Welcome to my AI Engineer portfolio! This repository showcases my expertise in artificial intelligence, machine learning, and deep learning.
I'm an AI Engineer passionate about building intelligent systems that solve real-world problems. My work spans across:
- Machine Learning: Supervised & unsupervised learning, model optimization
- Deep Learning: Neural networks, computer vision, NLP
- MLOps: Model deployment, monitoring, and maintenance
- AI Applications: End-to-end AI solutions from research to production
AI-Engineer/
├── projects/ # Showcase of AI/ML projects
├── notebooks/ # Jupyter notebooks with experiments and analyses
├── models/ # Trained models and architectures
├── docs/ # Technical documentation and blog posts
└── resources/ # Useful resources and references
Advanced image classification using transfer learning and custom CNN architectures.
Highlights:
- 🎯 95.8% accuracy with EfficientNetB0
- 🔄 Transfer learning with ResNet50, VGG16
- 📊 Comprehensive data augmentation pipeline
- 🚀 GPU-accelerated training
Tech Stack: TensorFlow, Keras, OpenCV
State-of-the-art sentiment analysis using transformers and LSTM networks.
Highlights:
- 🎯 95.8% accuracy with BERT
- 🤖 Multiple architectures (LSTM, Bi-LSTM, Transformers)
- 💬 Real-time sentiment prediction
- 🔍 Attention visualization
Tech Stack: TensorFlow, Transformers, NLTK
Advanced forecasting models for financial and operational predictions.
Highlights:
- 📈 Stock price prediction with 94% R² score
- ⏰ Multi-step ahead forecasting
- 🧮 Feature engineering with technical indicators
- 📊 Transformer-based architecture
Tech Stack: TensorFlow, Prophet, StatsModels
Scalable recommendation engine using collaborative filtering and deep learning.
Highlights:
- 🎬 25M+ ratings from MovieLens
- 🤝 Neural collaborative filtering
- 🎯 78% Hit Rate @ 10
- 🔀 Hybrid recommendation approach
Tech Stack: TensorFlow, Surprise, Implicit
Explore hands-on Jupyter notebooks demonstrating various ML/AI concepts:
- EDA Workflow: Complete exploratory data analysis pipeline
- Neural Networks from Scratch: Understanding the fundamentals
- Model Comparisons: Benchmarking different algorithms
- Research Implementations: Paper reproductions and experiments
In-depth articles on AI/ML topics:
- Understanding Transformer Architecture: Deep dive into attention mechanisms
- Model deployment best practices
- Feature engineering techniques
- MLOps workflows
Useful utilities and reference materials:
- ML Cheat Sheet: Quick reference for common ML tasks
- Helper scripts and utilities
- Data preprocessing tools
- Model evaluation frameworks
Languages: Python, R, SQL
ML/DL Frameworks:
- TensorFlow, PyTorch, Keras
- Scikit-learn, XGBoost, LightGBM
- Hugging Face Transformers
MLOps Tools:
- Docker, Kubernetes
- MLflow, Weights & Biases
- AWS SageMaker, Azure ML, Google Cloud AI
Data Processing:
- Pandas, NumPy, Polars
- Apache Spark, Dask
- Model Development: End-to-end ML pipeline creation
- Model Deployment: Production-ready AI systems
- Data Engineering: ETL, feature engineering, data pipelines
- Research: Staying current with latest AI research and implementations
- Collaboration: Working with cross-functional teams
Feel free to reach out for collaborations or opportunities!
This repository is for portfolio purposes. Individual project licenses may vary.
Last updated: November 2025