Random Forest Training in Reading

Random Forest Training in Reading

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

NobleProg -- Your Local Training Provider

Reading

Reading Thames Valley Park
400 Thames Valley Park Drive Thames Valley Park
Reading, BRK RG6 1PT
United Kingdom
,
See map: Google Maps
Berkshire GB
Reading
Learn Random Forest in our training center in Reading. The Thames Valley Park business centre is in a prestigious, modern 200-acre business park and nature reserve idyllically positioned close to the south bank of the River Thames on the east side of Reading. The city has a significant historical involvement in the information technology industry and this attractive and highly sought-after location is home to an impressive array of well-known information technology companies. It is the UK headquarters for a world-leading software giant as well as a... Read more

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Random Forest Course Events - Reading

Random Forest Course Outlines in Reading

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