Free PDF Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
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Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
Free PDF Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
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About the Author
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.D. in Electrical Engineering and Computer science, and B.A. degrees in Computer Science in Mathematics, all from U.C. Berkeley.
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Product details
Paperback: 218 pages
Publisher: O'Reilly Media; 1 edition (April 14, 2018)
Language: English
ISBN-10: 1491953241
ISBN-13: 978-1491953242
Product Dimensions:
7 x 0.4 x 9.1 inches
Shipping Weight: 12.5 ounces (View shipping rates and policies)
Average Customer Review:
4.1 out of 5 stars
8 customer reviews
Amazon Best Sellers Rank:
#114,128 in Books (See Top 100 in Books)
It's great that someone's written a book about Feature Engineering and it contains a lot of interesting material. I also like that clear readable coding examples are included (Python), so that the reader can try at home.But, it feels unfinished. The book is pretty short and the set of topics seems more like what the authors were interested in than a survey of the field. The level of explanation varies from super-detailed to glossing over concepts and technical terms that most readers won't know.The biggest issue is that the graphics are mostly hand-drawn on some kind of tablet and look like they were scrawled out quickly. I've attached one example. As you can see, they didn't even bother to adjust the contrast. So, the images have a dull gray background. Disappointing that O'Reilly didn't take care of this for the authors.
A good reference book for those of us in the daily engagement of machine learning.
Good intro to the feature engineering with clear examples in Python. Overall 5 stars as the book is easy to read and it contains useful hints.
Well written. Highly recommended.
This book does a great job explaining the "why".
At the end of the preview, the book recommends converting song listening counts into a binary 0 1 variable. This is TERRIBLE advice. It mentions binning as an alternative but goes ahead with binary code. The authors argument is that "listening 10 times does not mean liking the song as much as someone who listens ,,20 times."In other words, if the user listened to a song at least once, then we count it as the user liking the song. This way, the model will not need to spend cycles on predicting the minute differences between the raw counts. The binary target is a simple and robust measure of user preference.". There are many ways to deal with possible non-proportional effects. Going to a 1 treats someone who tried a song and did not like it the same as someone who listens daily!I would love a good book on this topic for my students, but this is not it..
I have read almost half of the book. It is amazingly useful, concrete and helpful. I can't stop finishing the book and then write my review and I would like to appreciate authors for their great work.Based on my experience the process of machine learning is 80% data wrangling, cleaning, feature engineering,... and 20% running the model or ML algorithm. This book aims for the first 80%.
I’ve read the pre-release version on safari’s books. Excellently written and great examples. I thoroughly enjoyed the initial chapters on transforming input data.
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Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists PDF
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists PDF
