Artificial Intelligence
What is Machine learning?
Machine learning is a branch of AI where computers learn to make predictions or decisions by finding patterns in data, instead of being explicitly programmed with rules. The more relevant data it sees, the better it gets.
See it, don’t just read it.
Watch a 2-minute lesson with voice + animation that explains machine learning.
Key things to understand
- 1Supervised learning trains on labeled examples (e.g. photos tagged 'cat' or 'dog') to predict labels for new data.
- 2Unsupervised learning finds hidden structure in unlabeled data, like grouping similar customers.
- 3Reinforcement learning trains an agent through trial and error using rewards and penalties.
- 4A model is the trained system; training adjusts its internal numbers (parameters) to reduce errors.
Frequently asked questions
- How is machine learning different from traditional programming?
- Traditional programs follow rules a developer writes. Machine learning derives the rules itself by learning from examples.
- What is training data?
- The examples a model learns from. Its quality and breadth largely determine how well the model performs.
- What is overfitting?
- When a model memorizes its training data instead of learning general patterns, so it performs well in training but poorly on new data.