This article discusses the use of machine learning algorithms to classify spam and non-spam tweets in Twitter. It explores supervised and unsupervised models such as decision tree, random forest, Naïve Bayes, and logistic regression. The article emphasizes the importance of test and train data sets, and performance evaluation using confusion matrix, ROC, and AUC.