As a Machine Learning Intern, I developed and optimized machine learning models to extract actionable insights from data. My responsibilities included data preprocessing, feature engineering, and training models using libraries like scikit-learn, TensorFlow, and PyTorch. I worked on fine-tuning algorithms to improve model accuracy and efficiency, evaluating models through metrics such as accuracy, precision, and recall. This experience strengthened my skills in solving real-world problems through data-driven approaches and advanced machine learning techniques, fueling my enthusiasm for AI and innovation.

As a Junior AI Engineer Intern, I had the opportunity to design, implement, and fine-tune machine learning and deep learning models for analyzing visual data, including images and videos. My role involved working with frameworks like TensorFlow, PyTorch, and OpenCV, applying techniques such as object detection, image classification, and segmentation to deliver insights from visual content. From data preprocessing to model evaluation and deployment, I gained hands-on experience in building end-to-end solutions that make real-world applications of computer vision possible.