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Feature Engineering for Machine Learning
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Feature Engineering for Machine Learning

3.82  ·  Rating details ·  82 ratings  ·  10 reviews
Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text ...more
Paperback, 218 pages
Published April 10th 2018 by O'Reilly Media
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Mahmoud Rabie
Apr 08, 2018 rated it really liked it  ·  review of another edition
I liked the book a lot, yes the book contains a lot of math but the author tried to always explain in simple words

The book don't focus on giving a tips and tricks or a how to guide for feature engineering, instead it focus on the effect of feature engineering on the data and the models

i.e. the effect of using log transformation on linear regression

As the book size is small - around 200 pages - the book don't cover a lot of topics, but at the end of the book your understanding for feature enginee
Amir Sarabadani
Apr 17, 2019 rated it really liked it
Most of the graphs are hand-written, that was weird.
Rick Sam
A Quick read, teaches you basics in Feature Engineering, this might give you raw knowledge about Feature Engineering, meanwhile in Production or Industry, one has to practice or apply based on the the raw-knowledge.

If someone could come up with a best way to gain know-how or procedural knowledge faster, do let me know.

I have a summary of this, if you want do PM me, it might save your time.

Here's an Outline of the Book:

0. Introduction and my thoughts
1. Machine Learning Pipeline
2. Fancy T
Sep 20, 2019 rated it it was ok
Fine start, but there's not a lot here. There are some good tricks for people who don't have a lot of experience building predictive models.

The book also focuses on some specific types of data that lots of people won't need to work with: images and text (as in trying to get a computer to understand text). More general discussion about categorical and numeric variables would have been better, since those ideas can be translated to other contexts more easily.

This book will probably be obsolete wi
Auggie Heschmeyer
Aug 28, 2019 rated it it was ok
This book is great if you want to know the exact mathematical expressions for the "feature engineering" part of the title, but a total drag if you're looking for anything practical. This book is clearly after a depth not breadth approach, but they go deep down the wrong paths. For instance, the final chapter is billed as a case study. However, rather than engineering the right features to do the task they lay out, they engineer some bad features then slap on some okay ones and say, "There you go ...more
Mehdi Ben Ayed
May 16, 2020 rated it liked it
Shelves: machine-learning
Good book. I liked Chapter 2 5 and 9.

I wish more time was spent on:
* Feature transformation of numerical values
* Feature selection
* Determining which transformation is adequate with which model type (NN, Random Forest, ..)
* The incremental impact of additional features over other selected features
Virajdatt Kohir
Jul 14, 2020 rated it liked it
Ummmm, good tricks thought it would help me (intermediate) but most of the things mentioned are for beginners. It did help me with revising things.
Mike Fowler
Sep 24, 2019 rated it it was amazing
Shelves: technology, data
Good clear explanations of concepts with plenty of examples. Appendix with useful refresher on linear algebra.
White Rose
Dec 23, 2018 rated it it was amazing
it is quite good, maybe not 100% noob-friendly, it is for me to reread again but there is a list of concepts I haven't heard of before
Jun 16, 2019 rated it liked it
Good for a general review, but too basic (e.g., not explaining the data distribution suitable for an engineering technique) and having some errors (the PCA chapter, although its explanation is good and useful).
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Alice is a technical leader in the field of machine learning. Her experience spans algorithm and platform development and applications. Currently, she is a Senior Manager in Amazon's Ad Platform. Previous roles include Director of Data Science at GraphLab/Dato/Turi, machine learning researcher at Microsoft Research, Redmond, and postdoctoral fellow at Carnegie Mellon University. She received a Ph. ...more

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