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Machine Learning for Hackers: Case Studies and Algorithms to Get You Started by [Conway, Drew, White, John Myles]
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Machine Learning for Hackers: Case Studies and Algorithms to Get You Started 1st , Kindle Edition

3.0 out of 5 stars 4 customer reviews

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Book Description

Case Studies and Algorithms to Get You Started

About the Author

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.


Product details

  • Format: Kindle Edition
  • File Size: 16163 KB
  • Print Length: 324 pages
  • Page Numbers Source ISBN: 1449303714
  • Simultaneous Device Usage: Unlimited
  • Publisher: O'Reilly Media; 1 edition (13 Feb. 2012)
  • Sold by: Amazon Media EU S.à r.l.
  • Language: English
  • ASIN: B007A0BNP4
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Not Enabled
  • Enhanced Typesetting: Not Enabled
  • Average Customer Review: 3.0 out of 5 stars 4 customer reviews
  • Amazon Bestsellers Rank: #345,973 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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3.0 out of 5 stars
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Format: Kindle Edition Verified Purchase
It's definitely not for hackers. More time is spent explaining trivial things such as extracting date or subject from an email message, rather than talking about actual algorithms. If you call a book 'for hackers', then you should assume that the reader is competitive enough to do basic text manipulation and this book is not doing that.

So much of the content is about those details, that I just got extremely bored and didn't read more than 1 chapter. Not recommended.
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Format: Kindle Edition Verified Purchase
Concepts are explained quite clearly.
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Format: Paperback Verified Purchase
This book isn't really about machine learning. There is relatively little in the book about the machine learning algorithms themselves. Most of it is plumbing: how to munge the data in using R. And there are some nice motivating examples on using the ggplot2 library in R to visualise the results. (However, on the downside, plots which require colour to be understood are presented in black and white, which doesn't help! The R code mostly works, so usually it is possible to produce your own, but it seems a waste of paper to output plots that show male vs female dots, say, in different shades of grey on the same graph.)

Also, when the algorithms are presented there are sometimes some serious errors (see the review on amazon's US site "Erroneous but entertaining" for more details). The single most shocking example was when a series of numbers was said to show the percentages of variation explained by an analysis, but the series added up to much than 100%. This was by no means the only error, however. The cumulative effect for me was that as I got further and further through the book, I began to have less and less trust in what was being presented to me.

I would characterise the book as being for hackers in the sense that you are encouraged to try a technique and see if it works. One good point is that the book emphasises having a separate test set from your training set.

Trying techniques until you find one that works is probably a good place to start, especially if your interest is in starting to learn the broader field of data science -- getting the data in, analysing it, visualising it -- rather than specialising in the selection and choice of machine learning algorithms themselves (for which Andrew Ng's coursera online course is a far better choice).
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If I knew it's for hackers,
I would not buy it
For beginning ,I want buy a book for learning .
But I can't return the item
for this book,I think it's a good book at least
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Most Helpful Customer Reviews on Amazon.com (beta) (May include reviews from Early Reviewer Rewards Program)

Amazon.com: 2.9 out of 5 stars 29 reviews
148 of 158 people found the following review helpful
3.0 out of 5 stars Machine Learning for Non-Hackers 21 Mar. 2012
By Voracious Reader - Published on Amazon.com
Format: Paperback Verified Purchase
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".
2.0 out of 5 stars Entry Level R Programming Book 13 Nov. 2016
By Graham - Published on Amazon.com
Format: Paperback Verified Purchase
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.
3.0 out of 5 stars R book with little machine learning 26 May 2017
By P. Kim - Published on Amazon.com
Format: Paperback Verified Purchase
Not much machine learning but more R.

Not really for hackers but those who want to learn and use R better.

I liked it but it did not help me much with machine learning.
3.0 out of 5 stars You can do better 25 Feb. 2016
By D Marx - Published on Amazon.com
Format: Paperback Verified Purchase
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.
8 of 9 people found the following review helpful
2.0 out of 5 stars Ok introduction to R but with bad code 2 Jun. 2013
By anthonyl - Published on Amazon.com
Format: Kindle Edition Verified Purchase
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
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