Hammad Ali Tahir
CS'26 | Data Science | Machine Learning | Neural Networks | LLMs & Generative AI | RAG | NLP
Sheikhupura District, Punjab, Pakistan
Summary: Driving AI Innovation
I am Hammad Ali Tahir, a final-year BS Computer Science student from Pakistan with a strong focus on Artificial Intelligence, Machine Learning, and Deep Learning. Over the past year, I have built and optimized numerous ML models, gaining hands-on experience in neural networks and NLP techniques including Transformers, BERT, attention mechanisms, and encoder-decoder architectures.
I am passionate about applying advanced AI methods to solve practical challenges and continuously advancing my expertise in this field.
Top Skills & Technical Prowess
Python & Data Science
Proficient in Python, SQL, NumPy, Pandas, Matplotlib, Seaborn for data manipulation and analysis.
ML/AI Frameworks
Expertise in PyTorch, TensorFlow, Scikit-learn, Transformers, Hugging Face, LangChain, LangGraph.
Advanced AI Concepts
Deep understanding of Diffusion Models, CNNs, RAG, Vector Stores (Chroma), LLMs, and Generative AI.
Deployment & Tools
Experience with FastAPI, Streamlit, Docker, Git/GitHub for application development and deployment.
Certifications: Validating Expertise
NumPy, Pandas, & Python for Data Analysis: A Complete Guide
Mastering essential libraries for robust data manipulation and analysis.
Data Science and Machine Learning with Python - Hands On!
Comprehensive practical experience in building and deploying ML models.
Supervised Machine Learning: Regression and Classification
In-depth knowledge of core supervised learning algorithms and their applications.
Data Visualization with Python
Creating impactful and insightful data visualizations using Python tools.
NVIDIA: Fundamentals of Machine Learning
Solid foundation in the principles and practices of machine learning from an industry leader.
Featured Project: AI-Powered Legal Case Management System
Uraan AI Techathone 1.0
Developed an AI-Powered Legal Case Management System integrating case classification, prioritization, and precedent retrieval using Machine Learning and RAG (Retrieval-Augmented Generation) techniques.
  • Achieved 91.5% classification and 95.2% prioritization accuracy using Support Vector Classifier, Naïve Bayes, and Random Forest models.
  • Improved legal data analysis efficiency through advanced ML and RAG.
  • Built an interactive Streamlit interface leveraging Hugging Face embeddings for seamless user interaction.
Featured Project: End-to-End Loan Approval Prediction
Predictive Model for Loan Approval
Constructed a comprehensive project pipeline by mapping all key phases, resulting in a streamlined workflow that reduced project turnaround time by 30%.
  • Coordinated all phases of the project lifecycle independently, delivering a fully functional solution on time and within scope.
  • Achieved a 100% completion rate without external assistance, demonstrating strong project management and technical execution.
Featured Project: Smart PDF Q&A System
LLM + RAG Document Assistant
Built an end-to-end retrieval-augmented QA system using LangChain, Groq/OpenAI models, ChromaDB, and Streamlit.
Semantic Chunking & Embeddings
Implemented semantic chunking, vector embeddings, and multi-retriever pipelines for high-accuracy retrieval.
Interactive Chat & Deployment
Developed an interactive chat interface and deployed the system as a local application.
Demonstrated Skills
Showcases strong skills in LLMs, embeddings, retrieval pipelines, and application deployment.
GitHub Repositories: A Glimpse into My Work
GEN-AI: Flagship AI/Agents/LangChain Project
Built a modular generative-AI system using LangChain and Groq models; implemented retrieval, tool-calling, and custom agent workflows; improved answer accuracy through prompt-routing and evaluation pipelines.
AI-Techathone: RAG + App-Dev Example
Developed a full RAG pipeline with semantic chunking, vector search, and a Streamlit interface; optimized retrieval latency and improved answer relevance through custom embedding strategy.
Loan_Approval_Prediction: Classical ML Pipeline
Delivered an end-to-end ML workflow for loan approval prediction including EDA, feature engineering, model selection, and deployment-ready inference script; achieved reproducible training with documented pipeline.
Historical-Event-Type-Predictor: NLP Modeling
Implemented an event-type prediction model using structured historical data; built preprocessing pipeline, baseline models, performance evaluation, and feature-importance analysis.
Financial Insights Dashboard: Data Visualization
Created an interactive financial dashboard analyzing multi-year Tesla and GameStop market trends; automated data ingestion and produced time-series insights for revenue and price movements.
ML Practice & Utilities: Fundamentals
Developed reusable Python utilities and multiple regression/classification experiments demonstrating proficiency with NumPy, Pandas, Scikit-Learn, evaluation metrics, and model debugging.
Connect With Me
GitHub
https://github.com/HammadAli08
My Vision: Shaping the Future with AI
My journey in Computer Science is driven by a profound curiosity and a commitment to innovation. I believe that AI and Machine Learning are not just tools, but transformative forces that can redefine industries and improve lives.
I am dedicated to continuous learning and applying cutting-edge techniques to develop intelligent systems that are efficient, ethical, and impactful. Let's build the future, one intelligent solution at a time.
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