Online learning: Difference between revisions

===Online Learning in Machine Learning==

Online learning is a machine learning method that enables the model to incrementally learn from individual examples and make predictions without waiting until all data has been processed. This approach works best when dealing with large streaming data sets that cannot be stored all at once in memory.

Traditional machine learning relies on offline processing of training data to determine optimal parameters for the model. However, in many real-world applications, data is constantly changing and must be adjusted in real-time; this is where online learning comes into play as it allows the model to continuously update its parameters as new information becomes available.

- Scalability: Online learning algorithms are capable of processing large amounts of data without experiencing a slowdown, making them ideal for big data applications.

 

- Real-time Adaptation: The model can adjust according to changes in data distribution in real time, enabling it to continuously improve its performance.

 

- Reduced Computation Complexity: Online learning's incremental nature reduces the computational complexity compared to traditional batch learning, making it more efficient in terms of memory usage and computing resources.

 

- Robustness: Online learning algorithms can handle non-stationary data, where the distribution changes over time, by continuously altering model parameters.