Machine Learning and
it’s Types:-
Machine learning is a
tool for turning information into knowledge. Machine learning techniques are used to
automatically find the valuable underlying patterns within complex data that we would otherwise struggle to discover. The
hidden patterns and knowledge about a problem can be used to predict future events and
perform all kinds of complex decision making.
Tom Mitchell gave a “well-posed”
mathematical and relational definition
that “A computer program its performance
on T, as measured by P, improves with
experience E”.
Types of Machine
Learning:-
- 1)
Supervised
Learning:- Supervised Learning is the one, where you can
consider the learning is guided by a teacher. We have a dataset which acts as a
teacher and its role is to train the model or the machine. Once the model gets
trained it can start making a prediction or decision when new data is given to
it.
- 2)
Unsupervised
Learning:- The model learns through
observation and finds structures in the data. Once the model is given a
dataset, it automatically finds patterns and relationship in the dataset by
creating clusters in it. What it cannot do is add labels to the cluster, like
it cannot say this a group of apples or mangoes, but it will separate all the
apples from mangoes.
- 3)
Reinforcement
Learning:- It is the ability of an agent to interact with the environment and
find out what is the best outcome. It follows the concept of hit and trial
method. The agent is rewarded or penalized with a point for a correct or a wrong answer, and on the basis of the
positive reward points gained the model trains itself. And again once trained
it gets ready to predict the new data presented to it
Comments
Post a Comment