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Course Outline
Introduction
- Overview of AdaBoost features and advantages for government applications
- Understanding ensemble learning methods in a public sector context
Getting Started
- Setting up the 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 alignment with public sector workflows
- Creating an instance with AdaBoostClassifier to support government initiatives
- Training the data model to enhance governance and accountability
- Calculating and evaluating the test data to ensure reliability for government purposes
Working with Hyperparameters
- Exploring hyperparameters in AdaBoost to optimize performance for government tasks
- Setting the values and training the model to meet public sector standards
- Modifying hyperparameters to improve performance in government applications
Best Practices and Troubleshooting Tips for government projects
Summary and Next Steps for government implementation
Requirements
- A comprehensive understanding of machine learning concepts for government applications
- Proficiency in Python programming for government tasks
Audience
- Data scientists working in the public sector
- Software engineers supporting government projects
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.
