I acquired fundamental knowledge in data mining, covering the identification of patterns and trends in large volumes of information. I became familiar with key technological trends driving deep learning, including supervised classification techniques (decision trees, K-NN) and clustering (K-Means, DBScan). I developed skills in building, training, and applying Bayesian network models, using metrics such as precision, recall, and F1-score to evaluate their performance. Finally, I applied these techniques in personal projects, using R and Python for advanced analyses.

I gained basic knowledge in pattern recognition, covering the classification of patterns using distance and similarity functions, as well as non-parametric classification techniques. I became familiar with feature selection and extraction, the design of linear discriminant functions, and basic probability concepts applied to the field. I developed skills in unsupervised learning and clustering, exploring algorithms such as K-means, LBG, and Isodata. Finally, I understood the fundamentals of syntactic pattern recognition, including the theory of formal languages and recognition grammars.

I acquired fundamental knowledge in Python programming, covering the basic syntax of the language, operators, control structures, and other essential resources. I became familiar with data structures such as strings, lists, tuples, sets, and dictionaries, and learned how to manipulate them efficiently. I developed skills in modular code organization using functions, classes, modules, and packages, as well as implementing programs under the object-oriented paradigm. Finally, I gained experience in handling errors and exceptions in Python, enabling me to develop more robust and reliable applications.