The introductory part of this paper discusses the development and importance of machine learning and gives a few examples of what it is used for. The first chapter gives a general picture and definition od machine learning. Machine learning is a branch of artificial intelligence that deals with algorithms that have the ability to learn based on previous experience. In this chapter, a more extensive picture of history or the development of machine learning is given. Machine learning is divided into: supervised learning, unsupervised learning and reinforcement learning. Unsupervised learning uses procedures that try to find the natural partition patterns while supervised learning implies learning mapping between input variables and output variables and applying that mapping to predict the output variable for new unseen input variables. The 51 supervised learning can be divided into regression and classification. The general classification problem is described in the example of car classification. The regression is used when the output variable is a numeric value and then we want to learn a function that can predict the output variables. Some of the most important regression methods are enhanced in this paper, namely: linear regression, multilinear regression, polynomial regression, logistic regression, regression with support vector machines. In the example with real data, it is shown how the Python programmer package solves the task of regression using machine learning.