The aim of the course is to deepen theoretical knowledge and practical skills acquired in the Data Science and Artificial Intelligence specialization through the execution of a specific data mining project.

1. Selection and interpretation of the data mining task, project planning, and documentation of the evaluation criteria for future solutions. Subsequently, complete data mining cycles and redefine them by evaluating the following work stages:
2. Data Preparation (selection of the database and data format, data cleansing, etc.)3. Data Visualization and Analysis (correlation analysis, explanatory variable selection, data transformations, etc.)4. Generation of Machine Learning Models (model selection, hybrid, deep learning, etc.)5. Evaluation of Machine Learning Models (metric selection, bootstrapping, improving results, hyperparameter tuning, applying boosting, etc.)6. Practical application of the generated data mining process (deployment to the cloud, ethical considerations, data protection).