I build full-stack web applications, train machine learning models, and understand deep learning systems end to end - from raw data to deployment.
Databases
- DBMS concepts, SQL, SQLite, PostgreSQL, SQLAlchemy (ORM)
Servers and APIs
- Flask, Jinja templating, CRUD APIs, JSON, CORS, forms
- Session and JWT authentication, database integration
- Background tasks with Celery and Redis
- Deployment on GitHub Pages and Railway
HTML, CSS, JavaScript
- Advanced JS: event loop, async, ES modules, Fetch API
- CSS layout with Grid and Flexbox, animations, clean and modern UI
Vue.js and Vite
- Vue components, Vue SFC, state management, Vite build tool
NumPy, Pandas, CSV, Spreadsheets
- Data cleaning and filtering in Python, Google Sheets
Exploration and Visualization
- Descriptive statistics, correlation, outlier detection
- Trend and pattern analysis, charts and diagrams
- Tableau, Power BI
Preprocessing and Feature Engineering
- Imputation, scaling, feature creation, handling class imbalance
Regression, Classification, Clustering
- Supervised and unsupervised learning
- End-to-end preprocessing and training pipelines
- Handling text features like comments and reviews
Fundamentals
- Partial derivatives, gradient descent, backpropagation, neurons, MLP
Tasks
- Regression and classification using MLP
- Image classification using CNNs (convolution, kernels)
- Audio classification using librosa
Sequential Models
- RNNs (LSTM, GRU) for memory over sequences
- Transformers and self-attention for understanding relationships across a full sequence
GAN - Generator and Discriminator competing to produce realistic outputs
Diffusion - Denoising from random noise to structured output
Large Language Models
- Tokenization, embeddings, positional encoding
- Transformer architecture, self-attention, next token prediction