We establish lower bounds on the singular values of certain random matrices. We assume that the matrix elements follow a joint Gaussian distribution or a mixture of Gaussians. Using these results, we apply the inverse Littlewood-Offord theory to obtain a useful lower bound on the singular values of a random matrix appearing in the numerical analysis of a randomized algorithm.