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Doing the full numpy and matplotlib chapters may be overkill for someone who wants to get a quick understanding of the basic before looking at a chapter like sympy, statistics, or scikit-learn.
The various application-specific chapters, such as the sympy, statistics, or scikit-learn could than refer to this section, for people who google and land directly on them.
The text was updated successfully, but these errors were encountered:
@GaelVaroquaux I will give it a try. Just starting to read the CONTRIBUTING.rst. Trying to figure out to translate the requirements section for MacOSX.
Doing the full numpy and matplotlib chapters may be overkill for someone who wants to get a quick understanding of the basic before looking at a chapter like sympy, statistics, or scikit-learn.
The way I tackle this is usually with a super brief introduction to the basic: https://github.com/GaelVaroquaux/sklearn_ensae_course/blob/master/rendered_notebooks/01_data_manipulation.ipynb
I suggest to add the above notebook as a last section to the introduction chapter (the first one), skipping the scipy sparse stuff, and the 3D plotting part.
The various application-specific chapters, such as the sympy, statistics, or scikit-learn could than refer to this section, for people who google and land directly on them.
The text was updated successfully, but these errors were encountered: