The humongous volume of unsolicited bulk e-mail (spam) which is further increasing, is the major cause for developing anti-spam protection filters. Machine learning provides a very optimized approach to automatically filter spams at a very successful rate. Here, in this paper, we survey some of the most popular machine learning algorithms (Naïve Bayes, k-NN, SVMs and ANN) and their applicability to the problem of spam e-mail classification. Descriptions of the algorithms are presented, and the comparison of their performance on the UCI spam base dataset is presented.