scikit-learn - Test Predictions Using Various Models [Video]
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.
|Course Length||2 hours 12 minutes|
|Date Of Publication||27 Feb 2018|