This repository offers a curated collection of end-to-end data science, machine learning, deep learning, and NLP projects designed to enhance resumes. It targets aspiring data scientists and ML engineers seeking practical, portfolio-worthy projects with modern MLOps and deployment practices.

How It Works

The repository provides comprehensive, step-by-step implementations of various ML/DL/NLP tasks. Projects cover diverse domains like student performance prediction, text summarization, computer vision (cell segmentation), audio classification, and generative AI. Key features include integration with MLOps tools (MLFLOW, DVC), cloud platforms (AWS Sagemaker), CI/CD (GitHub Actions), containerization (Docker), and LLM frameworks (Langchain, LlamaIndex, OpenAI, Google Gemini).

Quick Start & Requirements

• Projects typically require Python 3.x and standard data science libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy).
• Specific projects may require cloud credentials (AWS), API keys (OpenAI, Google Gemini), or specialized hardware (GPU for deep learning tasks).
• Links to detailed guides and demos are often provided within individual project folders.

Highlighted Details

• End-to-end implementations from data preprocessing to deployment.
• Integration of MLOps practices like MLFLOW, DVC, and GitHub Actions.
• Coverage of modern LLM applications with frameworks like Langchain and LlamaIndex.
• Examples include AWS Sagemaker deployment, YOLOv8 for computer vision, and RAG systems.

Maintenance & Community

• The repository is maintained by krishnaik06.
• Community engagement details (e.g., Discord, Slack) are not explicitly mentioned in the README.

Licensing & Compatibility

• The repository does not explicitly state a license in the provided README snippet.
• Compatibility for commercial use or closed-source linking is undetermined without a specified license.

Limitations & Caveats

The README lacks a clear license, making commercial use or integration into proprietary systems uncertain. Specific project dependencies and setup instructions are not consolidated, requiring users to navigate individual project folders.