COMPUTER FUNDAMENTALS

Detailed explanation-1: -Supervised learning uses a training set to teach models to yield the desired output. This training dataset includes inputs and correct outputs, which allow the model to learn over time. The algorithm measures its accuracy through the loss function, adjusting until the error has been sufficiently minimized.

Detailed explanation-2: -Example 1: We may use supervised learning to predict house prices. Data having details about the size of the house, price, the number of rooms in the house, garden and other features are needed. We need data about various parameters of the house for thousands of houses and it is then used to train the data.

Detailed explanation-3: -In supervised learning, training data requires a human in the loop to choose and label the features in the data that will be used to train the machine. Unsupervised learning uses unlabeled data to find patterns, such as inferences or clustering of data points.