Simple definitions

• AI (Artificial Intelligence): building systems that appear “intelligent” (reasoning, planning, perception, language).
• ML (Machine Learning): a subset of AI where systems learn from data.
• Deep Learning (DL): a subset of ML using neural networks with many layers.

  flowchart TD
  AI[Artificial Intelligence]
  ML[Machine Learning]
  DL[Deep Learning]

  AI --> ML
  ML --> DL

What belongs where?

AI without ML

• rule-based expert systems
• search/planning (A* search, game engines)

ML without deep learning

• linear regression
• logistic regression
• decision trees, random forests
• gradient boosting

Deep learning

• image recognition (CNNs)
• large language models (Transformers)
• speech recognition

When should you use deep learning?

Deep learning is powerful when:

• you have lots of data
• the relationship is highly complex (vision, audio, text)
• you can afford compute and training time

But in many business problems, classical ML is:

• faster to train
• easier to explain
• easier to debug

Key takeaway

AI is the umbrella goal.

ML is a major method for AI.

Deep learning is one ML family that dominates many unstructured data tasks.