There has probably never been a better time to build your AI skills, particularly if cost has been one of the things holding you back.
Over the past few years, the number of artificial intelligence courses available online has grown enormously, with universities, technology companies, governments and specialist training providers all developing programs. Some are excellent, some cover very similar ground and, increasingly, some of the best introductory learning is available completely free or at a relatively low cost.
Having worked across career development, VET and TVET, entrepreneurship, small business and innovation for many years, including increasingly at the intersection of workforce planning and AI, what interests me most is not simply whether someone has completed an AI course. It is whether they can take that learning back into their business, organisation, industry or community and actually use it.
That is an important distinction because AI literacy is rapidly becoming part of workforce literacy.
We have reviewed more than 60 courses and learning resources, including those from OpenAI, Microsoft, Google, IBM, AWS, NVIDIA, DeepLearning.AI, UNESCO, the University of Helsinki, Harvard, Cambridge, Oxford, Vanderbilt University, the University of Michigan, Lund University, Kaggle and Hugging Face.
There is plenty to choose from, so this isn’t a suggestion that anyone needs to work their way through the whole list. Instead, think about where you are starting from, what you need AI for and what you want to be able to do differently once you have completed the learning.
Where you might start
For someone who is relatively new to AI and wants practical skills that can be applied immediately, there are five options that stand out.
- OpenAI Academy – AI Foundations
Free | Beginner | Around 70 minutes
OpenAI Academy – AI Foundations
This is a particularly useful starting point if you are already using, or planning to use, ChatGPT for work. It explains AI and large language models without becoming unnecessarily technical and then moves into giving AI better instructions and context, reviewing outputs and using AI responsibly.
What I like about the approach is the connection to a real task. Rather than learning AI in isolation, participants are encouraged to think about something they already do and how AI could support that work. There is also an assessment and digital badge available.
- Elements of AI – University of Helsinki
Free | Beginner
Elements of AI has been around longer than many of the newer generative AI courses and remains an excellent choice for people who want to understand AI more broadly.
It covers what AI is, problem solving, machine learning, neural networks and the implications of artificial intelligence. For leaders, workforce professionals, educators and policy makers, that broader understanding can be valuable because AI is much bigger than ChatGPT and generative AI.
- Microsoft Learn – AI Learning
Free | Beginner through to advanced
Microsoft has developed an extensive range of free AI learning, which makes this a good option for organisations wanting people to build capability progressively.
The learning covers AI fundamentals, generative AI, Copilot, machine learning, responsible AI and more technical applications, with many modules designed so they can be completed in relatively short blocks of time.
For workplaces already using Microsoft 365, there is an obvious benefit in connecting AI learning with the tools people use every day.
- IBM SkillsBuild
Free | Beginner through to advanced
IBM SkillsBuild is another platform worth exploring because AI sits alongside broader digital capability, including data, cybersecurity, cloud computing and professional skills.
That makes it particularly relevant for students, jobseekers, career changers and people thinking about where their skills need to develop over the next few years rather than simply wanting a quick introduction to generative AI.
- Google AI Essentials
Low cost | Beginner | Under 10 hours
Google AI Essentials is not generally free, but it remains worth including because it provides a structured introduction to using AI productively at work.
It covers AI fundamentals, productivity, prompting, responsible use and continuing to build AI capability. For someone wanting a recognised credential for their CV or LinkedIn profile, the modest investment may also be worthwhile, although current pricing should always be checked before enrolling.
AI fundamentals and AI literacy
If you are starting from scratch, there is no need to complete five introductory courses covering much the same material. Choose the one that best matches how you like to learn, complete it and then move into applying the knowledge.
- AI for Everyone – DeepLearning.AI
Andrew Ng’s well-known course is aimed at non-technical learners and explains what AI can and cannot do, where organisations might use it and some of the broader implications of adoption.
- Generative AI for Everyone – DeepLearning.AI
A useful progression for business leaders and professional users who want to understand generative AI and where it can create value without needing to become developers.
- Introduction to Artificial Intelligence – University of Illinois Urbana-Champaign
This university-level option provides more depth for learners who have completed basic AI literacy and want to understand the field more comprehensively.
