Do I need to learn maths to work with AI?

Only for one of the two paths. If you want to build or train models, yes — linear algebra and statistics at working level, and there is no shortcut past it. If you want to use models well, which is where most of the value companies are currently capturing sits, no. The applied path is retrieval, evaluation, prompting and knowing the failure modes, and it takes weeks rather than months. Decide which one you are on before you buy a course, because the two share almost no material.

Is an AI certificate worth anything?

A fundamentals badge certifies vocabulary, and employers read it that way — it says you can hold a conversation about the platform, not that you have shipped anything. That is genuinely useful if you are moving into an AI-adjacent role from marketing, support or analysis, and close to useless on its own if you are applying for an engineering job. The associate-level data exams are a different proposition: they certify plumbing that a team actually needs. The failure mode we see most often is collecting three fundamentals badges from three vendors and no project that ever ran.

Which vendor should I certify with?

Whichever cloud your employer already runs, or the one named most often in the job postings you are actually applying to. The fundamentals exams from Microsoft and AWS cover substantially the same ideas in two dialects; the ideas transfer and the dialects do not. Picking on merit rather than on your target employer's stack is the most common wasted exam fee in this field.

Why is there no AI tool or product recommendation here?

Because we are not in any AI affiliate programme, so we have nothing to sell you and no basis for a ranking we would stand behind. The course links on this page are affiliate links to the directory and they are disclosed on every course page. If that changes, the disclosure will change with it, on this page, before any recommendation appears.

Artificial intelligence

The vocabulary, the credentials, and where the bill arrives

AI is the one field on this site where the vocabulary moves faster than the substance. That gap is where most of the wasted money goes: courses named after a product that will be renamed next year, certificates that prove you can define a term, and a great deal of confident writing that never says what anything costs. This portal is the counterweight — 13 concepts in the order they make sense, 7 vendor exams with what each one actually signals, and the two course shelves that teach them.

There are two honest ways into this field and one dishonest one. The builder path is Python, then the maths you can tolerate, then training models on real data. It takes months and it is the only route into research or model engineering. The applied path is learning to use models well — retrieval, evaluation, and the limits of the tools — inside a job you already understand. It takes weeks, and it is where most of the value being captured right now actually sits. The dishonest path is collecting fundamentals badges from three vendors and never shipping anything; employers have learned to ask what you built.

Read the AI pillar if you want the argument in one piece. Use this page if you want the route.

The vocabulary, in three layers

Not alphabetical, and not by popularity. In the order where each term is explained by the ones above it.

Foundation — how a machine learns anything at all

Three terms that have not changed in a decade and explain every headline that has. Read these first; everything else is a special case of them.

Architecture — the designs that won

One architecture underlies every chat assistant you have used, and two long-standing problem families explain what it was built to do.

Application — where most people actually meet AI

This is the layer that pays for most readers of this site. None of it requires training a model; all of it requires knowing what the model is doing.

The two that decide whether any of it ships

Every AI project that dies, dies here rather than in the modelling. Learn them early enough to argue with a plan, not late enough to explain a failure.

The credentials, and what each one signals

Every price below is the vendor's list price, read on the date shown, before the tax and country adjustment that will change it. The foundational exams certify vocabulary. The associate exams certify plumbing. Neither certifies that you have built anything, and no employer reads them as if they did.

Pick the vendor your employer already runs, or the one your target job postings name. The fundamentals exams cover substantially the same ideas in different dialects — the ideas transfer, the dialects do not. The full exam catalogue, including the security, networking and project-management ladders, is on the certifications hub.

Where AI meets the rest of the site

AI is not a vertical on this site so much as a pressure applied to the other four. These are the places where that pressure turns into a decision someone has to make.

Hosting & cloud

This is where an AI project stops being free. A model that runs on someone else's API is a per-token bill that scales with success; a model you host yourself is a GPU you rent by the hour whether it is busy or not. Neither is the cheap option by default, and the decision is the same one the hosting ladder describes: the lowest rung that meets the real need.

Data & analytics

Every serious practitioner in this field spends most of their time on data, not on models. That is not a slogan — it is why the associate-level exams above are data-engineering exams rather than AI exams, and why a retrieval system fails on the documents long before it fails on the model.

Programming

Assistants changed how code gets written and not what makes it correct. The skills that decide whether an AI-assisted codebase survives its second year — reading code you did not write, reviewing, testing, knowing what to refuse — are the same ones that decided it before.

Digital assets

Generated images are the live licensing question of the moment: what you may sell, what a marketplace now requires you to declare, and what a client's legal team will ask for. The licence vocabulary that answers it is the same one that governs stock and templates.

Courses from the AI shelves

From the directory's generative-AI and data-science shelves — every card leads to the course page, which carries the provider link and the affiliate disclosure.

Questions people actually arrive with

Do I need to learn maths to work with AI?: Only for one of the two paths. If you want to build or train models, yes — linear algebra and statistics at working level, and there is no shortcut past it. If you want to use models well, which is where most of the value companies are currently capturing sits, no. The applied path is retrieval, evaluation, prompting and knowing the failure modes, and it takes weeks rather than months. Decide which one you are on before you buy a course, because the two share almost no material.
Is an AI certificate worth anything?: A fundamentals badge certifies vocabulary, and employers read it that way — it says you can hold a conversation about the platform, not that you have shipped anything. That is genuinely useful if you are moving into an AI-adjacent role from marketing, support or analysis, and close to useless on its own if you are applying for an engineering job. The associate-level data exams are a different proposition: they certify plumbing that a team actually needs. The failure mode we see most often is collecting three fundamentals badges from three vendors and no project that ever ran.
Which vendor should I certify with?: Whichever cloud your employer already runs, or the one named most often in the job postings you are actually applying to. The fundamentals exams from Microsoft and AWS cover substantially the same ideas in two dialects; the ideas transfer and the dialects do not. Picking on merit rather than on your target employer's stack is the most common wasted exam fee in this field.
Why is there no AI tool or product recommendation here?: Because we are not in any AI affiliate programme, so we have nothing to sell you and no basis for a ranking we would stand behind. The course links on this page are affiliate links to the directory and they are disclosed on every course page. If that changes, the disclosure will change with it, on this page, before any recommendation appears.

Last reviewed 30 August 2026 · Getting Digital