AI Fundamentals· Lesson 1 of 10
- 1What Even Is AI? AI vs. ML vs. Deep Learning6 min
- 2How Models Learn: Training vs. Inference6 min
- 3What Is an LLM?8 min
- 4Tokens — the Currency of Everything7 min
- 5Why AI Output Is Probabilistic (and Why It Changes How You Build)7 min
- 6Hallucination — Why Models Make Things Up7 min
- 7What AI Is Genuinely Good and Bad At — a Capability Map7 min
- 8AI Systems vs. Traditional Software — the Mindset Shift7 min
- 9Where AI Fits in a Product (and Where It Shouldn't)7 min
- 10Responsible AI Basics for Builders7 min
What Even Is AI? AI vs. ML vs. Deep Learning
In one line · AI is the umbrella, Machine Learning is an approach inside it, and Deep Learning is ML built on large neural networks.
Don't conflate the three. AI is the goal; ML is how you get there; Deep Learning is a powerful kind of ML. The LLMs you'll build on are all Deep Learning.
Once the distinction is clear, you'll quickly grasp why AI errs, why it needs data, and why it isn't ordinary programming.
Think of AI as a field, ML as a specialty within it, and Deep Learning as a sub-specialty within ML — each narrower than the last.
Like transport includes cars, and cars include electric ones: all Deep Learning is ML, but not all AI is ML.
An old spam filter that blocks any email with a banned word is rule-based AI, no ML. A filter that learns from the emails you mark as spam is ML.
In ML you feed the model many examples and it infers the rules itself. Deep Learning does the same with networks of millions of parameters, so it captures complex patterns like language and images.
The LLM you talk to is a deep neural network trained on huge text. Your API call runs that network (inference); there is no database it looks things up in — it computes a prediction.
🧪 Try it: list three products you use daily and classify each as rule-based AI or learning ML. Unsure? Ask: 'does it improve as it sees more examples?'
The big mistake is thinking AI 'knows' the right answer like a database. It predicts the most likely answer. Another: using ML, AI, and Deep Learning interchangeably with your team.
Say AI for the general goal, ML when a model learns from data, and Deep Learning for large networks like LLMs.
If a problem is solved by simple, clear rules (like computing tax), you don't need ML at all — AI isn't always the right fit.