Introduction to Artificial Intelligence
9–11. Microsoft AI Fundamentals
Microsoft Learn has several introductory options, including Get Started with AI, Explore AI Basics and Introduction to AI Concepts. They are useful for people who prefer short modules and want to progressively build their understanding rather than commit immediately to a longer course.
12–13. IBM AI Literacy and AI Fundamentals
IBM SkillsBuild includes introductory learning covering AI literacy and AI fundamentals, which can then be connected with learning in areas such as data, cybersecurity and cloud computing.
Explore IBM SkillsBuild learning
14–15. AWS AI Learning
AWS provides learning covering artificial intelligence fundamentals, AI use cases and applications, with pathways that can eventually lead into machine learning and the development of AI solutions.
Generative AI and using AI at work
Once the fundamentals make sense, this is where I think the learning becomes much more interesting. Rather than continuing to collect introductory courses, start looking at the work you actually do and where AI could make a meaningful difference.
- Applied AI Foundations – OpenAI Academy
Applied AI Foundations is a useful progression from the introductory OpenAI learning because the focus starts moving from individual prompts towards repeatable ways of working.
This is where you can begin asking better questions about their own role. Which tasks are repetitive? Where does research consume time? What information needs to be synthesised? What could AI draft or analyse, and where does a person still need to review, challenge or make the decision?
- Generative AI Primer – Vanderbilt University
Vanderbilt’s Generative AI Primer provides an accessible introduction to the technologies and applications of generative AI and is suitable for professional users as well as beginners.
- Getting Started with Generative AI
This Coursera option provides another accessible entry point for people who understand the basics of AI but want to focus more specifically on generative AI.
Getting Started with Generative AI
- Generative AI Foundations – Edureka
The course introduces foundational generative AI concepts and can be useful for learners wanting a more structured progression.
- Generative AI and Large Language Models
For people who have been using tools such as ChatGPT but want to understand what sits behind them, this provides a useful introduction to generative AI and large language models.
Generative AI and Large Language Models
- IBM Generative AI Fundamentals
IBM’s learning provides another pathway from introductory AI into generative AI and its practical applications.
IBM Generative AI Fundamentals
- Developing Generative AI Solutions – AWS
This is more appropriate once you are ready to move from understanding generative AI towards developing solutions and exploring how AWS technologies can support implementation.
AI for trainers, facilitators and educators
This is an area that particularly interests me because of the potential impact across VET, TVET, Higher Education, workplace learning and professional development.
AI changes both sides of the equation. It changes the skills people need to learn, but it also gives trainers and educators new ways of researching, designing, developing and delivering learning.
- Introduction to Generative AI for Trainers – Microsoft
Microsoft has developed a learning pathway specifically for trainers covering generative AI, large language models, the development of training materials, prompting and critical thinking.
For trainers, facilitators and VET/TVET practitioners wondering where to start, this is one I would put quite high on the list.
Introduction to Generative AI for Trainers
- Introduction to Artificial Intelligence for Trainers – Microsoft
This provides a broader introduction to AI for people responsible for developing the capability of others and complements Microsoft’s generative AI learning.
Microsoft training for educators and trainers
Responsible AI, ethics and governance
Responsible AI cannot be something organisations think about after implementation. As AI becomes embedded into recruitment, workforce decisions, customer interactions, education, health, government services and business processes, questions around privacy, transparency, bias, accountability and human oversight become increasingly important.
There are some very good courses in this area.
- Global MOOC on the Ethics of AI – UNESCO
UNESCO brings an international and societal perspective to AI ethics, making this particularly relevant for policy, education, development and international work.
- Ethical AI: AI Essentials for Everyone – University of Cambridge
This provides a university perspective on some of the ethical questions emerging alongside AI adoption.
Ethical AI: AI Essentials for Everyone
- Artificial Intelligence: Ethics & Societal Challenges – Lund University
This is worth considering for anyone interested in the relationship between AI, people and society rather than looking at AI solely through a technology or productivity lens.
AI Ethics & Societal Challenges
- Ethics of Artificial Intelligence – Politecnico di Milano
Another university-based option examining ethical considerations associated with AI development and adoption.
Ethics of Artificial Intelligence
- AI Ethics, Responsible Use and Creativity – University of Michigan
This course brings together responsible use and some of the emerging creative applications of generative AI.
