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. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine.
This course go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases.
Medicine is one of the fastest-growing and important application areas, with unique challenges like handling missing data. You’ll start by learning the nuances of working with 2D and 3D medical image data. You’ll then apply tree-based models to improve patient survival estimates. You’ll also use data from randomized trials to recommend treatments more suited to individual patients. Finally, you’ll explore how natural language extraction can more efficiently label medical datasets.
● AI for Medical Diagnosis
● AI for Medical Prognosis
● AI For Medical Treatment
Start learning the AI in Medicine with outside of business hours schedule!
The 12 hours of schedule is as follows:
April 12 – 19 – 26 and May 3
Sundays, from 9:00 am to 12:00 pm
The venue for the bootcamp is Magnimind Academy Sunnyvale Campus: 830 Stewart Dr #182, Sunnyvale, CA 94085. The capacity is limited to 20 people.
AI in Medicine Specialized Bootcamp is now also available online. Anyone who wants to attend this mini bootcamp can join online live webinars where the same course content will be taught. Online sessions will be distributed through zoom conferences. Students will have access to the screen of the instructor, external camera showing class atmosphere, whiteboard, and be able to ask questions through chat. You may attend this mini bootcamp no matter where you are.
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After you finish filling your application form, the website will direct you to the payment page. There, you can select available payment options.
If you’re not satisfied with the course you may cancel your application.
Abdullah Karasan is a principal data scientist at Magnimind Academy and an instructor for O’Reilly and Springboard. He’s published several papers in prestigious journals in the field of financial data science and is the sole author of Machine Learning for Financial Risk Management with Python (forthcoming December 2021). Born in Berlin, Germany, he holds a master’s degree from the University of Michigan-Ann Arbor and a Ph.D. in financial mathematics from Middle East Technical University.
He has several published scientific articles and is the author of the book in progress titled “Machine Learning for Financial Risk Management in Python”.