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Anjila S - Django developerLD Talent logo

Anjila S

The star rating is a representation of the overall rating, calculated as the mean of the client satisfaction rating, the average client interview rating, and internal interview scores.

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Data Scientist Preferred Title
$19.00 /hr $ 17.0K /yr Hourly Rate and Yearly Salary
Overview Experiences Qualifications Vetting
Overview
Basic Summary
LD Talent History
  • Average Count of Messages/Day 0.4
  • Project Completion Rate 2/4
  • Interview Acceptance Rate 4/6
  • Timezone Overlap 21h PST, 28h UTC
Vetting Summary
  • No. of Onboarding MCQs Completed 6
  • More details
Interview Scores (3.2/5)
  • Software Engineering Process 3.0/5
  • Design Practice 3.0/5
  • Design Theory 3.0/5
  • Technical Breadth 3.0/5
  • Logical Thinking 4.0/5
  • Technical Strength 4.0/5
  • System Design 3.0/5
  • Productivity and Responsiveness 3.0/5
  • Teamwork 3.0/5
  • Agile Development Process 3.0/5
  • Intellectual Merit 3.0/5
  • English Communication 3.0/5
  • Documentation 3.0/5
Talent's Devices
  • Desktop Windows
  • Phone iPhone
General
  • Member Since Mar 17, 2024
  • Profile Last Updated Jun 17, 2024
  • Last Activity June 17, 2024, 8:41 a.m. UTC
  • Location Nepal
  • Current Status Student
Profile Summary
I am currently a third-year Btech in AI student here at Kathmandu University, Nepal. I am very enthusiastic about Data Science and Machine Learning. I have also collaborated in Omdena Local Chapters and Innovation challenges to hone my skills. With AI/ML I know backend Django RestFrameWork and Frontend basics HTML/CSS/JS.
Skills
Total Experience: 1+ years
  • Django (2E, 2Y)

    2 experiences, across 2 years
  • Convolutional Neural Networks (2Y)

    2 years of experience
  • Python (2Y, 1C)

    2 years of experience, with 1 course
  • Graphs And Networks (2Y)

    2 years of experience
  • Natural language processing (2Y)

    2 years of experience
  • LSTM (2Y)

    2 years of experience
  • BERT (2Y, 1C)

    2 years of experience, with 1 course
  • Transformer (2Y, 1C)

    2 years of experience, with 1 course
  • Deep Learning (Theory) (1C)

    1 course
  • Neural Networks (1C)

    1 course
Weekly Availability
Timezone Overlap with 06 - 21 per Week: 21h PST, 28h UTC
Day
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
UTC
12 - 16
12 - 16
12 - 16
12 - 16
12 - 16
12 - 16
12 - 16
PST
05 - 09
05 - 09
05 - 09
05 - 09
05 - 09
05 - 09
05 - 09
Vetting
  • Interview Data
  • Software Engineering Process
  • Design Practice
  • Design Theory
  • Technical Breadth
  • Logical Thinking
  • Technical Strength
  • System Design
  • Productivity and Responsiveness
  • Teamwork
  • Agile Development Process
  • Intellectual Merit
  • English Communication
  • Documentation
Experience
Machine Learning Engineer
Course Project
Apr 2023 - Apr 2024
Kathmandu University Company
Technology Industry
Project: Sheild Talk
  • LSTM
  • Django
  • Natural language processing
  • BERT
  • Transformer
  • Sheild Talk was a full-stack web project with Django as the backend. We used NLP to make our network from the very scratch and learned how to make Natural language processing networks.
  • We used Bidirectional LSTMs for toxicity classification because they capture the context and dependencies in text data, which can be difficult to model with other models.
  • We then upgraded our model with a Transformer i.e. BERT, We finetuned the model and deployed the model into the streamlit. I learned how to integrate and finetuned the transformer according to need.
Github Links
  • https://github.com/Anjila-26/ShieldTalk
  • https://github.com/Anjila-26/Toxicitity_Pretrained
Machine Learning Engineer
Passion Project
Feb 2023 - Mar 2024
Shequal Foundation Company
Technology Industry
Project: SignBloom
  • Convolutional Neural Networks
  • Graphs And Networks
  • Python
  • Django
  • SignBloom was a full-stack project with Frontend and Backend integrated. It Started as a hackathon Project where we were able to integrate the AI/ML with the Django(Python) backend.
  • I was able to make a Convolutional Neural Networks model that could correctly classify up to 10 sign language letters/ as in images and use that to predict the sign in real-time.
  • To Upgrade this project, We took this as and Semester project and used GAT(Graphs and Networks) i.e Graph Attention Network to capture similar patterns while training the collected images.
Github Links
  • https://github.com/Anjila-26/Signlanguagerecognition
Qualifications
Education
Kathmandu University
Feb 2021 - Dec 2025
B.tech (Artificial Intelligence)
Courses
Neural Networks and Deep Learning
Course Certificate Link
May 2023 - Jun 2024
DeepLearning.AI
  • Deep Learning (Theory)
  • Python
  • Neural Networks
Transformers and BERT
Course Certificate Link
Feb 2024 - Feb 2024
Google Cloud Training
  • BERT
  • Transformer
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