”Learning” in machine learning (ML) refers to how the ML algorithm looks for patterns that exist in data and automatically adjust itself to reflect what it has found. Effective “learning” in ML largely depends on more data in order to make accurate predictions.
Contrary to humans, machines find it difficult to generalize the acquired knowledge in most cases or transfer the knowledge gained from one application to another. This generalization problem in machine learning has become one of the key research focus in ML
Learning Categories
