Random Forest Training in Leicester

Random Forest Training in Leicester

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 Leicester or in NobleProg corporate training centres in Leicester.

NobleProg -- Your Local Training Provider

Leicester - Knighton Road

College Court Conference Centre
Knighton Road
Leicester, LEI LE2 3UF
United Kingdom
,
See map: Google Maps
Leicestershire GB
Leicester - Knighton Road
Learn Random Forest in our training center in Leicester. A passion for hospitality… A quick glance at College Court and it is clear to see – we are different. Our Grade II listed building is packed with quirky design features that reflect its 1960 origins. Originally built as a hall of residence for University of Leicester students, the days of student living have long since gone and following a £17m refurbishment, College Court is now a state-of-the-art conference venue and hotel. Maybe it is the warm welcome visitors receive or the bespoke... Read more

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Random Forest Course Events - Leicester

Random Forest Course Outlines in Leicester

Course Name
Duration
Overview
Course Name
Duration
Overview
14 hours
This instructor-led, live training in Leicester (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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