The purpose of this master’s thesis is creating a foundation for the introduction of some elements of machine learning into secondary education. In today’s age, machine learning is universally used in almost all aspects of life. From advertisement, voice recognition, value estimate of real estate, data analysis to determining patient diagnosis and architecture. Because of the rapid development of internet and technology and the announcement of internet of things it is important to familiarize students with the modern use of computers. In today’s education there’s an emphasis on lifelong learning and the skill of learning how to learn to achieve lifelong learning. Machine learning encompasses basic principles of learning within itself and therefor is a good way to develop the skill of learning how to learn. It also represents a multidisciplinary approach which uses and encompasses the knowledge of psychology, mathematics and computer science. Machine learning can be used as a good example of applied abstract mathematical knowledge on real life problems. Because of everything mentioned, it is my opinion that machine learning should be introduced into classes at schools. In this thesis I have made a basic separation of machine learning into supervised, unsupervised and reinforced learning. I have named some algorithms commonly used in machine learning and talked about: decision tree, linear regression and naive Bayes in more detail. For each above mentioned algorithm I gave one possible implementation in the programming language Python. At the end of the thesis I have given examples of the use of machine learning in everyday life as well as a few events from popular culture tied to the subject.