Andrew Ng, a prominent name in the field of Artificial Intelligence has introduced a new specialization course AI for Medicine featuring TensorFlow. The specialization has been launched by Coursera in collaboration with and will be taught by Pranav Rajpurkar, who is a 5th year Ph.D. candidate in the Stanford Machine Learning Group.

Source: YouTube

About the Specialization

The specialization is aimed at widening the access of Machine Learning problems in medicine and to inspire more people to pursue this field. It is available on Coursera and can be audited for free.

The course requires some basic knowledge of Tensorflow as a prerequisite to be able to grasp concepts such as Convolutional Neural Networks (CNNs), Transfer Learning, and Natural Language Processing to solve interesting problems. The specialization is divided into three courses as follows,

1. AI For Medical Diagnosis:

In this first course, students will create convolutional neural network image classification and segmentation models to make diagnoses of lung and brain disorders. 

2. AI For Medical Prognosis:

In this second course, students will learn to build models to predict future patient health. Specifically, students will build risk models and survival estimators for heart disease using statistical methods and a random forest predictor to determine patient prognosis.

3. AI For Medical Treatment:

In the final course, students will build a treatment effect predictor, apply model interpretation techniques, and use natural language processing to extract information from radiology reports.

According to Coursera, “This three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine. These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases.”

In Conclusion

AI is rapidly transforming the practice of medicine, ranging from helping doctors diagnose patients more accurately, to recommending better treatments. This specialization is expected to provide AI practitioners with the necessary tools for a more effective experience in applying machine learning to concrete problems in medicine.

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