ML & AI Projects 🚀

A curated collection of practical Machine Learning and Artificial Intelligence projects by Farhan Rana.

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🔍 Overview

This repository contains projects across:

• Supervised & Unsupervised Learning
• Deep Learning
• Natural Language Processing (NLP)
• Computer Vision
• AI Applications

Each project is designed for learning, experimentation, and building real-world AI applications.

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📂 Project Structure

ML_and_AI_Projects/ ├── Scikit-Learn/ ← Classic ML projects using scikit-learn ├── Deep_Learning/ ← Neural networks, CNNs, RNNs, etc. ├── NLP/ ← Text processing, language models ├── Computer_Vision/ ← Image classification, object detection, etc. ├── AI_Assistant_OpenAI_API/ ← Example project: AI assistant using OpenAI API ├── AI_Resume_Critiquer/ ← Example: automated resume critiquing tool ├── requirements.txt ← Library dependencies └── .gitattributes ← Git attributes

Tip: You can reorganize or rename folders as your project list evolves.

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🛠 Setup / Installation

1. Clone the repository

git clone https://github.com/FarhanRana234/ML_and_AI_Projects.git cd ML_and_AI_Projects

2. (Optional) Create a virtual environment

On Unix/macOS

python3 -m venv venv source venv/bin/activate

On Windows

python -m venv venv venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Run Jupyter notebooks

Navigate to any project folder and open the notebooks to explore or run experiments:

jupyter notebook

🎯 Usage

• Use the Jupyter notebooks to learn: follow annotated steps, experiment with parameters, visualize results.
• Use modular code modules (model.py, train.py, utils.py) to build your own extensions.
• Reuse datasets or preprocessing scripts for experimentation.
• Use as a baseline for real-world applications: data ingestion → modeling → evaluation → deployment.

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🧠 Featured Projects

• AI Assistant using OpenAI API – Interactive assistant leveraging large language models.
• AI Resume Critiquer – Automated system to analyze and critique resumes.
• NLP Folder – Text classification, sentiment analysis, language modeling projects.
• Scikit-Learn Folder – Classical ML algorithms: regression, classification, clustering, dimensionality reduction.
• Deep Learning / Computer Vision – CNNs, transfer learning, object detection, etc.

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✅ Why Use This Repository?

• Ready-to-use: Code, notebooks, and dependencies are organized.
• Learning-oriented: Great for students, hobbyists, or those wanting hands-on AI experience.
• Modular & Extensible: Fork or adapt parts of the code for your own projects.
• Variety: Covers multiple ML/AI domains — ideal for learning trade-offs between methods.

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📌 How You Can Contribute

• Add your own project folders with clear documentation.
• Improve existing notebooks with comments, visualizations, or experiments.
• Update requirements.txt as dependencies evolve.
• Add a README.md in each project folder explaining the project.
• Fix bugs, add tests, or provide performance benchmarks.

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⚠️ Notes & Considerations

• Some projects may require large datasets or GPU resources — check each notebook.
• Ensure compatible library versions (see requirements.txt).
• Verify dataset licenses before using in production.
• This repository is for educational and experimentation purposes, not production-ready deployment.

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📬 Contact

• GitHub: FarhanRana234
• Email: rana.tdm@gmail.com
• Instagram: farhan_.rana
• Indeed: Farhan Rana

Feel free to open an issue or create a Pull Request for questions, suggestions, or contributions.

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⭐ If you find this repository useful, give it a star!