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manthan89-py (Manthan Bhikadiya) · GitHub

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Think , Code, Compile & Fire 🔥
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Think , Code, Compile & Fire 🔥
126 followers · 58 following

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manthan89-py/README.md

👋 Hello, I'm Manthan

AI/ML Engineer | LLM & GenAI Specialist | Software Engineer II at Bristol Myers Squibb 🚀

Transforming ideas into AI realities through code and innovation.

🎯 What I'm Up To

  • 🤖 Specializing in Large Language Models, Generative AI, and AI Agents
  • 🔬 Researching and implementing cutting-edge AI solutions in healthcare
  • 🌱 Building scalable AI/ML pipelines and architectures
  • 👥 Open to collaboration on innovative AI projects
  • 📚 Exploring the path to Artificial General Intelligence (AGI)

🛠️ Technology Arsenal

🧠 AI/ML Ecosystem

LLM & GenAI Tools   │  ML Frameworks       │  Data Science
────────────────────────────────────────────────────────────
▣ LangChain         │  ▣ PyTorch           │  ▣ NumPy
▣ CrewAI            │  ▣ TensorFlow        │  ▣ Pandas
▣ LangGraph         │  ▣ Keras             │  ▣ Scikit-learn
▣ Phidata           │  ▣ Transformers      │  ▣ SciPy

🔧 Development Stack

Backend             │  Frontend           │  DevOps & Tools
────────────────────────────────────────────────────────────
▣ FastAPI           │  ▣ ReactJS          │  ▣ Docker
▣ Django            │  ▣ Streamlit        │  ▣ Git
▣ Flask             │  ▣ Gradio           │  ▣ MLflow
▣ PostgreSQL        │  ▣ Plotly           │  ▣ DVC

🎨 Visualization & Analysis

Data Visualization │  Computer Vision        │  NLP Tools
────────────────────────────────────────────────────────────
▣ Matplotlib       │  ▣ OpenCV               │  ▣ spaCy
▣ Seaborn          │  ▣ PIL                  │  ▣ NLTK
▣ Plotly           │  ▣ Scikit-Learn Image   │  ▣ Hugging Face
▣ Bokeh            │  ▣ MediaPipe            │  ▣ Tokenizers

🚀 ML Deployment & Scaling

Model Serving      │  Monitoring     │  Infrastructure
────────────────────────────────────────────────────────────
▣ BentoML          │  ▣ MLflow       │  ▣ AWS
-                  |  ▣ Grafana      |  ▣ GCP

💻 Expertise Level

{
    "Expert":        ["Python", "AI ML Development"],
    "Intermediate":  ["Backend Development", "Visualization &  Analysis",],
    "Learning":      ["Model Serving", "Model Deployment", "Monitoring Tools"]
}

🎓 Core Expertise Areas

mindmap
  root((AI Expertise))
    LLM and GenAI
      Prompt Engineering
      Fine-tuning
      RAG Systems
    AI Agents
      Multi-agent Systems
      Autonomous Agents
      Agent Orchestration
    Traditional ML/DL
      Computer Vision
      Natural Language Processing
      Time Series
    MLOps
      Pipeline Design
      Model Deployment
      Cloud Infrastructure & Monitoring
    Software Developlemt
      API Development
      Database Management
      Basic UI Development
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🎯 Project Focus Areas

pie title Current Project Distribution
    "LLM Applications" : 30
    "Generative AI" : 10 
    "AI Agents" : 30
    "Full Stack AI Application" : 20
    "Traditional ML" : 10
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🤝 Connect & Collaborate

Pinned Loading

  1. Heart-Disease-Prediction Heart-Disease-Prediction Public

    This project is combination of data analysis and machine learning. In this project First I try to find features and make different three type of models. Do some analysis and finally getting accurac…

    Jupyter Notebook 10

  2. Image-Creation-Using-GANs Image-Creation-Using-GANs Public

    In this Project, We are Simple Creating Images Which have a similar look like images that we have trained our Model. Note that image can be anything but it generated by itself.

    Jupyter Notebook 1

  3. Tic-Tac-Toe-Reinforcement-learning Tic-Tac-Toe-Reinforcement-learning Public

    This is a simple game of tic-tac-toe with Artificial Intelligence

    Jupyter Notebook 1

  4. Plant-Disease-Detection Plant-Disease-Detection Public

    Bases on Leaf images we are trying to predict plant disease using convolutional neural network. PyTorch implementation

    Jupyter Notebook 376 233

  5. Denoise-the-Image-using-Autoencoder Denoise-the-Image-using-Autoencoder Public

    In this project we are simply add a noisy images in out model and model simple some % noise in the image return cleanned image as output. Autoencoder is used because it used encoder and decoder con…

    Jupyter Notebook 1

  6. Fake_News_detection Fake_News_detection Public

    This Project is based on Binary Classification of News. The Model Will detect whether the news is fake or not. This is project implement using Keras and NLTK Libraries.

    Jupyter Notebook 15 5

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