A collection of practical Jupyter notebooks for applied machine learning, deep learning, generative AI, and analytics.
Developed alongside technical articles on Relataly.com, this repository has grown to nearly 160 stars and 90+ forks.
| Area | Examples |
|---|---|
| Applied machine learning | Anomaly detection, feature selection, hyperparameter tuning, and churn prediction |
| Deep learning | Neural networks, computer vision, and recurrent models |
| Time series | Forecasting and stock-market prediction |
| Generative AI | OpenAI and ChatGPT examples, prompt engineering, and API-based workflows |
| Recommender systems | Collaborative filtering and content-based methods |
| Analytics and data engineering | Visualization, geographic analysis, and PySpark |
- Browse the repository for individual
.ipynbprojects. - Open a notebook locally or in a compatible hosted notebook environment.
- Review its imports and install dependencies in an isolated Python environment.
- Consult the related article on Relataly.com when available.
The notebooks were published over time and may reflect APIs or library versions current at their original publication date. Validate dependencies and outputs before using them in production.
Relataly Public Python API Tutorials
This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.