Blog Posts
Apparently there are two popular free online courses to learn the basics of machine learning. One is by Stanford, and the other is released by Caltech. Having taken one of them, I can say that it probably doesn't really matter which one you take, as long as you take the class seriously.
Machine learning generally concerns itself with analyzing and processing data to find a pattern or trend. This trend is then applied to real world situations in a variety of ways - classifying data, or predicting values. As machine learning is strongly related to data, there are many statistics concepts involved. For example, one of the more basic yet efficient examples of machine learning is to use linear regression. For datasets that have a moderate to strong linear relationship, a regression line can be used with surprising accuracy to classify or predict data. If the relationship in the data is not linear, then either a transform can be applied to the data or another machine learning method can be used. One of the more popular methods being neural networks. Neural networks have multiple layers and nodes that each apply a regression.