Machine Learning
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
Types of
classification algorithms in machine learning:-
1) Nearest
Neighbour:- The k-nearest neighbour algorithm is a classification algorithm.
And it is supervised; it takes a bunch of labelled points and uses them to
learn how to label other points.
2)
Logistic
Regression(Predictive Learning Model):- It is a statistical method for analysing a
data set in which there are one or more independent variables that determine an
outcome.
3)
Decision
Trees:- Decision tree builds
classification or regression models in the from of a tree structure. It breaks
down a data set into smaller and smaller subsets while at the same time an
associated decision tree is incrementally developed. The final result is a tree
with decision nodes and leaf nodes.
4)
Random
Forest:- Random forests or random
decision forests are an ensemble learning method for classification, regression
and other tasks. Random decision forests correct for decision trees habit of
over fitting to their training set.
5)
Neural
Network:- A neural network consists of
units (neurons), arranged in layers, which convert on input vector into some
output. Each unit takes an input, applies a (often nonlinear) function to it
and then passes the output an to the next layer.
6)
Naïve
Bayes classifier (Generative Learning Model) :-
It is a classification technique based on Bayes’ Theorem with an
assumption of independence among predictors. Naïve Bayes is Known to outperform
ever highly sophisticated classification methods.
https://rgpvnotesforcsestudents.blogspot.com/2021/03/machine-learning-its-types.html
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