This repository is a living collection of my work in Machine Learning, Deep Learning, NLP, and Generative AI. Instead of being a fixed project archive, this repo grows continuously as I learn new concepts, build experiments, and implement ideas from scratch.
It represents a hands‑on learning journey — from core algorithms to advanced deep learning and real‑world applications.
- Practical implementations of ML, DL, and NLP concepts
- Small experiments, mini-projects, and full applications
- Implementations from scratch to understand core logic
- End-to-end projects with training, evaluation, and deployment
- Continuous additions as new topics are explored
This is not a static portfolio — it is an evolving technical notebook of progress.
- Regression and classification problems
- Real-world prediction systems
- Data preprocessing and feature engineering
- Artificial Neural Networks (ANNs)
- Convolutional Neural Networks (CNNs)
- Computer vision tasks (e.g., handwritten digit recognition)
- Sentiment analysis
- Recommendation systems
- Duplicate question detection
- Transformer-based models
- LLM-based applications
- Retrieval-augmented systems and chatbots
- Vector search using FAISS
- Gradient descent
- Neural networks
- Core ML algorithms and mathematical intuition
- Strengthen fundamentals through hands-on coding
- Build an applied ML/AI portfolio with practical examples
- Experiment with new architectures and ideas
- Track personal learning progress over time
- Explore any project folder or notebook.
- Open notebooks to see experiments and explanations.
- Run applications using the provided scripts where available.
- Use projects as reference implementations.
Typical Python project setup:
python -m venv .venv
# On macOS / Linux
source .venv/bin/activate
# On Windows (PowerShell)
.\.venv\Scripts\Activate.ps1
# On Windows (cmd)
.\.venv\Scripts\activate
pip install -r requirements.txt
python app.pyTip: Check each project folder for a
requirements.txtand a small README with project-specific instructions.
Learn → Implement → Experiment → Improve → Repeat
Each project is an opportunity to deepen understanding and iterate on ideas. Some experiments are intentionally rough — learning is the focus.
- Transformer-based applications
- Multimodal AI systems
- Real-time ML deployments
- Research-inspired implementations and end-to-end AI products
I’m a student and AI enthusiast building projects across Machine Learning, Deep Learning, NLP, Generative AI, and full-stack AI applications.
This repository is offered for learning and reference purposes. You may use the code for educational or non-commercial projects.