Author profiling is the task of determining age, gender, native language or personality type of author by studying their sociolect aspect, that is, how language is shared by people. In this paper, we propose a Machine Learning approach to determine unknown author's age and gender. The approach uses three types of features: content based, style based and topic based. We were able to achieve an accuracy of 64.08%, 64.30% for age and 56.53%, 64.73% for gender in English and Spanish respectively.