Reinforcement Learning Course (Reinforcement Learning, RL) + 8 ECTS credits

The ML supervised learning course is designed to acquire the necessary skills in a sector in full boom, where the demand for trained professionals does not stop growing. This course will provide you with a solid base in regression, classification and evaluation techniques, essential for any candidate for Data Scientist. You will learn from the foundations of automatic learning to advanced methods such as neuronal networks and support vectors machines, ensuring that you prepare to face the current challenges of the work world. This online course offers flexibility and accessibility, allowing you to advance at your own pace while you become an expert in supervised learning.

- Understand the foundations of supervised learning in Machine Learning. - Learn how to prepare and prept data for Machine Learning models. - Apply linear regression to solve prediction problems. - Use logistics regression in binary classification tasks. - Evaluate models through cross validation techniques. - Implement decision and assembly trees such as Random Forest. - Develop neural networks focused on supervised learning.

The main professional outputs of the supervised learning course in ML are in areas such as data science focused on predictive analysis, Machine Learning engineering in technical companies, artificial intelligence consulting for models development, data analysis in financial and marketing sectors, among others.

The ML supervised learning course prepares you to address complex data analysis problems through advanced Machine Learning techniques. You will learn from the preparation and preprocessing of data to the application of regression and classification algorithms. You will dominate models such as decision trees and neuronal networks, evaluating its efficiency with cross validation, among other things.

The ML supervised learning course is designed for professionals and graduates who wish to deepen automatic learning techniques. It addresses from the introduction to supervised learning to the application of neuronal networks, through linear regression, decision trees and SVM, providing essential tools for data analysis and modeling.

La presente formación no está incluida dentro del ámbito de la formación oficial reglada (Educación Infantil, Educación Primaria, Educación Secundaria, Formación Profesional Oficial FP, Bachillerato, Grado Universitario, Master Oficial Universitario y Doctorado). Se trata por tanto de una formación complementaria y/o de especialización, dirigida a la adquisición de determinadas competencias, habilidades o aptitudes de índole profesional, pudiendo ser baremable como mérito en bolsas de trabajo y/o concursos oposición, siempre dentro del apartado de Formación Complementaria y/o Formación Continua siendo siempre imprescindible la revisión de los requisitos específicos de baremación de las bolsa de trabajo público en concreto a la que deseemos presentarnos.