
Beth Perez developed a suite of educational data science and machine learning resources for the Fernando-JAL/Neurociencias-2025-2 repository, focusing on reproducible Jupyter Notebooks. She upgraded the Python environment metadata to ensure compatibility with modern tooling and authored tutorials on data visualization, normalization, covariance, and singular value decomposition using Python, NumPy, and Matplotlib. Beth also created hands-on materials for exam preparation, including theoretical overviews of supervised and unsupervised learning, and implemented a decision tree classifier on the Iris dataset with Scikit-learn. Her work emphasized clear documentation, structured notebook organization, and maintainable code, supporting both student onboarding and collaborative development.

Concise monthly summary for 2025-05 highlighting features delivered in Fernando-JAL/Neurociencias-2025-2. Focused on delivering hands-on, reusable ML learning resources for exam preparation and practical model evaluation, while improving project hygiene and documentation to support maintainability and collaboration.
Concise monthly summary for 2025-05 highlighting features delivered in Fernando-JAL/Neurociencias-2025-2. Focused on delivering hands-on, reusable ML learning resources for exam preparation and practical model evaluation, while improving project hygiene and documentation to support maintainability and collaboration.
February 2025 performance summary for Fernando-JAL/Neurociencias-2025-2. Focused on modernizing the notebook environment and expanding educational resources with a data visualization tutorial. No major bugs reported this month.
February 2025 performance summary for Fernando-JAL/Neurociencias-2025-2. Focused on modernizing the notebook environment and expanding educational resources with a data visualization tutorial. No major bugs reported this month.
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