Svm in machine learning: Support Vector Machine (SVM) is

Support Vector Machine (SVM) is a supervised learning algorithm used for classification and regression. It finds the optimal boundary to separate classes, ensuring maximum margin. This article explores SVM’s working, mathematical foundation, types, real-world applications, and implementation with examples. 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 ... Learn what SVM is, how it works, and why it is effective for classification and regression tasks. Explore its features, examples, applications, advantages, and disadvantages with Ze Learning Labb's courses. 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 ...