AI Ethics, Responsible Use and Creativity
- Ethics and Responsible Practices in GenAI – Alberta Machine Intelligence Institute
This focuses specifically on responsible practices associated with generative AI.
Ethics and Responsible Practices in GenAI
- Responsible and Ethical AI – Northeastern University
Another option for people wanting to strengthen their understanding of responsible AI implementation.
- Intro to AI Ethics – Kaggle
Kaggle’s AI Ethics course is particularly attractive because it is free and practical, covering areas including human-centred design, bias, fairness and model cards.
- Responsible Artificial Intelligence Practices – AWS
For people working with AWS technologies, responsible AI learning can be incorporated into the broader AWS learning pathway.
- Responsible AI Ethics and Data Practice – Microsoft
This connects responsible AI with data practices, an important consideration for organisations moving beyond experimentation into implementation.
Responsible AI Ethics and Data Practice
- AI Governance – Oxford Saïd Business School
Governance becomes increasingly important for leaders, boards and organisations moving from individuals experimenting with AI towards organisation-wide adoption. Check current enrolment and certificate costs before signing up.
- AI Governance & Regulation – Edureka
This provides another option for people interested in the developing regulatory and governance environment surrounding AI.
Moving from prompts to AI agents and workflows
This is an area to watch closely. Much of the early discussion around generative AI focused on how to write a good prompt. That remains useful, but AI is rapidly moving towards workflows and agents that can use tools, work through multiple steps and undertake more complex tasks.
For people who already use AI comfortably, this is probably a more valuable next learning step than completing another beginner course.
- Agents & Workflows – OpenAI Academy
OpenAI’s learning provides a progression into thinking about structured workflows, agents, context, boundaries and human review.
- Hugging Face AI Agents Course
For people with some technical capability, the Hugging Face Agents Course is an excellent free resource covering agent fundamentals and frameworks including smolagents, LlamaIndex and LangGraph.
It is much more hands-on than an introductory AI literacy course, so some Python and an understanding of large language models will help.
- Learn AI Agents – Scrimba
Scrimba provides another hands-on option for learners who want to start developing AI agents rather than simply understanding them conceptually.
Machine learning, large language models and technical AI
Not everyone needs to become a machine learning engineer, but for people who enjoy the technical side or want to work directly in AI, there is a substantial amount of high-quality learning available without committing immediately to an expensive qualification.
- Fundamentals of Machine Learning and Artificial Intelligence – AWS
A useful introduction to the concepts underpinning machine learning and AI.
AWS Machine Learning and AI learning
- Introduction to Machine Learning Concepts – Microsoft
Microsoft provides free learning covering fundamental machine learning concepts as part of its broader technical training catalogue.
Microsoft Machine Learning training
42–47. Kaggle Learn
Kaggle is one of the strongest free options for people wanting to learn by actually doing things. Its learning catalogue includes Intro to Machine Learning, Intermediate Machine Learning, Intro to Deep Learning, Computer Vision, Machine Learning Explainability, and Intro to Game AI and Reinforcement Learning.
Rather than just watching videos, learners can work with data and code, which makes Kaggle worth exploring if you want to move into more practical technical skills.
- Hugging Face LLM Course
Hugging Face’s free LLM course goes considerably deeper into large language models, natural language processing, transformers, tokenisation and the technologies underpinning modern generative AI.
This is one for technical learners rather than complete beginners.
- NVIDIA AI Training
NVIDIA’s training catalogue covers generative AI, deep learning, accelerated computing and other advanced areas. There is a mix of free and paid learning, so check the individual course before enrolling.
AI security
AI capability also brings new security considerations, including the security of AI systems themselves and the ways AI can be used in cyber threats. For cybersecurity professionals, technology teams and organisations implementing AI at scale, this is becoming another important capability area.
- AI Security: Security in the Age of Artificial Intelligence
This specialisation looks specifically at the intersection between artificial intelligence and cybersecurity.
AI Security: Security in the Age of Artificial Intelligence
- AI Security
Another introductory Coursera option focusing on security considerations associated with artificial intelligence.
