AI for Clinical Practice Learning Path

This course discusses the psychological impact of AI, its implications on health, and its clinical application for mental health professionals. It reviews the progression of machine learning, ethical dilemmas, and current research into AI's use in mental health service delivery.

A course with 14 short lessons on how to use ChatGPT to optimize administrative tasks in healthcare outside of direct patient contact.

This program explores the application of AI in healthcare, covering ethical considerations and practical applications to improve patient care and industry transformation.

This specialization provides a deep dive into how artificial intelligence can transform healthcare delivery, covering predictive analytics and clinical applications of machine learning with hands-on projects.

This course is outdated because it is based on PyTorch lightning and alot of thing has been changed since the release of this course.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)

Artificial Intelligence (AI) revolution is here and TensorFlow 2.0 is finally here to make it happen much faster! TensorFlow 2.0 is Google’s most powerful, recently released open source platform to build and deploy AI models in practice.AI technology is experiencing exponential growth and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries. The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab.The course provides students with practical hands-on experience in training Artificial Neural Networks and Convolutional Neural Networks using real-world dataset using TensorFlow 2.0 and Google Colab. This course covers several technique in a practical manner, the projects include but not limited to:(1) Train Feed Forward Artificial Neural Networks to perform regression tasks such as sales/revenue predictions and house price predictions(2) Develop Artificial Neural Networks in the medical field to perform classification tasks such as diabetes detection.(3) Train Deep Learning models to perform image classification tasks such as face detection, Fashion classification and traffic sign classification.(4) Develop AI models to perform sentiment analysis and analyze customer reviews.(5) Perform AI models visualization and assess their performance using Tensorboard(6) Deploy AI models in practice using TensorFlow 2.0 Serving The course is targeted towards students wanting to gain a fundamental understanding of how to build and deploy models in TensorFlow 2.0. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this course will master AI and Deep Learning techn

Did you ever want to apply Deep Neural Networks to more than MNIST, CIFAR10 or cats vs dogs?Do you want to learn about state of the art Machine Learning frameworks while segmenting cancer in CT-images?Then this is the right course for you!Welcome to one of the most comprehensive courses on&nbsp; Deep Learning in medical imaging!This course focuses on the application of state of the art Deep Learning architectures to various medical imaging challenges.You will tackle several different tasks, including cancer segmentation, pneumonia classification, cardiac detection, Interpretability and many more.The following topics are covered:Num PyMachine Learning Theory Test/Train/Validation Data Splits Model Evaluation - Regression and Classification Tasks Tensors with PyTorch Convolutional Neural Networks Medical Imaging Interpretability of a network's decision - Why does the network do what it does?A state of the art high level PyTorch library: PyTorch-lightning Tumor Segmentation Three-dimensional dataand many more Why choose this specific Deep Learning with PyTorch for Medical Image Analysis course ?This course provides unique knowledge on the application of deep learning to highly complex and&nbsp; non-standard (medical) problems (in 2D and 3D) All lessons include clearly summarized theory and code-along examples, so that you can understand and follow every step. Powerful online community with our QA Forums with thousands of students and dedicated Teaching Assistants, as well as student interaction on our Discord Server.You will learn skills and techniques that the vast majority of AI engineers do not have!</ul