For Python programmers Harrison's Website is awesome resource
Introduction to Machine Learning for Developers
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Re: Introduction to Machine Learning for Developers
#22I've made a similar list for economists. It included a list of practical applications of ML. Developers can get a sense of what the discipline can do before jumping in. APPLIED MACHINE LEARNING CASES ## Business 1. Kaggle, Data Science Use cases. An outline of business applications. Few companies have the data to implement these things. https://www.kaggle.com/wiki/DataScienceUseCases 2. Kaggle, Competitions. (Make su…
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...
Re: Introduction to Machine Learning for Developers
#23The description of Naive Bayes is misleading. Almost all supervised learning problems assume that "Inputs are classified in isolation where no input has an effect on any other inputs" (quote from the article), but that's not why Naive Bayes is called naive.
The naive assumption made by Naive Bayes is that the features (or attributes) of each input point are independent. Let me explain from a simple example:
Suppose you want to find people who receive benefits they are not entitled to. The input data might have two attributes: cash on bank account, and amount received in benefits. Although you could look for data that have a high value in both attributes, the naive assumption made in Naive Bayes says that you can in fact make your classification without correlating multiple attributes; Naive Bayes assumes you can explain the labeling of data just by looking at attributes in isolation. In this example, this assumption is clearly unfounded, since if you only look at benefits or only at cash balance, you won't be able to tell how a person should be classified.
The data independence assumption made by almost all ML algorithms is that different data points are not correlated: the label of a single data point (person in the above problem) does not depend on the attributes of other data points.
Re: Introduction to Machine Learning for Developers
#24[deleted]
Re: Introduction to Machine Learning for Developers
#25The description of Naive Bayes is misleading. Almost all supervised learning problems assume that "Inputs are classified in isolation where no input has an effect on any other inputs" (quote from the article), but that's not why Naive Bayes is called naive. The naive assumption made by Naive Bayes is that the features (or attributes) of each input point are independent. Let me explain from a simple example: Suppose y…
I agree. Most supervised learning classifiers are derived based on the independent and identically distributed assumption for each (x,y) pair.
To be more specific about the Naive Bayes assumption, the features of a data point are conditionally independent instead of simply independent. This means that given a certain label, these set of features are independent.
Re: Introduction to Machine Learning for Developers
#26In the slides for unsupervised learning, what is meant by "Maximum Entropy"? Doesn't this just imply that the distribution will be uniform; i.e. it's no better than making a blind guess?
Re: Introduction to Machine Learning for Developers
#27In the slides for unsupervised learning, what is meant by "Maximum Entropy"? Doesn't this just imply that the distribution will be uniform; i.e. it's no better than making a blind guess?
I have only seen a maximum entropy model as part of the supervised realm where it is a discriminative model. In other words, given some labeled data, we can draw a decision boundary. Maximum entropy in this context is almost certainly associated with the information theory definition, where the entropy of a collection of data based on the distribution of classes is measured. High entropy if each class is equally probable. Lower Entropy otherwise.
Re: Introduction to Machine Learning for Developers
#28Honestly, 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 points on ycombinator?
Re: Introduction to Machine Learning for Developers
#29The description of Naive Bayes is misleading. Almost all supervised learning problems assume that "Inputs are classified in isolation where no input has an effect on any other inputs" (quote from the article), but that's not why Naive Bayes is called naive. The naive assumption made by Naive Bayes is that the features (or attributes) of each input point are independent. Let me explain from a simple example: Suppose y…
Hi, I'm Stephanie Kim and wrote the talk/post. Thanks for the comment! Yes you are correct I should have specified that it is the features of each input rather than the inputs that are regarded as independent from one another! I will revise that in the post. Again, thanks for pointing that out since it's an important distinction, especially for people just starting out!
Re: Introduction to Machine Learning for Developers
#30Honestly, 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 experience in machine learning. Thanks for your feedback.