Machine Learning for Hackers: Case Studies and Algorithms to Get You Started

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.

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most Springer texts.Con's:A substantial part of the code is peripheral tasks; this can be skipped.Some of the code is out of date.These Con's are trivial.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.

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.

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.

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.

As other reviews have noted, this book is R-heavy. And R turns out to be a poor choice for a lot of this data manipulation. As a "hacker" I'd prefer to use python or something similar for a lot of the code and then use R for the stuff that it's good at. Also the kindle formatting of this book is terrible. The code is formatted very poorly (I hope this is a kindle translation problem and not the way that it looks in the book). That poor formatting makes it difficult to read through the code. I'd recommend anyone who knows a decent programming language should try a different book.

Excelente libro

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).

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.

The book is real good but I wouldn't prefer it if you are new to machine learning. You would find it to be real good if have basic knowledge of the subject

Concepts are explained quite clearly.