Support Vector Machine (SVM) is one of the machine learning’s classifier. Its goal is to find the optimal separating hyperplane which maximizes the margin of the data train. there are three important parts of this algorithm,namely the optimal separating hyperplane, the margin, and the data train. SVM will be implemented in a data train, so it is a supervised learning algorithm. this algorithm classifies the data into a certain class which makes it as a classification algorithm. To predict a class of a new data, SVM uses a hyperplane as the model that separates the classes and we can classify a new data just by looking at its position towards the hyperplane. What Is Hyperplane? we know that the data train can be implemented in a space having any dimensions. If we use a two-dimensional space, the hyperplane becomes a line. If we use a three-dimensional space, it becomes a plane. And in more dimensions, it becomes a hyperplane. So, a hyperplane is just a generalization
We are running in the most quality period of human race. when you open some article about machine learning, you see dozens of detailed descriptions. The idea behind writing this blog is to get the knowledge about Machine learning across the world. Through this blog, ML provides potential solutions in all different domains and more, and is set to be a pillar of our future civilization.. I am providing a flow level understanding about various machine learning Types along with description. These should be sufficient to get your hands dirty. So what exactly is “machine learning” Machine Learning (ML) is coming into its own, It is playing a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems. Machine Learning is all around us. Apple, Amazon, Microsoft, Uber and many more companies are using machine learning. Generally there are four approaches in Machine Learning -: 1) Sup