Machine Learning is a branch of Artificial Intelligence that involves the study of algorithms and models that enables machines to learn through experience in the form of inputted data , abstraction and generalization.
Data Input : Past experience in the form of inputted data is utilized for exploring the pattern and for decision making.
Abstraction : Abstraction helps in deriving the concepts or model from the input data and pattern. This concept or model may be in the any of the following form : Mathematical Equations, Computational blocks like if else rules , Data structures like trees , graphs or tables and logical grouping of observations.
- Deriving the concept say bird by observing instances like sparrow, pigeon , eagle , hen, crow ,etc.
- Deriving the equation like y = x2 from the series of input 1,4,9,16,——–
Generalization : The abstracted representation is generalized in a broader way through the underlying algorithm. It represents the model’s ability to adapt properly to new, previously unseen data, drawn from the same distribution as the one used to create the model.
Example : Ability to classify the things as birds , animals or car based on the concept learned.