Deliver huge improvements to your machine learning pipelines without spending hours fine-tuning parameters! This book’s practical case-studies reveal feature engineering techniques that upgrade your data wrangling—and your ML results.

In Feature Engineering Bookcamp you will learn how to:

• Identify and implement feature transformations for your data
• Build powerful machine learning pipelines with unstructured data like text and images
• Quantify and minimize bias in machine learning pipelines at the data level
• Use feature stores to build real-time feature engineering pipelines
• Enhance existing machine learning pipelines by manipulating the input data
• Use state-of-the-art deep learning models to extract hidden patterns in data

Feature Engineering Bookcamp guides you through a collection of projects that give you hands-on practice with core feature engineering techniques. You’ll work with feature engineering practices that speed up the time it takes to process data and deliver real improvements in your model’s performance. This instantly-useful book skips the abstract mathematical theory and minutely-detailed formulas; instead you’ll learn through interesting code-driven case studies, including tweet classification, COVID detection, recidivism prediction, stock price movement detection, and more.

About the technology

Get better output from machine learning pipelines by improving your training data! Use feature engineering, a machine learning technique for designing relevant input variables based on your existing data, to simplify training and enhance model performance. While fine-tuning hyperparameters or tweaking models may give you a minor performance bump, feature engineering delivers dramatic improvements by transforming your data pipeline.

About the book

Feature Engineering Bookcamp walks you through six hands-on projects where you’ll learn to upgrade your training data using feature engineering. Each chapter explores a new code-driven case study, taken from real-world industries like finance and healthcare. You’ll practice cleaning and transforming data, mitigating bias, and more. The book is full of performance-enhancing tips for all major ML subdomains—from natural language processing to time-series analysis.

What's inside

• Identify and implement feature transformations
• Build machine learning pipelines with unstructured data
• Quantify and minimize bias in ML pipelines
• Use feature stores to build real-time feature engineering pipelines
• Enhance existing pipelines by manipulating input data

About the reader

For experienced machine learning engineers familiar with Python.

Table of Contents

1 Introduction to feature engineering

2 The basics of feature engineering

3 Healthcare: Diagnosing COVID-19

4 Bias and fairness: Modeling recidivism

5 Natural language processing: Classifying social media sentiment

6 Computer vision: Object recognition

7 Time series analysis: Day trading with machine learning

8 Feature stores

9 Putting it all together

Review

"An excellent resource for the often-undervalued task of feature

engineering. It demonstrates the 'when, why, and how' of techniques

you can use to improve your models."

—Josh McAdams, Google

"The most overlooked step in a data science pipeline, simplified!"

—Shaksham Kapoor, UBS

"Make major improvements in your models by improving the features."

—Harveen Singh Chadha, Thoughtworks

"Detailed and practical."

—Satej Kumar Sahu, Honeywell

"The practical case studies reveal core feature engineering

techniques that improve process and ML workflow."

—Maria Ana, CG

About the author

Sinan Ozdemir is the founder and CTO of Shiba, a former lecturer of Data Science at Johns Hopkins University, and the author of multiple textbooks on data science and machine learning.