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If you want to get into machine learning, it is actually pretty easy, provided you studied Math, Engineering, or Science in University. The papers are hard at f

If you want to get into machine learning, it is actually pretty easy, provided you studied Math, Engineering, or Science in University. The papers are hard at first, but they don't hide like other papers do. Everything is laid out there for you to code and run with your own data. In a field like, say, Structural Engineering, the paper writers can make claims about the structural resilience of an Ultra High Performance Concrete that they tested, without you ever being able to hope to repeat the exact experiment. You may not even be able to get your hands on the proprietary compound they used. In ML, you might not be able to use the same corpus / training set, but you're usually able to recode the actual algorithm and you're usually able to find a compatible type of data set to work off of. Also, most ML people are lazy, so if they don't work for Google or Facebook they're usually using open data datasets anyway, which are trivial to drop in and verify.

nsomaru 12y ago
I'm curious -- does one need to consult these papers when using algorithms from libraries such as SciKit (Python)? I ask because the field seems so broad, and having completed Andrew Ng's original ML course, I'm not sure which way to go... thanks.
- agibsonccc 12y ago
  Hi Machine learning instructor from Zipfian Academy here. We teach scikit learn at our bootcamp and people who have some sort of scientific background, have completed Ng's course, or are software engineers typically apply to our program. What we have found in the class room is that if you understand the fundamentals of machine learning algorithms, you don't have to understand all the minutiae of an algorithm as a user. The hardest problem to solve is figuring out how to encode data such that the algorithm learns properly as well as all the pre processing steps that typically go in to machine learning. The only thing that would be worth understanding for an algorithm are the hyperparameters associated with the respective models. An example of this would be learning rate or regularization coefficients. If you understand what those are useful for, you should be fine.
- nandemo 12y ago
  FWIW, I'm studying ML with Introduction to Statistical Learning, which was recommended to me by several people. Personally I didn't enjoy Coursera's ML course (though I believe Ng is a great teacher) and dropped out, but I'm enjoying this book. It's available as PDF for free: http://www-bcf.usc.edu/~gareth/ISL/ http://www-bcf.usc.edu/~gareth/ISL/
visarga 12y ago
> most ML people are lazy That's a great virtue for programmers in general. "Laziness: The quality that makes you go to great effort to reduce overall energy expenditure. It makes you write labor-saving programs that other people will find useful, and document what you wrote so you don't have to answer so many questions about it. Hence, the first great virtue of a programmer. Also hence, this book. See also impatience and hubris. (LarryWall, ProgrammingPerl, 1st edition)" > they're usually using open data datasets anyway They do that in order to be able to judge the performance of various algorithms - they need to have a common standard to test against.
- 3pt14159 12y ago
  I use lazy in the positive sense, I'd actually forgotten that it had negative connotations when I wrote the above.
- mandeepj 12y ago
  Bill Gates once said - "I choose a lazy person to do a hard job, Because a lazy person will find an easy way to do it." source - http://en.wikiquote.org/wiki/Talk:Bill_Gates#Lazy_person_to_do_a_hard_job http://en.wikiquote.org/wiki/Talk:Bill_Gates#Lazy_person_to_...
icelancer 12y ago
>Also, most ML people are lazy As someone who has written a fair amount of machine learning code, I could not agree more. It is an inelegant way to solve a problem by brute force. Which, of course, is occasionally very useful! But it is often lazy. Though it sounds really cool.

nsomaru 12y ago

I'm curious -- does one need to consult these papers when using algorithms from libraries such as SciKit (Python)? I ask because the field seems so broad, and having completed Andrew Ng's original ML course, I'm not sure which way to go... thanks.

agibsonccc 12y ago
Hi Machine learning instructor from Zipfian Academy here. We teach scikit learn at our bootcamp and people who have some sort of scientific background, have completed Ng's course, or are software engineers typically apply to our program. What we have found in the class room is that if you understand the fundamentals of machine learning algorithms, you don't have to understand all the minutiae of an algorithm as a user. The hardest problem to solve is figuring out how to encode data such that the algorithm learns properly as well as all the pre processing steps that typically go in to machine learning. The only thing that would be worth understanding for an algorithm are the hyperparameters associated with the respective models. An example of this would be learning rate or regularization coefficients. If you understand what those are useful for, you should be fine.
nandemo 12y ago
FWIW, I'm studying ML with Introduction to Statistical Learning, which was recommended to me by several people. Personally I didn't enjoy Coursera's ML course (though I believe Ng is a great teacher) and dropped out, but I'm enjoying this book. It's available as PDF for free: http://www-bcf.usc.edu/~gareth/ISL/ http://www-bcf.usc.edu/~gareth/ISL/

visarga 12y ago

> most ML people are lazy That's a great virtue for programmers in general. "Laziness: The quality that makes you go to great effort to reduce overall energy expenditure. It makes you write labor-saving programs that other people will find useful, and document what you wrote so you don't have to answer so many questions about it. Hence, the first great virtue of a programmer. Also hence, this book. See also impatience and hubris. (LarryWall, ProgrammingPerl, 1st edition)" > they're usually using open data datasets anyway They do that in order to be able to judge the performance of various algorithms - they need to have a common standard to test against.

3pt14159 12y ago
I use lazy in the positive sense, I'd actually forgotten that it had negative connotations when I wrote the above.
mandeepj 12y ago
Bill Gates once said - "I choose a lazy person to do a hard job, Because a lazy person will find an easy way to do it." source - http://en.wikiquote.org/wiki/Talk:Bill_Gates#Lazy_person_to_do_a_hard_job http://en.wikiquote.org/wiki/Talk:Bill_Gates#Lazy_person_to_...

icelancer 12y ago

>Also, most ML people are lazy As someone who has written a fair amount of machine learning code, I could not agree more. It is an inelegant way to solve a problem by brute force. Which, of course, is occasionally very useful! But it is often lazy. Though it sounds really cool.