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 Madrid can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Madrid - Chamartín Business Area
NobleProg Madrid, Street Bausá 27, Madrid, spain, 28033
Madrid is a municipality and city of Spain is the most populated city of the country, with 3 165 235 inhabitants. On the econ...
Madrid is a municipality and city of Spain is the most populated city of the country, with 3 165 235 inhabitants. On the economic level, it is the fourth richest city in Europe, after London, Paris and Moscow. It is an influential cultural center and has museums of international reference, among which stand out the Prado Museum, the National Museum of Art Reina Sofia, Thyssen-Bornemisza and CaixaForum Madrid.
Being one of the largest cities in all of Spain and one of the most populated, it is characterized by having an extensive transport network consisting of rail, metro and bus lines, in addition to its airport.
Located in the business area of Chanmartin five minutes from Plaza Castilla and less than ten minutes from the airport Madrid Barajas and Madrid downtown area.
This instructor-led, live training in Madrid (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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