Random Forest is an algorithm for machine learning that is used mostly for classification and regression. It utilizes multiple decision trees to generate more precise and accurate predictions.
This instructor-led, live training (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.
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
주말Random Forest코스, 밤의Random Forest트레이닝, Random Forest부트 캠프, Random Forest 강사가 가르치는, 주말Random Forest교육, 밤의Random Forest과정, Random Forest코칭, Random Forest강사, Random Forest트레이너, Random Forest교육 과정, Random Forest클래스, Random Forest현장, Random Forest개인 강좌, Random Forest1 대 1 교육
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