Naila Nausin
MIG 1/81 Hudco Bhilai, Chhattisgarh
� +91 9340984243
#nausinnaila@gmail.com �
linkedin.com/in/nausinnaila �
github.com/NAUSINNAILA Summary
Highly motivated professional with a robust educational foundation in both Data Science and Software Engineering.
My unique skill set, seamlessly integrating expertise in Data Science and Software Engineering, positions me as a
versatile and innovative contributor in the field of computer science.
Education Dhirubhai Ambani Institute of Information and Communication Technology
2022 &ndash; 2024
M.Tech. in Machine Learning CPI:7.36
Bhilai Institute of Technology, Durg 2017 &ndash; 2021
B.Tech. in Information Technology CPI:8.54
Experience Knowledge Gate Eduventure - LLP
June 2021 &ndash; July 2022
Content and Tech Writer Hybrid
I collaborated with educators and designers to understand feature requirements and designs, contributing to the
creation of accurate documentation. I played a vital role in analyzing and preparing performance reports, offering
valuable insights into user experiences. This role honed my skills in collaborative communication, documentation,
and data analysis in a dynamic hybrid work environment.
Seed IT Solution, Bhilai May 2019 &ndash; August 2019
Vocational Training Bhilai, Chhattisgarh
During my vocational training at Seed IT Solution, Bhilai, I undertook the responsibility of developing and
implementing a user-friendly alert system. This involved utilizing embedded C programming with a focus on IoT
concepts. Engaging in the end-to-end development process, from conceptualization to execution, I coded in
embedded C to facilitate seamless communication between devices. Leveraging IoT principles, I ensured efficient
data exchange and real-time monitoring. Additionally, I integrated sensors and actuators to enhance the alert
system&rsquo;s responsiveness and reliability.
Technical Skills Languages
: C/C++, Python, HTML/CSS, SQL
Developer Tools &amp; IDEs : Jupyter-Notebook, Google-Colab, Visual Studio Code.
Technologies/Frameworks : Git/ GitHub, Plotly, Scikit-Learn, Matplotlib, Pandas, NumPy, TensorFlow, Keras.
Knowledge of Core Sub jects : Machine Learning, Deep Learning, Natural Language Processing, Information Retrieval,
Operating Systems.
Leadership / Extracurricular Teaching Assistant (TA)
August 2022 &ndash; Present
As a Teaching Assistant (TA), I played a crucial role in supporting the teaching and learning process,
collaborating closely with professors and instructors to enhance the academic journey of students. In my first
semester, I served as a TA for &ldquo;Language and Literature&rdquo; under Prof. Amishal Modi for B.Tech first-year
students. This involved conducting labs and tutoring a sizable batch of 360 students, ensuring a clear
understanding of the covered concepts. In subsequent semesters, I took on the role of a TA for &ldquo;Software
Engineering&rdquo; (IT-314) under Prof. Saurabh Tiwari for B.Tech Pre-final year students. My responsibilities
included conducting quizzes, labs, and tutoring, as well as grading assignments, providing constructive
feedback, facilitating discussions, and group work, and offering mentorship and support to students.

Technical Pro jects
Multi-class fake news detection using IR Models
|Python October 2023
In October 2023, our pro ject focused on combating the rising tide of fake news in the digital realm. We aimed to
assess the efficacy of advanced language models&mdash;specifically GPT-2, Recurrent BERT, and RoBERTa&mdash;in
detecting fake news within the English language context. Leveraging the transformative capabilities of models like
BERT and RoBERTa, we scrutinized their performance, ultimately finding that GPT-2 exhibited superior
classification abilities for English news. Our evaluation extended to practical application, testing the model on news
from the Politifact website, where it demonstrated moderate effectiveness according to the &rdquo;truth-o-meter.&rdquo; This
work contributes to the ongoing endeavors to enhance fake news detection using state-of-the-art language models.
Probabilistic Forecasting using deep learning models |Python July 2023
In this pro ject, we addressed the challenges presented by the chaotic and uncertain nature of real-world time-series
data. Recognizing the limitations of single-point forecasts in capturing the inherent complexities, our ob jective was
to enhance reliability by obtaining a narrow interval for future value estimation. Leveraging deep learning models in
Python, we focused on quantifying uncertainties within the data. The methodology involved advanced techniques
to navigate the chaotic and random aspects of the time-series data, ensuring a more accurate and nuanced
forecasting approach.
Collaborative-based recommendation system on Movie lens dataset(100k) |Python February 2023
I undertook the development of a collaborative-based recommendation system using the Movie Lens dataset (100k)
with the primary goal of analyzing and evaluating data within the movie domain. The pro ject involved the creation
of a collaborative system, encompassing the writing of code and utilizing exploratory data analysis (EDA)
techniques to extract valuable insights from the MovieLens dataset. Through this initiative, the aim was to enhance
the understanding and utilization of collaborative filtering methodologies for effective and personalized movie
recommendations.
Classification on MNIST-fashion dataset |Python October 2022
In October 2022, I engaged in a pro ject focused on image classification using the MNIST-Fashion dataset. The
ob jective was to design a robust model incorporating various layers to extract features from images and facilitate
accurate classification into distinct fashion categories. Employing a convolutional neural network (CNN)
architecture, the pro ject aimed to leverage the power of deep learning to enhance the model&rsquo;s ability to discern
intricate patterns within the dataset, contributing to effective and precise classification outcomes.