  Every quantum system is coupled to an environment. Such system-environment
interaction leads to temporal correlation between quantum operations at
different times, resulting in non-Markovian noise. In principle, a full
characterisation of non-Markovian noise requires tomography of a multi-time
processes matrix, which is both computationally and experimentally demanding.
In this paper, we propose a more efficient solution. We employ machine learning
models to estimate the amount of non-Markovianity, as quantified by an
information-theoretic measure, with tomographically incomplete measurement. We
test our model on a quantum optical experiment, and we are able to predict the
non-Markovianity measure with $90\%$ accuracy. Our experiment paves the way for
efficient detection of non-Markovian noise appearing in large scale quantum
computers.
