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About Me
About Me
Monireach Tang
Monireach Tang
Machine Learning Engineer | AI Aficionado
My journey into AI and Data Science began with a deep love for math and programming, leading me to pursue a Master's degree in AI to turn my passion into expertise. Inspired by visionaries like Tony Stark, I dream of becoming an AI engineer who builds cutting-edge solutions for real-world problems. Before formal studies, I was a self-taught developer and DevOps enthusiast, mastering languages like Python, Dart, and Flutter, along with tools like Linux, Git, Spreadsheets, and SQL. Now, I’m fully dedicated to deepening my knowledge—taking courses like Introduction to AI with Python on Domestika to strengthen my grasp of machine learning algorithms and use cases. Through hands-on projects and academic rigor, I’ve built a strong foundation in AI and am eager to apply these skills to meaningful challenges. My goal is to keep pushing boundaries, learning relentlessly, and contributing to innovative AI advancements. The future is intelligent, and I’m excited to be part of shaping it!
Skills
Skills
Machine Learning
Machine Learning
Built ML models using Supervised, Unsupervised, and Reinforcement Learnings.
Neural Networks
Neural Networks
Designed and trained deep learning models (CNNs, RNNs) for computer vision and sequence prediction tasks.
Python
Python
Built end-to-end ML pipelines using Python libraries (TensorFlow, PyTorch, Scikit-learn
Cloud Platforms
Cloud Platforms
Deployed scalable ML models on AWS, leveraging cloud computing and storage.
Docker
Docker
Containerized ML APIs and microservices for portable, reproducible deployments.
Git
Git
Managed version control for collaborative ML projects and CI/CD workflows.
SQL
SQL
Processed and analyzed structured data for feature engineering and model training.
Linux Shell
Linux Shell
Automated server tasks and ML pipeline orchestration using Bash scripting.
Portfolio Projects
Portfolio Projects
Fraud Detection in Digital Payment
Fraud Detection in Digital Payment
Highlight:
Highlight:
Built a machine learning system to detect fraudulent transactions using Logistic Regression, Random Forest, and XGBoost.
Developed a REST API with FastAPI for real-time predictions and containerized the application with Docker.
Achieved 95% precision and 90% recall using SHAP for model interpretability.
and containerized the application with Docker.
Cherry-Raspberry Image Classification
Cherry-Raspberry Image Classification
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Highlight:
Built MobileNetV2 classifier (TensorFlow) with data augmentation (PIL/OpenCV).
Applied transfer learning for efficient binary classification.
Cats vs Dogs Image Classification using CNN
Cats vs Dogs Image Classification using CNN
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Highlight:
Built CNN (TensorFlow/Keras) for cats/dogs classification (85% accuracy).
Optimized with EfficientNetB0 transfer learning and OpenCV preprocessing.
California Housing Market Segmentation Analysis
California Housing Market Segmentation Analysis
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Highlight:
Segmented housing markets using K-means (12 clusters) with dynamic analysis.
Engineered features (population density) and visualized insights with Seaborn.
Contact Me
Contact Me
📞 : +855 (0)70 489 488
✉️ : monireach.tang@gmail.com
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