Mastering Feature Engineering Principles and Techniques for Data Scientists

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
• Explore various techniques such as feature scaling, bin-counting, and frequent sequence mining
• Understand what is unsupervised feature learning and how it works in deep learning
• See the methods in action for text mining, image tagging, churn prediction, and targeting advertising

Table of Contents

Chapter 1. The Machine Learning Pipeline
Chapter 2. Fancy Tricks With Simple Numbers
Chapter 3. Text Data: Flattening, Filtering, And Chunking
Chapter 4. The Effects Of Feature Scaling: From Bag-Of-Words To Tf-Idf
Chapter 5. Categorical Variables: Counting Eggs In The Age Of Robotic Chickens
Chapter 6. Dimensionality Reduction: Squashing The Data Pancake With Pca
Chapter 7. Nonlinear Featurization Via K-Means Model Stacking
Chapter 8. Automating The Featurizer: Image Feature Extraction And Deep Learning
Chapter 9. Back To The Feature: Building An Academic Paper Recommender
Appendix A. Linear Modeling And Linear Algebra Basics