Author: Ross Stevenson

Whenever someone asks me, “How do I learn AI?” I have to pause because it feels like it’s a double-loaded question.

I understand why people want to ask me that, especially in my line of work and visibility in showcasing different use cases of artificial intelligence specifically in the realm of learning and development.

Despite this, it’s still really hard for me to give a concrete and clear answer.

It’s difficult because there are so many courses, resources, and experiences that are available to you. For the most part, they all sound the same, look the same, and tell us that they’re gonna give us the same outcome.

I’m always of the belief that there isn’t one tool, one method, or one strategy to rule them all.

And… to top it all off, the term “learn AI” is pretty ambiguous.

What I assume they mean is to understand how a generative tool, like an LLM, works and how they can work with it to get their desired results. That’s how I’ll frame this for today’s advice.

How do I know which AI course is right for me?

To help someone, you need to understand:

• What is the context of the individual and organisation?
• What are the constraints they may be experiencing?
• What is their current starting point?

Without understanding all of these key inputs, it’s very hard for anyone to give you a clear answer to the question I’m constantly posed.

Now I know you’re probably reading this and thinking “oh God please stop writing so much and just tell me what I should do.”

So that is exactly what we are going to do today.

While I’m not gonna give you a clear 100% “this is the course to rule them all”, what I will do is share a selection of choices.

How I assessed these zero-cost AI courses

Okay, here’s the method to my madness with this assessment.

I’ve looked across the market at all of the courses which are tagged with ‘AI fluency’.

There are a lot of free resources on YouTube with a varying degree of quality to consider, but I’ve not included these here because that’s really based on your own taste.

This led me to focus my testing on the leading AI and tech companies building the tools we know. The idea is since they’re building these tools, they might just be a good place to learn about their capabilities.

With that focus, I spent the last six weeks completing courses from the following companies:

• Google
• DeepLearning.ai
• Anthropic
• Microsoft

That gives us four courses in total.

You’ll notice one big omission here from OpenAI.

I did visit OpenAI’s Academy to try and find some resources and courses for the general population. But I found the Academy very difficult to navigate. I didn’t really find anything beyond the ability to sign up to webinars, and rather outdated looking help articles.

That’s not to say that I dislike OpenAI at all.

I would be more than happy to add them into this once I see an improved offer and can feel confident in recommending that to you.

My recommendations for each course will include:

1. What does the course cover?
2. Who would it benefit the most?
3. What will you tangibly walk away with that you can use the next day?

Now, a disclaimer for you…

The right choice for you is contextual, and depends on the 3 questions I posed in the opening section. I’ll give you my recommendations on which courses are truly worth your time in the sea of hundreds currently available.

Yet, your choice on which of the 4 is most useful to you is…well, down to you.

You can use these options for your company, too

Long-time readers will know I’m all about the value!

Since I’m not part of the AI bros club (let’s be honest my hair is far too fabulous and fashion sense too cool for that), where you get literally nothing outside of “This changes everything” comments, I’m making these recommendations not only for your development, but that of the teams you support too.

A lot of organisations are asking you to lead or play a big support role in adoption of AI.

The good news is once you’re confident with the content, you can then use it to your advantage to enable your org to be just as smart with AI (if you want to, of course).

The 4 AI fluency courses I’d recommend for L&D pros

Let’s get this show on the road.

This will be the only playbook you need, and I’ll do my best to keep it updated as the years roll or until my battery dies (hopefully, not anytime soon).

AI For Everyone by Deeplearning.AI

Funny name for this one because I don’t believe it’s for everyone.

Of all the courses I share today, this one is the most advanced-ish, so I’d reserve this for those who are farther down the yellow brick road of AI awareness.

Who it’s for:

This was one of the first AI courses I came across in mid-2023, although it covers much more than Generative AI. It’s taught by Andrew Ng, Founder of Coursera and Google Brain (a deep learning predecessor to the more well known Google DeepMind), and an actual computer scientist.

