Random Forest Training in Cambridge

Random Forest Training in Cambridge

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

NobleProg -- Your Local Training Provider

Cambridge

Cambridge
Compass House Vision Park, Chivers Way, Histon
Cambridge, CAM CB24 9AD
United Kingdom
,
See map: Google Maps
Cambridgeshire GB
Cambridge
Learn Random Forest in our training center in Cambridge. Vision Park is within easy reach of Cambridge city centre and has the advantage of excellent links with the M11 and A14. Trains from London Kings Cross arrive every 30 minutes, with a 15 minute journey by taxi to the Park. Histon Railway Station is on the boundary of Vision Park, which provides high quality, frequent local transport from St Ives to Cambridge. Guided Bus Vision Park lies alongside the Cambridgeshire Guided Bus which offers fast and frequent buses from Histon to... Read more

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Random Forest Course Events - Cambridge

Random Forest Course Outlines in Cambridge

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