Machine Learning for Hackers: Case Studies and Algorithms to Get You Started 1st Edition
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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.
- Publisher : O'Reilly; 1st edition (March 6, 2012)
- Language: : English
- Paperback : 324 pages
- ISBN-10 : 1449303714
- ISBN-13 : 978-1449303716
- Item Weight : 1.14 pounds
- Dimensions : 7 x 0.75 x 9.19 inches
- Best Sellers Rank: #1,232,653 in Books (See Top 100 in Books)
- Customer Reviews:
Top reviews from the United States
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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.
my advice find a different book there any many, many more accurate and detailed books on R and machine learning
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.
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Some of the code is out of date.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.
Top reviews from other countries
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).