1. Wrap-up
Congratulations on completing the course “Feature Engineering for Machine Learning in Python”. This course set out to teach you about understanding your data types and how best to prepare your dataset for a machine learning model. Let's take a moment to recap what you have covered.
2. Chapter 1
In chapter one, you learned how to better understand the underlying types of data contained in your dataset, how to create features out of categorical columns and how to bin continuous columns.
3. Chapter 2
In chapter two, we moved on to exploring how to deal with some of the challenges of real world data, such as missing values and non desirable characters in your data.
4. Chapter 3
Chapter 3 discussed how different distributions can effect your models and how to mitigate it, and different ways to deal with spurious outlier values in your dataset.
5. Chapter 4
Finally in chapter 4, we explored how to deal with non tabular data such as free text and different ways to encode it for use with a machine learning model.
6. Next steps
Hopefully these newly learned skills should benefit both your personal projects and your professional careers. A great place to test out these skills is to try applying them to kaggle competitions or any of your own pet projects to see if they improve your results.
Or, if you want to explore these topics further, perhaps you could try out some of the other related courses on DataCamp.
7. Thank You!
This is the final video, and would like to thank you for going through this course. I hope you have learned from it and it provides value in your machine learning work ahead.