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
IBM on applying generative AI to project delivery rather than to product design. Delivered through Coursera; the browse listing publishes no price.
Not independently verified
IBM on generative AI for sales work, addressing the selling process rather than marketing. Delivered through Coursera; the browse listing publishes no price.
Not independently verified
Thirty-one hours on applying generative AI within software development work specifically, rather than on building models. Beginner level, from DeepLearning.AI.
Not independently verified
A fifteen-minute orientation to the AI features available in Asana, for people who already use the tool. Free with a free account.
Not independently verified
Getting started with the Model Context Protocol integration, connecting AI tooling to a Webflow project. Free from Webflow University, with an account offered to track progress.
Not independently verified
Google’s AI certificate, aimed at using AI competently in everyday work rather than at building models, and pitched at people with no technical background.
Not independently verified
IBM’s AI engineering certificate, aimed at building, training and deploying models rather than at using finished AI products, and pitched well above its introductory material.
Not independently verified
A product management certificate framed around AI products specifically, which is an unusual combination and directly useful to anyone shipping one.
Not independently verified
IBM’s generative AI engineering certificate, one of the larger structured programmes on the subject from a major vendor.
Not independently verified
Two and a half hours on applying AI in a working context rather than on building models, aimed at people who want to use the tools competently. Requires a paid plan, with no price stated on the catalog listing.
Not independently verified
A short foundational course on writing instructions for large language models, covering what separates a good prompt from a vague one, how the underlying models behave, and techniques for decomposing complex problems. Later modules deal with the failure modes rather than the happy path: conflicting outputs, hallucination, bias, and the ethical questions that follow. Ten modules, self-paced, sold through a subscription that also covers the publisher's other courses.
Not independently verified
An artificial intelligence course written specifically for web developers rather than for researchers or data scientists, which is unusual enough to be the main reason to choose it. Part of the web.dev Learn series, free and open in the browser.
Not independently verified
A course on 3D machine learning using the libraries in the Hugging Face ecosystem. Free and openly readable, and narrow enough that the subject is the main thing to know before starting.
Not independently verified
A course on integrating AI and machine learning tooling into game development, published free by Hugging Face and openly readable.
Not independently verified
A three-course programme covering supervised learning, neural networks and unsupervised methods, taught by Andrew Ng and offered jointly by DeepLearning.AI and Stanford Online. It works up from linear and logistic regression through decision trees and neural networks to clustering, recommender systems and reinforcement learning, with practical guidance on diagnosing models rather than only building them. Delivered on Coursera, where the material can be audited free and a certificate requires a paid subscription.
Not independently verified
The mathematics behind machine learning taught as its own ninety-four hour specialization, for people who want the foundations rather than to take them on trust. Beginner level, from DeepLearning.AI.
Not independently verified
A twenty-minute course on applying Asana’s AI features at team rather than individual level, covering the shift from personal shortcuts to shared practice. Free with a free Academy account.
Not independently verified
A collection of open-source notebooks contributed by AI builders, published by Hugging Face as a practical companion to its courses. A reference collection rather than a designed syllabus, which is worth knowing before starting.
Not independently verified
A prompt engineering course taught through Vanderbilt University, one of the earliest university-published treatments of the subject and still among the most widely taken.
Not independently verified
A substantial twenty-six hour treatment of retrieval augmented generation, the technique most production language model applications depend on. Intermediate level, from DeepLearning.AI.
Not independently verified
A course on building robots using the LeRobot library, published free by Hugging Face. Unusual for a machine learning catalog in addressing physical hardware rather than software alone.
Not independently verified
A two-hour conceptual introduction to AI with no coding, aimed at understanding rather than building. Requires a paid plan.
Not independently verified
A one-hour course on prompt engineering, short enough to be an orientation rather than a full treatment. Requires a paid plan.
Not independently verified
An hour on using AI features to speed up creative work, taught through Canva’s own tooling rather than as a general introduction to the technology. From Canva Design School, and product-focused by design.
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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