Welcome

Here you will find the source code for the book Machine Learning Pocket Reference

Code Examples

Every chapter has a notebook with the code from that notebook.

Contents

1. Introduction
2. Overview of the Machine Learning Process
3. Classification Walkthrough: Titanic Dataset
4. Missing Data
5. Cleaning Data
6. Exploring
7. Preprocess Data
8. Feature Selection
9. Imbalanced Classes
10. Classification
11. Model Selection
12. Metrics and Classification Evaluation
13. Explaining Models
14. Regression
15. Metrics and Regression Evaluation
16. Explaining Regression Models
17. Dimensionality Reduction
18. Clustering
19. Pipelines

Thanks!

Thanks to readers for their support. If you enjoyed the book, please consider leaving a review on Amazon, or sharing it on social media.

Comments?

If you have comments or issues with the book, please consider filing an issue. The digital version may recieve updates. Big updates could be addressed in future versions of the book.

Thanks again! Matt Harrison