Read the following articles on types of machine learning.

- Supervised Learning
- Unsupervised Learning
- Self-organizing Map
- Adaptive Resonance Theory
- Semi-supervised Learning
- Co-training
- Maximum Likelihood
- Expectation Maximization

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.

Click http://en.wikipedia.org/wiki/Supervised_learning link to open resource.