This repository is about machine learning concepts that I learnt through the MOOC "Machine learning in python with scikit-learn" created by INRIA, and so Scikit-Learn's programers.
The background is all about "how to use the Scikit-Learn library" but the foreground of this MOOC is more of being critical in each step of the design of a predictive modeling pipeline: from choices in data preprocessing, to choosing models, gaining insights on their failure modes and interpreting their predictions.
Hence, this repository gives solid basis to initiate more complex insights of Machine Learning and gives also a general overview of the Scikit-Learn API.
This repository contains two different files :
MachineLearning_SumUp: focuses on general machine learning notions.Scikit-Learn_API: focuses on the Scikit-Learn API, so it is more a "how to use" file regarding the concepts established in the MachineLearning_SumUp file.
Machine learning contains a lot of notions whose overlaps with each others. I found interesting the mindnode format to get an overview of all the concepts and features to understand and understand the link between them.
I'm aware that everybody doesn't have the MindNode app so this repository contains various file formats :
.mindnodefor MindNode app.mmfor FreeMind app.pdf
All the code that you will find in those files are taken from the MOOC "Machine learning in python with scikit-learn" by INRIA.
Also, all the machine learning concepts, tips, and good practices come from the teachers and pedagogical team.
https://www.fun-mooc.fr/fr/cours/machine-learning-python-scikit-learn/
https://scikit-learn.org/stable/
All the teachers and the pedagogical team :
- Loïc Estève, Olivier Grisel, Guillaume Lemaître, Thomas Schmitt and Gaël Varoquaux which are or were Scikit-Learn developers
- Aurélie Lagarrigue, Laurence Farhi, Marie Collin and IT Benoit Rospars which are or were in the INRIA Learning Lab