Machine Learning works in three basic steps:
- Input Data (Learning from Examples):
AI is given data — for example, hundreds of images labeled apple or banana. - Training the Model (Finding Patterns):
The AI system analyzes all examples and discovers what features make apples and bananas different — like shape, color, and texture. - Testing and Prediction (Applying Knowledge):
When shown a new image, the AI applies what it learned to predict whether it’s an apple or banana.
The more accurate and diverse the data, the better the AI performs — just like how you learn better when you practice with good examples.