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. Federal District onsite live Random Forest trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Mexico City - Mariano Escobedo
Mariano Escobedo 510, Ciudad de Mexico, mexico, 11590
The Mexico City Mariano Escobedo Center is on the 12th floor and penthouse of a tall, slimline commercial building with bold ...
The Mexico City Mariano Escobedo Center is on the 12th floor and penthouse of a tall, slimline commercial building with bold vertical detailing giving it instant kerb-appeal.
Mexico City - Cygni Santa Fe
Alfonso Nápoles Gandara 50, Mexico City, mexico, 1210
The Mexico City Cygni Santa Fé Center is on the top floor of a four-story building that is one of Mexico City's foremost bus...
The Mexico City Cygni Santa Fé Center is on the top floor of a four-story building that is one of Mexico City's foremost business addresses. It has landscaped grounds and an imposing entrance, and the center enjoys incredible views across to the high-rise skyline of Santa Fé, a leading district for financial services businesses.
This instructor-led, live training in Federal District (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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