Practical feature engineering
Who is this presentation for?
- Data scientists, data engineers, and machine learning engineers
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 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
Ted Dunning is the chief technology officer at MapR. He’s also a board member for the Apache Software Foundation; a PMC member and committer of the Apache Mahout, Apache Zookeeper, and Apache Drill projects; and a mentor for various incubator projects. Ted has years of experience with machine learning and other big data solutions across a range of sectors. He’s contributed to clustering, classification, and matrix decomposition algorithms in Mahout and to the new Mahout Math library and designed the t-digest algorithm used in several open source projects and by a variety of companies. Previously, Ted was chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems and built fraud-detection systems for ID Analytics (LifeLock). Ted has coauthored a number of books on big data topics, including several published by O’Reilly related to machine learning, and has 24 issued patents to date plus a dozen pending. He holds a PhD in computing science from the University of Sheffield. When he’s not doing data science, he plays guitar and mandolin. He also bought the beer at the first Hadoop user group meeting.
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