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Course Outline
Introduction
- Overview of AdaBoost features and advantages
- Understanding ensemble learning methods
Getting Started
- Setting up the libraries (Numpy, Pandas, Matplotlib, etc.)
- Importing or loading datasets
Building an AdaBoost Model with Python
- Preparing data sets for training
- Creating an instance with AdaBoostClassifier
- Training the data model
- Calculating and evaluating the test data
Working with Hyperparameters
- Exploring hyperparameters in AdaBoost
- Setting the values and training the model
- Modifying hyperparameters to improve performance
Best Practices and Troubleshooting Tips
Summary and Next Steps
Requirements
- An understanding of machine learning concepts
- Python programming experience
Audience
- Data scientists
- Software engineers
14 Hours
Testimonials (5)
Ćwiczenia praktyczne.
Adam Borowski - NetWorkS! Sp. z o.o.
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Trener bardzo zrozumiale wytłumaczył trudne i zaawansowane tematy.
Leszek K
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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
Course - Applied AI from Scratch in Python
The details and the presentation style.
Cristian Mititean - Accenture Industrial SS
Course - Azure Machine Learning (AML)
Working from first principles in a focused way, and moving to applying case studies within the same day