This article provides a tutorial overview of machine learning classifiers in the context of functional magnetic resonance imaging (fMRI) data analysis. It discusses the methodology and key considerations in using classifiers for fMRI, including the process of creating examples, selecting features, choosing classifiers, and evaluating results. The article emphasizes the importance of understanding the stages of classifier-based analysis, from data preparation to training and testing, and the choices involved at each stage. It also addresses the challenges of overfitting, the need for feature selection, and the importance of cross-validation in ensuring reliable results. The text outlines various types of classifiers, such as linear and nonlinear models, and discusses their strengths and weaknesses. It also covers the statistical evaluation of results, including significance testing and confidence intervals, and the interpretation of classifier performance in the context of fMRI data. The article concludes with a discussion of the types of scientific questions that can be addressed using classifiers and the impact of the choice of question on the analysis process. Overall, the article aims to provide a comprehensive guide to the application of machine learning classifiers in fMRI research.This article provides a tutorial overview of machine learning classifiers in the context of functional magnetic resonance imaging (fMRI) data analysis. It discusses the methodology and key considerations in using classifiers for fMRI, including the process of creating examples, selecting features, choosing classifiers, and evaluating results. The article emphasizes the importance of understanding the stages of classifier-based analysis, from data preparation to training and testing, and the choices involved at each stage. It also addresses the challenges of overfitting, the need for feature selection, and the importance of cross-validation in ensuring reliable results. The text outlines various types of classifiers, such as linear and nonlinear models, and discusses their strengths and weaknesses. It also covers the statistical evaluation of results, including significance testing and confidence intervals, and the interpretation of classifier performance in the context of fMRI data. The article concludes with a discussion of the types of scientific questions that can be addressed using classifiers and the impact of the choice of question on the analysis process. Overall, the article aims to provide a comprehensive guide to the application of machine learning classifiers in fMRI research.