SVD is a dimensionality reduction technique like Principal Component Analysis (PCA). SVD is often used as a preprocessing step.

Support Vector Machine (SVM) is a supervised machine learning algorithm which is mostly used in classification problems.

The basic difference s that, SVD is dimension reduction technique and SVM is a classification technique.

SVM is one of the most famous and highly accurate machine learning algorithm. For classification it makes a hyper-plane and separate the different class as a cluster.

SVD works on the basis of matrix operation using decomposition.

Dear,

The SVM is a capable learning classifier capable of training polynomials, neural networks and RBFs. Singular Value Decomposition (SVD) are used for feature extraction and while SVM for classification.

You may use the Python 3.5 (Jupyter notebook from Anoconda navigator) for implementation purpose.