Welcome to my professional portfolio! Here, you'll find a selection of my best projects showcasing my expertise in Machine Learning, Full Stack Development, and MLOps.
- Description: A robust framework for evaluating classification models using multiple metrics and cross-validation.
- Technologies: Python, Scikit-learn, NumPy, Pandas, Stratified K-Fold.
- Key Features:
- Implements cross-validation for model evaluation.
- Supports multiple file formats for training/testing datasets (CSV, JSON, NPZ, XLSX, Parquet).
- Provides detailed accuracy, precision, recall, F1-score, and ROC-AUC metrics.
- Includes a final model training and evaluation pipeline.
- Description: A modular framework for extracting, processing, and evaluating features from images, using CNNs (VGG16, ResNet50) and Vision Transformers (ViTs).
- Technologies: Python, TensorFlow, Keras, Hugging Face Transformers, NumPy, OpenCV.
- Key Features:
- Supports CNN (VGG16, ResNet50) and Vision Transformer (ViT) feature extraction.
- Implements data augmentation (rotation, flipping, brightness adjustments).
- Provides PCA/t-SNE visualization for feature space analysis.
- Includes an evaluation module for intra-class and inter-class distance measurement.
Feel free to reach out via:
- LinkedIn: linkedin.com/in/elonesampaio
- GitHub: github.com/elonesampaio
- Email: [email protected]