So yes, this guy legit knows what he’s talking about.

While I found this experience great for my inner nerd, it’s certainly only for those who want a broader understanding of everything AI, not just LLMs and everything under the generative umbrella.

Maybe one best placed for my fellow learning tech folks.

What it does well:

The course gives you everything you’d need to know about AI without being an engineer.

Talking head videos and resources are clearly explained, and easy to download for your future viewing pleasure.

Where it falls short:

Now, this only falls short depending on your context.

If you’re not an engineer or don’t care about getting deeper into an AI specialised role, then it will probably fall short for you in many areas with its overwhelm. It is stuffed with lots of info, and most is non-relevant if you just use a standard LLM every day.

I mean, it has no hands-on use of AI tools but I find it hard to see where this could be weaved in with all the Matrix level explanations.

Verdict:

• ❌ Skip, if you’re 95% of average AI users.
• ✅ Dive in if you’re an engineer, curious nerd or want to make a career pivot to AI as a broader category.

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Google AI Essentials

Ok, let’s see what the big G has to offer.

While they came late to the useful LLM party, it can’t be denied they’re certainly a frontrunner with their abundance of tools and Godzilla-sized ecosystem.

Who it’s for:

This is pitched at the pure beginner. If you or your teams look at an LLM and have no clue what’s going on, this is the place to start.

Google’s team give you an overview of the basics that’ll give you a better standing in those workplace conversations.

What it does well:

Like I said, it’s framed perfectly for beginners, and in the traditional online course delivery we all know, but maybe don’t love. There’s plenty of focus on low-level tasks where you can collaborate with AI, and a surprisingly good overview of prompting too.

Where it falls short:

Like most choices on this list, the videos are super corny.

Even more so with these Google ones. I feel like the real humans could be avatars because the delivery is so scripted and robotic which breaks any form of immersion.

If you know the basics and are confident with generic LLM activities, you won’t get much from this.

Note: If you want to go to the next level, Google offers a paid professional certificate which bolts on a “How to build with AI” category, which is missing from all of the above. Free to access tools like Google AI Studio are criminally underrated because they’re pitched as “developer” tools but are very easy to use once you’re set up with some basic knowledge. This course provides that next step in your AI fluency journey.

Verdict:

• ❌ Skip if you’ve been using LLMs for a few years and know the basics.
• ✅ Dive in if you’re at beginner territory and need to build the foundations with understanding and working with LLMs

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AI Fluency: Framework and Foundations by Anthropic

I know I said “this isn’t a competition with one winner”…but…

This one is my most highly recommended for everyone.

I like this one because it’s focused on “how to work and think with Gen AI and LLMs as a category”, rather than, “here’s how to use our tool”. It does a good job on prompting you (pun intended) to think about how you engage with AI and its outputs.

This is more valuable for the long game with AI. The tools will change, but investing in how you think and work with AI won’t.

Who it’s for:

It’s for anyone ready to move beyond tools and templates and start thinking with AI.

So, basically, everyone reading this.

Simply having access to AI doesn’t make you competent and fluent with AI.

Anthropic

What it does well:

The course gives you a clear fluency framework you can pick up and implement with a few tweaks. The exercises (when you find them) are practical, immersive, and teach you to think about how you’re working with AI, not just what you’re asking it.

There’s a strong breakdown of techniques that improve both AI outputs and your own thinking in the process, and I liked the encouragement to work alongside a LLM to solve the problems the course unpacks.

Where it falls short:

The UX needs work.

Exercises are buried below the fold with no direction to scroll down. I was surprised an instance of Claude wasn’t embedded for these. Why not include live demo exercises in the videos to actually model thinking with AI?

There’s also a section covering transformers, compute, and tokens. Nice to know, but for most people this isn’t the advantage the course frames it as.

Verdict:

✅ Take it. 

It teaches you how to work with AI, not just how to use a tool. That alone sets it apart from 90% of AI courses out there right now.

