Fundamentals of Machine Learning:
– Basic Concepts of Machine Learning; motivation for Machine Learning; Machine Learning applications; types of Machine Learning

Machine Learning Process Overview:
– Problem Analysis; data analysis; Model construction; model evaluation; implementation and system maintenance

Data Pre-Processing Techniques:
– Data cleaning; data reduction; data balancing; data transformation

Training and Validation:
– Division of the data set; cross-validation; model selection; training and testing; validation and hyperparameter tuning

Supervised Methods:
– Class of Problems; classification; linear regression; performance evaluation measures

Unsupervised Methods:
– Class of Problems; proximity measures; clustering methods; K-Means algorithm; measures of performance evaluation

GRADUATE PROFILE
At the end of the course, you will be able to apply artificial intelligence techniques to analyze data in an objective and reliable way, in order to provide agile management and boost each of the spheres that include ESG.

COURSE PERIOD

05/02/2024 to 06/03/2024