In this post, we will learn about our next machine learning algorithm called support vector machine or SVM or support vector networks. This is a crucial concept and a powerful algorithm that has an advantage over neural networks when it comes to finding the optimum solution. We use SVM mainly for classification and regression tasks and sometimes even for clustering tasks. It is a supervised learning algorithm.
But what makes SVM truly special? How does it work its magic in discerning patterns within data? In this tutorial, we embark on a journey into the world of SVM, unraveling its concepts, breaking down its mathematical underpinnings, and exploring its applications across various domains. This post is everything you need to learn SVM from scratch to applications. I will explain the fundamentals and show you how you can apply them to real-world problems. So, let's get started.
Table of Contents
Prerequisites:
- Python, Numpy, Sklearn, Pandas and Matplotlib.
- Linear Algebra For Machine Learning.
- Statistics And Probability Theory.
- Advanced Calculus For Machine Learning – Lagrangian, Gradients etc.
What You Will Learn:
- Concept of Support Vector Machine
- Convex Sets and Convex Functions
- Concept of Duality
- Linear SVM
- Hard Margin Classifier
- Soft Margin Classifier
- Kernels
- Kernel Tricks
- Non-Linear SVM
- SVR, Implementation of SVM and more
Concepts And Definitions Of Support Vector Machine:
Imagine we have a dataset containing two classes and we wish to develop an algorithm that classifies them successfully and generalizes well on unseen data. That's where the SVM comes in. No doubt other algorithms out there can perform the same task but what sets SVM apart is its ability to offer unique solution to the problem as it uses the concept of margin. What it does is offer us an optimum hyperplane that maximizes the margin between these two classes.
We will come back to these concepts later but first, let's consider the possible representations of our dataset. In one case, we may have a dataset where classes are easily separable by a hyperplane which means data points from each class will be on either side of the hyperplane. In the next case, we will have a few data points that are not on the right side which means some of them will overlap. In the final case, our datasets will not be linearly separable at all. So, in a nutshell, we have to figure out the solution of these three cases so that we can develop our algorithm. Here we have three scenarios:
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