Share Description
In this course, you will :
- Investigate what feature engineering is and how to get started using it on real-world data.
- Learn about its underlying data types and why they affect how you should design your features.
- make new features out of both categorical and continuous columns
- introduces you to the reality of illegible and incomplete data
- You will learn how to locate missing values in your data and investigate various approaches for dealing with them.
- Concentrate on determining how the underlying distribution of your data will affect your machine learning pipeline.
- Learn how to deal with skewed data and situations where outliers may have an adverse effect on your analysis.
Syllabus :
- Creating Features
- Dealing with Messy Data
- Conforming to Statistical Assumptions
- Dealing with Text Data
