Support Vector Machines (SVMs) are powerful tools for classification and regression in machine learning. This article explores their geometric intuition, mathematical foundations, and use of kernels for handling non-linear data. It also covers Support Vector Regression (SVR), key applications across domains, strengths, limitations, and practical tips—offering a comprehensive, accessible guide to mastering margins.

Discover why more dimensions can spell trouble for data and learning, turning simple problems into complex, high-dimensional challenges!

Explore the depths of non-linear dimensionality reduction methods! Unravel complex data structures and enhance your analytics.

In this article, we explored PCA and SVD, the two most used linear dimensionality reduction techniques.