Learning from labeled examples with a teacher
Day 71 of 149
👉 Full deep-dive with code examples
The Flashcard Teacher Analogy
Remember learning with flashcards?
Teacher shows card: "This is the letter A"
Teacher shows card: "This is the letter B"
... hundreds of examples later...
Now YOU can recognize letters on your own!
The teacher supervised your learning with labeled examples.
How It Works
# Training: Teacher provides answers
training_data = [
(email1, "spam"),
(email2, "not spam"),
(email3, "spam"),
# ... thousands more
]
model.fit(training_data)
# Now the model learned the patterns!
model.predict(new_email) # "spam" or "not spam"
The model learns patterns between inputs and labels.
Two Types
| Type | What It Predicts | Example |
|---|---|---|
| Classification | Categories | spam/not spam, dog/cat |
| Regression | Numbers | house price, temperature |
Real Examples
- Email: Is this spam? (labeled by users marking spam)
- Credit: Will they default? (labeled by past defaults)
- Medical: Is this a tumor? (labeled by doctors)
- Faces: Who is this? (labeled by tagged photos)
The Catch
You need LABELED data! Someone had to manually mark:
- 10,000 emails as spam or not
- 1 million images as cat or dog
That's expensive and time-consuming.
In One Sentence
Supervised learning trains models using examples where the correct answer is already provided.
🔗 Enjoying these? Follow for daily ELI5 explanations!
Making complex tech concepts simple, one day at a time.
Top comments (0)