Wikipedia: "Types of Machine Learning"

Read the following articles on types of machine learning.

There are many learning methods, each having strengths and weaknesses in particular applications, for particular data sets and situations. Issues that have to be contended with include: bias (a predicted value of a learning algorithm is systematically incorrect when trained on several different data sets) and variance (variation of a predicted value for a given input when trained on different data sets), complexity of functions to be predicted, complexity of data, noisy data, missing data, etc.