Machine Learning basically has two definitions. They are:
- Arthur Samuel -: "The field of study that gives computers the ability to learn without being explicitly programmed." This can be considered as an older, informal definition.
- Tom Mitchell explains how the computer program became a Machine learning one here -: "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."
Now basically the question falls that how the machine is learning the things!
In an overview, it is being again subdivided as
- Supervised Learning
- Unsupervised Learning
- Supervised Learning:
Again this is being subdivided as:
- Regression :
- Predicts a continuous-valued output. Many parameters will be there to find out a result.
- Eg:- Given a picture find age.
- Classification :
- Separate the given data to discrete value of output.
- Eg:- Given many pictures classify them to different age categories.
Now, what is unsupervised. Complete mystery of finding the result. Yes, it is not having any feedback to correct means no input data early available to supervise. The data which are available to work on as well as the scenario to separate and find a result from the data is new in this case. We can conclude Unsupervised Learning as if we have some data and we can separate it into meaningful information without any external input.
- Clustering:
- Given data and separate finding factors into clusters.
- Eg:-Astronomical data to find similarities and cluster it.
- Non-Clustering:
- Given data instant of seperate time period or dimension and classify within from the data to find the difference.
- Eg:- from two videos of same two person in an area at different angle directed with seperate camera and from this data distinguish the person and label in the video without any previous learning data.