scikit-learn-mooc
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INRIA
Machine learning in Python with scikit-learn MOOC
In order to pay tribute to this fantastic content, I'd love to cite this repo or this course. It would be wonderful, if you could share in the `README.md` how...
As discussed in [this forum post](https://mooc-forums.inria.fr/moocsl/t/exercise-m3-02-scaling/8179), the narrative in the [Exercise M3.02 notebook](https://inria.github.io/scikit-learn-mooc/python_scripts/parameter_tuning_sol_03.html) does not correspond to the best model found by a `RandomizedSearchCV` nor the results shown with more...
In [this forum comment](https://mooc-forums.inria.fr/moocsl/t/confusing-definition-of-bootstrap-sample/9537/8) Olivier proposed revisiting the video on bagging to clarify the concept of bootstrapping. , we should probably change content of the [linear regularization notebook](https://inria.github.io/scikit-learn-mooc/python_scripts/linear_models_regularization.html) to not recommend to average the minimum of the best alpha values on each...
Originally posted [here in the forum](https://mooc-forums.inria.fr/moocsl/t/different-outputs-for-linear-regression-coefficients/8000/3). Ridge regression with `alpha=1.0` leads to same result as the ordinary linear regression depending on the machine. This is possibly an Issue worth looking...
As discussed in [this forum post](https://mooc-forums.inria.fr/moocsl/t/many-thanks-to-inria-for-this-great-mooc/10949), we can improve the friendliness of the MOOC by adding a short encouraging message somewhere at the end of the first Module. Maybe after...
Add hyperlinks whenever a new concept is introduced (and not covered in detail), or as a reminder in key places. This may help clarifying the content without adding more text....
The full data-set (no train-test split or cv) is used for modeling in the following notebooks: - [linear_regression_without_sklearn.py](https://inria.github.io/scikit-learn-mooc/python_scripts/linear_regression_without_sklearn.html) - [linear_models_ex_01.py](https://inria.github.io/scikit-learn-mooc/python_scripts/linear_models_ex_01.html) and its [solution](https://inria.github.io/scikit-learn-mooc/python_scripts/linear_models_sol_01.html) - [linear_regression_in_sklearn.py](https://inria.github.io/scikit-learn-mooc/python_scripts/linear_regression_in_sklearn.html) - [linear_models_ex_02.py](https://inria.github.io/scikit-learn-mooc/python_scripts/linear_models_ex_01.html) and its [solution](https://inria.github.io/scikit-learn-mooc/python_scripts/linear_models_sol_02.html)...
Revert https://github.com/INRIA/scikit-learn-mooc/pull/604. It seems the problem has been fixed in IPython 8.2 https://github.com/ipython/ipython/pull/13588. This is probably better to do it after the MOOC session 2 completes.