1. machine learning foundation

The pre-model data into:

1. Training set (Training data) -------------------- used to train, constructed model
2. The validation set (Validation data) ------------------ quality models used in the training phase of the test model
3. Test set (Testing data) --------------------- model training and other good after, and then the test set to evaluate the quality of the model

Training methods of machine learning

1. Supervised learning: the training data set with label
2. Unsupervised Learning: unlabeled data sets such as clustering
3. Semi-supervised learning: a learning supervised learning and unsupervised learning mode combination. To solve a small amount of data with labels and tags are not a lot of training and classification of problems

Common applications

1. Return    (future trend data based on historical data)
2. Classification    (image recognition, spam classification, text classification) classification basically tagged supervised learning
3. Cluster    (Cluster is no label classification), properties of similar classified as a class

Regression: prediction data is continuous value (Rate)

Category: Data Category forecast data, and the category is known (that is not a Class A Class B)

Clustering: forecast data categorical data, but the unknown category, no tag