AI, sustainability and ESG
There is another emerging area worth watching closely, particularly as governments, businesses and communities consider both the potential benefits and the infrastructure implications of AI.
AI can support areas such as energy optimisation, agriculture, transport, supply chains and environmental monitoring, while the rapid expansion of AI and data centres also raises questions around energy, water, infrastructure and sustainability.
- Generative AI and ESG – Rutgers University
This course examines generative AI through an environmental, social and governance lens.
- Gen AI for Sustainability – STARWEAVER
An option for people interested in how generative AI might be applied to sustainability challenges.
- Generative AI for Sustainability – AI CERTs
Another specialist option connecting generative AI with sustainability applications.
Generative AI for Sustainability
More introductory and practical AI courses
There are also many shorter courses available through Udemy and similar platforms. The quality, availability and pricing can change, so it is worth checking the course details, recent reviews and whether a supposedly free course is still free before enrolling.
Options from the broader review include:
- Introduction to Artificial Intelligence – Mohammed Shahin
View the course - Artificial Intelligence: Preparing Your Career for AI – Matthew Renze
View the course - Learn Basics About Artificial Intelligence – Nikola Milosevic
View the course - Understanding AI – Beginner’s Mini Course
View the course - Artificial Intelligence, GenAI and AI Agents for Beginners
View the course - Everyday AI: How to Use Artificial Intelligence Daily
View the course - Safe and Ethical Use of Artificial Intelligence
View the course - AI Agents for Beginners – SkillUP by Simplilearn
View the course - Artificial Intelligence for Beginners: AI Terms Explained
View the course - AI Literacy for Everyone
View the course - AI Literacy & Fundamentals: Skills for the Modern Workplace
View the course
So, which courses are actually worth doing?
With more than 60 options here, the answer is definitely not to enrol in all of them. There is quite a bit of overlap at introductory level, and completing another course explaining what generative AI is probably won’t add much value once you already understand the fundamentals.
For someone starting today, perhos choose one solid foundation course, with OpenAI Academy, Elements of AI, Microsoft Learn or IBM SkillsBuild among the strongest free options. From there, choose learning that relates to what you actually do, whether that is running a business, managing people, training and education, workforce planning, data analysis, policy, consulting, technology or something else entirely.
Responsible AI should be part of that development rather than treated as a specialist topic for someone else, particularly if AI is being used with organisational, customer, employee or commercially sensitive information. Once you are reasonably confident using generative AI, it also makes sense to start exploring workflows and agents rather than continually repeating introductory prompting courses.
For people wanting to move into AI professionally, the pathway will look different again, with machine learning, data, Python, large language models, APIs, RAG, evaluation, security and agent development becoming much more important.
From AI literacy to workforce capability
This is the part that interests me most from a Strategic Workforce Planning and Workforce Development perspective.
As AI becomes embedded across organisations and industries, we need to look beyond the number of people who have completed an AI course and think about what capability is actually required across different jobs, occupations and levels of an organisation.
There will be people who need enough AI literacy to use an assistant confidently and responsibly, others who need to redesign workflows and jobs, managers who need to understand implementation and risk, leaders and boards who need governance capability, trainers who need to develop the skills of others, and technical specialists who will build, integrate, secure and evaluate AI systems.
There are also important workforce questions about which tasks can be augmented, which might be automated, what new work will emerge and where human judgement, relationships, creativity, practical expertise and accountability remain essential.
For small businesses and regional communities, access matters too. One of the encouraging things about compiling this list is seeing how much quality learning is now available without a large training budget. That creates an opportunity to make AI capability much more accessible, provided people know where to find the good material and can connect the learning with practical support and real-world application.
My suggestion is to choose the courses that best matches where you are now, complete it and then deliberately apply the learning to a genuine piece of work. Look at what changed, what worked, what didn’t and what you need to learn next.
That connection between learning, practical application, workforce planning and responsible implementation is where AI training becomes much more valuable than another certificate sitting on a CV. And if you have completed a free or low-cost AI course that deserves to be on this list, please share it. I would love to keep building the directory as new learning becomes available.
Courses, pricing, free access and certificate arrangements change regularly, particularly through platforms such as Coursera and Udemy. Check the provider’s current information before enrolling. This list was compiled and reviewed in October 2026.