I am a Computer Science & Engineering student at National University, Bangladesh, focused on building data-driven and AI-powered systems.

I am transitioning from a MERN Stack background into a Machine Learning & Data Analytics–first profile, with hands-on experience across data preprocessing, model development, evaluation, and deployment.

I enjoy working on real-world problems where data, machine learning, and software engineering intersect, and my goal is to build scalable, production-ready AI solutions rather than just experimental notebooks.

📍 Location: Mirpur-14, Dhaka, Bangladesh
📧 Email: tanvirrahmanaz@gmail.com

• Machine Learning & Deep Learning model development
• Computer Vision & Image Processing
• Natural Language Processing (NLP)
• Data Analysis & Visualization
• ML model deployment using APIs
• Integrating ML systems with web applications

• Python (primary language for ML & Data)
• JavaScript, TypeScript
• SQL (PostgreSQL, MySQL)
• Data Structures & Algorithms (basic to intermediate)
• Object-Oriented Programming (OOP)

• Supervised Learning (Regression, Classification)
• Unsupervised Learning (Clustering, Dimensionality Reduction)
• Feature Engineering & Feature Selection
• Model Evaluation (Accuracy, Precision, Recall, F1, ROC-AUC)
• Cross-validation & bias–variance analysis

Libraries

• NumPy
• pandas
• Scikit-learn
• XGBoost (basic)
• LightGBM (basic)

• Neural Network fundamentals
• CNN architectures (VGG, ResNet, EfficientNet)
• RNN, LSTM, GRU (conceptual & basic implementation)
• Transfer Learning & Fine-tuning
• Regularization, optimization, learning-rate scheduling

Frameworks

• TensorFlow / Keras
• PyTorch

• Image preprocessing & augmentation
• Image classification
• Object detection (YOLO v5 / v8, SSD)
• Image segmentation basics (U-Net)
• OpenCV for image manipulation

• Text preprocessing & cleaning
• Tokenization & embeddings
• Transformer-based models (BERT-style)
• Sentiment analysis & text classification
• NLP pipelines for real-world applications

Tools

• Hugging Face Transformers
• spaCy
• NLTK

• Exploratory Data Analysis (EDA)
• Data cleaning & missing-value handling
• Statistical summaries & correlations
• Insight-driven visualization

Tools

• pandas
• NumPy
• Matplotlib
• Seaborn
• Plotly
• Power BI (basic)

• Model serving with Flask & FastAPI
• REST APIs for ML inference
• Docker & containerized ML apps
• Model versioning concepts (MLflow, DVC)
• GitHub Actions (CI basics)
• Cloud fundamentals (AWS EC2, S3, Firebase)

• React, Next.js
• Node.js, Express
• MongoDB, PostgreSQL
• API integration & authentication basics
• ML model integration into web applications

• End-to-end ML system thinking (data → model → API → app)
• Strong focus on clean structure and reproducibility
• Comfortable moving from research to production
• Consistent learner with daily hands-on practice

• Image Classification using Deep Learning
• Object Detection & Computer Vision Pipelines
• NLP-based Sentiment Analysis Systems
• ML-powered Web Applications
• Data Analysis & Insight Dashboards