scikit-learn - Test Predictions Using Various Models [Video]

scikit-learn - Test Predictions Using Various Models [Video]

Julian Avila

A one-stop solution to test model accuracy with cross-validation
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Video Description

Scikit-learn has evolved as a robust library for machine learning applications in Python with support for a wide range of supervised and unsupervised learning algorithms.

This course begins by taking you through videos on linear models; with scikit-learn, you will take a machine learning approach to linear regression. As you progress, you will explore logistic regression. Then you will build models with distance metrics, including clustering. You will also look at cross-validation and post-model workflows, where you will see how to select a model that predicts well. Finally, you'll work with Support Vector Machines to get a rough idea of how SVMs work, and also learn about the radial basis function (RBF) kernel.

Style and Approach

This course consists of practical videos on scikit-learn that target novices as well as intermediate users. It explores technical issues in depth, covers additional protocols, and supplies many real-life examples so that you are able to implement scikit-learn in your daily life.

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What You Will Learn

  • Evaluate and overcome shortfalls in the linear regression model.
  • Using sparsity to regularize models.
  • Handle data and quantize an image.
  • Search with scikit-learn.
  • Optimize an SVM.

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Video Details

ISBN 139781789133066
Course Length2 hours 12 minutes
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