With the development of machine learning, a greater supply and demand was created for the creation of systems that could be used for medical purposes to improve and speed up diagnostics. Such a program could also be used in dermatology to identify moles and melanomas and help doctors diagnose patients. The purpose of this study was to conduct training and evaluation in the classification of moles and melanoma images in the medical field and to examine the influence of the size of the training data set on the results of machine learning. It also further examined the process of collecting data for training and testing the system, creating the said program in the Orange application, and training the system on different sizes of the training set and showed their results. The paper consists of two main parts, the theoretical and the practical part. In the theoretical part, the concepts of machine learning and its subtypes were discussed, with an emphasis on supervised machine learning, classification algorithms, and the influence of the size of the set on the final result of the machine learning program. In the practical part of the work, the program for recognizing images of moles and melanoma in the Orange system was presented, and it was shown how different sizes of training sets affect the classification result through the logistic regression algorithm, neural networks and naive Bayes.