I can’t think of someone who wouldn’t benefit from it.

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AI Fluency by Microsoft

I know a lot of companies are using Microsoft infrastructure, so it was a no brainer to include this one.

The ultimate assessment of ‘if this is right for you and your teams’, is if you’re using Copilot as your go-to company LLM.

Who it’s for:

The easy answer is if your organisation’s LLM of choice is Copilot and you want to make the most of it across all the popular MS apps.

What it does well:

A comprehensive walkthrough of Copilot that’s perfect for any MS powered organisation. If you’re using Microsoft tools, you can’t go wrong here.

You also get a good overview of AI Fluency (according to Microsoft) and ideas on how to amplify its principles across a team, department and org level.

Where it falls short:

It’s painfully long, and stuffed with too much unnecessary content.

When you look at it, it’s just poor design. There’s some good stuff here but it’s buried by mountains of content. It felt like the brief was ‘how much can we stuff in?’. This is the biggest problem for this one. If you’re a copilot user, it’s made for you but you’ll be going through a lot of ‘nice to know’, not ‘this is actually gonna help me do better work’ content.

I don’t like the gamified angle with experience points either.

That might be just me, yet I felt like I was in some 90’s retro game at points, and I didn’t want to be there.

Verdict:

• ❌ Skip if you’re not using Copilot
• ✅ Dive in if you’re using Microsoft across your organisation with Copilot but select the parts that make most sense for your work, as you don’t need everything on offer here

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The elephant in the room

There is a rather oxymoronic angle to all these courses.

We’re being told as an industry that AI will change learning and education forever.

Yet, each of these companies decided to build courses around the traditional module structure with speaker videos and text based exercises.

I would have loved to see more application of AI in teaching AI.

Keep an eye out on my YouTube channel because I’m going to share how some of these could have incorporated the tools they’ve built.

You can start with this AI coach I built for one of my courses.

How I would learn about AI if I did it all over again

The biggest takeaway today might be this.

If I was starting all over again as an L&D pro with little understanding of AI, I’d:

1. Take the Google AI Essentials course to build your tech foundation

2. Take the Anthropic AI Fluency Frameworks course to build your “thinking with AI” foundation

3. Create a free Google account to access Google AI Studio, where you can test prompts, build apps and learn about agents in one place.

That’s it.

Google’s AI Essentials gives you everything you need to know about generative AI, and Anthropic’s fluency course builds on this by teaching you how to work and talk to AI without degrading your power of thought. Google’s AI Studio is the cherry on top as it lets you practice everything in one space with free access to top models.

That’s my zero-cost learning plan for you 😉.

Final thoughts

There you have it, friend.

These are my picks of the most impactful zero-cost AI courses worth checking out. Like I said, there’s no overall winner, just what works best based on your context, experience level, and goals.

I hope this helps you build your skills and enable those around you with AI too.

Let me know your thoughts on these courses, and any I missed that you think I should check out.

See you next time, human.

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Before you go… 👋

If you like my writing and think “Hey, I’d like to hear more of what this guy has to say” then you’re in luck.

You can join me every Tuesday morning for more tools, templates and insights for the modern L&D pro in my weekly newsletter.

L&D teams trying to and/or being responsible for total company AI adoption is a fools errand.

We both know this is stupid, yet I see too many instances of companies trying to “train” their way into successful AI adoption.

Of course, L&D teams alone aren’t going to make any organisation achieve meaningful AI adoption (however you measure that). Yet, we do have a part to play, and recognising where we can best support is critical.

So, lets explore Where L&D Can Support Meaningful AI Adoption.

There’s a ton of talk about AI adoption.

It’s odd because the validation of “adoption” has many definitions, dependent on the context and environment. The common pitfall is to measure adoption as ‘use of AI tools’ alone.

As we know, with previous technology, usage alone doesn’t mean meaningful adoption.

Setting what adoption looks like in your organisation is not a task for the L&D team.

