Continuous Learning: Online machine learning Course models are designed to learn and adapt in real-time. They can handle data streams that may be constantly changing and evolving.

Sequential Updates: The model is updated with each new data point or in small batches of data, rather than retraining on the entire dataset. This allows for efficient processing of streaming data.

Model Drift: Online models are particularly useful in scenarios where the underlying data distribution may change over time, causing what is known as "concept drift." The model can adapt to these changes more effectively than batch models.

Efficiency: Online learning is often computationally efficient because it only requires updating the model based on new data rather than retraining on the entire dataset, which can be costly and time-consuming.

Memory Management: Managing memory is a critical aspect of online learning. As the model processes new data, it may need to discard older data or use techniques like reservoir sampling to maintain a representative sample of historical data.

Examples: Online learning is commonly used in applications such as fraud detection, recommendation systems, natural language processing, and sensor data analysis, where data arrives continuously and the model must adapt to changing patterns.

Algorithms: Some machine learning Course algorithms are well-suited for online learning, such as stochastic gradient descent (SGD), online k-means clustering, and online random forests.

Online machine learning Course is valuable in scenarios where the data is dynamic and when timely updates to the model's predictions are essential. However, it also presents challenges related to model stability, parameter tuning, and managing the learning rate to balance adaptation to new data and preserving learned knowledge from the past.