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!
π I hold a Masters Degree in Business Analytics with a specialisation in Data Science from UT Dallas andI have 3+ years of experience building and deploying machine learning and deep learning models.
I have a strong practical and theoretical experience in the development of Large Language Models (LLMs) and Generative AI.
π 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
- Data Science Professional Certificate by IBM
- Python for Everybody Specialization by University of Michigan
π 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)
π 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
- The Prophet Forecasting Model by Facebook
π 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)
These valuable tools and techniques have empowered me to successfully develop and comprehend intricate machine learning projects.
The following links include detailed descriptions within each GitHub repository:
| π Predicting-Startup-outcomes-with-XGBoost-and-Machine-Learning) | π¨π»βπ» HR-Job-Market-Analysis-using-Light-GBM |
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| π World-Development-Indicators-Co2-Emission-Vs-GDP | βοΈ Telco Customer Churn Prediction |
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| β AI-driven-Chatbot-for-enhanced-question-answering-system |
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| β Create-Database-and-Tables-using-Athena | π» Using-AWS-S3-for-Data-Storage |
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ββ π Demystifying P-Values: A Guide for Non-Technical Stakeholders
ββ π Understanding Type I and Type II Errors in Hypothesis Testing: A Data Scientistβs Perspective
ββ π Head start your Machine Learning Journey!π
ββ π All about Data Preprocessing
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Tencent