Love it or fear it, that’s the question. Welcome to the series.
series: AI from zero to expert | introductionDisclaimer:
This series is written with the help of artificial intelligence (Claude), checking every technical claim against primary sources and official documentation (academic papers, technical documentation, specialized outlets) and citing the source for every relevant statement. Even so, it doesn’t replace your own judgment: verify it, look into it yourself, and be wary of any paragraph without a source behind it.
If you spot an error, let me know. You can email me at jenniffer@guaduastudio.com or reach me through my LinkedIn profile.
The series’ goal
I like writing about what I know and what I’m still learning. Sharing is living, even if we seem to have forgotten that. This AI series is born from that idea.
I tend to write about the most recurring topic of the last few years, in tech and in real life alike: AI.
And since it’s such a current topic, it’s in constant flux. You have to keep up with whatever’s new, and it feels like something new drops every five minutes. This series will probably be outdated in a couple of weeks, but it will have existed, and it will have done its job: being a free way to share knowledge.
This isn’t a coding course. You won’t see a single line of code in the whole series, that’s saved for a second part, later, hands on keyboard. This first pass is purely about understanding, about having the full map before touching anything.
What you’ll find here
We start with the fundamentals: what AI actually is, where the term comes from, and why “artificial intelligence,” “machine learning,” and “deep learning” aren’t synonyms even though marketing treats them that way. From there we move to classic machine learning (the algorithms that have been working for decades before anyone talked about generative AI), then to neural networks and deep learning, and from there to generative AI and language models: how they’re trained, how prompting works, what RAG is, why they hallucinate.
No chapter stops at the pretty part. There’s a whole block dedicated to the uncomfortable context: bias, regulation (the EU AI Act), the real energy cost of training these models, and the state of the job market in the field. And near the end, a few chapters go from general to specific: a handful of named AIs, which ones I’ll decide as the series moves forward.
Index
This will fill in chapter by chapter, as I publish. So far:
- Chapter 1. What AI Actually Is (and Isn’t)
- Chapter 2. AI winters, when the hype ran out of money
- Chapter 3. Types of AI: Narrow, General and Superintelligence
- Chapter 4. The minimum math you need to understand AI
- Chapter 5. Supervised, unsupervised, and reinforcement learning
- Chapter 6. Classic Machine Learning algorithms
- Chapter 7. Training a model
