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 training can be carried out locally on customer premises in Santiago or in NobleProg corporate training centers in Santiago.
The Center occupies the fifth and ninth floors of a modern office block with an attractive, spacious reception area. It has i...
The Center occupies the fifth and ninth floors of a modern office block with an attractive, spacious reception area. It has its own secure underground parking and is protected by 24-hour CCTV. It is just a few minutes' walk from an underground railway station. Arturo Meriono Benitez International Airport is 26km away.
The Santiago Vitacura Center is located on the 15th floor of one of the newest high-rise buildings in the city, situated near...
The Santiago Vitacura Center is located on the 15th floor of one of the newest high-rise buildings in the city, situated near El Bosque, an attractive business and residential district, and adjacent to the Santiago World Trade Center. The Center has its own on-site coffee bar and secure underground parking. There are good public transport connections, and the international airport is 40 minutes away.
This instructor-led, live training in Santiago (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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