Supervised Learning : Learning with a teacher
Imagine a student learning from past exams.
The machine gets both the questions and the correct answers.
Over time, it learns the pattern.
Real-Life Examples:
- Spam Email Detection
- House Price Prediction
- Disease Diagnosis
- Stock Market Forecasting
- Weather Predictions
Example:
You show a computer 1000 labeled images:
- dog
- cat
Later, it can say, “That’s a cat!” when shown a new image.
Unsupervised Learning : Learning without a teacher
Like a child sorting toys by shape or color without knowing their names.
The machine explores data without labels and discovers hidden patterns.
Real-Life Examples:
- Customer Segmentation for targeted ads
- Market Basket Analysis (product recommendations)
- Fraud and Anomaly Detection
Example:
Give it 1000 fruit pictures — no names. It might group them like:
- Round and red fruits
- Long and yellow fruits
- Small and purple fruits
It doesn’t know the fruit names but it finds structure.
Reinforcement Learning : Learning by doing
Like training a pet with rewards and corrections.
The machine (called an agent) tries different actions, gets rewards or penalties, and learns what works best over time.
Real-Life Examples:
- Self-driving Cars
- Game AI (Chess, Go, video games)
- Robotics and Automation
- Stock Trading Bots
Example:
- A robot tries to walk.
- Falls result in no reward.
- Moves forward result in a reward.
- It learns to stay balanced and move smart.
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