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Machine Learning for Hackers (Englisch) Taschenbuch – 1. April 2012


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Produktinformation

  • Taschenbuch: 320 Seiten
  • Verlag: O'Reilly & Associates; Auflage: 1 (1. April 2012)
  • Sprache: Englisch
  • ISBN-10: 1449303714
  • ISBN-13: 978-1449303716
  • Größe und/oder Gewicht: 17,8 x 1,9 x 23,3 cm
  • Durchschnittliche Kundenbewertung: 1.0 von 5 Sternen  Alle Rezensionen anzeigen (1 Kundenrezension)
  • Amazon Bestseller-Rang: Nr. 91.715 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)

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Produktbeschreibungen

Über den Autor und weitere Mitwirkende

Drew Conway is a PhD candidate in Politics at NYU. He studies international relations, conflict, and terrorism using the tools of mathematics, statistics, and computer science in an attempt to gain a deeper understanding of these phenomena. His academic curiosity is informed by his years as an analyst in the U.S. intelligence and defense communities. John Myles White is a PhD candidate in Psychology at Princeton. He studies pattern recognition, decision-making, and economic behavior using behavioral methods and fMRI. He is particularly interested in anomalies of value assessment.

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2 von 4 Kunden fanden die folgende Rezension hilfreich Von JimBob am 21. Oktober 2014
Format: Taschenbuch Verifizierter Kauf
Kann ich nur dringend von abraten. Klare vorsätzliche Täuschung der Titel. Es wir einem nicht erklärt was Machine Learning ist, es wird nur R benutzt die Algorithmen zu berechnen oder zu visualisieren und es wird weder R noch maschinelles Lernen erklärt.
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Die hilfreichsten Kundenrezensionen auf Amazon.com (beta)

Amazon.com: 25 Rezensionen
130 von 140 Kunden fanden die folgende Rezension hilfreich
Machine Learning for Non-Hackers 21. März 2012
Von Voracious Reader - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
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".
45 von 47 Kunden fanden die folgende Rezension hilfreich
Not for a hacker, probably for a scientist 4. Mai 2012
Von Sharon Talbot - Veröffentlicht auf Amazon.com
Format: Taschenbuch
In Machine Learning for Hackers by Drew Conway and John Myles White, the reader is introduced to a number of techniques useful for creating systems that can understand and make use of data. While the book has solid topical material and is written in a fluid and easy to read manner, I don't feel that this book is really for hackers, unless the definition of hacker is vastly different from "programmer".

Much of the text is taken up explaining how to parse strings, change dates, and otherwise munge data into shape to be operated on by statistical functions provided by R. In fact, there is so much of the book in that fashion that I end up skipping through large portions to get back to something that is worth spending time reading about. I can't understand why a programmer would need significant education in string parsing. I was also put off by the vast amount of text explaining basic statistics. Maybe a recent computer science graduate is simply the wrong reader for this book?

I think it is certainly possible to learn the basic principles of machine hacking from this book, and even to put them to good use with R in the same manner displayed in the examples. Indeed, the code and data available for this book would be very useful as prep for an introductory course at an academic institution. To make the best use of the text, you really should be sitting at your computer, reading the text side by side with the code, and operating on the data with R as instructed to do.

Personally, I found that wading through this text wasn't enjoyable it due to the lack of density of material at the depth I was looking for. Other readers may find it is just right for them, but I suspect those readers would not be hackers, contrary to the implication of the title. As best as I can figure, this book would best serve a student scientific researcher who wanted to understand what machine learning was about, and did not have significant prior experience in programming or statistics. Alternatively, if you are significantly distant in years from your time in statistics, or considered learning R one of your goals, this book could work well for you.

I received this book for free as part of the O'Reilly Blogger Review program, which is neat.

I should note that I read this book on the iPhone as an ePub. There were some formatting problems with tables that were distracting, but otherwise it was readable.
56 von 60 Kunden fanden die folgende Rezension hilfreich
Erroneous but entertaining 27. Juli 2012
Von Kurt - Veröffentlicht auf Amazon.com
Format: Taschenbuch
I used this book to teach students about data mining and machine learning with a hands-on approach. I intended it to be used as a book for the students to rely and fall back on. It is not suited well for that purpose.