Yet, we have an opportunity to contribute to long-term and meaningful adoption of AI across workforces as part of a wider collaboration in a community.

Let’s talk about that…

It takes more than access

Let’s go beyond the veil of bullshit we see online.

Access to an AI tool alone means nothing, and putting on one hour lunch and learns to “make people learn AI” is a comical up-skilling strategy.

If you’re a long-time reader, you’ve heard me become a broken record when I talk about what it takes to nurture meaningful and long-term change. We have much to consider with context, culture and constraints in each environment. No two workplaces are the same, that’s why the cookie-cutter “adoption frameworks” make me laugh.

They’re a good point of inspiration, but you shouldn’t follow them like a strict set of instructions.

Saying that, what is it we need to consider beyond tools?

People, Systems and Tools

As you’ve probably guessed, launching new technology and tools alone rarely leads to meaningful adoption.

There’s a bigger ecosystem at play.

We have to consider:

1/ People

Where are people at today, and how do we meet them?

Everyone will have a different understanding, maturity and receptiveness to something new and unknown. In AI’s case, we have a mix of emotions from “will this take my job” to “I want it to do all this stuff I hate doing”.

The most difficult part of a change process is people, because we’re all so unpredictable.

2/ Systems

Quite simply, how we work today.

What are the tried, tested and trusted conscious and unconscious systems we have in place? This covers both how we execute tasks and how we think about executing those tasks (deep, I know).

We each follow different types of systems in our day to day.

Understanding what these are and how AI will impact those is key to this change.

3/ Tools

The part you’re most likely more familiar with.

Here, we should consider the tools in use today alongside new ones being deployed, and how to bridge the gap in both understanding and knowing when and where to deploy them.

Too many forget the ‘when and where’ part at their own peril.

Where you can add value

For us to recognise where we can provide support and drive value, we must recognise what’s changing.

I think this framework from BCG can help recognise the moments where performance support is most needed with AI transformation.

They propose it for navigating AI transformation at scale, and through an L&D lens, I see this as a conversation point of what to map against when focusing on how best to support workforces.

It’s built on two key dimensions:

1️⃣ AI Maturity

It progresses from tool-based adoption by individuals to workflow transformation, to full, agent-led orchestration. Most organisations, and even teams within them, operate across multiple stages at once, not in a linear path.

2️⃣ Workforce Impact

This spans how tasks are executed, to what skills are needed, to how teams are structured, to how organisational culture must evolve to support new ways of working.

While this covers the wider transformation AI brings across businesses, it acts as a roadmap for L&D.

A roadmap is often what we need because it’s not uncommon for senior leaders to treat “training” (as they call it) as a boomerang that’s thrown at will when they decide people need to know stuff.

The framework above provides a view of where the friction/pain points/ problems exist in the cycle of change. That’s where we should focus.

Map it out

I mentioned before not to blindly follow frameworks, and that advice is the same here.

This view from BCG is a useful foundation for each of us to think about “where can we add value”, but it will look different for each environment.

So, I’d recommend you map out what your organisational journey looks like today.

Explore the 3 pillars of tasks, talent and teams across your business and how/where AI is starting to and might impact these. It’s here that you will uncover the friction and pain points where we can be of most service.

Some of that will be through tooling, no doubt. Yet, I feel pretty safe in saying you’ll be spending a good deal of your time navigating changes within people and systems.

Final thoughts

I’m going to leave it here for now, folks.

There’s much to say, of course, but only so much attention span I can ask you to give.

I’m thinking of expanding some of this thought into a long-form video. If that sounds like something you’d like to see, let me know.

In the meantime, some additional resources to explore on this include:

• Build your AI confidence with this strategy
• The ultimate guide to using (or avoiding) AI
• How to meaningfully drive AI adoption

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Before you go… 👋

If you like my writing and think “Hey, I’d like to hear more of what this guy has to say” then you’re in luck.

You can join me every Tuesday morning for more tools, templates and insights for the modern L&D pro in my weekly newsletter.