  This paper proposed a new methodology for machine learning in 2-dimensional
space (2-D ML) in inline coordinates. It is a full machine learning approach
that does not require to deal with n-dimensional data in n-dimensional space.
It allows discovering n-D patterns in 2-D space without loss of n-D information
using graph representations of n-D data in 2-D. Specifically, it can be done
with the inline based coordinates in different modifications, including static
and dynamic ones. The classification and regression algorithms based on these
inline coordinates were introduced. A successful case study based on a
benchmark data demonstrated the feasibility of the approach. This approach
helps to consolidate further a whole new area of full 2-D machine learning as a
promising ML methodology. It has advantages of abilities to involve actively
the end-users into the discovering of models and their justification. Another
advantage is providing interpretable ML models.
