Ensemble Machine Learning Techniques [Video]

Ensemble Machine Learning Techniques [Video]

Arish Ali

Combine multiple machine learning algorithms to build models with higher accuracy
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Video Description

Ensemble is a powerful way to upgrade your model as it combines models and doesn't assume a single model is the most accurate. But what if we combine these models as a way to drop those limitations to produce a much more powerful classifier or regressor?

This course will show you how to combine various models to achieve higher accuracy than base models can. This has been the case in various contests such as Netflix and Kaggle, where the winning solutions used ensemble methods.

If you want more than a superficial look at machine learning models and wish to build reliable models, then this course is for you.

The code bundle is placed at this link https://github.com/PacktPublishing/Ensemble-Machine-Learning-Techniques-

Style and Approach

This fast-paced course offers practical and hands-on guidance with step-by-step instructions. This course will enable you to develop your own ensemble learning models and methods to use them efficiently.

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What You Will Learn

  • Apply ensemble techniques in your models with the power of machine-learning algorithms and the simplicity of Python
  • Apply decision trees to real datasets
  • Develop robust models using the bagging technique
  • Transform your weak models to strong models using boosting
  • Learn how to combine different types of model sequentially

Authors

Video Details

ISBN 139781788392716
Course Length1 hours 23 minutes
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