AdaBoost is an algorithm that is used together with other machine learning models for optimal performance. It uses ensemble learning techniques, combining weaker models to form more accurate predictions.
This instructor-led, live training (online or onsite) is aimed at data scientists and software engineers who wish to use AdaBoost to build boosting algorithms for machine learning with Python.
By the end of this training, participants will be able to:
- Set up the necessary development environment to start building machine learning models with AdaBoost.
- Understand the ensemble learning approach and how to implement adaptive boosting.
- Learn how to build AdaBoost models to boost machine learning algorithms in Python.
- Use hyperparameter tuning to increase the accuracy and performance of AdaBoost models.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
- An understanding of machine learning concepts
- Python programming experience
- Data scientists
- Software engineers
- Overview of AdaBoost features and advantages
- Understanding ensemble learning methods
- 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