Explore how to make powerful, accurate predictions with ensemble learners, one of the most common classes of machine learning algorithms.
Overview
Syllabus
Introduction
- Explore ensemble learning
- What you should know
- What tools you need
- Using the exercise files
- 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
- Reading in the data
- Cleaning up continuous features
- Cleaning up categorical features
- Write out all train, validation, and test sets
- What is ensemble learning?
- How does ensemble learning work?
- Why is ensemble learning so powerful?
- 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
- 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
- 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
- Compare the three methods
- Compare all models on validation set
- How to continue advancing your skills
Taught by
Derek Jedamski
