AI for Medicine is a three-course Specialization that equips learners with practical experience in applying machine learning to solve real-world medical challenges. The courses delve into diagnosing diseases from medical images, predicting patient survival rates, estimating treatment effects, and automating medical dataset labeling using natural language processing. Throughout the program, participants will gain insights into the nuances of applying AI to medical use cases and develop a deeper understanding of neural networks.
This Specialization is suitable for individuals with some understanding of the math and coding behind AI algorithms. While no prior medical expertise is required, a working knowledge of deep neural networks and proficiency in Python programming at an intermediate level are essential. Learners new to machine learning or neural networks are recommended to first take the Deep Learning Specialization offered by deeplearning.ai and taught by Andrew Ng.
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The AI for Medicine Specialization comprises three courses that provide practical experience in applying machine learning to medical challenges. Participants will learn to diagnose diseases from medical images, predict patient survival rates, and estimate treatment effects, among other skills.
AI for Medical Diagnosis: This module focuses on creating convolutional neural network image classification and segmentation models for diagnosing lung and brain disorders.
AI for Medical Prognosis: In this module, learners will apply tree-based models to estimate patient survival rates, navigate practical challenges in medicine, and address issues like missing data.
AI For Medical Treatment: This module covers estimating treatment effects using data from randomized control trials, exploring methods to interpret diagnostic and prognostic models, and applying natural language processing to extract information from unstructured medical data.
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