AI for Medicine Specialization from deeplearning.ai

We have already looked at TOP 100 Coursera Specializations and today we will check out AI for Medicine Specialization from deeplearning.ai.

Coursera Specialization is a series of courses that help you master a skill. To begin, you can enroll in the Specialization directly, or review its courses and choose the one you’d like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. You can either complete just one course or you can pause your learning or end your subscription at any time.

This three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine.AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments.

These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend taking the Deep Learning Specialization.

What will you learn

• Diagnose diseases from x-rays and 3D MRI brain images
• Estimate treatment effects on patients using data from randomized trials
• Predict patient survival rates more accurately using tree-based models
• Automate the task of labeling medical datasets using natural language processing

There are 3 Courses in this Specialization.

#1.AI for Medical Diagnosis

This program will give you practical experience in applying cutting-edge machine learning techniques for concrete problems in modern medicine. In this course, you will create convolutional neural network image classification and segmentation models to make diagnoses of lung and brain disorders.

#2.AI for Medical Prognosis

Machine learning is a powerful tool for prognosis, a branch of medicine that specializes in predicting the future health of patients. In this second course, you’ll walk through multiple examples of prognostic tasks. You’ll then use decision trees to model non-linear relationships, which are commonly observed in medical data, and apply them to predicting mortality rates more accurately. Finally, you’ll learn how to handle missing data, a key real-world challenge.

What will you learn

• Walk through examples of prognostic tasks
• Navigate practical challenges in medicine like missing data
• Apply tree-based models to estimate patient survival rates

#3.AI For Medical Treatment

Medical treatment may impact patients differently based on their existing health conditions. In this third course, you’ll recommend treatments more suited to individual patients using data from randomized control trials. In the second week, you’ll apply machine learning interpretation methods to explain the decision-making of complex machine learning models. Finally, you’ll use natural language entity extraction and question-answering methods to automate the task of labeling medical datasets.

What will you learn

• Estimate treatment effects using data from randomized control trials
• Apply natural language processing to extract information from unstructured medical data
• Explore methods to interpret diagnostic and prognostic models

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