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 byStanford University
• Deep Learning Specialization byAndrew Ng
• Deploying AI & Machine Learning Models for Business fromUdemy
• Python for Time Series Data Analysis byJose 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 forGenerative 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 toDevOps 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.

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