Feature Engineering Bookcamp 1st Edition Sinan Ozdemir by Sinan Ozdemir 9781617299797, 1617299790 instant download after payment.

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 reader
For experienced machine learning engineers familiar with Python.
 
About the author
Sinan Ozdemir is the founder and CTO of Shiba, a former lecturer of Data Science at Johns Hopkins U