Over the past few decades, Machine Learning (ML) has been dynamically becoming the state-of-art technology in various real time applications. ML steps into various fields mainly medical diagnosis with computational approaches, statistics, image processing, graphical games and so on. This post focuses on reviewing the concepts of machine learning algorithms that are significant in recent applications. Also, discuses about ML role in Big Data Analytics. Various applications are glimpsed with suitable examples. Major analysis has been done on medical diagnosis. Further, the limitations of ML are also listed.
Machine learning
Machine learning is
the subset or an application of the AI that will provide computers and machines
the ability to automatically learn and improve from experience without being
directly programmed or developed. Machine learning mainly gives importance on
the development of computer programs or modules that can access datasets and
use it for self learning. Over the past two decades ML has become one of the
mainstreams for information technology and Computer Science. In today's world
more than million data is generated per day With those ever increasing amounts
of data becoming available there is good reason to believe that Big data
analysis will become even more pervasive as a necessary ingredient for
technological thus improving several solutions by Machine learning and Deep
learning .
Arthur Samuel, an expert in the field of AI and computer gaming, coined the word “Machine Learning”. He gave the definition of machine learning as – “The Field of study that gives computers the capability to learn without being explicitly programmed”.
Basic Difference
in ML and Traditional Programming
- Traditional Programming: Feed DATA (Input) + PROGRAM (logic), run it on machine and get output.
- Machine Learning: Feed DATA(Input) + Output, run it on machine during training and the machine creates its own program(logic), which can be evaluated while testing.
ML programming
The machine
learning program or code learns from its experience ‘E’ when doing some set of tasks ‘T’ and performance measure ‘P’,
if its performance at those tasks in T, as measured by P,
then it improves with that experience E and it steadily continues to learn For example: playing a
checkers game. E is the experience of playing
from the past games. T is
the task for playing checkers. P is
the probability that the program will win in the next game.
This post mainly focuses on machine
learning and its applications. The data in machine learning gives a brief intro
on what is data and the importance of a data with its types. There are several
machine learning algorithms in which some of the general algorithms are also
mentioned in this post can be broadly
classified under four branches namely supervised, unsupervised,
semi-supervised, reinforcement learning algorithms. Machine learning is now in
its golden age thanks to the development bigdata and its analytical tools. It
serves a main part in developing the future due to its vast applications in
every field which are mentioned in machine learning applications. Everything
has its dark side said that machine learning has also its limitations and there
are multiple areas of research waiting for improvement in machine learning. In
Conclusion this postdiscusses about machine learning applications and its
impact on humans and virtual Internet world.
