Medical Diagnosis using Support Vector Machines

In this one-hour long project-based course, you will learn the basics of support vector machines using Python and scikit-learn. The dataset provided contains anonymized diagnostic measurements for a set of female patients from the National Institute of Diabetes and Digestive and Kidney Diseases. Throughout the course, you will be guided to train a support vector machine to predict whether a new patient has diabetes based on these measurements.

By the end of the course, you will be equipped with the knowledge to model an existing dataset with the goal of making predictions about new data. This course is a great first step for anyone looking to master machine learning and is particularly well-suited for learners based in the North America region.

• Duration: 1 hour
• Skills: Python, scikit-learn, machine learning, support vector machines
• Learning Outcome: Create a machine learning model using industry standard tools and solve a medical diagnosis problem