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. Onsite live Random Forest trainings in Mexico City can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Mexico City - Colonia Del Valle
Av. Insurgentes Sur N° 800 , Mexico City, mexico, 3100
The Colonia Del Valle (HQ) Center is on the eighth floor of an attractive, 20-story steel-gray building on the Avenida de los...
The Colonia Del Valle (HQ) Center is on the eighth floor of an attractive, 20-story steel-gray building on the Avenida de los Insurgentes. The landmark Mexicana de Aviatión Tower is nearby, and the World Trade Center (WTC) is only two blocks away.
Mexico City - Torre Angel
Paseo de la Reforma 350, Mexico City, mexico, 6600
The Mexico City Torre Angel Center is on the 10th and 11th floors of a prestigious curved, reflective high-rise building with...
The Mexico City Torre Angel Center is on the 10th and 11th floors of a prestigious curved, reflective high-rise building with magnificent views to The Angel, the iconic monument to independence on the Paseo de la Reforma.
This instructor-led, live training in Mexico City (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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