Medical Diagnosis using Support Vector Machines

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En este proyecto guiado, tú:

Create a machine learning model using industry standard tools and solve a medical diagnosis problem

Clock1 hour
IntermediateIntermedio
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

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.

Habilidades que desarrollarás

  • Python Programming
  • Machine Learning
  • Scikit-Learn

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  1. Load a dataset from file

  2. Split a dataset into training and testing subsets

  3. Create a support vector machine

  4. Make a medical diagnosis for a new patient

  5. Evaluate the accuracy of the SVM classifier

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

Reseñas

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