Medical Diagnosis using Support Vector Machines
61 ratings

2,209 already enrolled
Create a machine learning model using industry standard tools and solve a medical diagnosis problem
61 ratings
2,209 already enrolled
Create a machine learning model using industry standard tools and solve a medical diagnosis problem
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.
Python Programming
Machine Learning
Scikit-Learn
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Load a dataset from file
Split a dataset into training and testing subsets
Create a support vector machine
Make a medical diagnosis for a new patient
Evaluate the accuracy of the SVM classifier
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
by AP
Jul 10, 2020A short duration course but with deep and effective learnings. This will give you some insights regarding the power of SVMs
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