Requirements: Basic knowledge of Python, Math, statistics and databases.
Participants will learn to identify different strategies for the execution of machine learning methods with Python. The course includes examples that allow understanding basic applications from the perspective of the CRISP-DM cycle for Data Science, starting with the approach of a problem to be solved, passing through the stages of model execution, validation of regression performance and classification. The machine learning methods are: linear regression, logistic regression, decision trees, random forest and ensembles. The total hours assigned to the course is 16 hours, the course material includes guided exercises and their answers. The course includes theoretical concepts of mathematics and statistics which will be explained in a general way without including a specific review of the theoretical bases.