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LinkedIn Learning

Applied Machine Learning: Ensemble Learning

via LinkedIn Learning

Overview

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Explore how to make powerful, accurate predictions with ensemble learners, one of the most common classes of machine learning algorithms.

Syllabus

Introduction
  • Explore ensemble learning
  • What you should know
  • What tools you need
  • Using the exercise files
1. Review Machine Learning Basics
  • What is machine learning?
  • What does machine learning look like in real life?
  • What does an end-to-end machine learning pipeline look like?
  • Bias-Variance trade-off
2. Preparing the Data
  • Reading in the data
  • Cleaning up continuous features
  • Cleaning up categorical features
  • Write out all train, validation, and test sets
3. What is Ensemble Learning?
  • What is ensemble learning?
  • How does ensemble learning work?
  • Why is ensemble learning so powerful?
4. Boosting
  • What is boosting?
  • How does boosting reduce overall error?
  • When should you consider using boosting?
  • What are examples of algorithms that use boosting?
  • Explore boosting algorithms in Python
  • Implement a boosting model
5. Bagging
  • What is bagging?
  • How does bagging reduce overall error?
  • When should you consider using bagging?
  • What are examples of algorithms that use bagging?
  • Explore bagging algorithms in Python
  • Implement a bagging model
6. Stacking
  • What is stacking?
  • How does stacking reduce overall error?
  • When should you consider using stacking?
  • What are examples of algorithms that use stacking?
  • Explore stacking algorithms in Python
  • Implement a stacking model
Conclusion
  • Compare the three methods
  • Compare all models on validation set
  • How to continue advancing your skills

Taught by

Derek Jedamski

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