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ImageI am an experienced AI Trainer with over 1 years specializing in data annotation, labeling, and validation for machine learning and AI projects across NLP and computer vision domains. My expertise includes annotating text, image, audio, and video data using tools like Labelbox, Prodigy, CVAT, and Amazon SageMaker, as well as prompt engineering for large language models such as ChatGPT and Claude. I am skilled in data cleaning, preprocessing, and model evaluation, and have contributed to projects like chatbot improvement and large-scale image recognition datasets. I am passionate about ensuring data quality, reducing bias, and promoting responsible AI practices, and I enjoy collaborating with teams to deliver high-quality, ethical AI solutions.
Sentiment Analysis (Beginner): Uses Natural Language Processing (NLP) to classify text as positive, negative, or neutral. It is commonly performed on product reviews or Twitter data using libraries like NLTK and scikit-learn.
Autonomous Driving Perception: Drawing bounding boxes or 3D cuboids around pedestrians, vehicles, and traffic signs in street-level imagery. Advanced versions use semantic segmentation to color-code every pixel (e.g., distinguishing "road" from "sidewalk") or polylines to track lane markings. Medical Diagnostic Imaging: Annotating X-rays, MRIs, and CT scans to identify tumors, abscesses, or anatomical structures. In one case study, human intervention improved AI diagnostic accuracy for breast cancer from 94.6% to 99.5%. Retail Inventory Management: Tracking customer movements and item selections in video frames to optimize store layouts
Bachelor of Science, Science
A TRAINER
