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Course Outline
Introduction
- Overview of AdaBoost features and advantages for government applications
- Understanding ensemble learning methods in the context of public sector data analysis
Getting Started
- Setting up necessary libraries (Numpy, Pandas, Matplotlib, etc.) for government use
- Importing or loading datasets relevant to government operations
Building an AdaBoost Model with Python
- Preparing data sets for training in a government context
- Creating an instance using the AdaBoostClassifier for government applications
- Training the data model to meet public sector requirements
- Calculating and evaluating test data to ensure accuracy for government operations
Working with Hyperparameters
- Exploring hyperparameters in AdaBoost for optimizing government models
- Setting the values and training the model for government use
- Modifying hyperparameters to enhance performance in public sector applications
Best Practices and Troubleshooting Tips for Government Applications
Summary and Next Steps for Government Implementation
Requirements
- An understanding of machine learning concepts for government applications
- Python programming experience
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
Covered a lot of material.
