Understanding support vector machines - Géron, Aurélien (O'Reilly, 2017)
What you'll learn-and how you can apply it You'll learn the core concepts one of the most popular models in Machine Learning-support vector machines-how to use them, and how they work. Readers will gain an intuitive understanding of the mathematics involved in SVMs, including an introduction to using polynomial kernels. At the end of this Lesson, readers will be able to do binary classification for rather simple problems. This lesson is for you because You have some programming experience and you're ready to code a Machine Learning project. You want to classify attributes on small- to medium-sized datasets and possibly complex datasets. Prerequisites: Have some programming experience (know how to code in Python) Understanding of basic machine learning concepts (fitting a model to data) Materials or downloads needed: Python Scikit-Learn (code written and tested on v. 0.18).
"From Hands-on machine learning with Scikit-Learn and TensorFlow by Aurélien Géron"--Cover. - Date of publication from resource description page. - Online resource; title from title page (Safari, viewed May 25, 2017)
Keywords: Machine learning; Artificial intelligence; Artificial Intelligence; Machine Learning; Apprentissage automatique; Intelligence artificielle; artificial intelligence