- 🤖 Passionate about AI/ML, Deep Learning, and Computer Vision.
- 🔭 Currently working on a Machine Learning-based question-answering system and real-time transcription systems.
- ⚡ Exploring techniques like Transfer Learning, Natural Language Processing, and Reinforcement Learning.
- 📈 Enthusiastic about Data Science, Predictive Analytics, and AI Deployment.
- 🗨️ Feel free to ask me about ML frameworks, model optimization, or scalable AI systems.
- Supervised Learning: Logistic Regression, SVM, Random Forest, XGBoost, k-NN
- Unsupervised Learning: K-Means Clustering, PCA, DBSCAN
- Deep Learning: CNN, RNN, LSTM, GANs, Autoencoders
- NLP: Tokenization, POS tagging, Named Entity Recognition, Transformers (BERT, GPT)
- Model Optimization: Hyperparameter Tuning, Cross-Validation, GridSearchCV, Bayesian Optimization
- Deployment: Flask, FastAPI, Docker, AWS, TensorFlow Serving
- Frontend: HTML, CSS, JavaScript, React, Vue.js
- Backend: Node.js, Express.js, Flask, FastAPI
- Databases: MongoDB, MySQL, PostgreSQL
- Version Control: Git, GitHub, GitLab
- Deployment: Docker, Heroku, AWS, Netlify, Vercel
- Medical Transcription System: A real-time transcription system for medical data, processing speech using Faster-Whisper, followed by analysis through Llama 3.2 for actionable insights.
- RAG-Based Financial QA: Building a Retrieval-Augmented Generation model to answer questions from financial documents, providing robust solutions for the finance sector.
- AI-powered IoT Solutions: Combining Machine Learning with IoT to monitor water quality and detect system failures in real-time.