Machine Learning

Answer

Machine Learning is a subset of Artificial Intelligence in which systems learn from data to improve performance on a task without being explicitly programmed. It uses algorithms to find patterns, make…

Answer

Arthur Samuel coined the term Machine Learning in 1959 while at IBM. He defined it as the field of study that gives computers the ability to learn without being explicitly programmed.

Answer

It means the model learns patterns and correlations from examples in the data. As it sees more data, its predictions and decisions usually become more accurate and more useful for unseen cases.

Answer

The key types are supervised learning, unsupervised learning, reinforcement learning, and generative AI. These differ in the kind of data they use and the way they learn patterns or generate outputs.

Answer

Supervised learning is trained on labeled data, where each input has a known correct output. A spam filter is an example because it learns from emails already labeled as spam or not spam.

Answer

Unsupervised learning works on unlabeled data and finds patterns without a pre-defined output. Clustering is a common example, and it is used in customer segmentation.

Answer

Reinforcement learning is a type of machine learning in which an agent learns through trial and error by receiving rewards for correct actions and penalties for incorrect actions. It is used to train …

Answer

Generative AI creates new content such as text, images, or music by learning patterns from existing data and mimicking them.