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Course Outline
Introduction
- Overview of AdaBoost features and advantages.
- Understanding ensemble learning methods.
Getting Started
- Setting up libraries (Numpy, Pandas, Matplotlib, etc.).
- Importing or loading datasets.
Building an AdaBoost Model with Python
- Preparing datasets for training.
- Creating an instance using AdaBoostClassifier.
- Training the data model.
- Calculating and evaluating the test data.
Working with Hyperparameters
- Exploring hyperparameters in AdaBoost.
- Setting values and training the model.
- Modifying hyperparameters to improve performance.
Best Practices and Troubleshooting Tips
Summary and Next Steps
Requirements
- A foundational understanding of machine learning concepts.
- Experience with Python programming.
Audience
- Data scientists.
- Software engineers.
14 Hours
Testimonials (3)
Training style and the overall knowledge of the trainer.
Kenosi - NWK Limited
Course - Laravel: Middleware Development
The lessons was very interactive and the excersices was good practical
Heino - NWK Limited
Course - Laravel and Vue.js
he was explaining and giving numerous examples to make us understand
