STRATIFICATION OF MACHINE LEARNING APPROACHES

Abstract

Machine learning works on terabytes of data and make predictions faster than ever before [1].
The buzz term Machine learning was coined by a renowned American pioneer Arthur Samuel in
1952 who developed a self-learning program that could play and learn Checkers game [2]. The
learning program could play and update its database periodically in order to ameliorate its selflearning capability. The more the computer plays, the more it develops the probability to succeed
from its counterpart [2]. This was possibly the first released version Reinforcement Learning [2].
It is discovered that machine learning concepts can be well-built using Python code as compared
to C++ or related programming codes [3].