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. Rennes onsite live Random Forest trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Rennes, City Center
Le Loft, 3, rue de Robien , Rennes, france, 35000
Subway Saint Anne
Parking Place Hoche
Bus 36 - 51 - 70 - 71 - 151ex
Training can also be organized on your premises in S...
Subway Saint Anne
Parking Place Hoche
Bus 36 - 51 - 70 - 71 - 151ex
Training can also be organized on your premises in Strasbourg or remotely. The 3rd option is to rent a room specifically for the duration of the course.
Training sessions can be given in English or French. For each session, we organize a coordination call with the trainer and participants to adapt the program to your expectations and level. Training includes theory, discussion, questions/answers and practical exercises.
Remote training is delivered via DaDesktop for training, a software package published by NobleProg that enables participants to have a workstation (VM) set up specifically for the training.
This instructor-led, live training in Rennes (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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