My Machine Learning Notes
There are 2 categories of learning: Supervised and Unsupervised. In Supervised learning, Extracting Features is the first step, then a training data set is used to test hypothesis and a model is created.The model is then applied to a larger data set to predict decisions. The results predicted and actual are analyzed,like mean square error is used and model is refined. Typically Supervised learning has below segments Classification and Regression. "....from Oriely book, Learning Spark, the authors have share below... Classification and regression are two common forms of supervised learning , where algorithms attempt to predict a variable from features of objects using labeled training data (i.e., examples where we know the answer). The difference between them is the type of variable predicted: in classification, the variable is discrete (i.e., it takes on a finite set of values called classes ); for example, classes might be spam or nonspam for emails, or the