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Product details
File Size: 18728 KB
Print Length: 324 pages
Simultaneous Device Usage: Unlimited
Publisher: O'Reilly Media; 1 edition (February 13, 2012)
Publication Date: February 13, 2012
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B007A0BNP4
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#819,804 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
This text offers a detailed description in each of 10 case studies about how to build a machine learning solution to the particular problem mentioned. The authors do this in R, and are extensively descriptive about the mechanics of writing R code. If you've never written a computer program, but want to understand how to implement a prewritten machine learning tool in R, this book could be of assistance. However, I'm not sure the authors actually understand the mathematical theory of machine learning; in this book they constantly substitute descriptions of how to select appropriate algorithm parameters with a trial-and-error approach, they do not explain how the algorithms work, and I'm not sure they ever mention the mechanics of "learning" with respect to mathematics. The book has brief passages about machine learning hidden amongst vast chapters about how to read computer directories and load data, etc. Again this book could be useful to the reader who understands ML, but not computer programming.
This book is just ok and barely touches the surface of the topics it discusses. If you're looking for an introduction to machine learning and the R language, I think you're better off with "Data Mining with R" by Torgo. It's a bit more expensive, but not without good reason.
I'm very disappointed that O'Reilly put there name on this book, it's boring doesn't really explain anything except how to use R to query data. But we all know machine learning is the process used to build that data. So the choice of ar arbitrary data sets to to explain what data is, its simply philosophical bull crap. It feels like this book is writen from the point of view of an idiot a proper title would be R query hack for databas tables. I really want a refund because I feel like I was duped ... and then Once you read the authors intro and that the book was written by someone who obviously has nothing to do with hacking or programming. Then it make sense, I don't see any actual maching learning period or detailed and correct explanation machine learning algorithms. O'R
This book is more of an introduction to R then anything to do with Machine Learning.. as a R introduction it's not bad minus the horibad sample code ... which won't even work if you copy and paste it,my advice find a different book there any many, many more accurate and detailed books on R and machine learning
This book may be good for those with little mathematical or statistical background, but its background information sections are too long and its treatment of ML topics too superficial for the book to be very useful for someone with the requisite background to actually implement the methods described in the book.
By page count, this is primarily a book on R, with some additional time spent on machine learning.There is way too much time spent on R, dedicated to such things as parsing email messages, and spidering webpages, etc. These are things that no-one with other tools available would do in R. And it's not that it's easier to do it in R, it's actually harder than using an appropriate library, like JavaMail. And yet, while much time is spent in details, like regexes to extract dates (ick!), more interesting R functions are given short shrift.There's some good material in here, but it's buried under the weight of doing everything in R. If you are a non-programmer, and want to use only one hammer for everything, then R is not a bad choice. But it's not a good choice for developers that are already comfortable with a wider variety of tools.I'd recommend Programming Collective Intelligence by Segaran, if you would describe yourself as a "Hacker".
I enjoyed reading this book.Pro's:The text is parsimonious.The examples are interesting.The coding is clever.The book is less expensive and easier to understand than most Springer texts.Con's:A substantial part of the code is peripheral tasks; this can be skipped.Some of the code is out of date.These Con's are trivial. The book is great. I would buy any other books written by these authors.
I learned a little about the R language and how a lot of scientist of physicist use it the write functions to dynamically create graphs to analyze the data they compiled. It also encompasses a lot of the same math you learn in statistics classes which makes sense. I even ended up downloading an R language reference app from the Apple app store to have an object reference of the language.
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