- ๐ญ Currently contributing to pgmpy (Probabilistic Graphical Models library)
- ๐ง Focused on Bayesian Networks & Probabilistic AI
- โ๏ธ Interested in improving real-world AI systems
- ๐ฑ Continuously learning advanced concepts in AI & ML
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Probabilistic Graphical Models
- Bayesian Networks (Structure & Parameter Learning)
- Exact & Approximate Inference (Variable Elimination, Sampling)
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pgmpy Internals
- Model architecture & CPDs
- Inference engine optimization
- Utility & performance improvements
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Mathematics for AI
- Probability Theory
- Conditional Independence
- Optimization basics
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Advanced Exploration
- Causal Inference
- Graph-based AI models
- ๐ฏ Looking to collaborate on AI/ML & Probabilistic Models
- ๐ค Seeking help in advanced inference & optimization techniques
- ๐ Python & Problem Solving
- ๐ง Bayesian Networks
- ๐ pgmpy usage & basics
- ๐ Probability & Inference
- ๐ ๏ธ Open Source Contribution
- Python (Strong)
- C++
- Basic Java
- C
- Probabilistic Graphical Models
- Bayesian Networks
- Machine Learning Basics
pgmpy (Bayesian Modeling & Inference)
scikit-learn (ML algorithms, preprocessing, evaluation)
NumPy (Numerical Computing)
Pandas (Data Analysis & Manipulation)
Matplotlib / Seaborn (Data Visualization)
- Git & GitHub
- VS Code
- Linux
- ๐น Enhancing usability & performance
- ๐น Exploring AI-based solutions
I enjoy solving complex problems and turning ideas into real working code ๐ก
โญ Focused on building intelligent systems using probability, logic, and code
