Supervised ML algorithms usually take a limited set of labeled data and build models that can make reasonable predictions for new data. We can split supervised learning algorithms into two main parts, classification and regression techniques, described as follows:

• Classification models predict some finite and distinct types of categories—this could be a label that identifies if an email is spam or not, or whether an image contains a human face or not. Classification models are applied in speech and text recognition, object identification on images, credit scoring, and others. Typical algorithms for creating classification models are Support Vector Machine (SVM), decision tree approaches, k-nearest neighbors (KNN), logistic regression, Naive Bayes, and neural networks. The following chapters describe the details of some of these algorithms.
• Regression models predict continuous responses such as changes in temperature or values of currency exchange rates. Regression models are applied in algorithmic trading, forecasting of electricity load, revenue prediction, and others. Creating a regression model usually makes sense if the output of the given labeled data is real numbers. Typical algorithms for creating regression models are linear and multivariate regressions, polynomial regression models, and stepwise regressions. We can use decision tree techniques and neural networks to create regression models too. The following chapters describe the details of some of these algorithms.