  Extracting environmental forces from noisy data is a common yet challenging
task in complex physical systems. Machine learning represents a robust approach
to this problem, yet is mostly tested on simulated data with known parameters.
Here we use supervised machine learning to extract the electrostatic,
hydrodynamic, and stochastic forces acting on micron-sized charged particles
levitated in an argon plasma. Trained on simulated particle trajectories using
more than 100 dynamical and statistical features, the model predicts system
parameters with 50\% better accuracy than conventional methods, and provides
non-contact measurements of the particle charge and Debye length.
