This module provides an overview of building a Personal Health Assistant, covering data preparation, model fine-tuning, and deployment. Learners will understand the steps involved in creating a health-focused AI application and gain practical skills in data cleaning, model training, and user interface integration.

This module provides an overview of building a mental health support chatbot using open source large language models. Learners will explore key steps such as setting up the environment, managing conversations, and integrating APIs for knowledge retrieval. The focus is on developing functional and ethical chatbot solutions in a web application framework.

This module introduces learners to the development of a medical tool detection system using computer vision techniques. It covers the training of models, integration of YOLO V9 with language models, and the implementation of a user-friendly interface using Gradio and LangChain. Learners will gain practical skills in building and deploying AI solutions for healthcare applications.

This module explores the process of detecting pneumonia using chest X-rays, covering model training, system implementation, and key concepts in medical image interpretation with AI tools.

This module explores the development and deployment of an AI-powered system for detecting skin cancer. Learners will gain insights into the technical components and workflow involved in building such a system, including model selection, user interface design, and deployment strategies.

This module explores the use of infrared camera technology for fever detection, covering system design, image processing, and data storage. Learners will gain hands-on understanding of how to build and implement a fever detection system using Python and common computer vision techniques.

This module explores the process of classifying brain tumors using MRI imagery, covering model training, implementation, and integration with AI applications. Learners will gain hands-on understanding of deep learning workflows and medical image analysis techniques.

This module explores the application of AI in detecting diabetic retinopathy using retina images. Learners will gain knowledge on model development, data preprocessing, and responsible AI deployment in healthcare settings. The content bridges theory and practical implementation in medical image classification.

This module explores how machine learning is applied in heart disease prediction, focusing on key concepts such as model selection, data preprocessing, and user experience design in healthcare. Learners will gain an understanding of the role of analytics in preventive medicine and how to integrate these tools effectively.

This module provides an in-depth look at the development and application of a Diabetes Prediction model. Learners will explore data preparation, model deployment, and performance evaluation techniques. The content emphasizes practical AI implementation in healthcare settings.

This module explores the development and implementation of a mortality prediction system for intensive care units, focusing on data handling, modeling techniques, and system deployment using machine learning. Learners will gain practical insights into building healthcare-based predictive models and understanding their real-world applications.

This module provides hands-on training in segmenting brain tumors using 3D-MRI imagery, covering the setup, model pipeline building, and visualization techniques. Learners will gain practical skills in medical image processing and front-end display integration.

This module focuses on the practical implementation of PolyP segmentation for gastrointestinal imaging, covering model setup, pipeline building, output analysis, and frontend integration. Learners will gain hands-on experience with deep learning workflows and data visualization techniques.

This module explores the application of deep learning in cell nuclei segmentation, covering best practices, model architecture, data handling, and performance evaluation techniques in biological image analysis.

This module explores the development and implementation of a Breathing Rate Monitor application, focusing on key components like face detection, signal processing, and system deployment using Python and Streamlit. Learners will gain practical knowledge on how to design and assess such systems for respiratory health monitoring.

This module explores the use of the YOLOR-v7 model for heart rate measurement, focusing on practical applications in healthcare. Learners will gain hands-on knowledge of how to implement the model and understand its relevance in real-world scenarios.