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.
Medical Diagnosis using Support Vector Machines
Taught in English
Instructor: Daniel Romaniuk
2,473 already enrolled
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(62 reviews)
What you'll learn
Create a machine learning model using industry standard tools and solve a medical diagnosis problem
Skills you'll practice
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Guided Project
(62 reviews)
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About this Guided Project
Learn step-by-step
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
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How you'll learn
Skill-based, hands-on learning
Practice new skills by completing job-related tasks.
Expert guidance
Follow along with pre-recorded videos from experts using a unique side-by-side interface.
No downloads or installation required
Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
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Reviewed on Jul 10, 2020
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