http://arogozhnikov.github.io/RocCurve.html

Touilleur de GB

http://arogozhnikov.github.io/2016/07/05/gradient_boosting_playground.html

Touilleur de CNN

https://poloclub.github.io/cnn-explainer/

https://arogozhnikov.github.io/2016/04/28/demonstrations-for-ml-courses.html

Everything you need to know about dataprepare.eda.

https://towardsdatascience.com/dataprep-eda-accelerate-your-eda-eb845a4088bc

https://towardsdatascience.com/understanding-generative-adversarial-networks-gans-cd6e4651a29

Soft introduction to some Machine Learning and Deep Learning concepts.

https://towardsdatascience.com/a-gentle-journey-from-linear-regression-to-neural-networks-68881590760e

http://gael-varoquaux.info/interpreting_ml_tuto/index.html

Understanding and diagnosing your machine-learning models

Achieving a good prediction is often only half of the job. Questions immediately arise: How to improve this prediction? What drives the prediction? Can we operate changes to the system based on the predictions? All these questions require understanding how good is the model prediction, and how do the model predict.

This tutorial assumes basic knowledge of scikit-learn. It will focus on statistics, tests, and interpretation rather than improving the prediction.