Machine Learning Basic - II

Semi - Supervised learning:

The challenge with supervised learning is that labelling data can be expensive and time consuming, if labels are limited, you can use unlabelled examples to enhance supervised learning because the machine is not fully supervised in this case, we say the machine is semi-supervised. With semi-supervised, you use unlabeled example with small amount of labeled data to improve the learning accuracy.

Unsupervised learning:

When performing unsupervised learning, the machine is presented with totally unlabeled data. It is asked to discover the intrinsic patterns that underlies the data, such as clustering structure, a low dimensional manifols, or asparse tree and graph.

·   Clustering: Grouping a set of data examples so that example is one group (or one cluster) are more similar (according to some criteria) than those in other groups.

·   Dimension reduction: Reducing the number of variables under consideration. In many applications. The row data have very high dimensional feature and some feature are redundant or irrelevant to the task. Reducing the dimensionality helps to find the true, latent relationship.