Requirements: Basic knowledge of machine learning with Python, math, statistics and databases.
Participants will learn intermediate methods of machine learning with Python in order to identify specific strategies to optimize the performance of a machine learning model, as well as dimensionality reduction methods, clustering by distances and probability, neural networks, deep learning and how to create models with tensorflow and keras. We will analyze basic strategies to manage a complete machine learning cycle using mlflow with the objective of keeping a log of tests and hypotheses to identify which methods and models are the most optimal to close the cycle through the operationalization of the model using mlflow functionalities for a deployment. The total hours assigned to the course is 24 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.