Dive into a comprehensive Python machine learning course focused on AI healthcare imaging using PyTorch and Monai. Master the creation of an automatic liver segmentation algorithm while improving your computer vision skills. Explore the U-Net architecture, learn to install necessary software and packages, and discover how to find and prepare datasets. Gain hands-on experience with preprocessing techniques, understand common errors and their solutions, and delve into advanced concepts like Dice Loss and Weighted Cross Entropy. Progress through the training and testing phases, and learn how to utilize the provided GitHub repository for practical implementation. Enhance your machine learning expertise with this in-depth, healthcare-oriented course led by Mohammed El Amine MOKHTARI.
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
4.5 rating, based on 2 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.
