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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 (2)
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
It helped me achieve my goal of understanding ML. Much respect for Pablo for giving a proper introduction in this topic, since it becomes obvious after 3 days of training how vast this topic is. I have also enjoyed A LOT the idea of virtual machines you have provided, which had very good latency! It allowed every coursant to do experiments at their own pace.
