Third-year Computer Science student with a solid foundation in data science, machine learning and deep learning. Experienced in data preprocessing, exploratory data analysis, and building predictive models on structured …
Third-year Computer Science student with a solid foundation in data science, machine learning and deep learning. Experienced in data preprocessing, exploratory data analysis, and building predictive models on structured datasets. Seeking an AI Engineer Internship to contribute to practical projects while further refining expertise in Artificial Intelligence, Machine Learning, and Data Science.
Conducted in-depth Exploratory Data Analysis and Feature Engineering on tabular survey data to train a robust Ensemble model, achieving an 87% F1-score., Built an end-to-end ML workflow for depression risk prediction, integrating model training, inference with Scikit-learn, and interpretability with SHAP., Containerized the full-stack web application using Docker and deployed the inference service on Render., Achievement: Gained hands-on experience in end-to-end ML pipeline; integrated interpretability using SHAP.
Engineered an audio processing pipeline using Librosa to convert time-series signals into Mel-spectrograms, applied Data Augmentation to enhance model generalization and robustness., Benchmarked and optimized CNN, CRNN, and Transformer models for urban sound classification, achieving validation accuracies of 85%, 83%, and 91%, respectively., Achievement: Proficient in DL frameworks and the implementation of architectures.
Developed a full-stack learning platform using Django and ReactJS, integrated with a content-based recommendation engine using Unsupervised K-Nearest Neighbors to intelligently match students with mentors., Evaluated the relevance–diversity trade-off, achieving an Average Cosine Similarity of 0.50–0.60 to ensure high relevance, while maintaining an Intra-List Diversity of 0.40–0.45 to mitigate filter bubbles., Achievement: Strengthened skills in applied Machine Learning and AI-Backend integration.
نظام التوظيف التشغيلي المعتمد على الذكاء الاصطناعي أولاً، الذي يوحّد ويؤتمت دورة التوظيف بالكامل للمؤسسات الحديثة حول العالم.
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