From the course: Data Science Tools of the Trade: First Steps

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Fundamentals

Fundamentals

From the course: Data Science Tools of the Trade: First Steps

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Fundamentals

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- There are several areas of well-known machine learning applications. Let's dive into three important ones. Classification, regression and clustering. Classification takes a dataset and divides it into two or more classes. For classification to work, a feature model needs to be set up to assign inputs into one of the classes. Fraud detection is an example of classification because it puts a customer transaction into one of the two classes. That is, fraud or legitimate action. Regression produces a continuous output as opposed to discrete outcomes like those created by classification. Regression is used when you are trying to make a prediction about a phenomenon. If your sample data shows that there is a direct positive relationship between the amount of hours students study and their final exam scores, you are able to establish a regression that can possibly predict a student's score based on their study hours. Clustering divides an input dataset into one or more groups. This is…

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