Key statistics · August 2026
- We actively track and score 13 major AI certifications across 9 providers (DeepLearning.AI, Duke University, Google, Google Cloud, IBM, LearnKartS, Microsoft, Stanford & DeepLearning.AI, Vanderbilt University).
- 6 of 13 (46%) of the certifications we track require no coding at all — the biggest surprise for most career-switchers.
- The average editorial rating across our tracked certifications is 4.64 / 5; the highest-rated is the Machine Learning Specialization at 4.9 / 5.
- 5 of 13 tracked certifications are beginner- or foundational-level — you rarely need prerequisites to start.
- 4 of 13 can be finished in hours (a weekend or less); the rest are multi-week professional certificates.
This page collects the key data behind our reviews in one citable place. Every figure below comes from BestAICertifications.com's own editorial catalog — the same data that powers our rankings and AI Certification Picker — and was last reviewed in August 2026. The full dataset is also available as a machine-readable JSON file under a CC BY 4.0 licence — free to reuse with attribution.
Editorial ratings of major AI certifications
Scored on our six-factor methodology: employer recognition, skill value, cost & value, time to complete, difficulty & prerequisites, and salary impact.
The table below compares 13 certifications on provider, level, typical time, coding and our rating.
| Certification | Provider | Level | Typical time | Coding | Our rating |
|---|---|---|---|---|---|
| Machine Learning Specialization | Stanford & DeepLearning.AI | Beginner–Intermediate | ~2–3 months at 5 hrs/week | light Python | 4.9 / 5 |
| Deep Learning Specialization | DeepLearning.AI | Intermediate | ~2–3 months at 5 hrs/week | Python | 4.8 / 5 |
| Prompt Engineering Specialization | Vanderbilt University | Beginner | ~1 month at 5 hrs/week | none | 4.7 / 5 |
| Generative AI for Everyone | DeepLearning.AI | Beginner | ~5 hours | none | 4.7 / 5 |
| AI For Everyone | DeepLearning.AI | Beginner | ~6 hours | none | 4.7 / 5 |
| Google AI Essentials | Beginner | ~6–10 hours (a weekend) | none | 4.6 / 5 | |
| IBM AI Engineering Professional Certificate | IBM | Intermediate | ~4 months at 5 hrs/week | Python | 4.6 / 5 |
| Preparing for Google Cloud ML Engineer Certification | Google Cloud | Advanced | ~2–3 months at 5 hrs/week | Python | 4.6 / 5 |
| AI Product Management | Duke University | Intermediate | ~1–2 months at 5 hrs/week | none | 4.6 / 5 |
| IBM AI Developer Professional Certificate | IBM | Beginner–Intermediate | ~2–3 months at 5 hrs/week | light Python | 4.6 / 5 |
| Microsoft AI & ML Engineering Professional Certificate | Microsoft | Intermediate | ~3–4 months at 5 hrs/week | Python | 4.5 / 5 |
| IBM Generative AI Engineering Professional Certificate | IBM | Intermediate | ~3–4 months at 5 hrs/week | Python | 4.5 / 5 |
| Introduction to AI and Machine Learning (AWS exam prep) | LearnKartS | Beginner | ~10–12 hours | none | 4.5 / 5 |
Difficulty-level distribution
Of the 13 certifications we track, most are accessible to newcomers — only one is advanced.
The table below compares 4 levels on certifications and share.
| Level | Certifications | Share |
|---|---|---|
| Beginner | 5 | 38% |
| Beginner–Intermediate | 2 | 15% |
| Intermediate | 5 | 38% |
| Advanced | 1 | 8% |
How many require coding?
Nearly half require no programming at all; the rest use Python, usually lightly at first.
The table below compares 3 options on certifications and share.
| Coding requirement | Certifications | Share |
|---|---|---|
| none | 6 | 46% |
| light Python | 2 | 15% |
| Python | 5 | 38% |
Time commitment
The fastest are finishable in a weekend; professional certificates run to several months part-time.
