  A quantum coherent screening cloud around a magnetic impurity in metallic
systems is the hallmark of the antiferromagnetic Kondo effect. Despite the
central role of the Kondo effect in quantum materials, the structure of quantum
correlations of the screening cloud has defied direct observations. In this
work, we introduce a machine-learning algorithm that allows to spatially map
the entangled electronic modes in the vicinity of the impurity site from
experimentally accessible data. We demonstrate that local correlators allow
reconstructing the local many-body correlation entropy in real-space in a
double Kondo system with overlapping entanglement clouds. Our machine learning
methodology allows bypassing the typical requirement of measuring long-range
non-local correlators with conventional methods. We show that our machine
learning algorithm is transferable between different Kondo system sizes, and we
show its robustness in the presence of noisy correlators. Our work establishes
the potential of machine learning methods to map many-body entanglement from
real-space measurements.
