Course Outline


  • 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


  • An understanding of machine learning concepts
  • Python programming experience


  • Data scientists
  • Software engineers
 14 Hours

Testimonials (4)

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