Monitor performance issues & errors in your code
Machine Learning with Python and scikit-learn
Episode #31,
published Tue, Oct 27, 2015, recorded Fri, Sep 25, 2015
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Machine learning allows computers to find hidden insights without being explicitly programmed where to look or what to look for. Thanks to the work of some dedicated developers, Python has one of the best machine learning platforms called scikit-learn. In this episode, Alexandre Gramfort is here to tell us all about scikit-learn and machine learning.
Links from the show:
scikit-learn: scikit-learn.org
Alexandre's website: alexandre.gramfort.net
Alexandre on Twitter: @agramfort
Novel Machine Learning: forbes.com/sites/85broads/2014/01/06/six-novel-machine-learning-applications
Kaggle competitions: kaggle.com
scikit-learn on github: github.com/scikit-learn/scikit-learn
scikit-learn datasets: scikit-learn.org/stable/datasets
Links from the show:
scikit-learn: scikit-learn.org
Alexandre's website: alexandre.gramfort.net
Alexandre on Twitter: @agramfort
Novel Machine Learning: forbes.com/sites/85broads/2014/01/06/six-novel-machine-learning-applications
Kaggle competitions: kaggle.com
scikit-learn on github: github.com/scikit-learn/scikit-learn
scikit-learn datasets: scikit-learn.org/stable/datasets
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Alexandre Gramfort
Alexandre Gramfort is currently an assistant professor at Telecom ParisTech and scientific consultant for the CEA Neurospin brain imaging center. His work is on statistical machine learning, signal and image processing, optimization, scientific computing and software engineering with primary applications in brain functional imaging (MEG, EEG, fMRI). Before joining Telecom ParisTech, he worked at the Martinos Center for Biomedical Imaging at Harvard in Boston. He is also an active member of the Center for Data Science at Universite Paris-Saclay.
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