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[META] Collection of Links for Beginners / FAQ

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MOOCs

Nowadays, there are a couple of really excellent online lectures to get you started. The list is too long to include them all. Every one of the major MOOC sites offers not only one but several good Machine Learning classes, so please check coursera, edX, Udacity yourself to see which ones are interesting to you.

However, there are a few that stand out, either because they're very popular or are done by people who are famous for their work in ML. Roughly in order from easiest to hardest, those are:

  • Andrew Ng's ML-Class at coursera: Focused on application of techniques. Easy to understand, but mathematically very shallow. Good for beginners!

  • Yaser Abu-Mostafa's Learning From Data: Focuses a lot more on theory, but also doable for beginners

  • Geoff Hinton's Neural Nets for Machine Learning: As the title says, this is almost exclusively about Neural Networks.

  • Daphne Koller's Probabilistic Graphical Models Is a very challenging class, but has a lot of good material that few of the other MOOCs here will cover

  • Hugo Larochelle's Neural Net lectures: Again mostly on Neural Nets, with a focus on Deep Learning


    Books

The most often recommended textbooks on general Machine Learning are (in no particular order):

Programming Languages and Software

In general, the most used languages in ML are probably Python, R and Matlab (with the latter losing more and more ground to the former two). Which one suits you better depends wholy on your personal taste. For R, a lot of functionality is either already in the standard library or can be found through various packages in CRAN, for Python, NumPy/SciPy are a must. From there, Scikit-Learn covers a broad range of ML methods.

If you just want to play around a bit and don't do much programming yourself then things like WEKA, KNIME or RapidMiner might be of your liking. Word of caution: a lot of people in this subreddit are very critical of WEKA, so even though it's listed here, it is probably not a good tool to do anything more than just playing around a bit. A more detailed discussion can be found here


Datasets and Challenges for Beginners

There are a lot of good datasets here to try out your new Machine Learning skills.

FAQ

How much Math/Stats should I know?

That depends on how deep you want to go. For a first exposure (e.g. Ng's Coursera class) you won't need much math, but in order to understand how the methods really work,having at least an undergrad level of Statistics, Linear Algebra and Optimization won't hurt.

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