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Manifold Learning and Dimensionality Reduction for Data Visualization... - Stefan Kühn

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Published on Aug 1, 2018

PyData Berlin 2018

Dimensionality Reduction methods like PCA - Principal Component Analysis - are widely used in Machine Learning for a variety of tasks. But besides the well-known standard methods there are a lot more tools available, especially in the context of Manifold Learning. We will interactively explore these tools and present applications for Data Visualization and Feature Engineering using scikit-learn.

Slides:
https://de.slideshare.net/StefanKhn4/...

Jupyter-Notebooks:
https://github.com/cc-skuehn/Manifold...
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