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Chapter 1. Machine learning basics > Key tasks of machine learning - Pg. 10

10 C HAPTER 1 Machine learning basics We've covered a lot of key terms of machine learning, but we didn't cover them all. We'll introduce more key terms in later chapters as they're needed. We'll now address the big picture: what we can do with machine learning. 1.3 Key tasks of machine learning In this section we'll outline the key jobs of machine learning and set a framework that allows us to easily turn a machine learning algorithm into a solid working application. The example covered previously was for the task of classification. In classification, our job is to predict what class an instance of data should fall into. Another task in machine learning is regression. Regression is the prediction of a numeric value. Most people have probably seen an example of regression with a best-fit line drawn through some data points to generalize the data points. Classification and regression are exam- ples of supervised learning. This set of problems is known as supervised because we're telling the algorithm what to predict. The opposite of supervised learning is a set of tasks known as unsupervised learning. In unsupervised learning, there's no label or target value given for the data. A task where we group similar items together is known as clustering. In unsupervised learn- ing, we may also want to find statistical values that describe the data. This is known as density estimation. Another task of unsupervised learning may be reducing the data