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Machine Learning for Hackers
- reader5000 15y agoPromising title, doesnt appear to have a table of contents currently.
- angrycoder 15y agoFrom 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 15y agousing 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 15y agoI 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 15y agoindeed, this was my first reaction as well...seems like a bit of marketing gimmick to toss the word "hacker" into the title.
- tutysara 15y agoPython or a jvm language should be a better choice
- Drbble 15y agoWhy are hackers allowed to use Python but not R? R is a quite powerful Scheme-like language with an incredible math library. Python is a "teaching language", officially.
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- neilkod 15y agoThat looks like TOC for "Machine Learning for Email", which is just a portion of the entire "Machine Learning for Hackers" book. The full "Machine Learning for Hackers" doesn't appear to be on Safari yet.
- jcfrei 15y agothanks, just what I was looking for! unfortunately I see here, that there seems to be a large part of the book spent on a statistics introduction (including R) and only one machine learning algorithm actually gets introduced. and on top of all it's one of the most simple ones (naive bayes). I expected at least some further description of support vector machines or other advanced techniques.
- wisty 15y agoIt doesn't look as advanced as Collective Intelligence (the Python-based book which covers most of Ng's ML course), but it looks like a cool R introduction.
- superxor 15y agoAre you referring to this book http://www.flipkart.com/books/8184043708?_l=rNOfRA_2s1pRNn46QI4BxA--&_r=JcznTdf9sAFqZnRRcpbz0g-- http://www.flipkart.com/books/8184043708?_l=rNOfRA_2s1pRNn46... ??
- danfitch 15y agoI 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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- hermanjunge 15y agoIt's a different ebook.
- MaxGabriel 15y agoIn corollary to this post, does anyone know what's up with the Stanford Machine Learning course? Haven't heard anything since the delay
- seancron 15y agoIt 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 http://www.coursera.org/modelthinking/lecture/preview Hopefully that means they're getting ready for the other classes to go live as well.
- ericlavigne 15y agoA followup to the AI class starts soon (I remember Feb 20 but can't find confirmation): "Due to popular demand, we are teaching a follow-up class: AI for Robotics at www.udacity.com . Also due to popular demand, we now have a programming environment, so you can develop and test software. Our goal is to teach you to program a self-driving car in 7 weeks. This is a topic very close to my heart, and I am eager to share it with you. (This class builds on the concepts in ai-class, but ai-class is NOT required)."
- fuzzythinker 15y agoThis Machine Learning in Action from Manning uses python instead of R: http://www.manning.com/pharrington/ 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.
- cageface 15y agoThis 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 15y agoThe 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 15y agoI 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.
- tel 15y agoThey're brutally simple to implement much of the time. The difficulty comes in two places: 1. Derivation of slight variations on the basic principles 2. Scaling. Both are very difficult.
- ramblerman 15y ago
- etrain 15y agoHave 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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- sbt 15y agoI 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.
- rylz 15y agoMaybe you're right, but I think there's something to be said for teaching applied ML separate from the theory. I took a very in-depth theory-focused ML class at Caltech, in which lectures made up a thoroughly rigorous mathematical introduction, and the problem sets were only maybe 10% applications. I and my classmates came out of the class feeling super comfortable with the theory behind learning in general and how it applied to many of the standard ML algorithms, but without any experience actually working with very large data sets and building ML algorithm implementations that scale. That's what I'm hoping this book will help with, and I think it is appropriate to separate that kind of material from the theory.
- iqster 15y agoGood point. Does one really need to know how to code up a neural network in order to just use it? This has been my biggest frustration with some teachers of Machine Learning. They need to abstract away the innards and provide a usable high-level interface. I know this is hard to do. ML isn't magic ... you have to know what you are doing. But the same can be said for the automobile when it was first invented. Today, to effectively use a car, you don't need to know anything about engines.
- readme 15y agoI 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 http://www.youtube.com/watch?v=vKmQW_Nkfk8
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- pauldix 15y agoI have an early review copy if this book and know both the authors. It's good and I highly recommend it!
- showerst 15y agoJust 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-Building-Applications/dp/0596529325/ref=sr_1_1?ie=UTF8&qid=1328721596&sr=8-1 http://www.amazon.com/Programming-Collective-Intelligence-Bu...