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Dismantling the problems into simpler architecture to execute in the best manne
:atom:
Dismantling the problems into simpler architecture to execute in the best manne

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murtazabaig/README.md

👋 Hi, I'm Murtaza Baig

🎓 BS Artificial Intelligence student at TUF
🤖 Building AI Agents & Automations
🐍 Python • 📊 Machine Learning • 👁️ Computer Vision

🌐 Portfolio • 💻 GitHub • 📧 Email • 💼 LinkedIn • 📸 Instagram • 📘 Facebook

🏆

🧠 About Me

My core strength lies in designing, implementing, and deploying AI-powered agents and automation pipelines that combine machine learning, orchestration logic, and real-world integrations.

🔬 Technical Expertise

  • 🤖 AI Agents & Orchestration

    • Tool-based agents, routing, memory design
    • Multi-step workflows, decision logic, evaluation loops
    • RAG-style systems and agent coordination concepts

  • 📊 Machine Learning & Data Science

    • Supervised & unsupervised learning
    • Model training, evaluation, and optimization
    • Feature engineering, preprocessing, and EDA
    • Experience with classification, regression, and ensemble methods

  • 👁️ Computer Vision

    • Image preprocessing and transformations
    • Feature extraction and model-based vision pipelines
    • OpenCV-based applications and integrations

  • 🧠 Deep Learning

    • Neural network fundamentals
    • CNN-based architectures
    • Hands-on with TensorFlow and PyTorch
    • Model experimentation and performance tuning

  • 🛠️ Automation & Scripting

    • API-driven automations
    • Web scraping with Selenium
    • Task scheduling, background jobs, and workflow logic
    • Bash scripting for tooling and pipelines

  • 🌐 Backend & Deployment

    • Django-based backends
    • Streamlit apps for ML demos
    • Cloud deployment using AWS, Azure, GCP, Firebase, and Vercel
    • CI/CD using GitHub Actions

🧩 Engineering Mindset

  • Strong understanding of end-to-end system design
  • Comfortable translating ideas into working prototypes
  • Focused on clean code, modularity, and scalability
  • Prefer building things that can be deployed, tested, and used

I am continuously improving my skills in AI architecture, agent systems, and automation-first design, with the goal of building intelligent systems that solve real problems rather than just producing notebooks.

🎯 Current Focus

  • 🤖 Agent workflows (tools, memory, routing, evaluation)
  • 🔄 Automation pipelines (APIs, schedulers, webhooks)
  • 🚀 Practical ML & CV projects with deployable demos

🛠️ Tech Stack

📊 GitHub Stats

🔥

✍️ Random Dev Quote

💡

👀 Visitor Count

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  1. AI-Rice-Image-Analysis AI-Rice-Image-Analysis Public

    Computer vision pipeline to detect, count, and measure rice grains. Includes a cleaned notebook with preserved outputs + modular script.

    Jupyter Notebook

  2. House-Price-Prediction House-Price-Prediction Public

    Welcome to my House Price Prediction project! This repository showcases a complete machine learning pipeline for predicting house prices based on real estate data.

    Jupyter Notebook

  3. MULTIPLE-DISEASE-DATA-ANALYSIS MULTIPLE-DISEASE-DATA-ANALYSIS Public

    Multiple Disease Data — EDA → preprocessing → ML models (LogReg, RF, optional XGBoost) with metrics (Accuracy/Precision/Recall/F1/ROC-AUC). Clean notebook + requirements for easy reproducibility.

    Jupyter Notebook

  4. Press_Release_Scraper_Analytics_Pipeline Press_Release_Scraper_Analytics_Pipeline Public

    Web scraping & analytics pipeline for corporate press releases (Engro + competitors). Playwright + BS4 + PDF (pdfminer) + TF-IDF + sentiment + Dash.

    Jupyter Notebook

  5. RFM-ML-pipeline RFM-ML-pipeline Public

    Comprehensive RFM segmentation pipeline combining statistical analysis, machine learning classifiers, and interactive Plotly visualizations to deliver actionable customer insights and portfolio-gra…

    Jupyter Notebook

  6. Spam-SMS-Classification-using-Machine-Learning Spam-SMS-Classification-using-Machine-Learning Public

    Spam SMS Classification using Machine Learning — End-to-end text classification pipeline to detect spam messages in SMS data. Includes data cleaning, exploratory analysis, feature engineering with …

    Jupyter Notebook