Supervised vs Unsupervised Learning

These are the two main flavors of ML. Let's break them down simply.

Supervised Learning = Learning with Answers

Think of a teacher showing flashcards:

• Shows picture of cat → "This is a cat"
• Shows picture of dog → "This is a dog"

After enough examples, the student can identify new animals.

The key: You provide labeled data (input + correct answer).

Supervised Learning Examples:

Task	Input	Label
Spam detection	Email text	Spam/Not spam
House prices	Size, location	Price
Disease diagnosis	Symptoms	Disease type
Image recognition	Photo	Object name

Two Types of Supervised Learning:

Classification - Predicting categories

# Is this email spam?
prediction = "spam" or "not_spam"

Regression - Predicting numbers

# What's this house worth?
prediction = 450000  # dollars

Unsupervised Learning = Finding Hidden Patterns

No labels. No right answers. The algorithm explores data on its own.

Like giving someone a box of Legos without instructions—they'll naturally group similar pieces together.

Unsupervised Learning Examples:

Task	What it does
Customer segmentation	Groups similar customers
Anomaly detection	Finds unusual transactions
Topic modeling	Discovers themes in documents
Recommendation	Finds similar items

Main Types:

Clustering - Grouping similar items

# Which customers are similar?
groups = [[user1, user4], [user2, user5], [user3]]

Dimensionality Reduction - Simplifying data

# Reduce 100 features to 10 important ones
simplified_data = reduce(complex_data)

Quick Comparison

Aspect	Supervised	Unsupervised
Has labels?	Yes	No
Goal	Predict specific output	Find patterns
Evaluation	Easy (compare to answers)	Harder (subjective)
Data prep	More work (need labels)	Less work
Examples	Spam filter, price prediction	Customer groups, anomaly detection

Which Should You Use?

Use Supervised when:

• You know what you want to predict
• You have labeled examples
• You need measurable accuracy

Use Unsupervised when:

• You want to explore data
• Labeling is expensive/impossible
• You're looking for unknown patterns

Real World Scenario

E-commerce website:

Supervised task: "Will this user buy?" (Yes/No labels from history)

Unsupervised task: "What types of shoppers do we have?" (No labels, find natural groups)

Both work together! Use unsupervised to find customer segments, then supervised to predict behavior within each segment.

Key Takeaway

• Supervised = "Here's the answer, learn to predict it"
• Unsupervised = "Find interesting patterns yourself"

Most real projects use both. Start with supervised if you have clear goals and labeled data.