We propose a support vector regression approach for symbol detection in large-MIMO systems employing spatial multiplexing. We explore the applicability of machine learning algorithms, in particular support vector machines, to address one of the recent research problem in communications.The machine learning capability is exploited to achieve fast detection in large dimension systems. The performance of the proposed method is compared with lattice reduction aided detection which is currently the popular choice and the improvement in terms of bit error rate is demonstrated. The sparse formulation of the problem matrix reduces the computational complexity and enables faster detection. The proposed detection algorithm is tailored to address both uncorrelated and correlated channel conditions as well.