Book Image

Artificial Intelligence and Machine Learning Fundamentals

By : Zsolt Nagy
Book Image

Artificial Intelligence and Machine Learning Fundamentals

By: Zsolt Nagy

Overview of this book

Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law. By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills!
Table of Contents (10 chapters)
Artificial Intelligence and Machine Learning Fundamentals

Classification with Support Vector Machines

We first used support vector machines for regression in Lesson 3, Regression. In this topic, you will find out how to use support vector machines for classification. As always, we will use scikit-learn to run our examples in practice.

What are Support Vector Machine Classifiers?

The goal of a support vector machines defined on an n-dimensional vector space is to find a surface in that n-dimensional space that separates the data points in that space into multiple classes.

In two dimensions, this surface is often a straight line. In three dimensions, the support vector machines often finds a plane. In general, the support vector machines finds a hyperplane. These surfaces are optimal in the sense that, based on the information available to the machine, it optimizes the separation of the n-dimensional spaces.

The optimal separator found by the support vector machines is called the best separating hyperplane.

A support vector machines is used to find one...