
Verification tells you that a course delivers the material it advertises. It cannot tell you whether the tools it teaches will still be current when you finish. These are the things worth checking yourself.
A course on how transformers work and a course on how to prompt a particular assistant are both legitimate and almost unrelated. Fundamentals stay useful when products change and cost more effort up front. Tool training is immediately applicable and has a shorter shelf life. Neither is a substitute, and the biggest source of disappointment in this subject is buying one while wanting the other.
This is the fastest-moving subject in the catalogue. A course recorded two years ago may still be excellent on backpropagation and useless on which model to use. Where a provider publishes a last-updated date we record it, and the summary usually says whether the course is taught around a specific product or around concepts that outlive it.
Much of the best AI training is published by the companies that make the models and libraries, and it is often genuinely the primary source. It is also, by construction, about their ecosystem. That is not a reason to avoid it, but it is worth knowing whether you are learning a field or a product. Each listing names the publisher, and where a course is built around one vendor the summary says so.
Courses in this subject range from requiring no technical background at all to assuming linear algebra, calculus and a year of Python. Providers usually state prerequisites plainly, and they mean them. The level on each listing reflects what the provider states.
AI attracts marketing about replacing work, multiplying output and career transformation. Treat those as claims about the future rather than descriptions of the course. What a course can honestly offer is that you will understand something or be able to do something specific, which is the part worth reading closely.
Standards
The published standard applies unchanged. These are the checks that tend to matter most in this subject.
A verification is dated, and in this subject the date matters more than in any other. It says nothing about whether the models, tools or techniques taught are still the ones you should be using.
Questions
Yes, and several in this category are explicitly written for a general or business audience. The level and the summary on each listing say who the course is pitched at, and courses assuming Python or mathematics normally state that prominently.
It depends entirely on what the course teaches. Foundations age slowly. Anything tied to a specific model version, product interface or pricing tier can be out of date within months. The listing names the publisher and, where stated, the last update.
Not at all. They are often the most accurate source on their own tools. Just read them as what they are: excellent on that ecosystem, and not a survey of the field.
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