Random Forest Training in Leeds

Random Forest Training in Leeds

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

NobleProg -- Your Local Training Provider

Leeds

No 2 Wellington Place
Leeds, WYK LS1 4AP
United Kingdom
,
See map: Google Maps
West Yorkshire GB
Leeds
Learn Random Forest in our training center in Leeds.

This centre is positioned in the middle of Leeds in Wellington Place, which covers 21 acres and will ultimately provide over three million square feet of offices, residential, hotel, retail and leisure facilities. Extensive River frontage, inspiring public spaces and piazzas and galleries add to its appeal. This development on a grand scale is designed to act as a catalyst for securing Leeds' status as the country's second commercial centre. The vision is to provide an environment that...

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Random Forest Course Events - Leeds

Random Forest Course Outlines in Leeds

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