Always learning. Always building.
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I build practical machine learning projects with a focus on tabular modeling, feature engineering, model evaluation, and clean Python/C++ implementation. My current goal is to become a Machine Learning Engineer who can connect data preparation, model training, validation, and deployment into reproducible engineering workflows. Machine Learning → Feature Engineering → Model Evaluation
Algorithms → Data Structures → Problem Solving
Software Systems → Docker / Linux → Deployment Basics |
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A system design portfolio project focused on backend engineering, API design, and practical software architecture. Focus
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C++ implementations for data structures, algorithms, and competitive programming. Focus
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IoT distance measurement system using ESP8266 and ultrasonic sensing. Focus
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ML Engineering
├── Data preprocessing
├── Feature engineering
├── Model training and validation
├── Ensemble learning
├── Experiment tracking
└── Deployment fundamentals
Computer Science
├── Linear Algebra
├── Discrete Mathematics
├── Data Structures
├── Algorithms
├── Operating Systems
└── Computer Architecture
Software Engineering
├── Git / GitHub workflow
├── Linux
├── Docker
├── MySQL
└── Backend fundamentalsI believe strong machine learning engineers should understand not only how to use tools, but also the principles behind them.
My goal is to grow into an engineer who can combine programming ability, algorithmic thinking, mathematical foundations, and real-world ML system development.
Always learning. Always building.
If you would like to connect, discuss machine learning, algorithms, software engineering, or potential collaboration, feel free to reach out.