AI, Machine Learning and Deep Learning

Definition

AI is the broad goal of making machines do intelligent tasks. Machine Learning is one way to reach it, by learning from data. Deep Learning is one kind of ML that uses neural networks with many layers. Generative AI is an application, mostly built with deep learning, that creates new content.

In simple words

Picture circles inside circles. The biggest circle is AI. Inside it is ML. Inside that is deep learning. So all deep learning is ML, and all ML is AI, but not all AI is ML. It's like: all mangoes are fruit, and all fruit is food, but not all food is fruit.

Why it matters

These words get mixed up all the time, in news, in job adverts and in pitches. Knowing the difference lets you judge what a product really does, pick the right tool, and read the rest of this course without confusion.

How it works

Term	What it is	Needs	Example
AI	Any technique that makes machines act intelligently	Rules, search or learning	A chess program with hand-written rules
Machine Learning	Learning patterns from data	Data, often in tables	Fraud detection with decision trees
Deep Learning	ML with many-layered neural networks	Lots of data and GPUs	Recognising faces in photos
Generative AI	Models that create new text, images or audio	Very large models	ChatGPT, image generators

Is data science part of this?

Data science overlaps but is a separate field. It means using statistics, programming and domain knowledge to get insights from data. A data scientist may use ML, or may only need a good chart.

Which one should you use?

If clear rules exist, write the rules. Plain software is cheaper and easier to check.
If you have tables of data, start with classic ML. It often wins on tables, with less data and cost.
If you have images, audio or lots of text, deep learning is usually the right choice.
If you need new content or natural conversation, use generative AI, and check what it produces.

Try it yourself

Which circle does it land in?

Tap each system and watch it jump to where it belongs.

Drag systems into the right circle

Choose the smallest circle each system belongs to.

Face unlock using a large neural network
A house-price predictor trained on past sales
A chatbot that writes a new poem
A spam filter that learns from emails marked as spam
A route planner that searches every road by fixed steps
An image generator that draws from a sentence

Common mistakes

Saying "AI" and "ChatGPT" as if they're the same. ChatGPT is one product in one small part of AI.
Assuming deep learning is always best. On small tables of data, simpler ML often beats it.
Calling every if-else rule "AI" because it sounds impressive.

Check yourself

1. Which statement is true?

2. You have a small table of 2,000 customer records. What should you try first?

Summary

AI ⊃ Machine Learning ⊃ Deep Learning.
Generative AI creates new content and is mostly built with deep learning.
Pick the simplest thing that works: rules, then classic ML, then deep learning.