Svm in machine learning: Support Vector Machine (SVM)

Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It tries to find the best boundary known as hyperplane that separates different classes in the data. Wondering what SVM is and why it sounds so complicated? Well, Support Vector Machine ( SVM) in machine learning stands as one of the most powerful yet flexible supervised algorithms you can master for classification and regression tasks. Support Vector Machines (SVM) work by creating an optimal hyperplane that maximizes the margin between different classes. This approach effectively separates data points while maintaining the highest possible distance from the closest points (known as support ... Learn how SVMs are used for classification and regression problems, and how they find a hyperplane that maximizes the margin between classes. See examples of SVM implementation in Python using sklearn library and kernel trick. Support Vector Machines (SVMs) is a supervised machine learning algorithms used for classification and regression tasks. They work by finding the optimal hyperplane that separates data points of different classes with the maximum margin. We can use Scikit library of python to implement SVM but in this article we will implement SVM from scratch as it enhances our knowledge of this algorithm and have better clarity of how it works. 1. Importing libraries and Data Visualization We will be using ...