Advanced Research Techniques: Python's Use in AI and Machine Learning

This report investigates the usefulness of Python in associating Artificial Intelligence (AI) and Machine Learning (ML). It begins with a literature review, defining machine learning, artificial intelligence, and Python, and then delves into the reasons for choosing Python for AI and ML projects, including its use of less code, prebuilt libraries, support, platform agnosticism, flexibility, and popularity. The report also discusses the advantages and disadvantages of using Python in AI and ML. The research methodology section details the research philosophy (positivism), research approach (qualitative and inductive), research design (descriptive), data collection process (secondary research), and ethical considerations. The study uses secondary data from books, online portals, journals, newspapers, and magazines to support its findings. Overall, the report provides a comprehensive overview of Python's role in AI and ML, highlighting its benefits and limitations, and outlining the research methods used to investigate the topic.

Running head: ADVANCED RESEARCH TECHNIQUES
Advanced Research Techniques
Name of Student-
Name of University-
Author’s Note-

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Topic:
Usefulness of Python in associating Artificial Intelligence and Machine learning
1. Literature Review:
1.1 Definition of machine learning, artificial intelligence and python
Machine learning:
As per [4], machine learning is the subset of Artificial Intelligence that states the ability
of a machine to learn in an automatic way. This automatic learning is done by including large
amount of data as well as helps to improve the experience of the users associated with the
project. Machine learning includes practice that includes machine for solving the problems for
gaining ability to think. In machine learning, if a good amount of data in provided to the system,
the system will automatically learn about how to improve the data as well as interpret the data. It
can also process the data and analyze the data that are used in the system using the concept of
machine learning.
Artificial Intelligence:
As stated by [8], the term of artificial intelligence can be termed as the science that gets
the machine to think about like humans and helps them ti make decisions similarly the human
takes. This was accomplished by Artificial Intelligence by creating robots or machines that are
able to be used in wide range [7]. The concept of AI is used in health care sectors, marketing, as
well as the business analytics. There are actually three types of AI available; artificial narrow
intelligence, artificial general intelligence, and artificial super intelligence. This is referred to as
any type of programming that helps to sort the machine learning.

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Python:
This is a programming language that helps to interpret, the languages as well as includes
dynamic semantics of high level languages. The python is considered as the data structures that
combines along with the dynamic typing feature [4]. The python includes dynamic binding as
well. The python programming also includes Development of Rapid Application as well as
includes scripting as well as language for connecting the existing components. The python
language includes supports from the module as well as includes packages that helps to encourage
the modularity of the program as well as helps in code reuse.
1.2 Reasons to choose python for AI and machine learning projects
[6] stated that for a startup company or for large company, python with AI and machine
learning helps to provide large number of benefits. Python is used in such a way that it is not
only limited to only one activity. The concept of python is used in more than one sector. The
popularity of python has allowed them to enter some of the popular as well as complex processes
of AI and ML. The reasons that explains the reasons to choose the python programming for the
AI as well as machine learning are explained below:
1. Uses less code: The AI concept include many algorithms and python helps the
algorithms to run easily and also helps to test the programs very easily [6]. Python is one of the
best competitors in AI. The concept of python also helps to write easily and helps to execute the
codes. Python helps to write easily as well as helps to implement logic within the code.
2. Uses prebuilt libraries: The language of python includes lot of programming libraries
that is needed for the AI project. Few of the libraries for python is scientific computation are

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Numpy, Scripy or AIIMA. The python has dedicated library so that it can be used for developing
the algorithm in the system.
3. Provides support: Python helps to open source with proper community. There are
many resources host that are available with python and those are included in the system [8]. The
host of resources includes developer to speed the programming in no time. There is huge
community in that includes the code of python in developing the AI programs.
4. Platform Agnostic: The concept of python in the AI and in ML helps flexibility to
provide the language in using the system. This platform of python is flexible and is a platform
independent. By changing few codes in the programming, the application might work in new
environment.
5. Very much flexible: The main advantage that this language provides is the flexibility of
programming. This programming can help to choose between the object oriented approaches or
can include scripting language in the system [3]. The best suitable language system is python.
This language works perfectly in the backend as well as is also suitable for linking different sets
if data structure in the system.
6. Very popular: With all the features provided by python, it has become very much
popular and many industries have starting taking the advantage of python along with AI and ML
[2]. the projects that are in AI mainly needs high experienced programmer and this can include
learning curve in the system. It is the extended libraries as well as includes active community
with developing the project code and improving the project code.

