> For the complete documentation index, see [llms.txt](https://aisviz.gitbook.io/documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://aisviz.gitbook.io/documentation/machine-learning.md).

# Machine Learning

- [Clustering with Scikit Learn](https://aisviz.gitbook.io/documentation/machine-learning/clustering-with-scikit-learn.md): Find shipping lanes, anchorages, and vessel behavior groups in one day of NOAA AIS data by running DBSCAN and KMeans over AISdb tracks.
- [Kalman Filters with FilterPy](https://aisviz.gitbook.io/documentation/machine-learning/kalman-filters-with-filterpy.md): Turn a jittery AIS track into a smooth path and a running velocity estimate with a constant-velocity Kalman filter in FilterPy, then flag GPS outliers with the filter's innovation.
- [seq2seq in PyTorch](https://aisviz.gitbook.io/documentation/machine-learning/seq2seq-in-pytorch.md): Build an attention-based encoder-decoder LSTM in PyTorch that forecasts a vessel's next positions from its recent AIS track, using AISdb to query, clean, and reshape the trajectories for training.
- [AutoEncoders in Keras](https://aisviz.gitbook.io/documentation/machine-learning/autoencoders-in-keras.md): A GRU autoencoder in Keras that compresses four hours of vessel track and decodes an eight-hour forecast, trained with probabilistic teacher forcing.
- [Embedding with traj2vec](https://aisviz.gitbook.io/documentation/machine-learning/embedding-with-traj2vec.md): Discretize AIS trajectories onto an H3 grid and train a t2vec-style encoder-decoder in PyTorch that embeds vessel tracks as fixed-length vectors.
- [Using Newtonian PINNs](https://aisviz.gitbook.io/documentation/machine-learning/using-newtonian-pinns.md): A physics-informed Seq2Seq model that penalizes unrealistic velocity and acceleration in predicted vessel tracks, so forecasts stay kinematically plausible even when AIS data is sparse or noisy.
- [TGNs with TorchGeometric](https://aisviz.gitbook.io/documentation/machine-learning/tgns-with-torchgeometric.md): Turn one day of NOAA AIS data into a temporal transit graph with AISdb and train a Temporal Graph Network on it with PyTorch Geometric.
- [Building a RAG Chatbot](https://aisviz.gitbook.io/documentation/machine-learning/building-a-rag-chatbot.md): Build a documentation chatbot with Retrieval-Augmented Generation. Scrape the AISViz docs, index them in ChromaDB, and ground Gemini answers in real pages that every response cites.
- [A No-Code Interface](https://aisviz.gitbook.io/documentation/machine-learning/a-no-code-interface.md): Preprocess, discretize, and enrich AIS tracks through a point-and-click Gradio app, with built-in plotting and a Gemini assistant, no Python script required.


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