Machine learning cookbook / reference card / cheatsheet? I find resources like the Probability and Statistics Cookbook and The R Reference Card for Data Mining incredibly useful. They obviously serve well as references but also help me to organize my thoughts on a subject and get the lay of the land. 
Q: Does anything like these resources exist for machine learning methods?
I'm imagining a reference card which for each ML method would include:


*

*General properties

*When the method works well

*When the method does poorly

*From which or to which other methods the method generalizes. Has it been mostly superseded?

*Seminal papers on the method

*Open problems associated with the method

*Computational intensity


All these things can be found with some minimal digging through textbooks I'm sure. It would just be really convenient to have them on a few pages. 
 A: Witten and Frank, "Data Mining", Elsevier 2005 is a good book for self-learning as there is a Java library of code (Weka) to go with the book and is very practically oriented.  I suspect there is a more recent edition than the one I have.
A: I have Machine Learning: An Algorithmic Perspective by Stephen Marsland and find it very useful for self-learning.  Python code is given throughout the book.
I agree with what is said in this favourable review:
http://blog.rtwilson.com/review-machine-learning-an-algorithmic-perspective-by-stephen-marsland/
A: "Elements of Statistical Learning" would be a great book for your purposes.  The and 5th printing (2011) of the 2nd edition (2009) of the book is freely available at http://www.stanford.edu/~hastie/local.ftp/Springer/ESLII_print5.pdf
A: 


*

*http://scikit-learn.org/stable/tutorial/machine_learning_map/

Often the hardest part of solving a machine learning problem can be finding the right estimator for the job.
  Different estimators are better suited for different types of data and different problems.
  The flowchart below is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data.
  Click on any estimator in the chart below to see it’s documentation.

A: The awesome-machine-learning repository seems to be a master list of resources, including code, tutorials and books.
A: If you want to learn Machine Learning I strongly advise you enroll in the free online ML course in the winter taught by Prof. Andrew Ng.
I did the previous one in the autumn and all learning material is of exceptional quality and geared toward practical applications, and a lot easier to grok that struggling alone with a book.
It's also made a pretty low hanging fruit with good intuitive explanations and the minimum amount of math.
A: Most books mentioned in other answers are very good and you can't really go wrong with any of them. Additionally, I find the following cheat sheet for Python's scikit-learn quite useful.
A: Microsoft Azure also provides a similar cheat-sheet to the scikit-learn one posted by Anton Tarasenko.

(source: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-cheat-sheet)
They accompany it with a notice:

The suggestions offered in this algorithm cheat sheet are approximate
  rules-of-thumb. Some can be bent, and some can be flagrantly violated.
  This is intended to suggest a starting point. (...)

Microsoft additionally provides an introductory article providing further details. 
Please notice that those materials are focused on the methods implemented in Microsoft Azure.
A: Some of the best and freely available resources are:


*

*Hastie, Friedman et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction

*David Barber. Bayesian Reasoning and Machine Learning

*David MacKay. Information Theory, Inference and Learning Algorithms (http://www.inference.phy.cam.ac.uk/mackay/itila/)


As to the author's question I haven't met "All in one page" solution   
A: I like Duda, Hart and Stork "Pattern Classification".  This is a recent revision of a classic text that explains everything very well.  Not sure that it is updated to have much coverage of neural networks and SVMs.  The book by Hastie, Tibshirani and Friedman is about the best there is but may be a bit more technical than what you are looking for and is detailed rather than an overview of the subject.
A: Yes, you are fine; Christopher Bishop's "Pattern Recognition and Machine Learning" is an excellent book for general reference, you can't really go wrong with it.
A fairly recent book but also very well-written and equally broad is David Barber's "Bayesian Reasoning and Machine Learning"; a book I would feel is slightly more suitable for a new-comer in the field.
I have used "The Elements of Statistical Learning" from Hastie et al. (mentioned by Macro) and while a very strong book I would not recommended it as a first reference; maybe it would serve you better as a second reference for more specialized topics. In that aspect, David MacKay's book, Information Theory, Inference, and Learning Algorithms, can also do a splendid job.
A: Since the consensus seems to be that this question is not a duplicate, I'd like to share my favorite for machine learner beginners:
I found Programming Collective Intelligence the easiest book for beginners, since the author Toby Segaran is is focused on allowing the median software developer to get his/her hands dirty with data hacking as fast as possible.
Typical chapter: The data problem is clearly described, followed by a rough explanation how the algorithm works and finally shows how to create some insights with just a few lines of code.
The usage of python allows one to understand everything rather fast (you do not need to know python, seriously, I did not know it before, too). DONT think that this book is only focused on creating recommender system. It also deals with text mining / spam filtering / optimization / clustering / validation etc. and hence gives you a neat overview over the basic tools of every data miner. 
A: For a first book on machine learning, which does a good job of explaining the principles, I would strongly recommend

Rogers and Girolami, A First Course in Machine Learning,
  (Chapman & Hall/CRC Machine Learning & Pattern Recognition), 2011.

Chris Bishop's book, or David Barber's both make good choices for a book with greater breadth, once you have a good grasp of the principles.
A: Don't start with Elements of Statistical Learning. It is great, but it is a reference book, which doesn't sound like what you are looking for. I would start with Programming Collective Intelligence as it's an easy read. 
A: I wrote a summary like that, but only on one machine learning task (Netflix Prize), and it has 195 pages:
http://arek-paterek.com/book
A: Check this link featuring some free ebooks on machine learning : http://designimag.com/best-free-machine-learning-ebooks/. it might be useful for you. 
A: A good cheatsheet is the one in Max Kuhn book Applied Predictive Modeling.  In the book there is a good summary table of several ML learning models. The table is in appendix A page 549:

