Published by
Conference Proceedings
Summary
Authored and presented research at an international scientific internet conference, focusing on contemporary challenges and advancements in physiology and rehabilitation.
Highly motivated AI/ML Engineer with a strong academic foundation and practical experience in Machine Learning and Deep Learning. Proven ability to deliver data-driven solutions, demonstrated by optimizing skin disease detection models by 20% on a 27,000+ image dataset and engineering a Federated Learning model with 89% accuracy for heart disease prediction. Seeking to leverage advanced technical skills to drive innovative AI-driven projects and solve complex real-world challenges.
AI/ML Intern
Nanded, Maharashtra, India
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Summary
Led the optimization of deep learning models and implemented advanced data augmentation techniques to enhance model performance and generalization for medical image analysis.
Highlights
Optimized deep learning models for skin disease detection, increasing accuracy by 20% on a dataset of 27,000+ images.
Implemented advanced data augmentation techniques, enhancing model generalization and bolstering accuracy for diverse training scenarios.
Integrated sophisticated data augmentation methods, expanding dataset samples and facilitating richer training scenarios to improve model robustness.
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M.Tech
Artificial Intelligence & Machine Learning
Grade: 8.13 CGPA
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B.Tech
Computer Science and Engineering
Grade: 7.33 CGPA
Published by
Conference Proceedings
Summary
Authored and presented research at an international scientific internet conference, focusing on contemporary challenges and advancements in physiology and rehabilitation.
Team Collaboration, Strategic Thinking, Problem-Solving, Data-Driven Problem-Solving.
Python, Embedded C, SQL.
TensorFlow, Keras, Scikit-Learn, Image Processing, Neural Networks.
NumPy, Pandas, Matplotlib.
Arduino, Raspberry Pi, Esp 8266.
Google Colab, Jupyter Notebook, Visual Studio Code, Tinkercad, ThingSpeak, Arduino IDE.
Volleyball.