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Build and train deep learning models to classify skin cancer images accurately using Convolutional Neural Networks (CNNs).
This project combines artificial intelligence with healthcare, equipping you to solve real-world challenges through machine learning. You will gain foundational knowledge in machine learning and explore deep learning methodologies for image classification. Using Convolutional Neural Networks (CNNs), you will develop models capable of analyzing and categorizing skin cancer images with precision. The hands-on nature of this project provides you with experience in data preprocessing, model training, and evaluation, ensuring they are prepared for entry-level roles in AI and machine learning.
Learn the basics of machine learning and set up Google Colab for development.
Understand key concepts in linear algebra and learn to use NumPy and Pandas for data manipulation.
Explore data preprocessing techniques and create insightful visualizations using Matplotlib.
Understand and implement a simple linear regression model for predictive analysis.
Dive into logistic regression for binary classification and evaluate its performance.
Learn the fundamentals of neural networks and implement a basic model.
Understand and implement a Convolutional Neural Network (CNN) for image classification, followed by a brief introduction to ResNet-50 and its key concepts.
Finalize project deliverables, review results, and present the findings.
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Sehaj Grover is a Software Development Build Fellow at Open Avenues Foundation, where he works with students leading projects in software development, backend engineering, and applied machine learning.
Sehaj is a Senior Full Stack Engineer at Attend (formerly Season Share), where he focuses on designing and implementing scalable web applications, developing advanced algorithms for optimized ticketing solutions, and mentoring junior engineers to foster a collaborative development environment. He also integrates machine learning techniques to enhance system capabilities and user insights.
Sehaj has over 5 years of professional experience in the software development field. He has led several high-impact projects, enhancing product efficiency, user experience, and system performance through modern technologies, innovative design approaches, and machine learning applications.
He holds a Master’s in Computer Science from the University at Buffalo (SUNY).
A fun fact about Sehaj is that he enjoys exploring mystery and thriller movies and audiobooks in his free time.