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Machine Learning and AI Foundations: Advanced Decision Trees with KNIME

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Every year, it seems, there is a new hot trend in data science. One of the hottest predictive analytics algorithms this year is gradient-boosted trees. One cannot hope to understand why it is popular and successful if one doesn’t understand the basics of decision trees. Specific tree algorithms have risen and fallen in popularity, but the core concepts have been fundamental to the discipline for at least 30 years. In this course, instructor Keith McCormick demonstrates and discusses a half-dozen popular decision tree algorithms. Keith shows how to access them using other open-source options from within the KNIME platform. He explains them and reverse engineers them to create a solid foundation on which to build more advanced data science skills.


01 - Introduction
  • 01 - Advanced decision trees
  • 02 - What you should know
  • 03 - Using the exercise files

  • 02 - 1. Exploring the Many Decision Tree Algorithms
  • 01 - Why are trees considered greedy algorithms
  • 02 - Why are there so many algorithms
  • 03 - Five low node or no code options in KNIME

  • 03 - 2. Using Extensions
  • 01 - Installing extensions
  • 02 - WEKA LMT demonstration
  • 03 - Interpreting the LMT results

  • 04 - 3. What Is Rule Induction
  • 01 - Comparing trees and rule induction
  • 02 - Rule induction demo
  • 03 - Interpreting the rules

  • 05 - 4. Low Code Python Options in KNIME
  • 01 - Low code options in KNIME
  • 02 - Python script node demo
  • 03 - CHAID demo in KNIME
  • 04 - Advanced code options in KNIME (optimal sparse trees)

  • 06 - 5. Ensembles and Random Forests
  • 01 - Introducing random forest
  • 02 - Random forests demo
  • 03 - Comparing two models

  • 07 - 6. Advanced Tips and Tricks
  • 01 - Data reduction with random forests
  • 02 - The XAI view node
  • 03 - Deployment

  • 08 - Conclusion
  • 01 - Final thoughts and recommendations
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    تاریخ انتشار: 29 دی 1401
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