

Machine Learning for Hackers - ahalan
http://shop.oreilly.com/product/0636920018483.do

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sbt
I hate to be high brow here, but I'm just waiting for O'Reilly to release
Brain Surgery for Hackers. Some things are just better learned the hard way,
sitting down and getting a more thorough introduction.

~~~
readme
I think these books are not really meant to be a PhD education but more of a
tutorial introduction to the field. What O'Reilly is doing right now is really
important. As Steve Yegge said, they are "trying to provoke a culture change."

Would-be brogrammers will find these books, read them, and graduate from cat-
picture projects into more sophisticated applications that can solve real
computer science problems. It matters less whether the readers of these books
actually get a comprehensive understanding of the theoretical knowledge behind
them, then it does that they enrich readers and spread the desire to learn and
attain true understanding of the field.

Steve Yegge reference: <http://www.youtube.com/watch?v=vKmQW_Nkfk8>

~~~
Drbble
You underestimate the value of ML for monetizing cat pictures.

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fuzzythinker
This Machine Learning in Action from Manning uses python instead of R:
<http://www.manning.com/pharrington/>

PS. Attended Machine Learning class in hacker dojo with the author, he's a
bright guy. Hopefully the book will be as good.

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danfitch
I would love to know more about this book but it seems to not be released yet
and there are no reviews. Not sure what the point of posting it is or why it
is up voted. More details would be great.

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etrain
Have interacted with both authors, however briefly. These are both smart guys
operating outside of the typical CS fields who have figured out how to apply
cutting edge computational techniques to their specialties and deliver
meaningful insight. Really looking forward to reading the book.

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MaxGabriel
In corollary to this post, does anyone know what's up with the Stanford
Machine Learning course? Haven't heard anything since the delay

~~~
seancron
It should be going live soon. I got an email about the Model Thinking class
which launched a preview at
<http://www.coursera.org/modelthinking/lecture/preview>

Hopefully that means they're getting ready for the other classes to go live as
well.

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reader5000
Promising title, doesnt appear to have a table of contents currently.

~~~
angrycoder
From Safari:

    
    
    		Preface
    		Machine Learning for Hackers: Email
    		How This Book is Organized
    		Conventions Used in This Book
    		Using Code Examples
    		How to Contact Us
    		Using R
    		R for Machine Learning
    		Further Reading on R
    		Data Exploration
    		Exploration vs. Confirmation
    		What is Data?
    		Inferring the Types of Columns in Your Data
    		Inferring Meaning
    		Numeric Summaries
    		Means, Medians, and Modes
    		Quantiles
    		Standard Deviations and Variances
    		Exploratory Data Visualization
    		Visualizing the Relationships between Columns
    		Classification: Spam Filtering
    		This or That: Binary Classification
    		Moving Gently into Conditional Probability
    		Writing Our First Bayesian Spam Classifier
    		Ranking: Priority Inbox
    		How Do You Sort Something When You Don’t Know the Order?
    		Ordering Email Messages by Priority
    		Writing a Priority Inbox
    		Works Cited
    		Books
    		Articles
    		About the Authors

~~~
plessthanpt05
using R seems a bit strange to me -- i have nothing against R (use it
regularly), but not exactly a "hacker" language. this seems like an ideal book
for python, but R?

~~~
levesque
I had the same feeling when I saw this. R is a great tool for statistics, very
useful when you need to do in-depth statistical analysis (analysis of
variance, etc.). It doesn't strike me as a good choice for a hacker's book -
which makes me think about their reasons to use this word, hacker. Maybe they
are just trying to benefit from the buzz that it generates these days?

~~~
plessthanpt05
indeed, this was my first reaction as well...seems like a bit of marketing
gimmick to toss the word "hacker" into the title.

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showerst
Just a quick note if you're interested, this book is similar and is an
absolutely fantastic 'applied beginner' ML book "Programming Collective
Intelligence"

[http://www.amazon.com/Programming-Collective-Intelligence-
Bu...](http://www.amazon.com/Programming-Collective-Intelligence-Building-
Applications/dp/0596529325/ref=sr_1_1?ie=UTF8&qid=1328721596&sr=8-1)

------
cageface
This seems like a field that could pretty easily be commodified. I can imagine
a service like the Google prediction API could meet the needs for this kind of
tech for many companies.

So while it's certainly an interesting field, I wonder how many hackers are
really going to need these skills.

~~~
law
The biggest problem with machine learning occurs when people subscribe to the
belief that it's a black-box solution. The truth is that you _can't_ just
drag-and-drop your data into a pre-existing solution. The types of algorithms
you use depend on the types of problems you're trying to solve (e.g.,
classification, regression, clustering). The data you collect depends on the
algorithms you use.

Sure, prediction APIs could arise that give detailed use cases for each
algorithm, but then there's a problem with the fringe cases: you might not
know that two pieces of data are so heavily correlated that they completely
shatter a conditional independence assumption, for example.

As a hacker who originally subscribed to the belief that a thorough
understanding of machine learning was overkill, it is without hesitation that
I admit being 100% wrong. The truth of the matter is that when it's done
properly, artificial intelligence and machine learning ought to be
inextricably linked with your core business processes.

~~~
cageface
I can certainly see a role for somebody that understands the tradeoffs of each
of these algorithms and that understands how to properly select and prepare
dataasets. But I wonder how many people will really need to be able to
actually _implement_ these algorithms.

~~~
ramblerman
That was also the whole point of the stanford ML course. TO teach exactly that
skillset.

Sure we did some basic implementations in octave, it helps to have some idea
of the internals. But that wasn't the goal of the course.

~~~
Drbble
Well, no. ml-class was a series of demos. Plugging in a formula is 1-line
developing an algorithm. The original cs229 is more like what you describe.

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pauldix
I have an early review copy if this book and know both the authors. It's good
and I highly recommend it!

