Advantages of Support Vector Machines (SVM)
SVM is one of the supervised algorithms mostly used for classification problems. This article will give an idea about its advantages in general.

SVM is very helpful method if we donβt have much idea about the data. It can be used for the data such as image, text, audio etc.It can be used for the data that is not regularly distributed and have unknown distribution.

The SVM provides a very useful technique within it known as kernel and by the application of associated kernel function we can solve any complex problem.
Kernel provides choosing a function which is not necessarily linear and can have different forms in terms of different data it operates on and thus is a nonparametric function. 
In Classification problems, there is a strong assumption that is Data have samples that are linearly separable but with the introduction of kernel, Input data can be converted into High dimensional data avoiding the need of this assumption.
K(x1, x2)=γf(x1), f(x2)γWhere K is the kernel function, x1, x2 are ndimensional inputs and f is a function that is used to map ndimensional space into mdimensional space and γx1, x2γis used to specify/indicate the dot product 
SVM generally do not suffer condition of overfitting and performs well when there is a clear indication of separation between classes. SVM can be used when total no of samples is less than the no of dimensions and performs well in terms of memory.

SVM performs and generalized well on the out of sample data. Due to this as it performs well on out of generalization sample data SVM proves itself to be fast as the sure fact says that in SVM for the classification of one sample , the kernel function is evaluated and performed for each and every support vectors.

The other important advantage of SVM Algorithm is that it is able to handle High dimensional data too and this proves to be a great help taking into account its usage and application in Machine learning field.

Support Vector Machine is useful in finding the separating Hyperplane ,Finding a hyperplane can be useful to classify the data correctly between different groups.

SVM has a nature of Convex Optimization which is very helpful as we are assured of optimality in results So the answer would be global minimum instead of a local minimum.

In SVM, we can due to the large margin that it likes to generate, we can fit in more data and classify it perfectly.

Outliers have less influence in SVM Algorithm therefore there are less chances of skewing the results as outliers affect the mean of the data and therefore mean cannot represent the data set which it was able to do before the effect of having outliers ,Thus as there is less influence of outliers in SVM ,it proves to be helpful.

As In SVM , the Classifier is dependent ideally only on a subset of points , while maximizing distance between closest points of two classes (Margin) , So We do not need to take care and take into account all the points but taking taking only subset of points become helpful

There are many algorithms used for classification in machine learning but SVM is better than most of the other algorithms used as it has a better accuracy in results.

SVM Classifier in comparison to other classifiers have better computational complexity and even if the number of positive and negative examples are not same ,SVM can be used as it has the ability to normalize the data or to project into the
space of the decision boundary separating the two classes. 
The other reason to say that SVM is better than other algorithms is the reason
that it can also perform in nDimensional space. 
The Execution time comes out to be very little in comparison to algorithm such as Artificial Neuron Network.

The other reason to say SVM better is the fact that after doing little modification in the feature extracted data does not affect the results which were
expected before. It is converging very fast and as earlier stated in the article
Kernel Functionality, In general Polynomial kernel proves out to be a better
factor in terms of Support Vector Machine. 
In comparison with Naive Bayes Algorithm which is also a technique used for
classification, Support Vector Machine Algorithm has a faster prediction along with better accuracy. 
In comparison with Logistic Regression which is also a classification method SVM proves itself to be cheaper , it has a time complexity of O(N^2*K) where K is no
of support vectors whereas logistic Regression had the time complexity of O(N^3). 
SVMs can be robust, even when the training sample has some bias and one of the reasons that SVM proves out to be robust is its ability to deliver unique solution better than Neural Networks where we get more than one solutions corresponding to each local minima for different samples.

The Other Important advantage is that SVM can be applicable to also semi β supervised Learning Models. It can be applicable to not only unlabeled data but also with the labeled data.

SVM has a concept of "Transductive SVM" , in such a concept only one thing is needed to satisfy that is Minimization problem and can be applied accordingly when
needed. 
We can also used Inbuilt functionality of SVM, It is available in languages such as Python and Matlab and SVM can also be used for non  linearly separable data having soft margin and Linearly separable data having hard margin.

Although There are other some libraries where kernel can be implemented but in the
case of SVM, all SVM libraries, kernels are already implemented, well researched
and documented, so it's far easier to use them with Support Vector Machines. 
There are many applications Of SVM in real world that is Sentiment Analysis of Emotions of speech , video , image , Handwriting recognition , Cancer diagnosis etc.
Conclusion
This Article, at OpenGenus, has explained advantages of SVM in terms of its contribution in Ml field, Inbuilt usages , Kernel Function and so on along with specifying how SVM proves out to be better than other ML algorithms and an ability to solve some Real life Problems. Hope this article helped you and give comments of your feedback. Thank you for reading the article.