Add to ORKGWe present an application of a machine learning method to the estimation of photometric redshifts for the galaxies in the SDSS Data Release 9 (SDSS-DR9). Photometric redshifts for more than 143 million galaxies were produced. The MLPQNA (Multi Layer Perceptron with Quasi Newton Algorithm) model provided within the framework of the DAMEWARE (DAta Mining and Exploration Web Application REsource) is an interpolative method derived from machine learning models. The obtained redshifts have an overall uncertainty of σ=0.023 with a very small average bias of about 3x10-5 and a fraction of catastrophic outliers of about 5%. After removal of the catastrophic outliers, the uncertainty is about σ=0.017. The catalogue files report in their name the ran...