AI for Healthcare Learning Path

A specialized course exploring the adoption of AI in medical imaging, covering both technical aspects and ethical challenges. The program is particularly relevant for radiologists and imaging specialists and focuses on AI applications in image analysis, ethical considerations in AI imaging, and clinical validation methodologies.

This course explores the application of AI in revolutionizing energy systems and advancing healthcare. In the energy sector, it covers AI-driven techniques like predictive maintenance, demand forecasting, and energy storage optimization.

This course equips healthcare professionals and enthusiasts with practical AI skills to improve patient care and streamline operations. It covers AI fundamentals, machine learning, natural language processing, predictive analytics, and ethical healthcare practices. Learners will explore the application of AI in medical imaging, diagnostics, treatment planning, and personalized medicine while understanding compliance and regulatory standards.

From Johns Hopkins University, this course focuses on the principles of hypothesis testing as applied to public health research questions.

This course introduces advanced machine learning and NLP techniques for parsing and extracting information from unstructured text documents in healthcare, such as clinical notes and radiology reports.

This course focuses on applying statistical methods in R to public health research, covering data management, descriptive statistics, and basic inferential statistics.

This specialization, offered by Stanford University, covers the current and future applications of AI in healthcare, aiming to equip learners with the knowledge to bring AI technologies into clinical practice safely and ethically. It is designed for both healthcare and computer science professionals to foster collaboration. The series includes a capstone project with a hands-on experience following a patient's journey.

This specialization helps learners understand AI as a process of intelligent decision-making to solve challenges in health systems and apply AI solutions responsibly.

AI in Healthcare: Diagnosis and Treatment

This course is outdated because it is based on PyTorch lightning and alot of thing has been changed since the release of this course. Further some of datasets in this course are no more available for public anymore. So I am not providing support for this course. I want to make this course free, but udemy is not allowing to do so because of content length. The reason why I am not archiving this course, because its still relevant if you want to gain concept of medical imaging competition. Greetings. This course is not intended for beginners, and it is more practically oriented. Though I tried my best to explain why I performed a particular step, I put little to no effort into explaining basic concepts such as Convolution neural networks, how the optimizer works, how Res Net, Dense Net model was created etc. This course is for those who have worked on CIFAR, MNIST data and want to work in real-life scenarios My focus was mainly on how to participate in a competition, get data and train a model on that data, and make a submission. In this course PyTorch lightning is used The course covers the following topics Binary Classification Get the data Read data Apply augmentation How data flows from folders to GPU Train a model Get accuracy metric and loss Multi-class classification (CXR-covid19 competition)Albumentations augmentations Write a custom data loader Use publicly pre-trained model on XRay Use learning rate scheduler Use different callback functions Do five fold cross-validations when images are in a folder Train, save and load model Get test predictions via ensemble learning Submit predictions to the competition page Multi-label classification (ODIR competition)