The other night, while setting up a Docker container and trying to explain vector embeddings to my 11-year-old daughter, I paused mid-sentence and smiled. Not because she got it (which she kind of did), but because I realized this is the kind of moment I dreamed of: learning, teaching, struggling, and sharing the journey of tech with my kids.

Over the past few months, I’ve been slowly building my own roadmap into machine learning and AI. And while this roadmap is inspired by frameworks I’ve seen online (especially one great breakdown I came across on LinkedIn), I’ve made it my own.

More importantly I’ve made it something I can share with my kids. And maybe you can too.

So here’s a plain-language breakdown of how I’m learning ML, how I’m teaching it to my daughter, and how we’re both growing through the process.


Before we get into the technical roadmap, I used stories and analogies to help her relate to these concepts:

Training a model became the story of teaching a robot to recognize birds in our yard. Each bird it sees helps it learn patterns shape, color, sound.

Prompt engineering was like giving instructions to her younger siblings: “Ask nicely, speak clearly, and explain what you want.”

AI agents were explained as farm animals with different jobs the goat explores, the chicken remembers, the sheep makes decisions. Together, they run the farm.

Databases were the shelves in our pantry: items grouped by type, easily found when you know the structure.

Fine-tuning was like tweaking our homemade bread recipe same ingredients, but a different flavor for a special day.


These stories made the ideas real. They helped her feel that machine learning isn’t just about machines. It’s about learning, adapting, and communicating just like we do every day.

1. Start with Code (Gently)

We began with Python. Not with libraries, not with AI. Just Python. Simple things. A calculator. A guess-the-number game. Drawing stars with loops. I wanted her to feel the logic first before touching models or data.

We also touched a bit on Bash commands and file structures just enough to teach her how the machine talks back. We’re not chasing speed here. Just fluency.


2. What Are LLMs and Why Do They Matter?

Once we had the coding basics down, we stepped into the world of large language models LLMs. Think of them like really powerful text prediction engines. But explaining that to an 11-year-old isn’t straightforward.

So instead, we used stories. I’d write a sentence, she’d guess what comes next. Then I’d show her how a model might do the same.

We talked about structured output, multi-modal inputs (text + images), and how AI isn’t just smart it’s trained. Like a child who reads a lot. It mimics what it’s seen. That made sense to her.


3. Prompt Engineering and the Art of Asking

This part was surprisingly fun. Prompt engineering is basically learning how to talk to AI.

We played games like: “If you wanted a story about a goat that learns to code, what exactly would you say?” Then we’d test it out. Adjust. Try again.

We also started exploring the idea of fine-tuning taking an existing model and teaching it new tricks. She loved the analogy of upgrading a recipe, adding flavors to an already baked cake.


4. Where Does All This Info Live? (Databases!)

We talked about how AI needs memory. How it retrieves info. Vector databases and graph databases are fancy, but I broke it down as a giant filing cabinet. Similar files live together.

We also looked at what hybrid retrieval means mixing different ways of finding data.

She now calls our notes “vector cabinets” and honestly, I’m not correcting her.


5. Designing AI Agents (Like Characters in a Game)

I introduced AI agents as characters in a game who have to work together to solve a mission.

Some remember things. Some ask questions. Some make decisions.

We sketched our own agents. Gave them jobs. She named one “Agent Feather” who helps lost children find books. I wasn’t ready.


6. Deployment (Where the Magic Goes Live)

We started to look at how models go from local machines to the world. I introduced Kubernetes, CI/CD (in concept), and cloud basics.

We didn’t get too deep. But the main idea stuck: your model needs a home, and it needs to be able to grow without breaking.


7. Evaluation: Is It Even Working?

We ran little tests. Changed prompts and compared results. Talked about what makes a response “good.”

We touched on instrumentation and observability tools though honestly, this part was more me talking to myself while she built Lego near me.

Still, she asked questions. That’s all I wanted.


8. Ethics and AI Safety: Where Islam Meets Engineering

This one was big.

We talked about fairness. Bias. Guardrails. How AI can reflect the good or the harm of the people who build it.

And then I reminded her of a simple Islamic principle:

> Whatever you build, you’re responsible for it. Not just technically but morally.

This hit home. For both of us.


9. Bonus: Creative Agents and Future Paths

We played with image models. Tried out speech-based tools. Talked about robots and voice agents and whether AI can help find lost goats (jury’s out).

She had ideas. I had concerns. And in between, we just kept learning.


Final Thoughts: This Isn’t About AI It’s About Process

This journey isn’t about raising a future ML engineer. It’s about slowing down and learning deeply. For her.

She doesn’t need to master models right now. But she’s learning how to think, how to ask, and how to approach new knowledge without fear.

And that’s more valuable than any API key.

So if you’re on a similar journey with kids, with your team, or even just for yourself feel free to use this roadmap. Adapt it. Simplify it. Make it your own.

Because the goal isn’t to finish fast. It’s to think well.

And to remember that learning, like barakah, grows best when it’s shared.