A Review on Machine Learning Algorithms,
Tasks and Applications
Diksha Sharma1, Neeraj Kumar2
ABSTRACT: Machine learning is a field of
computer science which gives computers an
ability to learn without being explicitly
programmed. Machine learning is used in a
variety of computational tasks where
designing and programming explicit
algorithms with good performance is not
easy. Applications include email filtering,
recognition of network intruders or
malicious insiders working towards a data
breach. One of the foundation objectives of machine learning is to train computers to
utilize data to solve a specified problem. A
good number of applications of machine
learning like classifier training on email
messages in order to differentiate between
spam and non-spam messages, fraud
detection etc. In this article we will focus on
basics of machine learning, machine
learning tasks and problems and various
machine learning algorithms.
Keywords: Machine learning, supervised
learning, unsupervised learning,
classification
1. INTRODUCTION
Machine learning is a branch of artificial
intelligence that allows computer systems to
learn directly from examples, data, and
experience. Through enabling computers to
perform specific tasks intelligently, machine
learning systems can carry out complex
processes by learning from data, rather than
following pre-programmed rules. Increasing
data accessibility has endorsed machine
learning systems to be trained on a bulky
pool of examples, while growing computer
processing power has supported the critical
capabilities of these systems. Within the
field itself there have also been algorithmic
advances, which have given machine
learning better power. As a outcome of these
noticeably below-human levels can now go
better than humans at some definite tasks.
Many people now cooperate with systems
based on machine learning each day, for
example in image recognition systems.
Now-a-days the concept of machine learning
is used in many applications and is a core
concept for intelligent systems [1][3] .As the
field develops further, machine learning
shows promise of supporting potentially
transformative advances in a range of areas,
and the social and economic opportunities
which follow are significant. In healthcare,
machine learning is creating systems that
can assist doctors give more correct or
efficient diagnosis for definite conditions.
For public services it has the potential to
target support more effectively to those in
need, or to tailor services to users. Machine
learning is helping to make sense of the
gigantic quantity of data accessible to
researchers today, offering new insights into
biology, physics & medicine.
II. MACHINE LEARNING TASKS
Machine learning tasks are typically
classified into three broad categories,
depending on the nature of the learning
"signal" or "feedback" available to a
learning system.
Supervised learning Unsupervised learning Reinforcement learning
Supervised Learning: It is the machine
learning task of inferring a function from
labeled training data. The training data
consists of a set of training examples. A
supervised learning algorithm analyzes the
training data and produces an inferred
function that can be utilized for mapping
fresh examples. To work out on a given
problem of supervised learning, one has to
carry out the following steps:
(i) Decide the kind of training examples.
The user should decide what kind of data is
to be used as a training set.
(ii) Collect a training set. The training set
needs to be envoy of the real-world use of
the function. Thus, a set of input objects is
collected and corresponding outputs are also
collected.
(iii) Decide the input feature depiction of the
learned function. The accuracy of the
learned function relies sturdily on how the
input object is represented. Normally, the
input object is altered into a feature vector
that contains a number of features that are
descriptive of the object. The number of
(iv) Decide the structure of the learned
function and corresponding learning
algorithm.
(v) Complete the design. Run the learning
algorithm on the gathered training set. Some
supervised learning algorithms need the user
to find out certain control parameters.
(vi) Assess the accuracy of the learned
function. After parameter adjustment and
learning, the performance of the resulting
function should be measured on a test set
that is separate from the training set.
Unsupervised learning: It is the machine
learning task of inferring a function to depict
concealed structure from "unlabeled" data.
Since the examples specified to the learner
are unlabeled, there is no assessment of the
accuracy of the structure that is output by
the relevant algorithm—which is one way of
distinguishing unsupervised learning from
supervised learning and reinforcement
learning. A central case of unsupervised
learning is the problem of density estimation
in statistics [1].
Reinforcement learning: A computer
program interacts with a vibrant
environment in which it must perform a
certain goal. The program is provided
feedback in terms of rewards and
punishments as it navigates its problem
space.
III. MACHINE LEARNING
ALGORITHMS
There are number of machine learning
algorithms such as Linear Regression,
Logistic Regression, Decision Tree, SVM
[2], and KNN. Linear Regression is used to
estimate real values (cost of houses, number
of calls, total sales etc.) based on continuous
variable(s). Here, we establish relationship
between independent and dependent
variables by fitting a best line. Logistic
Regression is used to estimate discrete
values based on given set of independent
variable(s). In simple words, it predicts the
probability of occurrence of an event by
fitting data to a logit function.Decision Tree
is a type of supervised learning algorithm
that is mostly used for classification
problems.SVM is a classification method. In
this algorithm, we plot each data item as a
point in n-dimensional space (where n is
number of features you have) with the value
of each feature being the value of a
particular coordinate.K nearest neighbors is
a simple algorithm which stores the entire
available cases and classifies new cases by a
Fig.1: Machine learning algorithms
IV. MACHINE LEARNING
APPLICATIONS
Machine learning algorithms are widely
used in variety of applications like digital
image processing(image recognition)[5], big
data analysis[4], Speech Recognition,
Medical Diagnosis, Statistical Arbitrage,
Learning Associations, Classification,
Prediction etc.
V.CONCLUSION
The article illustrates the concept of machine
learning with its tasks and applications. The
article also highlights the various types of
learning such as supervised learning,
unsupervised learning and reinforcement
learning. In this article a detailed procedure
for solving a problem using supervised
learning has also been discussed..
VI. REFERENCES
1. Talwar, A. and Kumar, Y., 2013. Machine
Learning: An artificial intelligence methodology.
International Journal of Engineering and Computer
Science, 2, pp.3400-3404.
2. Muhammad, I. and Yan, Z., 2015. Supervised
Machine Learning Approaches: A Survey. ICTACT
Journal on Soft Computing, 5(3).
3. Singh, S., Kumar, N. and Kaur, N., 2014. Design
Anddevelopment Of Rfid Based Intelligent Security
System. International Journal of Advanced Research
in Computer Engineering & Technology (IJARCET)
Volume, 3.
4. Sharma, D., Pabby, G. and Kumar, N., Challenges
Involved in Big Data Processing & Methods to Solve
Big Data Processing
Problems.IJRASET,5(8),pp.841-844.
5. Kumar, N. and Gupta, S., 2016. Offline
Handwritten Gurmukhi Character Recognition: A
Review. International Journal of Software
Ms. Diksha completed her
B.Tech from Chitkara University, Himachal
Pradesh in the stream of Electronics and
Communication Engineering. She is now
planning to pursue Masters in science from
abroad.
Mr. Neeraj Kumar is
presently working as Assistant Professor in
Electronics and Communication
Engineering Department at Chitkara
University, Himachal Pradesh, India. He has
more than 6 years of teaching experience.
His area of interest is digital image
