Support Vector Machines

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About this Free Certificate Course

Support vector machines or support vector networks are models based on supervised learning models that are primarily used for classification and regression analysis. Support vector machine or SVM is a robust and highly efficient prediction method with a firm foundation in statistical methods It was developed at AT&T Bell Laboratories by Vladimir Vapnik and his fellow workers.

SVM works like any other machine learning algorithm. An SVM is fed a set of training examples with each marked as belonging to one of two classes; its training algorithm builds a model that assigns new examples to one class or the other, so in essence, it is a non-probabilistic binary linear classifier. Geometrically, an SVM maps training examples to points in space and then tries to maximize the width of the gap between the two categories. For prediction, the input value is mapped into that same space and predicted to belong to a class based on which side of the margin they fall. In simple terms, an SVM classifies the data into classes by creating a line or a hyperplane which separates the data into classes.

When compared to more advanced systems like deep neural networks, SVM holds two main advantages i.e., SVM has higher speed and has better performance even with a small sample set. 

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Course Outline

Introduction to Machine Learning and Linear Regression

Data is the soul of Machine Learning, and there are specific methods to deal with it efficiently. This module first introduces Machine Learning and talks about the mathematical procedures involved. You will learn about supervised and unsupervised learning, Data Science Machine Learning steps, linear regression, Pearson's coefficient, best fit line, and coefficient of determinant. Lastly, you will be going through a case study to help you effectively comprehend Machine Learning concepts. 

 

Understanding Support Vector Machines
What are Kernel Functions?
Advantages and Disadvantages of SVMs
Popular Applications of SVMs
SVM Demo in Python

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Support Vector Machines

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Frequently Asked Questions

What is a support vector machine? Explain it with an example?

A support vector machine (SVM) is a class of supervised deep learning algorithms that performs supervised learning for classification or regression of a data set segregated into classes.SVM finds huge applications in computational biology. One famous use case of SVM is 'Protein Fold and Remote Homology Detection.' Here SVM is used to model and analyze different models of the protein sequences. Internally, it uses different methods to solve the deployed kernel functions that help to find the similarity between different protein sequences.

What are support vector machines good for?

SVM finds application in many fields including computer vision, NLP, content moderation, review and recommendation systems, computational biology, sales and analytics, biotech etc. 

When should I use SVM?

SVM is primarily a classification algorithm but it can be used for regression and outliers’ detection as well. SVM can be used irrespective of the linearity of the problem i.e., it can be applied for both linear and nonlinear problems. For SVM to give a good result, the following conditions must hold:

  1. The dataset has to be linearly separable else the data needs to be transformed using various Kernels offered. Transformation is not part of SVM and it can blow out the number of features.

  2. SVM’s performance deteriorates with an increase in the number of dimensions as compared to other methodologies such as a random forest. SVM uses constrained optimization problems and that deteriorates with an increase in input feature count. 

  3. SVM is not suitable for a dataset where the number of features is much larger than the number of observations. 

  4. Categorical variables are not handled out of the box by the SVM algorithm.

What is a kernel from SVM's perspective?

A kernel is a method of projecting a two-dimensional plane into a higher-dimensional space, so as to make it curved in the higher dimensional space. In layman's language, a kernel is a function that maps data from the low dimensional space into a higher-dimensional space.

What is the ‘kernel trick’ employed in SVM?

Kernel trick is employed to transform non-linear data to linear form. A Kernel Trick is a mathematical way to project non linear data onto a higher dimension space so as to make it easier to classify the data by dividing it linearly in a plane. This is done by using the Lagrangian formula using Lagrangian multipliers.

How can I learn the support vector machine for free?

There are various sources available on the internet for free to learn SVM. Great Learning is an excellent platform that offers free online courses on supervised learning methods including SVM. Medium, Towards DataScience, real python etc. are online websites that give detailed articles on SVM. Github is a great source to get free source code of various implementations of SVM. 

Can you explain about SVM Regression?

The Support Vector Regression or SVR uses the same principles as the SVM for classification with only a few minor differences. The major difference is that it becomes very difficult to predict the information at hand because the output is a real number. Moreover, the margin of tolerance is set in approximation to the SVM.

Will I get a certificate after completing this Support Vector Machines free course?

Yes, you will get a certificate of completion for Support Vector Machines after completing all the modules and cracking the assessment. The assessment tests your knowledge of the subject and badges your skills.
 

How much does this Support Vector Machines course cost?

It is an entirely free course from Great Learning Academy. Anyone interested in learning the basics of Support Vector Machines can get started with this course.
 

Is there any limit on how many times I can take this free course?

Once you enroll in the Support Vector Machines course, you have lifetime access to it. So, you can log in anytime and learn it for free online.
 

Can I sign up for multiple courses from Great Learning Academy at the same time?

Yes, you can enroll in as many courses as you want from Great Learning Academy. There is no limit to the number of courses you can enroll in at once, but since the courses offered by Great Learning Academy are free, we suggest you learn one by one to get the best out of the subject.

Why choose Great Learning Academy for this free Support Vector Machines course?

Great Learning Academy provides this Support Vector Machines course for free online. The course is self-paced and helps you understand various topics that fall under the subject with solved problems and demonstrated examples. The course is carefully designed, keeping in mind to cater to both beginners and professionals, and is delivered by subject experts.

Great Learning is a global ed-tech platform dedicated to developing competent professionals. Great Learning Academy is an initiative by Great Learning that offers in-demand free online courses to help people advance in their jobs. More than 5 million learners from 140 countries have benefited from Great Learning Academy's free online courses with certificates. It is a one-stop place for all of a learner's goals.

What are the steps to enroll in this Support Vector Machines course?

Enrolling in any of the Great Learning Academy’s courses is just one step process. Sign-up for the course, you are interested in learning through your E-mail ID and start learning them for free online.
 

Will I have lifetime access to this free Support Vector Machines course?

Yes, once you enroll in the course, you will have lifetime access, where you can log in and learn whenever you want to. 
 

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