Feature engineering is essential to applied machine learning, but using domain knowledge to strengthen your predictive models can be difficult and expensive. To help fill the information gap on feature engineering, this complete hands-on guide teaches beginning-to-intermediate data scientists how to work with this widely practiced but little discussed topic.
Author Alice Zheng explains common practices and mathematical principles to help engineer features for new data and tasks. If you understand basic machine learning concepts like supervised and unsupervised learning, you’re ready to get started. Not only will you learn how to implement feature engineering in a systematic and principled way, you’ll also learn how to practice better data science.
- Learn exactly what feature engineering is, why it’s important, and how to do it well
- Use common methods for different data types, including images, text, and logs
- Understand how different techniques such as feature scaling and principal component analysis work
- Understand how unsupervised feature learning works in the case of deep learning for images