Machine learning algorithms are categorized into supervised, unsupervised and semi-supervised. This presentation will discuss how to analyze a given dataset and applying an appropriate model. The steps are: getting and cleaning data, extracting and selecting features and finally developing an appropriate classifier. Some of the popular classifiers such as Na√Øve Bayes, SVM and Neural Network will be discussed. Generalizing the algorithm on test dataset and calculating error rate is an important part in developing a robust model on any given dataset. The algorithms will be discussed briefly with some practical examples.