Introduction to Data Science - Unit : 2 - Topic 4 : TYPES OF ML

TYPES OF ML

Broadly speaking, we can divide the different approaches to machine learning by the amount of human effort that’s required to coordinate them and how they use labelled data—data with a category or a real-value number assigned to it that represents the outcome of previous observations.

Ø  Supervised learning techniques attempt to discern results and learn by trying to find patterns in a labeled data set. Human interaction is required to label the data.

Ø  Unsupervised learning techniques don’t rely on labeled data and attempt to find patterns in a data set without human interaction.

Ø  Semi-supervised learning techniques need labeled data, and therefore human interaction, to find patterns in the data set, but they can still progress toward a result and learn even if passed unlabeled data as well.

 

Supervised learning

Supervised learning is a learning technique that can only be applied on labeled data. An example implementation of this would be discerning digits from images. Let’s dive into a case study on number recognition.