The field of neutrino physics has begun making precision measurements of neutrino oscillation parameters. NOvA is a two-detector, long-baseline, neutrino oscillation experiment measuring the appearance of electron neutrinos in a muon beam produced at Fermilab. Key to the NOvA experiment is the use of machine learning algorithms for reconstruction of neutrino interaction flavor and particle identification. The use of these tools adapted from computer vision is becoming more widespread within NOvA and the field. These algorithms require rigorous validation to both understand and develop. This talk will present NOvA's development of machine learning algorithms, sources of uncertainty, and the latest results using neutrinos and antineutrinos.