Random Forest Training in Plymouth

Random Forest Training in Plymouth

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. Plymouth onsite live Random Forest trainings can be carried out locally on customer premises or in NobleProg corporate training centres.

NobleProg -- Your Local Training Provider

Plymouth - Charles Street

Regus Drake Circus
Unit MSU9A, Level 1 1 Charles Street
Plymouth, DEV PL1 1EA
United Kingdom
,
See map: Google Maps
Devon GB
Plymouth - Charles Street
Learn Random Forest in our training center in Plymouth. Business travellers now have somewhere to work and meet while on the go – walk-in workspace at Drake Circus shopping centre in Plymouth. Our business lounge features flexible workspace and desks along with meeting rooms to rent by the day, half-day or hour. Work smart and meet your clients in a professional environment. A virtual office service is also available. It’s easy to access by car and there’s plentiful parking at this site close to Plymouth University and the City Museum. The... Read more

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Random Forest Course Events - Plymouth

Random Forest Course Outlines in Plymouth

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