Random Forest Training in Brighton

Random Forest Training in Brighton

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 Brighton can be carried out locally on customer premises or in NobleProg corporate training centres.

NobleProg -- Your Local Training Provider

Brighton

3rd Floor, Queensberry House 106 Queens Road
Brighton, ESX BN1 3XF
United Kingdom
,
See map: Google Maps
East Sussex GB
Brighton
Learn Random Forest in our training center in Brighton.

This centre occupies a prominent location on Queens Road which links Brighton station to the Churchill Square shopping centre and the seafront. Queensberry House, which offers some sea views, is in the heart of the business district in this city. Brighton is a highly sophisticated and modern resort with a thriving economy and tourist industry. Millions of visitors flock here every year, both for leisure and to attend conferences and exhibitions at the Brighton Centre, one of the largest...

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Random Forest Course Events - Brighton

Random Forest Course Outlines in Brighton

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