ABOUT ME
As I embarked on my journey to pursue a Master's degree, I was thrilled to delve into the world of Data Science. While the initial exposure to the plethora of concepts and ideas was challenging, I was determined to overcome this obstacle and embraced the learning experience with enthusiasm. I received my Bachelor's degree in Information Technology where I became fascinated with the potential of machines to make accurate predictions better than humans based on large amounts of data. This curiosity led me to gain expertise in Python, SQL, R, and various core machine learning and deep learning algorithms.
The opportunity to intern at Fidelity Investments as a Data Scientist also honed my skills in ETL pipelines, AWS, and collaborating with cross-functional teams. This experience has allowed me to approach problems with a strategic mindset, and drive innovative solutions that positively impact businesses and stakeholders.
In my free time, you can find me exploring new datasets, reading research papers on the latest techniques, and working on personal projects that stretch my abilities. I'm excited to continue growing and pushing boundaries in the field of data science, and I can't wait to see what the future holds!
Any Questions?
Why did you make this website?
Firstly, I wanted to showcase my work and enthusiasm for the field of Data Science in an appealing and accessible way.
Additionally, I saw this as an opportunity to expand my skill set by learning more about website design and creation. As a Data Scientist,
I believe that having these skills is increasingly important, especially as we work more closely with
cross-functional teams to build and deploy end-to-end Machine Learning products.
It's essential to understand the technical infrastructure necessary to
put a product into production. By gaining experience in website creation, I hope to
better understand these important considerations and become a more effective member of
cross-functional teams.
How long have you been practicing Machine Leanring?
I first became familiar with the term 'Machine Learning' during my
undergraduate studies in 2020, which happened to be during the pandemic. Since then, I've been
dedicated to learning as much as possible about the field.
I've utilized a variety of resources,
including articles on Medium, research papers, and data science courses and books, to develop
my understanding of key concepts and techniques. Within my specialization in Data Science, I've
built models for a variety of use cases, such as recommendation systems, outlier detection, and
clustering. Additionally, I've worked extensively with NLP techniques like sentiment analysis,
named entity recognition, and topic modeling, among others.
Why are you interested in NLP/Machine Learning?
Large language models (LLMs) are an incredibly exciting development
in the field of natural language processing (NLP). These models are designed to understand
the nuances of language in a way that was previously impossible, and they have the potential
to transform the way we interact with machines.
At the same time, LLMs are just one part of the larger NLP landscape. NLP encompasses a
wide range of techniques and approaches, from text classification and named entity recognition
to machine translation and speech recognition. What makes NLP so fascinating is that it requires
a deep understanding of human language, which is incredibly complex and context-dependent.
Overall, I'm really passionate about NLP because it's such an exciting and rapidly evolving field,
and I believe that the insights and innovations that are emerging from this work have the potential
to transform the way we communicate and interact with technology.
What kind of work do you see yourself doing?
I see myself working in a team where I can contribute my skills and knowledge
to deliver end-to-end machine learning solutions that are integrated with existing production systems.
This would require a strong understanding of MLops, data engineering, machine learning, and statistics,
as well as the ability to collaborate with cross-functional teams such as data scientists, software
engineers, and business stakeholders.
I am also very interested in building machine learning products that can help businesses and
stakeholders understand how they can benefit from the predictions and insights generated by machine
learning models. This would require me to be able to effectively communicate complex technical
concepts to non-technical stakeholders and to be able to tailor my solutions to meet the
specific needs of the business.