Motivated and detail-oriented AI & Machine Learning engineering student with strong hands-on experience in Python, Machine Learning, Deep Learning, and Computer Vision. Skilled in building and deploying intelligent syste…
Motivated and detail-oriented AI & Machine Learning engineering student with strong hands-on experience in Python, Machine Learning, Deep Learning, and Computer Vision. Skilled in building and deploying intelligent systems using TensorFlow, Scikit-Learn, OpenCV, and MediaPipe, with practical project experience in breast cancer detection, human pose estimation, and data-driven applications. Possesses a solid foundation in data science, cloud computing, and software development, with a passion for solving real-world problems through AI-based solutions. Adept at learning new technologies quickly, collaborating in team environments, and delivering reliable, high-quality results.
Developed CodeGenie, an AI-powered coding assistant capable of natural language → code generation, resulting in a 30% increase in user engagement and satisfaction.
Implemented transformer-based text generation models, delivering human-like outputs and improving content generation efficiency by 25%.
Engineered scalable backend APIs and optimized inference pipelines, reducing latency by 40% for real-time coding assistance.
Developed CodeGenie, an AI-powered coding assistant capable of natural language → code generation, resulting in a 30% increase in user engagement and satisfaction., Implemented transformer-based text generation models, delivering human-like outputs and improving content generation efficiency by 25%., Engineered scalable backend APIs and optimized inference pipelines, reducing latency by 40% for real-time coding assistance.
Developed a human pose estimation system using deep learning models, achieving a 30% improvement in keypoint detection accuracy and optimizing performance with TensorFlow/PyTorch.
Designed and implemented an anomaly detection system using unsupervised learning, identifying critical issues in real-time and preventing potential system failures, resulting in a 25% reduction in downtime.
Conducted transfer learning experiments with pre-trained language models, reducing training time by 40% while maintaining high accuracy in sentiment analysis.
Developed a human pose estimation system using deep learning models, achieving a 30% improvement in keypoint detection accuracy and optimizing performance with TensorFlow/PyTorch., Designed and implemented an anomaly detection system using unsupervised learning, identifying critical issues in real-time and preventing potential system failures, resulting in a 25% reduction in downtime., Conducted transfer learning experiments with pre-trained language models, reducing training time by 40% while maintaining high accuracy in sentiment analysis.
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