These educational videos present the Edge-ML solutions. They are for Data-Scientists and Data-Engineers, but also for Business Managers, Marketing and deciders. These videos are classified into 3 categories in order to address the different audiences in a better way.
Mathematics and algorithmics
Technique and implementation
Applications and use cases
This video presents the mathematical approches MODL used by Edge-ML in the phase of pre-treatment of data :
Those stages come before the construction of the Set classifier which will be introduced in the next video.
This video presents the next step of automated pipe of Machine Learning implemented by Edge-ML. The aim is to build multi-varied classifier without parameters to optimise, starting from mono-varied pre-treatments (discretisation and grouping).
More precisely, this talk presents 4 ways of improvement of the Naive Bayes classifier, in order to make this classifier more accurate while keeping its robustness.
This video presents the MODL approch devised for the preparation of the sequential data (texts, web sessions, logs…). The aim is to identify the sub-sequences of the data set allowing to describe in a precise and robust way the distribution of the classes to be predicted (Auto Features Engineering).