Introduction In the realm of machine learning, online learning refers to algorithms that learn incrementally, processing one data point at a time. This stands in contrast to batch learning, where the model is trained on the entire dataset at once. Online learning is particularly valuable in situations where the data is too large to be…
Introduction Clustering is a type of unsupervised machine learning technique used to group similar data points together. It plays a pivotal role in various machine learning applications, including anomaly detection, data compression, and market segmentation. One of the most powerful clustering algorithms is DBSCAN (Density-Based Spatial Clustering of Applications with Noise), which groups data based…
Introduction Active learning is a machine learning paradigm that is used to solve problems where labeled data is scarce or expensive to obtain. In traditional machine learning, a model is trained on a large, fully labeled dataset. However, in many real-world scenarios, labeling data is time-consuming and expensive, particularly when expert knowledge is required. Active…