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Introduction to Machine Learning with R: Rigorous Mathematical Analysis
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About the Author
Scott Burger is a senior data scientist living and working in Seattle. His programming experience comes from the realm of astrophysics, but he uses it in many different types of scenarios ranging from business intelligence to database optimizations. Scott has built a solid career on explaining terse scientific concepts to the general public and wants to use that expertise to shed light on the world of machine learning for the general R user.
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Product details
Paperback: 226 pages
Publisher: O'Reilly Media; 1 edition (April 1, 2018)
Language: English
ISBN-10: 1491976446
ISBN-13: 978-1491976449
Product Dimensions:
7 x 0.4 x 9.1 inches
Shipping Weight: 13.3 ounces (View shipping rates and policies)
Average Customer Review:
3.0 out of 5 stars
6 customer reviews
Amazon Best Sellers Rank:
#400,400 in Books (See Top 100 in Books)
This book very nicely introduces basic machine learning concepts like regression, decision trees, and neural networks and how to easily build, train, and evaluate models in R. In the final chapter, the author ties everything together nicely by showing how to tie everything together using the excellent caret package.The overall information is fantastic. However, this book has a surprising number of errors. These were mostly instances where the text showed one value, but the sample output showed another, perhaps due to code being re-run without using the same random seed. There were also instances where figure references were wrong. Although they didn't hurt my ability to learn, they were a big distraction, and could make things difficult for someone new to R or to ML.
This is a nice, simple, and comprehensive introduction on how to go about doing Machine Learning in R programming environment and I would have definitely recommended it for beginners were it not for the incredibly high number of either minor typos or just outright wrong text included in this book. There are pages where the author is saying one thing, while the code and the results are showing something else. In some places, the author refers to Appendix for additional statistical details, but there is no such Appendix to be found. As a beginner myself, I spent many minutes self-flagellating over why I didn't understand something that was obvious to the author before I realized that there was an error in the book. If you were to buy this book, I would recommend that you code along and not rely on the outputs shown in the book. When I shell out about $50 on a book, the *least* I expect is that somebody has proofread it before publishing and mass-distributing it. Really disappointed with O'Reilly Publishers.
The output of R code does not match typed up equations, and in turn does not match up printed coefficients on graphs. Someone needs to proof-read before publish it. Very shoddy job on the editor's side.
The print quality is terrible, cant even read the images. Almost like this is a bootleg print, copied from the internet and printed in some shady warehouse in china. Junk. And pages falling out!
I found this to be a very friendly introduction to machine learning with R. It had a good combination of explanation and code examples. It covered all the major machine learning algorithms without getting too much in the weeds. I feel my knowledge and comfort with machine learning and R improved as a result. Highly recommended.
This book really breaks down machine learning in a way that allows anyone to learn it. It was perfect for my first exposure!
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