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. Malaga onsite live Random Forest trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Malaga City Center
NobleProg Málaga, Street Cortina del Muelle, 1, Malaga, spain, 29015
Malaga, Andalusian city with more than 3,000 years of history, from its Phoenician origins to the beautiful and cosmopolitan ...
Malaga, Andalusian city with more than 3,000 years of history, from its Phoenician origins to the beautiful and cosmopolitan city that loves citizens from all over the world. Located in the south of the Iberian Peninsula, it is characterized by having multiple geographical sectors, such as Its abundant beaches or its high mountains.
The airport is connected to the city center and with the Málaga-María Zambrano Station by the Cercanías railway line and by the A Express of the EMT, which also has a stop inside the Bus Station, where you can link to the destination To other Spanish cities and the Costa del Sol.
This instructor-led, live training in Malaga (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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