Random Forest Training in Glasgow

Random Forest Training in Glasgow

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

NobleProg -- Your Local Training Provider

Glasgow

Glasgow
69 Buchanan St
Glasgow, LKS G1 3HL
United Kingdom
,
See map: Google Maps
Lanarkshire GB
Glasgow
Learn Random Forest in our training center in Glasgow.

The Buchanan Street Centre is located in the heart of Glasgow, in Scotland's most famous shopping and retail district. A major feature of this stunning large Victorian building is the impressive sandstone entrance archway giving access to four floors. Glasgow's fantastic array of shops, hotels, restaurants and bars are on the doorstep and Buchanan Street itself is home to a number of exclusive shops and recently won the Academy of Urbanism 'Great Street Award'. One of the fastest growing...

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Random Forest Course Events - Glasgow

Random Forest Course Outlines in Glasgow

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