Hands-on machine learning projects with full source code, detailed technical write-ups, and one-click Google Colab launchers. This page is designed to help you move from theory to practice: each category includes reproducible experiments, implementation notes, and model evaluation decisions so you can learn both how the pipeline works and why each choice matters.
Build end-to-end computer vision pipelines including SRGAN super-resolution and real-time face/eye detection with OpenCV. You will find data preprocessing, model architecture choices, qualitative/quantitative evaluation, and practical trade-offs for real-world inference speed.
Explore builds
Explore NLP systems from multilingual abstractive summarization with mT5 to document-based question answering with TF-IDF and NLTK. These projects cover tokenization, retrieval logic, decoding strategies, and how to evaluate output quality beyond simple accuracy metrics.
View NLP projects
Compare statistical and deep-learning forecasting methods on real temporal data, including S&P 500 ARIMA/LSTM modeling and demographic age-pyramid projections. The notebooks walk through stationarity checks, feature engineering, backtesting logic, and error analysis.
View forecasts
Understand reinforcement learning fundamentals through a Q-learning agent that improves game after game. You will see reward design, exploration-vs-exploitation behavior, convergence patterns, and how policy quality evolves with additional training episodes.
Open notebooksEach project category is designed to be practical and reproducible: you get end-to-end notebooks, documented modeling choices, and evaluation workflows you can reuse in your own portfolio, coursework, or client work.
If you are building a portfolio, start with one project per category and focus on documenting your assumptions, feature choices, and evaluation results. If you are studying for interviews, use the notebooks to practice explaining model decisions clearly, including why a simpler baseline may be preferable in some contexts.
Every category was selected to reflect common production scenarios: noisy inputs, limited compute, and the need for interpretable outcomes. This makes the content useful not just for learning concepts, but for building project stories that resonate with hiring managers, clients, or academic reviewers.