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 local-first daily planner that combines task scheduling, execution tracking, analytics, and ML-based duration prediction in a Dockerized multi-service system. Focus
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An end-to-end machine learning web application for training, comparing, versioning, and serving Titanic survival models through REST APIs. Focus
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An end-to-end regression project for Kaggle House Prices, combining robust feature engineering, strict cross-validation, model ensembling, calibration, and reproducible MLOps tracking. Focus
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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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AI / ML Engineering
├── Data analysis, preprocessing and visualization
├── Feature engineering and dimensionality reduction
├── Machine learning, validation and ensemble methods
├── Deep learning, CNNs and transfer learning
├── NLP, transformers, embeddings and semantic search
├── LLM applications, RAG, agents and evaluation
└── MLOps, APIs, Docker and model monitoring
Computer Science Foundations
├── Data Structures & Algorithms
├── Linear Algebra & Discrete Mathematics
└── Operating Systems & Computer Architecture
Software Engineering
├── Git / GitHub, Linux and Docker
├── Backend development and system design
├── PostgreSQL and MySQL
└── Testing and reproducible developmentI 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.