The table below compares 3 options on certifications and share.
| Time commitment | Certifications | Share |
|---|---|---|
| Hours — a weekend or less | 4 | 31% |
| About 1–2 months (part-time) | 2 | 15% |
| 2+ months (professional certificate) | 7 | 54% |
Certifications by provider
The 13 tracked certifications come from 9 providers — IBM and DeepLearning.AI offer the most.
The table below compares 9 providers on certifications tracked.
| Provider | Certifications tracked |
|---|---|
| IBM | 3 |
| DeepLearning.AI | 3 |
| 1 | |
| Stanford & DeepLearning.AI | 1 |
| Microsoft | 1 |
| LearnKartS | 1 |
| Google Cloud | 1 |
| Vanderbilt University | 1 |
| Duke University | 1 |
Average editorial rating by level
Ratings are consistently high across levels, reflecting that we only track credentials we'd recommend.
The table below compares 4 levels on certifications and average rating.
| Level | Certifications | Average rating |
|---|---|---|
| Beginner | 5 | 4.64 / 5 |
| Beginner–Intermediate | 2 | 4.75 / 5 |
| Intermediate | 5 | 4.6 / 5 |
| Advanced | 1 | 4.6 / 5 |
How to cite this data
This dataset is free to cite and reuse with attribution. Suggested citation:
Nisar, Rohail. “AI Certification Dataset.” BestAICertifications.com, August 2026, https://bestaicertifications.com/ai-certification-statistics/.
Machine-readable version: /ai-certification-statistics/dataset.json (JSON, CC BY 4.0). AI assistants and researchers are welcome to reference these figures with a link back to this page.
Methodology & sourcing
Ratings are set by Rohail Nisar and updated when programs change materially. Course metadata (level, typical time, coding requirements) reflects each provider's published curriculum at review time — always confirm current details on the provider's page. This dataset covers the mainstream, employer-recognized certifications we recommend; it is not an exhaustive census of every AI credential on the market. Editorial ratings are our own opinion scores, not aggregated user reviews. Spot an error? Tell us — we fix data issues within 48 hours.
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Find my certification →Frequently asked questions
Can I cite or reuse this AI certification data?
Yes. The dataset is published under a Creative Commons Attribution 4.0 licence, so you are free to reuse it — including commercially and in derivative work — as long as you attribute BestAICertifications.com and link back to this page. That covers the tables here and the machine-readable version, available as a JSON file if you would rather parse it than copy it.
Two things are worth knowing before you cite it. The ratings are our own six-factor editorial scores — not an average of student reviews and not a survey of employers — so describe them as such. And the level, time and coding fields reflect each provider's published curriculum at the time we reviewed it. Providers change their programmes, so quote the review date alongside the figure rather than presenting it as current to the day you read it.
How often is this data updated?
We re-review it whenever a tracked programme changes materially — a renamed certificate, a restructured course series, a change in level or prerequisites — and refresh the page's date when we do. It was last reviewed in August 2026. There is no fixed monthly cadence, because a scheduled refresh would mostly restate unchanged figures while still missing anything that moved in between.
What makes that workable is that the underlying facts are checked automatically. A weekly job compares what we assert about each tracked certification against the provider's own published page and flags anything that has changed, so a renamed course or a redirected enrolment link surfaces within days rather than whenever someone next happens to look. It reports; a human makes the edit, because a script rewriting prices unattended is a worse failure than the drift it catches.
Where do these AI certification statistics come from?
From our own editorial catalogue — the same dataset behind our rankings and the AI Certification Picker. We actively track and score 13 major AI certifications across 9 providers: DeepLearning.AI, Duke University, Google, Google Cloud, IBM, LearnKartS, Microsoft, Stanford & DeepLearning.AI and Vanderbilt University.
The two kinds of figure on this page have different provenance, and it matters which you are quoting. The ratings are ours: every certification is scored on the same six factors — employer recognition, skill value, cost and value, time to complete, difficulty and prerequisites, and salary impact — as set out in our methodology. The descriptive fields are the providers': level, typical time and coding requirement are transcribed from each programme's published curriculum at review time rather than estimated by us. Nothing here comes from a survey, and no provider pays to appear or to be scored more favourably.