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:

Books

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

Note that these books delve deep into math, and might be a bit heavy for complete beginners. If you don’t care so much about derivations or how exactly the methods work but would rather just apply them, then the following are good practical intros:

There are of course a whole plethora on books that only cover specific subjects, as well as many books about surrounding fields in Math. A very good list has been collected by /u/ilsunil here

Deep Learning Resources

Math Resources

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 Visions of Chaos, 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

Deep Learning Software, GPU’s and Examples

There are a number of modern deep learning toolkits you can utilize to implement your models. Below, you will find some of the more popular toolkits. This is by no means an exhaustive list. Generally speaking, you should utilize whatever GPU has the most memory, highest clock speed, and most CUDA cores available to you. This was the NVIDIA Titan X from the previous generation. These frameworks are all very close in computation speed, so you should choose the one you prefer in terms of syntax.

Theano is a python based deep learning toolkit developed by the Montreal Institute of Learning Algorithms, a cutting edge deep learning academic research center and home of many users of this forum. This has a large number of tutorials ranging from beginner to cutting edge research.

Torch is a Luajit based scientific computing framework developed by Facebook Artificial Intelligence Research (FAIR) and is also in use at Twitter Cortex. There is the torch blog which contains examples of the torch framework in action.

TensorFlow is a python deep learning framework developed by Google Brain and in use at Google Brain and Deepmind. The newest framework around. Some TensorFlow examples may be found here Do not ask questions on the Google Groups, ask them on stackoverflow

Neon is a python based deep learning framework built around a custom and highly performant CUDA compiler Maxas by NervanaSys.

Caffe is an easy to use, beginner friendly deep learning framework. It provides many pretrained models and is built around a protobuf format of implementing neural networks.

Keras can be used to wrap Theano or TensorFlow for ease of use.

Datasets and Challenges for Beginners

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

Research Oriented Datasets

In many papers, you will find a few datasets are the most common. Below, you can find the links to some of them.

Communities

ML Research

Machine Learning is a very active field of research. The two most prominent conferences are without a doubt NIPS and ICML. Both sites contain the pdf-version of the papers accepted there, they’re a great way to catch up on the most up-to-date research in the field. Other very good conferences include UAI (general AI), COLT (covers theoretical aspects) and AISTATS.

Good journals for ML papers are the Journal of Machine Learning Research, the Journal of Machine Learning and arxiv.

Other sites and Tutorials

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.