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

More Information
  • 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.

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.

  • Optimizing the ridge regression parameter
  • Analyze and plot an ROC curve without context
  • Dummy Estimators and Persisting models with joblib
  • Using k-means for outlier detection
Course Length 2 hours 12 minutes
ISBN 9781789133066
Date Of Publication 27 Feb 2018


Julian Avila

Julian Avila is a programmer and data scientist in finance and computer vision. He graduated from the Massachusetts Institute of Technology (MIT) in mathematics, where he researched quantum mechanical computation, a field involving physics, math, and computer science. While at MIT, Julian first picked up classical and flamenco guitars, Machine Learning, and artificial intelligence through discussions with friends in the CSAIL lab.

He started programming in middle school, including games and geometrically artistic animations. He competed successfully in math and programming and worked for several groups at MIT. Julian has written complete software projects in elegant Python with just-in-time compilation. Some memorable projects of his include a large-scale facial recognition system for videos with neural networks on GPUs, recognizing parts of neurons within pictures, and stock-market trading programs.