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Freelancer Overview

I have hands-on experience in building AI-driven systems with a strong focus on data quality and model accuracy. My work has involved curating, labeling, and preprocessing large-scale datasets—most notably the UNSW-NB15 dataset—for machine learning tasks in cybersecurity, including anomaly detection and attack classification. I’ve trained models using techniques like Isolation Forest and XGBoost, emphasizing the importance of well-labeled data in ensuring reliable performance in real-world scenarios. My skill set spans data cleaning, feature engineering, and validation, combined with practical knowledge of model training pipelines. I also bring experience in full-stack environments, including Node.js for system integration. My understanding of both the data side and the deployment side of AI systems allows me to contribute meaningfully to end-to-end model development. This blend of machine learning and security insight helps me ensure that training data not only reflects real-world diversity but is also robust against noise and edge cases.

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