Online or onsite, instructor-led live H2O AutoML training courses demonstrate through interactive hands-on practice how to use H2O AutoML to automoate the process of building and selecting the best machine learning algorithm and parameters.
H2O AutoML training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Barcelona onsite live H2O AutoML trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Barcelona City
NobleProg Barcelona, C/ de Mallorca, 501, Barcelona, spain, 08013
With a population of 1 604 555 inhabitants and constantly growing, Barcelona is the second most populated city of Spain after...
With a population of 1 604 555 inhabitants and constantly growing, Barcelona is the second most populated city of Spain after Madrid, and the eleventh of the European Union. It is located on the shores of the Mediterranean Sea, about 120 km south of the A mountainous chain of the Pyrenees and the border with France and is globally recognized as a global city because of its cultural, financial, commercial and tourist importance.
It has a great service of public transport called TMB formed by diverse lines of railroad, tram, metro and buses that circulate throughout the city.
Located in a magnificent location in "L'Esquerra de l'Eixample", one of the main business centers of Barcelona, with universities, metro, buses, Sants station, post office and all the services you can need a few steps away
This instructor-led, live training in Barcelona (online or onsite) is aimed at data scientists who wish to use H2O AutoML to automoate the process of building and selecting the best machine learning algorithm and parameters.
By the end of this training, participants will be able to:
Automate the machine learning workflow.
Automatically train and tune many machine learning models within a specified time range.
Train stacked ensembles to arrive at highly predictive ensemble models.
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