Book Image

The Machine Learning Workshop - Second Edition

By : Hyatt Saleh
Book Image

The Machine Learning Workshop - Second Edition

By: Hyatt Saleh

Overview of this book

Machine learning algorithms are an integral part of almost all modern applications. To make the learning process faster and more accurate, you need a tool flexible and powerful enough to help you build machine learning algorithms quickly and easily. With The Machine Learning Workshop, you'll master the scikit-learn library and become proficient in developing clever machine learning algorithms. The Machine Learning Workshop begins by demonstrating how unsupervised and supervised learning algorithms work by analyzing a real-world dataset of wholesale customers. Once you've got to grips with the basics, you'll develop an artificial neural network using scikit-learn and then improve its performance by fine-tuning hyperparameters. Towards the end of the workshop, you'll study the dataset of a bank's marketing activities and build machine learning models that can list clients who are likely to subscribe to a term deposit. You'll also learn how to compare these models and select the optimal one. By the end of The Machine Learning Workshop, you'll not only have learned the difference between supervised and unsupervised models and their applications in the real world, but you'll also have developed the skills required to get started with programming your very own machine learning algorithms.
Table of Contents (8 chapters)
Preface

The 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 the SVM Algorithm Work?

The following diagram shows a simple example of an SVM model. Both the triangles and circular data points represent the instances from the input dataset, where...