Model Representation To establish notation for future use, we’ll use x^{(i)}x(i) to denote the “input” variables (living area in this example), also called input features, and y^{(i)}y(i) to denote the “output” or target variable that we are trying to predict (price). A pair (x^{(i)} , y^{(i)} )(x(i),y(i)) is called a training example, and the dataset […]

Supervised Learning In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output. Supervised learning problems are categorized into “regression” and “classification” problems. In a regression problem, we are trying to predict results […]

What is Machine Learning? What is Machine Learning? Two definitions of Machine Learning are offered. Arthur Samuel described it as: “the field of study that gives computers the ability to learn without being explicitly programmed.” This is an older, informal definition. Tom Mitchell provides a more modern definition: “A computer program is said to learn […]

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