Book Image

Machine Learning Fundamentals

By : Hyatt Saleh
Book Image

Machine Learning Fundamentals

By: Hyatt Saleh

Overview of this book

As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains you how to use the syntax of scikit-learn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithms over real-world datasets, to discover patterns and profiles, and explore the process to solve an unsupervised machine learning problem. The focus of the book then shifts to supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You'll also learn how to perform coherent result analysis to improve the performance of the algorithm by tuning hyperparameters. By the end of this book, you will have gain all the skills required to start programming machine learning algorithms.
Table of Contents (9 chapters)
Machine Learning Fundamentals

Support Vector Machine Algorithm

The support vector machine (SVM) algorithm is a classifier that finds the hyperplane that effectively separates the observations into their class labels. It starts by positioning each instance into a data space with n dimensions, where n represents the number of features. Next, it traces an imaginary line that clearly separates the instances belonging to a class label from the instances belonging to others.

A support vector refers to the coordinates of a given instance. According to this, the support vector machine is the boundary that effectively segregates the different support vectors in a data space.

For a two-dimensional data space, the hyperplane is a line that splits the data space into two sections, each one representing a class label.

How Does It Work?

The following diagram shows a simple example of an SVM model. Both the green and orange dots represent the instances from the input dataset, where the colors define the class label to which each instance...