What Online Learning Meaning, Applications & Example
Learning from data streams in real-time.
What is Online Learning?
Online Learning is a machine learning approach where models learn from data in real-time or in sequential batches. Unlike traditional batch learning, where the model is trained on the entire dataset at once, online learning updates the model incrementally as new data becomes available.
Key Features of Online Learning
- Incremental Updates: The model is updated continuously as new data arrives.
- Low Memory Usage: It processes data in small chunks, reducing the need to store large datasets in memory.
- Adaptability: It allows the model to adapt to new patterns or changes in data over time.
Applications of Online Learning
- Real-Time Predictions: Used in applications like stock market forecasting or recommendation systems where new data is constantly generated.
- Data Streams: Applied in scenarios where data is received as a continuous stream, such as sensor data in IoT devices.
Example of Online Learning
In real-time traffic prediction, an online learning model can be trained on incoming traffic data to make predictions about traffic conditions at any given moment, updating its knowledge as more data is collected.