Medical Diagnosis using Support Vector Machines

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Medical Diagnosis using Support Vector Machines provided by Coursera is a comprehensive online course, which lasts for 1 hour of material. Medical Diagnosis using Support Vector Machines is taught by Daniel Romaniuk. Upon completion of the course, you can receive an e-certificate from Coursera. The course is taught in Englishand is Paid Course. Visit the course page at Coursera for detailed price information.

Overview
  • In this one hour long project-based course, you will learn the basics of support vector machines using Python and scikit-learn. The dataset we are going to use comes from the National Institute of Diabetes and Digestive and Kidney Diseases, and contains anonymized diagnostic measurements for a set of female patients. We will train a support vector machine to predict whether a new patient has diabetes based on such measurements. By the end of this course, you will be able to model an existing dataset with the goal of making predictions about new data. This is a first step on the path to mastering machine learning.

    Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.