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"Open Source Models with Hugging Face" course empowers you with the skills to leverage open-source models from the Hugging Face Hub for various tasks in NLP, audio, image, and multimodal domains.
A real-time voice-to-text and text-to-speech AI pipeline using Whisper, an LLM, and Edge-TTS with tunable parameters for low-latency audio processing and response generation.
Spam Detector is a Data Science Project built using Pytorch and Hugging Face library. Used BERT model based on Transformer Architecture and got 99.97% accuracy on train set and 98.76% accuracy on test set.
Build a sentiment analysis tool that processes user reviews from various platforms (like Amazon or Yelp) and provides insights on sentiment trends over time. Use advanced NLP techniques like Transformers (BERT, GPT).
A FastAPI-powered REST API offering a comprehensive suite of natural language processing services using machine learning models with PyTorch and Transformers, packaged in a Docker container to run efficiently.
A web-based utility for fetching, categorizing, summarizing and managing global news and articles using the GDELT 2.0 API. Designed for content creators, news aggregators, and researchers, this tool simplifies access to up-to-date articles with an intuitive UI and customizable configurations.
Deployed an interactive web platform for exploring and utilizing language models. Features include real-time text analysis and translation, built with Django for robust performance and scalability
Successfully developed a fine-tuned BERT transformer model which can accurately classify symptoms to their corresponding diseases upto an accuracy of 89%.
Successfully developed a fine-tuned DistilBERT transformer model which can accurately predict the overall sentiment of a piece of financial news up to an accuracy of nearly 81.5%.