# AI News, Understanding Support Vector Machine algorithm from examples (along with code)

- On Wednesday, June 6, 2018
- By Read More

## Understanding Support Vector Machine algorithm from examples (along with code)

Note: This article was originally published on Oct 6th, 2015 and updated on Sept 13th, 2017 Mastering machine learning algorithms isn’t a myth at all.

Think of machine learning algorithms as an armory packed with axes, sword, blades, bow, dagger etc. You have various tools, but you ought to learn to use them at the right time.

In this article, I shall guide you through the basics to advanced knowledge of a crucial machine learning algorithm, support vector machines.

However, it is mostly used in classification problems. In this algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate.

In Python, scikit-learn is a widely used library for implementing machine learning algorithms, SVM is also available in scikit-learn library and follow the same structure (Import library, object creation, fitting model and prediction).

The creation of a support vector machine in R and Python follow similar approaches, let’s take a look now at the following code:

Let’s look at the example, where we’ve used linear kernel on two feature of iris data set to classify their class.

Example: Have linear kernel Example: Have rbf kernel Change the kernel type to rbf in below line and look at the impact.

would suggest you to go for linear kernel if you have large number of features (>1000) because it is more likely that the data is linearly separable in high dimensional space.

I discussed its concept of working, process of implementation in python, the tricks to make the model efficient by tuning its parameters, Pros and Cons, and finally a problem to solve.

- On Wednesday, May 22, 2019

**Support Vector Machine - Georgia Tech - Machine Learning**

Watch on Udacity: Check out the full Advanced Operating Systems course for free ..

**Support Vector Machine Algorithm**

Support Vector Machines are one of the most popular and talked about machine learning algorithms. This algorithm is used for classification. It is done through ...

**Support Vector Machine - Machine Learning**

Support Vector Machine” (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However, it is ...

**Support Vector Machine SVM-IRIS dataset**

SVM or support vector machine is a very widely used robust algorithm used for classification. Its a optimization problem mainly solved via a hyperplane. Look at ...

**Support Vector Machines - The Math of Intelligence (Week 1)**

Only a few days left to signup for my Decentralized Applications course! Support Vector Machines are a very popular type of machine ..

**Mod-09 Lec-31 Support Vector Machines -- Introduction, obtaining the optimal hyperplane**

Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IISc Bangalore. For more details on NPTEL visit ...

**Predicting the Winning Team with Machine Learning**

Only a few days left to signup for my Decentralized Applications course! Can we predict the outcome of a football game given a dataset ..

**Lecture 15 - Kernel Methods**

Kernel Methods - Extending SVM to infinite-dimensional spaces using the kernel trick, and to non-separable data using soft margins. Lecture 15 of 18 of ...

**Nonlinear SVM and Kernel Function**

**Still Support Vector Machines - Georgia Tech - Machine Learning**

Watch on Udacity: Check out the full Advanced Operating Systems course for free ..