Explanation regarding Machine Learning Techniques

Running head: MACHINE LEARNING
MACHINE LEARNING
Name of the Student
Name of the University
Author Note:

MACHINE LEARNING1
Summary of paper and Explanation regarding Machine Learning Techniques
It can be stated as the domain of Artificial intelligence, which completely push forward
by concept by providing complete access to data. Machine can easily learn themselves about the
ways to tackle a given problem. By making use of complex mathematical and statistical tool,
machine learning can easily perform in independent way (Xu et al. 2020). The whole idea
regarding automation of complex task is all about generate of high interest in the domain of
networking. Expectation of different activities are there in both design and operation for
communication network can be easily offloaded to various machines. Machine learning in
various domain of networking have already matched the expectation in some areas like intrusion
detection, classification of radio and cognitive radios.
There are list of algorithm which are common algorithm which are classified as machine
learning. The approaches in machine learning can easily go far beyond the possibilities and
reader can have a number of fundamental number of books on the given subject. Some of the
commonly known machine learning are supervised learning, unsupervised learning, semi-
supervised learning, reinforcement learning, overfitting, underfitting and model selection.
Supervised learning is generally used a range of application like speech recognition, detection of
spam and recognition of object (Klaine et al. 2017). The mere goal is all about predicting the
value of one or more kind of output variable which has a given value for the vector of input.
There is a need for training data collection, which consists of N samples of input variables.
In unsupervised learning, the algorithm completely identifies various kind of unusual
patterns in data, taking account the wavelength, BER and modulation. Some of the common
successful application of unsupervised learning method are genes clustering, analysis of social