Local instructor-led live Random Forest training courses in Moldova.
Mărturii
The trainer because he could handle almost every subject and situation.
Florin Babes - ANDREEA TOADER, eMAG IT RESEARCH SRL
Curs: Machine Learning with Python – 4 Days
very good trainer, very good explanations of every topic
ANDREEA TOADER, eMAG IT RESEARCH SRL
Curs: Machine Learning with Python – 4 Days
The manner in which the trainer explained the concepts, his positive and welcoming attitude and the real-world examples provided for each exercise.
Ovidiu Calita - ANDREEA TOADER, eMAG IT RESEARCH SRL
Curs: Machine Learning with Python – 4 Days
The amount of knowledge
ANDREEA TOADER, eMAG IT RESEARCH SRL
Curs: Machine Learning with Python – 4 Days
Very good training session with nice documentation and exercises and Kristian did it like a professional he is.
Adrian Boulescu - ANDREEA TOADER, eMAG IT RESEARCH SRL
Curs: Machine Learning with Python – 4 Days
I like that he is very skilled and has lots of knowledge in his domain.
dan dumitriu - ANDREEA TOADER, eMAG IT RESEARCH SRL
Curs: Machine Learning with Python – 4 Days
rich documentation and many resources as course support, as well as resources for the post-course learning process
Virgil Mihai - ANDREEA TOADER, eMAG IT RESEARCH SRL
Curs: Machine Learning with Python – 4 Days
Random Forest Course Outlines
Numele cursului
Durata
Sinoptic
Numele cursului
Durata
Sinoptic
14 hours
This instructor-led, live training in Moldova (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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