RAM usage after importing numpy in python 3.7.2
You can't avoid this cost, but it's likely not as bad as it seems. The numpy libraries (a copy of C only libopenblasp, plus all the Python numpy extension modules) occupy over 60 MB on disk, and they're all going to be memory mapped into your Python process on import; adding on all the Python modules and the dynamically allocated memory involved in loading and initializing all of them, and 80 MB of increased reported RAM usage is pretty normal.
That said:

The C libraries and Python extension modules are memory mapped in, but that doesn't actually mean they occupy "real" RAM; if the code paths in a given page aren't exercised, the page will either never be loaded, or will be dropped under memory pressure (not even written to the page file, since it can always reload it from the original DLL).
On UNIX-like systems, when you fork (multiprocessing does this by default everywhere but Windows) that memory is shared between parent and worker processes in copy-on-write mode. Since the code itself is generally not written, the only cost is the page tables themselves (a tiny fraction of the memory they reference), and both parent and child will share that RAM.

Sadly, on Windows, fork isn't an option (unless you're running Ubuntu bash on Windows, in which case it's only barely Windows, effectively Linux), so you'll likely pay more of the memory costs in each process. But even there, libopenblasp, the C library backing large parts of numpy, will be remapped per process, but the OS should properly share that read-only memory across processes (and large parts, if not all, of the Python extension modules as well).
Basically, until this actually causes a problem (and it's unlikely to do so), don't worry about it.
I run conda 4.6.3 with python 3.7.2 win32. In python, when I import numpy, i see the RAM usage increase by 80MB. Since I am using multiprocessing, I wonder if this is normal and if there is anyway to avoid this RAM overhead? Please see below all the versions from relevant packages (from conda list):

python...........3.7.2 h8c8aaf0_2
mkl_fft...........1.0.10 py37h14836fe_0
mkl_random..1.0.2 py37h343c172_0
numpy...........1.15.4 py37h19fb1c0_0
numpy-base..1.15.4 py37hc3f5095_0

thanks!