What is AI, ML and DL?

Introductory lesson explaining the differences between Artificial Intelligence, Machine Learning, and Deep Learning.

Summary

Understand the differences between Artificial Intelligence, Machine Learning, and Deep Learning, and how they relate to each other.

10 min
basic
core-ai

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are terms often used interchangeably — but they represent different layers of abstraction.

Let’s break them down clearly.

Artificial Intelligence (AI)

Artificial Intelligence is a broad field focused on creating machines that can simulate human intelligence.

It includes:

  • Reasoning
  • Learning
  • Problem solving
  • Understanding language
  • Perception

Examples:

  • Voice assistants like Siri or Alexa
  • Navigation systems using real-time traffic

Machine Learning (ML)

Machine Learning is a subset of AI.

It focuses on algorithms that learn from data rather than following hard-coded rules.

In ML, we don't program the behavior — we feed examples and let the machine learn patterns.

Types of ML:

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

Deep Learning (DL)

Deep Learning is a subset of Machine Learning.

It uses large neural networks with many layers (aka “deep” networks) to learn complex patterns in data.

This is what powers:

  • Large Language Models (LLMs)
  • Image recognition (e.g., facial recognition)
  • Voice assistants
  • Autonomous driving

Note: Not all AI systems rely on deep learning — traditional AI still includes rule-based approaches that don’t involve learning from data.

Visual Overview

AI, ML, and DL Relationship - Diagram showing Artificial Intelligence as the broadest field, containing Machine Learning as a subset, which in turn contains Deep Learning as the most specific subset

Key Differences

AI

  • Scope: Broad
  • Approach: Rules or learning
  • Example: Chatbots, Planning

ML

  • Scope: Data-driven
  • Approach: Learn from data
  • Example: Movie recommendations

DL

  • Scope: Neural networks
  • Approach: Deep learning from data
  • Example: GPT-5, Facial recognition

Real-World Examples

  • Playing chess — classic AI with predefined logic
  • Predicting house prices — ML model trained on historical data
  • ChatGPT — DL model using transformer architecture
  • Photo recognition — convolutional neural networks (DL)

Wrapping Up

  • AI includes ML and DL as subfields.
  • ML uses data to learn patterns.
  • DL is a powerful subset of ML using neural networks.

You're now ready to explore real-world applications of AI and how these models operate in practice.

Self-Check

  • What is the key difference between ML and DL?
  • Give one example of an AI application in daily life.
  • True or False: All AI systems use deep learning.