Feature engineering is generally the section that gets left out of machine learning books, but it’s also the most important part of successful models, even in today’s world of deep learning. While academic courses on machine learning focus on gradients and the latest flavor of recurrent network, Ted Dunning (MapR) explores the techniques that practitioners in the real world are seeking out better features and figuring out how to extract value using a variety of time-honored (and occasionally exceptionally clever) heuristics.
In a sense, feature engineering is the Rodney Dangerfield of machine learning, never getting any respect. It is, however, the task that will get you the most value for time spent in terms of model performance. This work is not just the work of the data scientist. Good features encode business realities as well and are the cross-product of good business sense and good data engineering.
- A basic understanding of how machine learning is used to teach models
What you'll learn
- Learn some surprising techniques that can help you solve some really hard problems
This session is from the 2019 O'Reilly Strata Conference in New York, NY.
Table of contents
- Title: Practical feature engineering
- Release date: February 2020
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 0636920371809
You might also like
Spark: The Definitive Guide
Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the …
Designing Data-Intensive Applications
Data is at the center of many challenges in system design today. Difficult issues need to …
Python Crash Course, 2nd Edition
This is the second edition of the best selling Python book in the world. Python Crash …
Strata Data Conference 2019 - San Francisco, California
Thousands of the data scientists, analysts, engineers, developers, and executives converged at the Strata Data Conference …