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I Built a Free AI Curriculum With 7 Chapters, a Browser Playground, and Zero Paywalls. Here's Why.

By Luigi Pascal Rondanini

There are thousands of AI courses online. Most cost money. Many are outdated before they launch. Almost none let you run code without installing anything.

I built something different.

Berta Chapters is a free, open-source AI curriculum that takes you from Python basics to supervised machine learning — with interactive notebooks, exercises, professional diagrams, and a Python playground that runs in your browser.

No paywall. No signup. No tracking. Just learning.

chapters.berta.one

The problem I kept seeing

I've spent 35 years in financial systems — FX trading floors in London, Milan, Basel, Riyadh. Treasury systems. Organizational transformation. Over the past few years, the question I hear most often is:

"How do I get started with AI?"

The answer should be simple. It isn't.

The current landscape looks like this:

  • University courses: Rigorous but expensive and slow. A semester to cover what a motivated learner could absorb in weeks.
  • Online platforms: Polished videos, but often locked behind subscriptions. $40/month adds up.
  • YouTube tutorials: Free but fragmented. You spend more time searching for the next video than actually learning.
  • Documentation: Written for people who already know. Not for people who are starting.

What's missing is something that is simultaneously rigorous, practical, complete, and free.

That's what I built.

What Berta Chapters actually is

It's a structured curriculum — 25 chapters planned, 7 available today — that covers:

Foundation Track (complete): 1. Python Fundamentals for AI 2. Data Structures & Algorithms 3. Linear Algebra & Calculus 4. Probability & Statistics 5. Software Design & Best Practices

Practitioner Track (in progress): 6. Introduction to Machine Learning 7. Supervised Learning: Regression & Classification

Each chapter includes:

  • 3 progressive Jupyter notebooks (introduction → intermediate → advanced)
  • Professional SVG diagrams that explain concepts visually
  • Production scripts you can reuse in your own projects
  • 5-6 exercises with complete solutions
  • Practice datasets

The entire thing is on GitHub: github.com/luigipascal/berta-chapters

What makes it different

You can read everything on the website

Every notebook is converted to a readable web page. You don't need to install Python, Jupyter, or anything else. Just open chapters.berta.one and start reading.

There's a Python playground in the browser

Go to chapters.berta.one/playground. Write Python. Click Run. See results. Real Python running via WebAssembly — no server, no installation, no account.

There are 14 pre-built exercises you can load from a dropdown, covering chapters 1 through 6. And if your code has an error, the playground explains it in plain English:

"You tried to use a variable called 'x', but it hasn't been created yet. Did you forget to define it? Python is case-sensitive, so 'Name' and 'name' are different variables."

That's the kind of feedback a beginner actually needs.

Every concept is explained before the code

I've seen too many tutorials that show code first and explain later (or never). In Berta Chapters, every concept gets 3-5 sentences of plain English explanation before any code appears.

Variables are introduced as "labeled containers." Functions are "reusable recipes." Classes are "blueprints for houses." Gradient descent is "walking downhill to find the lowest point."

The notebooks are 60-75% explanatory text. The code is there to illustrate, not to overwhelm.

It's generated by AI, curated by a human

Every chapter is generated by Berta AI — the system I built at berta.one. I review, test, and refine every piece of content before it goes live.

This isn't "AI slop." It's AI used the way it should be used: to scale the creation of educational content that one person couldn't write fast enough alone, with human oversight ensuring quality.

Every chapter is transparently marked as AI-generated.

The numbers so far

Metric Value
Chapters available 7
Jupyter notebooks 21
SVG diagrams 21
Exercises with solutions 37
Hours of learning content 56
Practice datasets 5
Cost $0

What's coming next

One new chapter per week. Next up:

  • Chapter 8: Unsupervised Learning
  • Chapter 9: Deep Learning Fundamentals
  • Chapter 10: Natural Language Processing

The goal is 25 chapters covering everything from Python basics to multi-agent systems, reinforcement learning, AI safety, and building production AI systems.

The full roadmap is at chapters.berta.one/guides/roadmap.

Who this is for

If you're new to programming: Start with Chapter 1. It assumes nothing. It explains what a variable is before asking you to use one.

If you know Python but not AI: Skip to Chapter 6. It takes you from "what is machine learning?" to building your first model in one notebook.

If you're an experienced developer: Browse the chapters, grab what you need. The exercises are non-trivial. The production scripts are patterns you can use in real projects.

If you're a manager or executive: There's a learning path designed for you — 6 chapters, 38 hours, focused on understanding AI capabilities and limitations for decision-making.

How to start

Three ways, depending on your comfort level:

  1. Just read on the website — chapters.berta.one/chapters. Zero setup.

  2. Use the playground — chapters.berta.one/playground. Write and run Python in your browser.

  3. Clone and run locally — For the full Jupyter experience. There's a step-by-step guide that assumes you've never opened a terminal before.

Why free?

Because education shouldn't have a paywall.

I've been fortunate in my career. I've worked at major banks, consulted on complex systems, built digital properties through Rondanini Publishing. I'm in a position to give this away.

And I believe that AI literacy is too important to be gated by money. The people who most need to understand AI — career changers, students, professionals in non-tech roles — are often the ones who can least afford another $40/month subscription.

So Berta Chapters is free. Open source. MIT licensed. Fork it, modify it, teach with it, build on it. Just include attribution.

Subscribe for updates

I'm releasing one new chapter per week. To get notified:

Luigi Pascal Rondanini is an author, publisher, and treasury systems consultant. He's the founder of Rondanini Publishing and the creator of Berta AI. He writes about AI, education, and organizational transformation.

If you found this useful, a clap or share helps others find it. And if you'd like a chapter on a specific AI topic, request one — Berta will build it for you.