Importance of Machine Learning Report 2023

Running head: IMPORTANCE OF MACHINE LEARNING
Importance of Machine Learning
Name of Student-
Name of University-
Author’s Note-

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Executive summary
The below report deals with the details of machine learning that organization uses to automate
the work of the systems. This report includes a case study of JD Company who is the online
business retailer in Australia. The company wants to implement machine learning in the working
system and this report states all such information related to ML that would help the company to
implement ML in the working process. Proper definition of ML is provided so that the company
would get an understanding about how the ML would enhance it working process and would
help the company to improve its customer satisfaction.

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Table of Contents
1. Introduction..................................................................................................................................4
1.1 Brief Description...................................................................................................................4
1.2 Problems Identified................................................................................................................4
1.3 Main Purpose.........................................................................................................................4
1.4 Objectives..............................................................................................................................4
2. Definition of machine learning and its relationship with AI.......................................................5
2.1 Definition of Machine Learning............................................................................................5
2.2 Difference and relationship with artificial learning...............................................................5
3. Use of Machine learning in three different industries.................................................................6
3.1 ML used is healthcare industry..............................................................................................6
3.2 ML used is financial services industry..................................................................................7
3.3 ML used is automotive industry............................................................................................7
4. Adoption of machine learning in JD............................................................................................8
4.1 Process of implementing Machine learning in JD.................................................................8
4.2 Application of Machine learning in two functional areas.....................................................9
4.3 Advantages and disadvantage of applying machine learning................................................9
5. Ethical Legal and social issues of machine learning.................................................................10
5.1 Ethical issues.......................................................................................................................10
5.2 Legal Issues.........................................................................................................................10
5.3 Social Issues.........................................................................................................................11
6. Conclusion.................................................................................................................................11
7. Recommendations......................................................................................................................12
References......................................................................................................................................13

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1. Introduction
1.1 Brief Description: The case study that is outlined for applying the concept of
machine learning is JD online retailer. This organization is an Australian online retailer involved
in selling merchandise along with electronics, books and different apparel online to the
customers. The company has implemented online sales and has analyzed increase in sales and
thinks that with the adoption of machine learning in the business process, the company would be
able to increase its sales along with the customer satisfaction.
1.2 Problems Identified: The problem that is identified in its business process of JD is
that it was facing market pressure as because other companies was capturing the market and this
company was losing market share. Other companied in the same sector was using machine
learning that would attract more customers to them resulting in loss for JD.
1.3 Main Purpose: The main aim of this report is to implement the concept of machine
learning so that JD can increase its market share and will be able to increase its customer
satisfaction and increase its sales. This report discuses different features that would be required
to learn the advantage of machine learning on the company.
1.4 Objectives: The objective associated with this project are:
To study the definition of machine learning and their relationship with the artificial
intelligence.
To study three other different industries that uses the concept of machine learning to
enhance its business.
To observe the changes that JD might incorporate after implementing machine learning.
To investigate the legal, ethical as well as social issues of machine learning.

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To recommend the process that JD should follow to implement machine learning in their
business.
2. Definition of machine learning and its relationship with AI
2.1 Definition of Machine Learning: Machine Learning includes getting the computers
do the program by themselves without human help. In machine learning, the programming is
automated and this machine learning can be stated as automating the programming automation
process. In machine learning, the data is left to do the work that human does. The concept of
machine learning helps to make the program scalable. With traditional programming, the data
and the program is made to run on a system so that the machine can provide an output. While in
machine learning, the data and the output is made to run on a computer system so that it can
create a program. This program is then used for traditional programming.
There are many field of machine learning. The concept of machine learning is used for
web search which helps to rank the pages dependent on what the users frequently clicks on. The
machine learning is also used in the field of computational biology that helps in designing the
rational drugs dependent on the past experiments (Jiang et al., 2016, p. 34). The concept of
machine learning is also used in finance, e-commerce, robotics, social networking sites,
information extraction, debugging as well as in space exploration. All the component that are
involved in machine learning includes three specified algorithm, representation, optimization as
well as evaluation.
2.2 Difference and relationship with artificial learning: With both machine learning as
well as artificial intelligence becoming part of daily life, there are lots of difference between
them. Machine learning is considered as a part of artificial intelligence. Stated by Lu, Li, Chen,

