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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
Testimonials (5)
The trainer showed that he has a good understanding of the subject.
Marino - EQUS - The University of Queensland
Course - Machine Learning with Python – 2 Days
Keeping it short and simple. Creating intuition and visual models around the concepts (decision tree graph, linear equations, calculating y_pred manually to prove how the model works).
Nicolae - DB Global Technology
Course - Machine Learning
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
Convolution filter
Francesco Ferrara
Course - Introduction to Machine Learning
The trainer was so knowledgeable and included areas I was interested in.
