Online or onsite, instructor-led live Random Forest training courses demonstrate through interactive hands-on practice how to use Random Forest to build machine learning algorithms for large datasets.
Random Forest 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. Toulouse onsite live Random Forest trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Toulouse, City Center
Mutliburo Toulouse, 9 Rue de Sébastopol, Toulouse, france, 31000
The business center is located in the Compans business district on the edge of Compans Caffarelli Park, which extends over...
The business center is located in the Compans business district on the edge of Compans Caffarelli Park, which extends over 10 hectares.
Many businesses have settled in this area, attracted both by the situation between the historic heart and the ring road that leads to the airport and the modernity of the site. Note the proximity of the conference center of Toulouse.
The business center offers its fully equipped offices and 2 meeting rooms to organize trainings and business meetings. All rooms are air-conditioned. Your visitors will be welcomed by our teams who will organize your day (preparation of rooms, internet connections, coffee break, catering, secretarial, hotel booking
This instructor-led, live training in Toulouse (online or onsite) is aimed at data scientists and software engineers who wish to use Random Forest to build machine learning algorithms for large datasets.
By the end of this training, participants will be able to:
Set up the necessary development environment to start building machine learning models with Random forest.
Understand the advantages of Random Forest and how to implement it to resolve classification and regression problems.
Learn how to handle large datasets and interpret multiple decision trees in Random Forest.
Evaluate and optimize machine learning model performance by tuning the hyperparameters.
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