Fernando-JAL/Neurociencias-2025-2
May 2025 – May 2025
1 Month active
Languages Used
MarkdownPython
Technical Skills
Academic WritingCNNData AnalysisData PreprocessingData VisualizationDeep Learning
Alissongm04
PROFILE
Alissongm04
During May 2025, Alisson Garcia developed three core assets for the Fernando-JAL/Neurociencias-2025-2 repository, focusing on machine learning education and reproducible research. He authored comprehensive ML theory documentation in Markdown, covering foundational topics such as supervised learning, model evaluation, and neural network components. For the brain tumor imaging project, he implemented data preprocessing, exploratory data analysis, and compared models including CNNs, MobileNetV2, SVM, and Random Forest using Python, Keras, and Scikit-learn. Alisson also improved code readability through notebook refactoring and annotation. The work emphasized maintainable code, clear documentation, and reusable workflows, supporting both instruction and future development.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
8Total
Bugs
0
Commits
8
Features
3
Lines of code
7,843
Activity Months1
Your Network
37 peopleSame Organization
@comunidad.unam.mx17
Work History
May 2025
8 Commits • 3 Features
May 1, 2025Month: 2025-05 — Fernando-JAL/Neurociencias-2025-2 delivered three major assets: (1) ML Theory Q&A Documentation (Markdown) covering supervised vs unsupervised learning, classification vs regression, overfitting/underfitting, evaluation metrics, and neural network components; (2) Brain Tumor Imaging Project and Exam Materials including data preprocessing, exploratory data analysis, model comparisons (CNNs, MobileNetV2, SVM, KNN, Random Forest), and practical exam materials plus related image assets; (3) Code Cleanup and Notebook Formatting to improve readability with Python script annotations, output formatting refactors, and notebook section markers. No critical bugs reported this month; focus was on feature delivery and code quality. Business value includes ready-to-share learning resources, reproducible ML workflows, and a maintainable codebase for future work. Technologies demonstrated include Python, notebook-based experiments, Markdown documentation, ML model evaluation across CNNs and classical algorithms, and Git-based version control.
8 Commits • 3 Features
May 1, 2025Month: 2025-05 — Fernando-JAL/Neurociencias-2025-2 delivered three major assets: (1) ML Theory Q&A Documentation (Markdown) covering supervised vs unsupervised learning, classification vs regression, overfitting/underfitting, evaluation metrics, and neural network components; (2) Brain Tumor Imaging Project and Exam Materials including data preprocessing, exploratory data analysis, model comparisons (CNNs, MobileNetV2, SVM, KNN, Random Forest), and practical exam materials plus related image assets; (3) Code Cleanup and Notebook Formatting to improve readability with Python script annotations, output formatting refactors, and notebook section markers. No critical bugs reported this month; focus was on feature delivery and code quality. Business value includes ready-to-share learning resources, reproducible ML workflows, and a maintainable codebase for future work. Technologies demonstrated include Python, notebook-based experiments, Markdown documentation, ML model evaluation across CNNs and classical algorithms, and Git-based version control.
May 2025
Activity
Loading activity data...
Quality Metrics
Correctness90.0%
Maintainability87.4%
Architecture90.0%
Performance82.6%
AI Usage22.4%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
Academic WritingCNNData AnalysisData PreprocessingData VisualizationDeep LearningExploratory Data AnalysisImage ClassificationImage ProcessingKerasMachine LearningMatplotlibPandasScikit-learnTensorFlow
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline