Applied Machine Learning: Ensemble Learning (2022)

Course details

Do you want to grow your skills as a machine learning practitioner, but don’t know where to begin? You don’t need any formal training in data science to start working toward your goal. In this course, instructor Derek Jedamski shows you how to harness messy data, find signal in it, and build models that make powerful predictions with ensemble learners, one of the most common classes of machine learning algorithms.

Review the basics of the machine learning pipeline to find out where ensemble learners sit within it. Learn about the underlying theory that drives ensemble learners, covering examples of ensemble learning in Python and then implementing models of your own. Explore concepts like boosting, bagging, and stacking, and how to use each and when. Get the tools you need to ramp up your predicting power and advance your machine learning skills today.

Concepts

0. Introduction

01 - Explore ensemble learning
02 - What you should know
03 - What tools you need
04 - Using the exercise files

1. Review Machine Learning Basics

05 - What is machine learning
06 - What does machine learning look like in real life
07 - What does an end-to-end machine learning pipeline look like
08 - Bias-Variance trade-off

2. Preparing the Data

09 - Reading in the data
10 - Cleaning up continuous features
11 - Cleaning up categorical features
12 - Write out all train, validation, and test sets

3. What is Ensemble Learning

13 - What is ensemble learning
14 - How does ensemble learning work
15 - Why is ensemble learning so powerful

4. Boosting

16 - What is boosting
17 - How does boosting reduce overall error
18 - When should you consider using boosting
19 - What are examples of algorithms that use boosting
20 - Explore boosting algorithms in Python
21 - Implement a boosting model

5. Bagging

22 - What is bagging
23 - How does bagging reduce overall error
24 - When should you consider using bagging
25 - What are examples of algorithms that use bagging
26 - Explore bagging algorithms in Python
27 - Implement a bagging model

6. Stacking

28 - What is stacking
29 - How does stacking reduce overall error
30 - When should you consider using stacking
31 - What are examples of algorithms that use stacking
32 - Explore stacking algorithms in Python
33 - Implement a stacking model

Conclusion

34 - Compare the three methods
35 - Compare all models on validation set
36 - How to continue advancing your skills