Pros: The book is affordable and nicely written. The authors take great care in making the book useful and entertaining and one can immediately start putting things into practise. Also, the R examples are interesting and by itself motivating.

Cons: The book has a couple of very grievous errors, that make me wonder the authors understand the subject matter. This is especially striking in the chapters on PCA and Multidimensional Scaling (which I covered in some depth in the class), but also to a lesser degree in other parts of the book that I have read more thoroughly (like optimization and linear and nonlinear regression). Many errors are not typos or simple mistakes but seem to be proof of a profound misunderstanding of concepts by the authors. I am sorry to be so blunt, but one should not write a book about topics that one is not intimate with. Given that the book is probably quite successful, it propagates error into a community whose members may not have the statistical background to spot the errors immediately. Some methods used in the book are quite hard to understand even for graduate students and to be so nonchalant about the underlying theory can be dangerous. I realize that the book is intended to be superficial with regards to mathematical or conceptual depth, but this combined with some of the presented high-level techniques can easily backfire when people are given the tools, but not the understanding. Especially when the explanations on interpretation are plainly wrong (I am talking about using standard deviations instead of variances, substantive interpretation of methodological artifacts, wrong explanation of R output, etc.). Additionally, certain parts of the book became outdated as soon as the book came out, such as the Google example.

Overall, I do not recommend the book. I now only use it as a collection of nice examples and sometimes borrow bits of their R code.
36 von 43 Kunden fanden die folgende Rezension hilfreich
Excellent and immediately practical, if you already know some R 20. Februar 2012
Von Ravi Aranke - Veröffentlicht auf Amazon.com
Format: Taschenbuch
I started my journey in the machine learning / data mining field thanks to curiosity generated by Toby Segaran's classic Programming Collective Intelligence: Building Smart Web 2.0 Applications. The book by Drew Conway and John White continues in the same excellent tradition. It presents case studies which are interesting enough that you can appreciate them without too much domain knowledge and without getting too deep into technical nitty-gritty. At the same time, the case studies are meaty enough that you can adapt them to real life problems and hack together a quick working prototype in your practice.

By many estimates (and my own experience), 80% of time in machine learning is spent in data cleaning and exploratory data analysis. This book has very good coverage of both areas. Authors use Hadley Wickham's excellent packages viz. ggplot2, plyr and reshape2. If you are doing serious exploratory data analysis in R, these packages are a must and the book does a great job in showing them in action.

The reason I suffixed the review with 'if you know a little R' is that data cleansing requires one to be fairly comfortable with somewhat arcane R syntax. If you don't know any R at all, it would be helpful to get a more gentle introduction - such as R Cookbook (O'Reilly Cookbooks) - before you tackle this book.

In summary, this is an excellent 2nd book on R to have as you try to deploy machine learning in real life.
BTW, if you are looking for 3rd R book, my vote is Data Mining with R: Learning with Case Studies (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
9 von 11 Kunden fanden die folgende Rezension hilfreich
Brilliant examples but too many errors 3. Oktober 2012
Von Stefan Zapf - Veröffentlicht auf Amazon.com
Format: Taschenbuch
There are substantial errors in the R code that are not typos. Let me give you a specific example.

"First, notice that we are wrapping the strsplit command in R's error-handling function, tryCatch. Again, not all of the entries are of the proper "City, State" form, and in fact, some do not even contain a comma. The strsplit function will throw an error if the split character is not matched; therefore, we have to catch this error. In our case, when there is no comma to split, we will return a vector of NA to indicate that this entry is not valid." (p. 16)

As the trycatch error handling is essential for the correct data analysis, I tested the function with erroneous input. The wrong data was not marked as NA as explained in the book. The reason is that the strsplit doesn't throw an exception according to the R documentation. The code was clearly not tested and claims are made without consulting the R documentation. This leads to erroneous data being accepted as correct data. Unfortunately this is not the only example of errors in the first chapter.

I have sufficient experience to spot programming errors, I won't be able to spot the errors in the Machine Learning concepts. Maybe the authors only misunderstand some R functions, but too little care was taken to fact check the book by the editors or authors.

Why did I give the book 3 stars? To learn about ML by studying the impact of seasons on UFO sightings is just plain fun. The authors can explain concepts really well and in an entertaining way. Publish a new edition that corrects the errors and I'll recommend this book to my friends.
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