Machine Learning for Hackers: Case Studies and Algorithms to Get You Started 1st Edition

by Drew Conway (Author), John Myles White (Author)

30 customer reviews

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ISBN-13: 978-1449303716

ISBN-10: 1449303714

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If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.

Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.

Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text
Use linear regression to predict the number of page views for the top 1,000 websites
Learn optimization techniques by attempting to break a simple letter cipher
Compare and contrast U.S. Senators statistically, based on their voting records
Build a “whom to follow” recommendation system from Twitter data

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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.

Paperback: 324 pages
Publisher: O'Reilly; 1st edition (February 25, 2012)
Language: English
ISBN-10: 1449303714
ISBN-13: 978-1449303716
Product Dimensions: 7 x 0.8 x 9.2 inches
Shipping Weight: 1.4 pounds (View shipping rates and policies)
Average Customer Review: 30 customer reviews
Amazon Best Sellers Rank: #794,078 in Books (See Top 100 in Books)
- #91 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Natural Language Processing
- #158 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Machine Theory
- #183 in Books > Textbooks > Computer Science > Algorithms

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Graham

Entry Level R Programming Book

November 13, 2016

Format: PaperbackVerified 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.

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P. Kim

R book with little machine learning

May 25, 2017

Format: PaperbackVerified 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.

2 people found this helpful

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February 24, 2016

Format: PaperbackVerified 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.

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Anon

This book is about data mining and data science and giving symantics to the data sets .... Oreilly needs to remove this book,

December 28, 2016

Format: PaperbackVerified Purchase

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

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Voracious Reader

Machine Learning for Non-Hackers

March 20, 2012

Format: PaperbackVerified 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".

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Amazon Customer

This book may be good for those with little mathematical or statistical background

October 7, 2016

Format: PaperbackVerified Purchase

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.

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W. Hogan

An interesting and easy read

December 12, 2013

Format: Kindle EditionVerified Purchase

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.

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Chris Weathers

Learned something new

October 2, 2012

Format: PaperbackVerified Purchase

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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Most recent customer reviews

alberto

Learn R

Not machine learning

Published on April 10, 2016

John C.

Ultra practical approach to a collection of machine learning problems

Unlike most books on AI and machine learning, this book attempts to be fully practical solutions to common machine learning problems, like separating spam from genuine...Read more

Published on December 1, 2014

MCNAUGHTON NEIL

Introduction to use of the ‘R’ statistics and data manipulation language - the "lingua franca" of analytics.

Drew Conway and John Myles White’s credentials are impeccable—Conway used to work in intel, White is a psychologist, statistician and maintainer of several ‘R’ packages.Read more

Published on January 3, 2014

Austin Clemens

Do not buy on Kindle

The way this is formatted for Kindle makes it basically unusable. Page numbers in the middle of the text, mangled figures and tables, no chapter breaks and more.Read more

Published on October 6, 2013

anthonyl

Ok introduction to R but with bad code

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 ...Read more

Published on June 2, 2013

Filipe

Great book

It goes through the very basics of statistics to build the necessary knowledge to the machine learning algorithms.Read more

Published on April 28, 2013

Michael Flynn

Too many errors, code doesn't even work correctly

This book is an attempt to give someone an introduction to the very, very basic concepts used in Machine Learning, using R.Read more

Published on December 21, 2012

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