Batch Vs Online Learning

Batch learning is a term in artificial intelligence that refers to the process of training a machine learning model on a large set of data all at once, instead of continuously updating the model as new data comes in. This method allows for greater consistency and efficiency in the training process, as the model can learn from a fixed set of data before being deployed for use.

In batch learning, the model sees the entire dataset multiple times (known as epochs), refining its understanding with each pass. By processing data in large chunks, it converges more slowly but generally achieves higher accuracy.

Online learning takes a continuous, incremental approach. Instead of waiting for all the data to be available, you feed it to the model bit by bit, just like learning something new every day instead of cramming for a final exam. The model updates with each new data point, so it's constantly learning and evolving.

For example, imagine you're monitoring customer behavior on a website. Every time a user clicks or makes a purchase, your model gets smarter, learning from that single interaction and refining its predictions for the next.