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. Valencia onsite live H2O AutoML trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Valencian Community
NobleProg Valencia, Plaza Manuel Sanchis Guarner, 10, Valencia, spain, 46004
Valencia is the third most populous city and metropolitan area in Spain, behind Madrid and Barcelona. With a population of mo...
Valencia is the third most populous city and metropolitan area in Spain, behind Madrid and Barcelona. With a population of more than 700,000.
Among its most representative monuments are the Miguelete, the Cathedral, the Towers of Serranos and Quart, the Lonja de la Seda, declared a World Heritage Site by Unesco in 1996, and the City of Arts and Sciences.
In addition to its airport the city of Valencia has lines of rail, tram, metro and buses that circulate throughout the metropolis.
The Hotel Senator Parque Central is located in the heart of Valencia, just a few minutes from the old town. Surrounded by shops, restaurants and entertainment venues, the hotel is well-connected: in front of the property you will find a bus stop that will take you to the beaches (5 km) and Xàtiva station is only a 15-minute walk away.
This instructor-led, live training in Valencia (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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