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Kim and Serikawa (2018), machine learning is known as the study which involves computer
algorithms so that the computer programs runs automatically via experience. Machine learning is
one single way of achieving artificial intelligence (Ghahramani, 2015, p. 76). The concept of
artificial intelligence is a broader concept and is considered to imitate the abilities of human
intelligence. The concept of algorithms in machine learning provides limited version of human
intelligence. While with Artificial Intelligence, all the abilities of human intelligence are
included.
Machine learning is known as another concept of data mining (Russell, & Norvig, 2016,
p. 34). The data mining technique includes examining large database that are pre-existing as well
as extraction the new information that can be obtained from the database. The machine learning
includes technique of data mining.
AI is completely different from machine learning as well as deep learning. The
techniques of machine learning and deep learning can be considered as the sub-set of artificial
intelligence. AI can be defined as the computer program that acts like human brain. AI is
duplicate of human brain and works in similar way that human brain works as well as functions.
3. Use of Machine learning in three different industries
3.1 ML used is healthcare industry: Concept of machine learning is used in healthcare
checking wearable devices as well as includes sensors that helps to monitor the patients in real
time. There are millions of people who takes the helps of machine learning to monitor their
health and to keep a track on their health related issues (Kumar, Kabra, Mussada, Dash, & Rana,
2019, p. 88). The machine learning in the advanced devices helps to examine the vital signs of
disease in a human body through the sensors that are attached with the body. The information

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gathered by the sensors are then sent to the analytics center of machine learning where data are
analyzed. By this process the problems are detected in the body and also provides alert to visit
some specific healthcare professionals (Jordan & Mitchell, 2015, p. 67). The technology of
machine learning also on the other hand also helps medical experts to analyze the data so that the
trends can be identified so that might improve the health of the patient.
3.2 ML used is financial services industry: The concept of machine learning is also used
in the financial sectors. The banks as well as other business that are related to financial industries
uses the concept of machine learning so that they can improve the technology used in those
industries (Christensen, Nørskov, Frederiksen, & Scholderer, 2017, p. 99). The technology of
machine learning is mainly used for two main purposes; helps to identify the important sights of
data as well as helps to prevent any type of fraud involved in those sectors. This technology
helps in identifying the investment opportunities as well as helps the visitors to know about the
trade that is being carried out in the industry.
3.3 ML used is automotive industry: The machine learning concept is also used in
automotive industry that helps to take steps so that it can differentiate the capabilities to leverage
the concept of machine learning. The automotive industry also uses the analytics of big data for
improving the operations, the marketing strategies as well as includes enhance customer
experience before purchasing or after purchasing (Singla, & Sharma, 2019, p. 78). The predictive
analytics in machine learning helps the manufacturers to monitor as well as share all the vital
information related with the potential vehicle or are associated with part failures that deals with
dealerships, and reduces the cost of customer maintenance. Using the machine learning concept,
the trends as well as the patterns can be identified form large datasets related with the vehicle

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ownership. The networks of the dealers can also be optimized so that parts inventory of real time
can be optimized and improves the customer care service.
4. Adoption of machine learning in JD
4.1 Process of implementing Machine learning in JD: The organization of JD can adopt
the concept of machine learning by implementing four different used for machine learning. The
process of adopting machine learning are explained below:
1. Introducing some specialized roles of machine learning: JD should implement new
roles who are experts in data operations as well as ML engineers (Brynjolfsson, & Mcafee, 2017,
p. 88). Those people should be capable of building as well as developing the models of machine
learning in the business process of JD.
2. Implement specific success metrics of machine learning: As JD is a well-established
organization with good revenue, it can be considered as sophisticated organization. So, this
company should assign team priorities that that would be done by data science leads. The
company should also have more than one success metrics.
3. Approach of building ML model in different way: JD should not use common ways
such as Kanban way of software development or agile method of software development to adopt
machine learning in the business process. The company should understand that building of ML
model automatically does not starts working. This includes model deployment, different
operations to be carried out and monitor the process.
4. Build a checklist model that is robust: Along with other model-building checklists, the
company should have transparency as well as data privacy checklists (Chalfin et al., 2016, p. 18).
There should be data protection design in the checklists that are to be included by the company.

