1. IntroductionHuman beings can expand their knowledge to adapt to the changing environment. To do this they must “learn”. Learning can simply be defined as the acquisition of knowledge or skills through study, experience, or teaching. While learning is an easy task for most people, gaining new knowledge or skills from data is too difficult and complicated for machines. Furthermore, a machine's level of intelligence is directly relevant to its ability to learn. The study of machine learning seeks to address this complicated task. In other words, machine learning is that branch of artificial intelligence that tries to find an answer to this question: how to make the computer learn? When we say that the machine learns, we mean that the machine is able to make predictions from examples of desired outcomes. past behavior or observations and information. A more formal definition of machine learning by Tom Mitchell is: A computer program is said to learn from experience E with respect to some class of tasks T and measure performance P, if its performance on the tasks in T, measured by P, they improve with experience E The definition also indicates the main goal of machine learning: the design of such programs2. Machine Learning Taxonomy Machine learning systems can be classified according to many different criteria. We will discuss three criteria: Classification on the basis of the learning strategies used, Classification on the basis of the representation of the knowledge or skill acquired by the learner, and Classification in terms of the application domain of the performance system for which the knowledge is acquired. There are two main types of machine learning systems based on the underlying learning...... middle of paper ......d can learn its face. Subsequently the system will be able to recognize and classify this person. References Tom, M. (1997). Machibe learning. Machine Learning, Tom Mitchell, McGraw Hill, 1997:McGraw Hill.Mitchell, TM (2006). The discipline of machine learning. Department of Machine Learning Technical Report CMU-ML-06-108, Carnegie Mellon University.Alpaydin, E. (2004). Introduction to machine learning. Massachusetts, USA: MIT Press.Taiwo Oladipupo Ayodele (2010). Types of Machine Learning Algorithms, New Advances in Machine Learning, Yagang Zhang (ed.), ISBN: 978-953-307-034-6, InTech, available from: http://www.intechopen.com/books /new- advances in machine learning/types-of-machine-learning-algorithms1. T. Mitchell, Generative and discriminative classifiers: Naive Bayes and logistic regression. Draft version, download 2005