A highly motivated and detail-oriented individual currently pursuing a B.E. in Computer Science and Engineering. Committed to data-driven decision-making and business process improvement, with a strong eagerness to learn and implement emerging technologies.
Convolutional Neural Networks for Non-Invasive Diagnosis of Androgenetic Alopecia Using Dermoscopic Hair Images, 01/01/24
Successfully developed an AI-based application that demonstrates the practical implementation of machine learning algorithms. The project involved data collection, preprocessing, model training, and deployment using tools such as Python, TensorFlow/PyTorch, and relevant libraries. Focused on solving real-world problems by integrating intelligent systems capable of learning from data and making predictions or decisions with minimal human intervention. Applied concepts like supervised/unsupervised learning, neural networks, and natural language processing to enhance the model’s accuracy and efficiency. The project showcased not only technical skills but also problem-solving, analytical thinking, and a solid understanding of AI workflows from concept to execution
Python, C, C++, Java, NumPy, Pandas, TensorFlow, Cloud, Web3, IoT, SQL, Tableau, Power BI