Artificial Intelligence vs Machine Learning vs Deep Learning

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.

diagram Diagram mermaid

  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

Matching real tasks to techniques

The book walks through a long list of real-world ML applications and the techniques typically used for each. A few highlights:

Task	Typical technique
Classify products on a production line	Image classification (CNN)
Detect tumors in brain scans	Semantic segmentation (CNN)
Classify news articles, flag offensive comments	NLP / text classification (RNN, Transformer)
Forecast next year’s revenue	Regression (Linear/Polynomial Regression, Random Forest)
React to voice commands	Speech recognition (RNN, CNN, Transformer)
Detect credit card fraud	Anomaly detection
Segment clients for marketing	Clustering
Visualize a high-dimensional dataset	Dimensionality reduction
Recommend a product	Recommender system (often neural net based)
Build a game-playing bot	Reinforcement Learning

Notice how “classic ML” (regression, clustering, anomaly detection) and “deep learning” (CNNs, RNNs, Transformers) both show up — the right tool depends on the task and the data, not on which family sounds more advanced.

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.

🧪 Try It Yourself

Exercise 1 – Match Task to Technique

Exercise 2 – Classic ML Without Deep Learning

Exercise 3 – Dimensionality Reduction for Visualization

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