Before jumping directly into what is Machine Learning lets starts with the meaning of individual words i.e. What is Machine and What is Learning.
- A machine is a tool containing one or more parts that transform energy. Machines are usually powered by chemical, thermal, or electrical means, and are often motorized.
- Learning is the ability to improve behaviour based on Experience.
What is Machine Learning? :-
According to Tom Mitchell, Machine Learning is
“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E”
- Task T is what machine is seeking to improve. It can be Prediction, Classification, Clustering etc.
- Experience E can be training data or input data through which Machine try to learn.
- Performance P can be some factor like improvements accuracy or new skills that Machine was previously unaware about of etc.
Machine Learning itself contains 2 main components the Learner and the Reasoner.
- Input/ Experience is given to the Learner that learn some new skills out of it.
- Background Knowledge can also be given to Learner for better learning.
- With the help of Input and Background, Knowledge Learner generates the Model.
- The model contains the information about how he has learnt from the Input and Experience.
- Now, the Problem/ task is given to Reasoner it can be Prediction, Classification etc.
- With the help of trained Model Reasonar tries to generate the Solution.
- Solution / Answer can be improved with adding additional input/ Experience Background knowledge.
How Machine Learning different from Standard Program? :-
In machine learning, you feed the computer the following things
- Input [Experience]
- Output [output corresponding to inputs]
And get Model/ Program as Output. With the help of this program, you can perform some tasks.
Whereas In Standard Program you feed the computer following things
- Program [how to process the input]
And get the Output a simple example can be to verify a number of Prime or not.
REFRENCES :- Machine Learning By Tom Michell