A Review on Machine Learning Algorithms, Tasks

International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 6, Issue 10, October 2017, ISSN: 2278 – 1323

A Review on Machine Learning Algorithms, Tasks and Applications Diksha Sharma1, Neeraj Kumar2

learning tasks and problems and various ABSTRACT: Machine learning is a field of

machine learning algorithms.

computer science which gives computers an

Keywords: Machine learning, supervised

ability to learn without being explicitly

learning,

programmed. Machine learning is used in a

classification

variety

of

designing

computational and

tasks

programming

where 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

unsupervised

learning,

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 advances, systems which performed at

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noticeably below-human levels can now go

 Supervised learning

better than humans at some definite tasks.

 Unsupervised learning

Many people now cooperate with systems

 Reinforcement learning

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

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:

can assist doctors give more correct or

(i) Decide the kind of training examples.

efficient diagnosis for definite conditions.

The user should decide what kind of data is

For public services it has the potential to

to be used as a training set.

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

(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.

biology, physics & medicine.

(iii) Decide the input feature depiction of the II. MACHINE LEARNING TASKS Machine

learning

classified

into

tasks

three

are

broad

typically categories,

depending on the nature of the learning "signal" or "feedback" available to a learning system.

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 features should not be too large.



(iv) Decide the structure of the learned

punishments as it navigates its problem

function

space.

and

corresponding

learning

algorithm.

III. MACHINE LEARNING ALGORITHMS

(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.

There are number of machine learning algorithms such as Linear Regression, Logistic Regression, Decision Tree, SVM

(vi) Assess the accuracy of the learned

[2], and KNN. Linear Regression is used to

function. After parameter adjustment and

estimate real values (cost of houses, number

learning, the performance of the resulting

of calls, total sales etc.) based on continuous

function should be measured on a test set

variable(s). Here, we establish relationship

that is separate from the training set.

between

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

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

in statistics [1].

of each feature being the value of a Reinforcement

computer

particular coordinate. K nearest neighbors is

vibrant

a simple algorithm which stores the entire

environment in which it must perform a

available cases and classifies new cases by a

certain goal. The program is provided

majority vote of its k neighbors.

program

feedback

learning:

interacts

in

terms

with

of

A a

rewards

and



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

Fig.1: Machine learning algorithms IV. MACHINE LEARNING APPLICATIONS

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

Machine learning algorithms are widely used in variety of applications like digital

in Computer Engineering & Technology (IJARCET) Volume, 3. 4. Sharma, D., Pabby, G. and Kumar, N., Challenges

image processing(image recognition)[5], big

Involved in Big Data Processing & Methods to Solve

data

Big Data Processing Problems.IJRASET,5(8),pp.841-

analysis[4],

Speech

Recognition,

Medical Diagnosis, Statistical Arbitrage, Learning

Associations,

Classification,

844. 5. Kumar, N. and Gupta, S., 2016. Offline Handwritten Gurmukhi Character Recognition: A

Prediction etc.

Review.

International

Journal

of

Software

Engineering and Its Applications, 10(5), pp.77-86.



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 Engineering

and Department

Communication at

Chitkara

University, Himachal Pradesh, India. He has more than 6 years of teaching experience. His area of interest is digital image processing, digital signal processing.
