Discover how machines learn through interactive simulations and visual explanations
Learning from labeled examples
In supervised learning, the model learns from labeled data - examples where we know the correct answer. It's like learning with a teacher who tells you if you're right or wrong. The model finds patterns in the input data to predict outputs for new, unseen examples.
Classify emails as spam or not spam based on labeled examples
Predict prices based on features like size, location, rooms
Predict scores based on study hours and past performance
Split data to learn patterns and verify accuracy
Learn the relationship between features and labels
Minimize prediction errors through iteration
Discovering hidden patterns
In unsupervised learning, the model explores unlabeled data to find hidden structures and patterns. There's no teacher - the algorithm discovers groupings and relationships on its own. It's like organizing a messy drawer by finding items that naturally belong together.
Group customers by behavior for targeted marketing
Find products frequently bought together
Organize photos by visual similarity automatically
Algorithm discovers structure without guidance
Find hidden structures in complex data
Let the data reveal natural categories
Learning through trial and error
In reinforcement learning, an agent learns by interacting with an environment. It takes actions, receives rewards or penalties, and gradually learns which actions lead to the best outcomes. It's like training a pet - rewarding good behavior and discouraging bad behavior over time.
Master chess, Go, and video games through practice
Teach robots to walk, grasp, and navigate
Learn optimal driving decisions from experience
Agent takes actions, environment responds
Feedback shapes future behavior
Strategy gets better over time
Understanding the key differences at a glance
| Feature |
📊 Supervised
|
🔮 Unsupervised
|
🤖 Reinforcement
|
|---|---|---|---|
| Data Type | Labeled | Unlabeled | Feedback-based |
| Learning Style | Teacher-guided | Pattern discovery | Trial & error |
| Output | Prediction | Clusters | Actions |
| Goal | Minimize error | Find structure | Maximize reward |
| Example | Spam detection | Customer grouping | Game AI |
"Here's the answer, learn from it"
"Find patterns yourself"
"Learn from consequences"