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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
Delivery Options
Private Group Training
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- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from £3800 online delivery, based on a group of 2 delegates, £1200 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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