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1.3 Advantages and disadvantages of using python in AI and machine learning
The benefits that python programming language provides to the organizations to integrate
AI and LM are:
1. The language of python is very much versatile to use, and is easy to use. This language
is very fast to develop as well.
2. The python language is considered to be an open source that includes a vibrant
community.
3. The python language has wide number of libraries that helps to make the work much
easier. With the libraries, the programmer can do more work from the web [5].
4. Python language include wide applicability as well as are used by the scientists,
engineers as well as mathematicians very widely. The python language is so used in the project
to create good prototype.
The disadvantages that python programming language provides to the organizations to
integrate AI and LM are:
1. The speed of python is limited.
2. The python language includes problem of threading that implementing multi thread
CPU programs makes the system slow.
3. The biggest disadvantage of python is that it is not available for the mobile
environment. Python language cannot be used for mobile applications [1].
2. Research Methodology:

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2.1 Research Philosophy:
The research philosophy is the way that specifies the phenomena that helps to carry out
the data that is gathered for the research, analyze the data as well as use the data for the research
[15]. The philosophy of research mainly deals with the source of knowledge, the nature of the
knowledge that is required to carry out the research and the process of developing the knowledge
needed to carry out the research. This is considered as the belief that includes the ways where the
data includes the phenomena that are collected as well as analyzed in the research.
The research philosophy that is used in the research includes positivism research. This
states that the research that is carried out is stable and this can be observed as well as described
from the viewpoint of the objective that is derived in the project. The positivism includes
phenomena that are to be isolated and all operations that are included are also repeatable in the
project.
2.2 Research Approach: The approach of research includes the plan as well as
procedure of the research that includes steps of broad assumptions for detailing the data
collection method that is carried out in the research [12]. This approach is also dependent on the
research analysis as well as research interpretation that are included in the system. The research
approach is mainly dependent on the research problem that are being addressed. Approach of
research is divided in to two parts, approach for data collection and the approach for data
analysis.
Approach of data collection helps to collect the meaning of the participants and helps to
focus on the phenomena of the research. This approach helps to validate the accuracy of the
findings and makes data interpretation. The approach that is used for collection data is

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qualitative approach. This includes constructive knowledge and includes phenomenology, case
study, and includes grounded theory.
Data analysis approach that is included in the research is inductive approach. This
approach provides emphasis on all methods that helps to collect as well as helps to generate the
data that are used in the project [11]. The inductive approach includes less emphasis on
analytical techniques so that the data interpretation can be taken place. This approach uses
detailed secondary data reading so that concepts can be derived along with models of research
and themes of research.
2.3 Research Design:
Research design includes framework method as well as techniques that are mainly chosen
by the researcher that combines different research components in logical manner to handle the
research problem in the research being carried out [15]. This design provides insights to conduct
the research with some particular methodology and research question is addressed within the
research design. There are five types of research design: descriptive design, experimental
design, correlational design, diagnostic design and explanatory design.
The research design for this research paper is descriptive design. This helps to describe
the particular case or the situation that is under the research case study. The descriptive research
design is basically theory based research design that helps to gather data, analyze the data that is
collected as well as helps to present the data that are collected [10]. This research design in
considered to be as the in depth design where the researchers helps to provide the insights about
how the research is carried out and why the research is carried out.

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2.4 Data Collection Process: The process of data collection includes colleting all the
information that are associated with the project and the information should be relevant. The
relevant sources helps to find all the answers that are can address the research problem, helps to
test hypothesis of the research, as well as evaluate the research outcome [14]. The methods that
are included in data collection are mainly categorized in two sections: secondary method as well
as primary method. The method that will be addressed in this research paper to collect data will
be secondary research.
This method of collecting data includes data that are already being published by various
authors and researchers. This method includes published data of books, online portals, journals,
newspapers as well as magazines [13]. The online sources have large variety of data related to
the topic about how python helps to link the artificial intelligence with machine learning. Some
set of criteria will also be used for selecting the secondary resources online which would include
the publication date, author credentiality, the quality of the paper or the article, and the source
reliability from where the article or paper has been taken.
2.5 Ethical Consideration: The ethics that are to be considered for carrying out a
research project includes some rules as well as includes the expectations of the researchers and
the reader’s behavior [16]. Research ethics are important to consider for a research study as
because they helps to promote the aim of the research and needs to support all such values that
are needed for collaborative work. The ethical considerations that are to be considered are:
1. The Research participants involved in the project should not be harmed while carrying
out the project.

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2. Respect should be provided to all the research participants and they should be
prioritized.
3. There should be full content of the respondents participating the research project.
4. The research respondents should be provided with proper protection and that has to be
ensured.
5. The research should include accurate confidentiality level of the data that are collected
and that also should be ensured [9].
6. Anonymity of the individuals as well as the organization from where the data will be
collected should be kept.
7. The aims and the objectives of the research study should not be changed.
8. The funding source and the affiliation forms are to be declared at the starting the
project research paper.
9. The communication that are associated in the project should be done honestly.
10. Biased data should be excluded from the research data.

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References
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the curse of imbalanced datasets in machine learning. The Journal of Machine Learning
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[3] E., Charniak, Introduction to deep learning. The MIT Press, 2019.
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[11] S.J., Taylor, R., Bogdan, and M., DeVault, Introduction to qualitative research methods: A
guidebook and resource. John Wiley & Sons, 2015.
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