I'm passionate about Machine Learning, Data Science, Large Language Models (LLMs), and Generative AI. I love exploring how AI can solve real-world problems and create new possibilities. Always excited to learn and share cool projects!
- 🔭 I hold a Master's Degree in Computer Science from New York University.
- 💼 I have 1+ years of experience building and deploying machine learning and deep learning models.
- 🔧 Worked in building real-time scalable ML systems for Time Series Data.
- 🤖 Experienced in developing Large Language Models (LLMs) and Generative AI solutions.
🔭 Some of the notable courses I have completed and that helped in gaining strong theoretical foundation include:
- Machine Learning Certification by Stanford University
- Deep Learning Specialization by Andrew Ng
- Deploying AI & Machine Learning Models for Business from Udemy
- Python for Time Series Data Analysis by Jose Portilla
🔭 I've used different Machine Learning and Deep Learning models in real-time projects. Below are some used models:
- Linear Regression
- Logistic Regression
- Support Vector Machines (SVM)
- Decision Trees (DT)
- Random Forests (RF)
- K-Nearest Neighbors (KNN)
- Deep Neural Networks
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Naive Bayes (NB)
- Gradient Boosted Decision Trees (GBDT)
- XGBoost
- Long Short-Term Memory (LSTM)
🔭 Below are some state-of-the-art (SOTA) time series forecasting models used in various real-time projects:
- Auto-Regressive (AR) Model
- Auto-Regressive Moving Averages (ARMA) Model
- Auto-Regressive Integrated Moving Averages (ARIMA) Model
- Neural Hierarchical Interpolation of Time Series (N-HiTS) Model
- Seasonal Auto-Regressive Integrated Moving Averages (SARIMA) Model
🔭 Furthermore, below are some of the tools used during my experience for Generative AI:
- Langchain
- LangGraph
- Retrieval Augmented Generation (RAG)
- Llama Index
- OpenAI API
- Mixtral (LLM)
- Llama 2 (LLM)
- GPT - 3 (LLM)
- GPT - 3.5 (LLM)
- GPT - 4 (LLM)
- Deep - Seek (LLM)
🔭 Here are some of the skillsets in regards to DevOps technologies:
- Docker
- Docker-Compose
- Kubernetes
- Linux
- Windows Subsystem for Linux (WSL)
- Travis CI
- Ubuntu
- GitLab CI/CD
These valuable tools and techniques have empowered me to successfully develop and comprehend intricate machine learning projects.
