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Plan Szkolenia
Introduction
- Overview of Random Forest features and advantages
- Understanding decision trees and ensemble methods
Getting Started
- Setting up the libraries (Numpy, Pandas, Matplotlib, etc.)
- Classification and regression in Random Forests
- Use cases and examples
Implementing Random Forest
- Preparing data sets for training
- Training the machine learning model
- Evaluating and improving accuracy
Tuning the Hyperparameters in Random Forest
- Performing cross-validations
- Random search and Grid search
- Visualizing training model performance
- Optimizing hyperparameters
Best Practices and Troubleshooting Tips
Summary and Next Steps
Wymagania
- An understanding of machine learning concepts
- Python programming experience
Audience
- Data scientists
- Software engineers
14 godzin
Opinie uczestników (5)
Ćwiczenia praktyczne.
Adam Borowski - NetWorkS! Sp. z o.o.
Szkolenie - AI Awareness for Telecom
Trener bardzo zrozumiale wytłumaczył trudne i zaawansowane tematy.
Leszek K
Szkolenie - Artificial Intelligence Overview
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
Szkolenie - Applied AI from Scratch in Python
The details and the presentation style.
Cristian Mititean - Accenture Industrial SS
Szkolenie - Azure Machine Learning (AML)
Working from first principles in a focused way, and moving to applying case studies within the same day