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People would want to master machine learning. The first thing that a beginner has to do to learn machine learning algorithms is through regression. It is simple to learn. The machine learning algorithms are power-packed with various tools, which would be a kind of sword, blade, bow, dagger, and so on. You cannot use all the tools for the same purpose. You should know when to use which tool. When you take the analogy of regression, you can consider it to be a sword using which you can slice or dice the data briskly, but it does not have the capability to deal with complicated data. The support vector machine would be like a sharp knife that helps you to work on the smaller dataset.

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Support vector machine is the most popular supervised learning algorithm that is used to solve different problems related to classification and regression. However, it is predominantly used to solve problems related to machine learning. The main aim of machine learning is to create the line that would separate the n-dimensional space into different classes so that it helps you to keep the data point in the right category later. The line is known as the hyperplane. The classification of data is done using the hyperplane, which would be acting as a decision boundary between various classes. The data points that are extreme in each class are known as support vectors. The SVM tries its best to find out the ideal hyperplane that would have the highest margin from every support vector.

Take an example where the KNN classifier is used. When you see a cat that is unique and looks similar to a dog, if you are looking for a model that would help you to identify whether it is a dog or a cat, you can easily create such a model with the help of the SVM algorithm. The model would be trained with a lot of images of dogs and cats so that the features of each animal can be learned and then check these features to match with the strange creature. The decision boundary would be created between two types of data, i.e. cat and dog, you must choose the extreme case. Based on the extreme case, it would classify the animal to be a cat.

**Linear SVM** - The linear SVM is used for the data that is easier to separate in a linear fashion. You can easily classify the data set into two different classes with the help of a single straight line this data is known as linearly separated data and the classifier is termed a linear SVM classifier. The students find it tough to write the assignment on this topic and would look for professional help. However, you can take our help to complete the assignment on time. We also revise the assignments as many times as possible without charging an extra penny.

**Non-linear SVM** - The non-linear SVM is used for the data that is easier to separate in a non-linear fashion. If it is impossible for you to classify the data using a straight line, then such data would be called non-linear data, and the classification is known as the non-linear SVM classifier. If you are stuck in the middle of writing an assignment on this topic, you can call us for help. Our team is available round the clock to complete the assignment request that is received at the last minute.

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Listed below are a few popular applications of SVM on which the assignments, homework, and coursework are based.

**Inverse Geosounding problem**- The inverse Geosounding problem can be solved using SVM and this would determine the layered structure of the planet. It can solve various problems related to geosounding. For the electromagnetic data, the linear function along with the SV learning algorithm would be used to solve the problem. These models are made with the help of linear programming techniques. The dimension in which the model is developed may be small due to the smaller size of the problem.**Data classification**- SVM would be used to solve many mathematical problems with ease. There are many smoothing methods that are used to solve math problems. However, it does not use the regular SVM method rather it uses the SSVM or smooth SVM.**Facial express classification**- There are various ways to classify the facial expression of people. You can use this for happy or sad classification. There are filters to be used. When there is a specific facial expression being added, there is a filter related to that expression gets added. The expression range would be between happy and sad faces. You can use SVM here.**Texture classification**- There are images of a specific texture being used. This data would be used to classify whether the surface of the texture is smooth or not. Using SVM is great for this type of application. If you are using a sensitive camera to capture the images and use this particular data in the model, you can train the model to make it powerful. If you capture the images of a surface, it is easy to determine whether the surface is smooth or gritty.**Speech recognition**- Speech recognition is where SVM is used to communicate with people who are deaf. The data here would be the acoustics. There are many functions such as MFCC, LPCC, and LPC that would be gathering the acoustic data. This data would be used by SVM to train the models. The results that are produced would be precise.

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