Machine Learning (ML) is a Programming Model which is quite good and faster. It helps in taking better decisions where domain knowledge is an important aspect. The Machine Learning models require some data and probable outputs if any and develop the program using the computer. The most popular and significant field in the world of technology today is machine learning. Thus, there is varied and diverse support offered for Machine Learning in terms of frameworks and programming languages. Ph.D. Assistance serves as an external mentor to brainstorm your idea and translate that into a research model. Hiring a mentor or tutor is common and therefore let your research committee known about the same. We do not offer any writing services without the involvement of the researcher. Learn More: https://bit.ly/3dcke6F Contact Us: Website: https://www.phdassistance.com/ UK NO: +44–1143520021 India No: +91–4448137070 WhatsApp No: +91 91769 66446 Email: info@phdassistance.com

1. MACHINE LEARNING
TECHNIQUES IN PYTHON
DISSERTATION
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, Phdassistance
Group www.phdassistance.com
Email: info@phdassistance.com

2. In-Brief
Introduction
How python is used in ML
ML methods for Python
Machine Learning Python Packages
Future ML topics in Python
Conclusion
Outline
TODAY'S
DISCUSSION

3. Machine Learning (ML) is a programming model which is quite good and faster. It
helps in taking better decisions where domain knowledge is an important aspect. The
Machine Learning models require some data and probable outputs if any and develop
the program using the computer. Better decisions can be made using this model in the
future and predictable outputs can be obtained from new inputs. Machine Learning
model can be developed and deployed easily and an intelligent system is built
around it that can not only monitor devices but it can be able to proactively determine
potential issues and even fix those issues before it crops up.
In-Brief

4. The most popular and significant field in the world of
technology today is machine learning.
Thus, there is varied and diverse support offered for
Machine Learning in terms of frameworks and
programming languages.
There are libraries available in ML for almost all
accepted l anguages like Julia, C++, Python, R, Scala,
etc.
Python is considered an appropriate language for
Machine Learning
Contd...
Introduction

5. Python- ML network has a broad collection of libraries that allow the developers to
perform data extraction, transformation, and data wrangling process, and in
applying robust ML algorithms and also in developing traditional algorithms easily.
These ML libraries include Numpy, pandas, scipy, scikit-learn, TensorFlow,
statsmodels, Keras, and so on.

6. To implement the machine learning model, a
programming language must be used that is flexible,
stable, and has available tools.
Python has all such facilities, which is why python is used
in most ML projects.
From improvement to use and maintenance, Python
helps software developers to be confident and productive
about the software they are developing.
Contd...
How
python is
used in ML

7. Python has a lot of advantages that make it the best fit for machine learning and
AI-based dissertation like simplicity and stability, access to vast frameworks and
libraries machine learning, platform independence, flexibility, and a wide
community.
Python provides brief and readable code.
While many versatile workflows and complex algorithms stand following machine
learning, the simplicity of Python allows developers to develop consistent systems.
Contd...

8. Developers put their whole effort into solving an ML crisis instead of focusing on
the scientific touch of the language. In addition to this, many developers feel that
Python is very attractive as it is simple to learn.
Python code is easily understandable, which makes it the easier language to
develop models for machine learning.
Contd...

10. ML Methods
for Python
a. Supervised Learning: Supervised learning methods
or algorithms are the most frequently used ML
algorithms.
This algorithm or learning method gets the data
sample i.e. the training data and its related output.
The main aim of the supervised learning method is
to discover a relationship between input data
samples and their corresponding outputs after
completing various training data instances.
Ml methods can be classified based on some wide
categories:

12. b. Unsupervised Learning: This learning method is opposite to supervised ML
algorithms. In this learning method, there will not be any supervisor to
grant any kind of guidance.
c. Semi-supervised Learning: These kinds of methods are neither completely
supervised nor completely unsupervised. They mostly fall between the
two categories i.e. supervised and unsupervised learning methods.

14. There are plenty of open-source libraries that are
accessible to assist in practical machine learning.
These are primarily recognized as technical Python
libraries and are generally used while performing
simple machine learning tasks.
These libraries, at a high level, can be divided into
data analysis and core machine learning libraries
based on their purpose.
Contd...
Machine
Learning
Python
Packages

15. i. Data analysis packages: These sets of packages offer us the scientific and
mathematic functionalities that are necessary to do data preprocessing and
transformation.
ii. Core Machine learning packages: This set of packages provides all the
essential machine learning methods and functionalities that can be useful on a
given dataset for extracting the patterns.
There are four key data analysis packages that are most commonly used for
data analysis. NumPy, Matplotlib, SciPy, Pandas. Pandas, Matplotlib, and
NumPy play a key role and are used to perform almost all data analysis tasks.

16. 1. A novel function for Design of VMware vSphere
Automatic Operation and Maintenance System Based
on Python
VMware vSphere automatic operation and maintenance
work use Python features that are smart, highly-efficient,
and simple, and also merges with powerful functions of
modules such as pyVmomi, pysphere, and MySQLdb,
and with VMware API support.
Contd...
Future
ML topics
in Python

17. 2. An effective performance for Pulse-Frequency Modulation Signal Generation for
Programmable Logic used by Python and VHDL system
The development and design of a signal generator circuit toolbox of pulse-frequency
modulation (PFM) targeting the programmable logic device (PLD) are provided in this
paper.
3. A novel method for Machine Learning for Multi-objective Evolutionary Optimization in
Python for EM Problems
EM problems are optimized using highly efficient algorithms which are based mostly on
Python libraries and used successfully in the development of antennas.
Contd...

18. 4. New-fangled mechanism for Development of Control Target Recognition intended for
Autonomous Vehicle used by FPGA with Python
The FPGA Design Competition is used in autonomous vehicles on miniature roads. The
ROS-based autonomous vehicle has been developed to be executed on an FPGA board
as a mock car.
5. An innovative method for NFDMLab: Simulating Nonlinear Frequency Division
Multiplexing in Python
Fiber-optic transmission is based on nonlinear frequency division multiplexing (NFDM).
NFDMLab is an open-source software used to stimulate transmissions on NFDM in
Python.
Contd...

19. Python provides a good blend of specialized packages,
and functionalities containing m achine learning m
ethods/algorithms.
Python is a language that has been used often for
generating compact and readable code.
Python has many libraries for statistical analysis, data
manipulation, machine learning, data visualization, and
deep learning.
The important libraries include Pandas, NumPy, SciPy,
Seaborn, Matplotlib, TensorFlow, and Scikit Learn.
Contd...
Conclusion

20. These libraries have made machine learning integrate easily with Python and are
also simpler.
As the Python community is growing quickly, multiple ML algorithms can be used
with Python in the future.
Linear Regression, Decision Tree, Logistic Regression, Naive Bayes, k-NN,
Random Forest, Gradient Boosting Algorithms like GBM, XGBoost, LightGBM,
CatBoost are the recent algorithms that can be applied to almost any data problem.

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