Final-year Artificial Intelligence student with a strong passion for applying AI and machine learning in real-world scenarios. Skilled in Python, deep learning, computer vision, and natural language processing. Currently…
Final-year Artificial Intelligence student with a strong passion for applying AI and machine learning in real-world scenarios. Skilled in Python, deep learning, computer vision, and natural language processing. Currently working on a Final Year Project focused on 3D crime scene reconstruction using AI and NLP. Also familiar with digital marketing concepts and AI automation tools. Actively seeking internship or entry-level opportunities in AI/ML, data science, or AI-driven marketing roles. Eager to contribute, learn, and grow in a dynamic, tech-forward environment.
As an AI Intern, I worked on a real-world project focused on facial emotion recognition using deep learning. My responsibilities included preprocessing the FERPlus dataset, building a custom PyTorch data pipeline, and training convolutional neural networks (CNNs) to classify facial expressions into multiple emotion categories. I explored pretrained models like ResNet18, applied data augmentation, and implemented evaluation metrics such as accuracy and confusion matrices. Additionally, I contributed to developing an inference API for real-time predictions, laying the foundation for integration into a MERN-based analytics platform. This hands-on experience strengthened my skills in Python, PyTorch, OpenCV, and model deployment for B2B AI solutions.
Developed a deep learning-based Cat Breed Classifier to distinguish between three cat breeds using a Convolutional Neural Network (CNN). The dataset consisted of labeled images for each breed, which were preprocessed and augmented to improve model performance. The CNN architecture was built and trained using PyTorch (or TensorFlow, if you used that), achieving accurate classification across the selected breeds. The model was evaluated using accuracy and confusion matrix, and later exported for deployment in an inference pipeline. This project enhanced my skills in image classification, data preprocessing, model tuning, and deep learning workflows.
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