Learn how to utilize PyTorch, Monai, and Python for healthcare imaging in this Python machine learning course. By creating an algorithm for automatic liver segmentation, you will enhance your machine learning skills. The course covers topics such as U-Net, software installation, dataset handling, preprocessing, error management, loss functions, training, testing, and GitHub repository usage. The teaching method includes practical demonstrations and code implementation. This course is designed for individuals interested in applying machine learning to healthcare imaging tasks.
Overview
Syllabus
) Introduction.
) What is U-Net.
) Software Installation.
) Finding the Datasets.
) Preparing the Data.
) Installing the Packages.
) Preprocessing.
) Errors you May Face.
) Dice Loss.
) Weighted Cross Entropy.
) The Training Part.
) The Testing Part.
) Using the GitHub Repository.
Taught by
freeCodeCamp.org
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Reviews
4.7 rating, based on 3 Class Central reviews
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As a beginner Python learner interested in applying machine learning to healthcare imaging, I found "PyTorch and Monai for AI Healthcare Imaging" to be a highly valuable and engaging course. Clear and Practical Instruction: The instructor's teachi…
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The course structure is well-paced, allowing students to grasp foundational concepts before diving into challenging projects. The hands-on nature of the course ensures that learners not only understand the theory behind computer vision but also gain valuable experience in implementing Unet for real-world applications. The well-designed projects provide a solid foundation for participants to tackle their own computer vision challenges confidently. Overall, this course is a commendable resource for those seeking a project-based approach to mastering computer vision with a focus on Unet.
