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

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most Springer texts.Con's:A substantial part of the code is peripheral tasks; this can be skipped.Some of the code is out of date.These Con's are trivial.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

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 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.

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)