Machine learning is a field of computer science which enables computers to learn how to manipulate and understand complex datasets through algorithm development. Various types of machine learning algorithms are trained on training datasets to apply specific tasks on new input data. Programmed machines gain experience with time which makes them more reliable and successful. There are two major classifications of machine learning algorithms. Supervised algorithms are trying to relate two types of data: predictors and response. They will estimate the response based on input predictor. On the other hand, unsupervised algorithms are used to group and describe the given dataset. Second classification is considering regression and classification algorithms. Algorithms that are used for computations with quantitative variables are called regression algorithms, and those manipulating qualitative variables are classification algorithms. In this bachelor’s thesis three machine learning methods are described. For each method examples are given with the application in molecular biology. Random forests is the first supervised method presented. It is based on constructing a forest of decision trees. Final response is estimated by averaging the response of all the trees. Support vector machine is using kernel functions to find the maximum margin hyperplane in order to divide the given dataset into two distinct groups. Last, K-means clustering algorithm will tend to divide the given dataset into predefined K number of groups based on similarity of the data. In the conclusion an overview of the machine learning field is given along with the additional information, ideas, methods and advices. Moreover, the importance of the steps that precede the algorithm implementation are highlighted and explained.