Introduction to Machine Learning for Developers • Hacker News Board

Re: Introduction to Machine Learning for Developers

#31

Post by lonewolf_ninja » Fri, Nov 11, 2016, 8:38 PM UTC

goberoi wrote: post #22

lonewolf_ninja wrote: post #13

Earlier quoted context omitted.

I recently started playing around with the data sets on past Kaggle competitions and have been learning a lot. The Data Science use cases there are quite interesting. Are there any publicly available data-sets (other than the ones available in competitions) to work with (especially for the marketing use cases)?

I highly recommend subscribing to the "Data is Plural" mailing list. You'll get interesting datasets mailed to you each week! Here's the last one: http://tinyletter.com/data-is-plural/letters/data-is-plural-... Also checkout "Academic Torrents". Lots of large datasets here, from millions of Tweets, to labeled photos of fish in the wild. http://academictorrents.com/browse.php?c6=1&sort_field=times...

Thanks for that! I looked it up and just subscribed to the mailing list. Looks quite interesting.

Re: Introduction to Machine Learning for Developers

#32

Post by machineman44 » Fri, Nov 11, 2016, 10:51 PM UTC

princesspea wrote: post #30

machineman44 wrote: post #28

Honestly, this is a good run through of resources and examples of different machine learning algorithms/techniques be it supervised, unsupervised, or model validation... however, the wording used and mistakes made when describing supervised learning or Naive Bayes shows that this is an attempt at taking an O'Rielly book and trying to summarize it in a short article... while making errors... How did it get so many poi…

Hi! I'm Stephanie Kim and wrote the article. This post and slides were from a talk I gave for a basic introduction to machine learning at a woman's programming conference in Seattle. I did update the language which was a mistake rather than a misunderstanding of Naive Bayes. I have professional machine learning experience and while I am definitely not an expert the talk was geared for web developers with no prior exp…

Hi Stephanie. Sorry if my comment sounded harsh and nit picky. I actually passed it off to a fellow software engineer at work and he found it really insightful and useful for the work he is doing. Not everybody makes the effort to share their knowledge and I really appreciate you doing so. Have a good day :)

Re: Introduction to Machine Learning for Developers

#33

Post by marcinzm » Sat, Nov 12, 2016, 3:51 PM UTC

mi100hael wrote: post #3

Cool, this is a helpful intro. Anyone have any recommended reading for ML in a JVM context?

If you're interested in the big data side of things there's Spark (http://spark.apache.org/) and MLlib for it (http://spark.apache.org/docs/latest/ml-guide.html). H20 (http://www.h2o.ai/) also provides ML algorithms on top of Spark (and I think independent of Spark as well, not sure of the current status). These are all written on the JVM either in Scala (Spark) or Java (H20).