Online machine learning, also known as incremental or streaming machine learning, is a type of machine learning paradigm where a model learns from data that arrives in a continuous stream, rather than in a batch or offline fashion. In traditional batch learning, a model is trained on a fixed dataset, and then its parameters are updated based on that static dataset. In online machine learning, the model continuously updates itself as new data points become available, making it suitable for real-time applications and scenarios where the data is constantly changing.
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