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parnerka/README.md

Hi 👋, I'm Pranav

Analytical machine learning/data science professional with a background in computer science, adept at delivering data-driven insights and solutions using statistical modeling, programming, and data visualization. Specialities include: machine/ deep learning, computer vision, time series analysis/forecasting, NLP.

parnerka

  • 🌱 I’m currently learning Advanced NLP, Generative AI, ML/DevOps, and Full Stack Web Development

  • 👨‍💻 All of my projects are available at https://parnerka.github.io/

  • 💬 Ask me about Computer Vision, Machine Learning, Data Science

  • 📫 How to reach me LinkedIn/Email

  • ⚡ Fun fact I am a songwriter/guitarist on the side

  • 👀 I'm actively looking for a Full-Time ML Engineer/Data Scientist role starting Jan 2025:)

Connect with me:

pranav-parnerkar

Languages and Tools:

aws bash bootstrap cplusplus csharp css3 d3js django docker dotnet express flask git heroku html5 java javascript linux mongodb mysql nodejs opencv pandas postgresql python pytorch react scikit_learn seaborn sqlite tailwind tensorflow typescript

parnerka

parnerka

Popular repositories Loading

  1. parnerka parnerka Public

    Config files for my GitHub profile.

  2. parnerka.github.io parnerka.github.io Public

    Forked from academicpages/academicpages.github.io

    Personal Website

    JavaScript

  3. CSCI-544-Project-Fall23-Group27 CSCI-544-Project-Fall23-Group27 Public

    Forked from jehilvora/CSCI-544-Project-Fall23-Group27

    Jupyter Notebook

  4. google-gemini-apps google-gemini-apps Public

    End-to-end applications powered by Google Gemini

    Python

  5. CV-EyeTracking CV-EyeTracking Public

    Pranav's code contribution during his internship at ARL-W. Includes gaze-based object detection GUI and screen tracking script.

    Jupyter Notebook

  6. Transformers-for-Language-Modeling-and-Sentiment-Analysis Transformers-for-Language-Modeling-and-Sentiment-Analysis Public

    Devised pre-training procedure for a BERT-style transformer model from scratch and achieved 92% test accuracy after fine-tuning for sentiment analysis.

    Jupyter Notebook