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4.2 Application of Machine learning in two functional areas: Two application areas that
JD can improve with the application of machine learning are explained below:
1. Improve recommendation engine: With the use of machine learning, the
personalization as well as recommendation engine of the company ca be improved. The data
analytics of machine learning can help the company to analyze all the online activities that
customer performs and recommendation is provided to the users on the basis of their search
(Carrasquilla & Melko, 2017, p. 56). The company would easily provide their customers with
product recommendations and can tailor to some customer group.
2. Improves personalization of content on company website: The machine learning the
business process of JD would help to personalize the content that are shown on the website of the
company. The algorithms in machine learning helps to find out the patterns that are based on
data and helps to process large amount of data that are structured as well as unstructured.
Personalization algorithms includes factors like most likely color and style, the image of the
product, or any other preferences based on the search of customer.
4.3 Advantages and disadvantage of applying machine learning:
The advantages of machine learning in an online shopping business includes (Librenza-
Garcia et al., 2017, p.76):
Helps to identify the patterns as well as trends that are related with the customer search.
Does not need any human intervention as because all analysis is done automatically.
Includes continuous improvement in terms of accuracy as well as efficiency.
Helps to handle multi-dimensional as well as multi variety of data in the organization.
Includes wide number of application that can perform machine learning.

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The disadvantages of machine learning in an online shopping business includes:
Data acquisition that includes massive data sets that are to be trained on.
Time needs to be given so that the algorithms of the system can learn as well as develop
so that the purpose of the organization is satisfied.
There also is high susceptibility of error by using machine learning in the system.
Machine learning also cannot interpret the results properly that are generated by the
algorithms of machine learning.
5. Ethical Legal and social issues of machine learning
5.1 Ethical issues: The ethical problem of machine learning includes the way that the
data are used in analysis. The concept of machine learning includes data from different sources
without the consent of the user. There is another ethical problems of machine learning. The
algorithms that are included in machine learning includes algorithms that could be black boxes
that the working process of the machine learning are not possible to understand. The decision
made by the machine learning is not possible to understand. It creates its own algorithm
depending on the data.
5.2 Legal Issues: The legal issues that are related with machine learning includes:
1. Responsibility and liability issues: With the decisions taken by the machine learning,
no one is accountable, liable or is responsible for actions or steps that are taken by the system.
2. Data Handling: In IoT system, there are many organizations that helps to collect, share
as well as helps to implement data that are related with each other. The organizations needs to
collect the data, store the data as well as process the data that are included in the system.

10IMPORTANCE OF MACHINE LEARNING
3. Privacy and Security: Machine learning deals with collecting haphazard data without
the consent of the user). This might lead to data privacy as well as data security with the data that
are being analyzed in the machine learning.
5.3 Social Issues: The social issues that are associated with the machine learning
includes:
1. Legislation: The legislation social issue are included in machine learning as because it
connects with the privacy as well as the anonymity related with the pubic domains. The ML
needs to follow the legislation that are applied to the specified industries.
2. Explainability: There are black boxes that states that the logic of data analyzing cannot
be specified in machine learning (Witten, Frank, Hall, & Pal, 2016, p. 34). It makes its own
algorithm and works accordingly. Interpreting the algorithm is not possible.
3. Privacy: The data included in the system of machine learning should be safe and
secured so that other might not use it. But the case is not so. For data analysis, data are taken
from the users who are not even aware of that their data is being analyzed.
6. Conclusion
From the above discussion, it can be summarized that machine learning is considered as
an important of information technology. Machine learning mainly states all the changes that the
system needs to perform some tasks that associated with the artificial intelligence. From the
above discussion it can be summarized that machine learning machine learning is directly
connected with the artificial intelligence that helps to provide the system to learn automatically
as well as helps to improve itself from its experience without manually programmed.

11IMPORTANCE OF MACHINE LEARNING
It can be analyzed from the above report that machine learning is most useful to establish
profitable business in this advance technology market. This report explains proper definition of
machine learning that would help the reader to get an idea about the concept of machine learning
and comparison of machine learning with that of artificial intelligence. This report also states use
of machine learning in different industries that state an example about how machine learning can
be used to enhance business sales. There is a particular case study that is taken in this report for
explaining the concept of machine learning in the organization. The organization that is taken in
online retailer who wants to introduce the concept of machine learning in it.
The organization JD wants to increase its sales and customer satisfaction with
implementing machine learning with AI. This report also explains how the company can adopt
machine learning explaining two of the functional areas that are included in the business process.
For the online retailers, the business needs to perform ethical, social as well as legal issues and
those are also discussed in the report above. Machine learning can be summarized as getting the
computers to program by themselves without involving human in programming.
7. Recommendations
For adopting the machine learning technology in the business process of JD, the
following seven steps are recommended to the development team.
1. Firstly, the problem that the organization is facing is to be articulated.
2. Consider different stages of adopting machine learning in the system.
3. Ensure a good quality of data in the company.
4. Preparing a gap between the business vision as well as technical vision.

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5. Exploring all the options for hiring a proper vendor to do all the job of adopting machine
learning.
6. Should also focus on replacing the model with time to time.
7. Deciding whether to include customer built algorithm.
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