An Introduction to Machine Learning
In today’s modern and digital world, everything is done easily by an automatic process. In earlier days, technologies believe in manual work. Manual work done by humans is believed to be secure, error-free, and assures confidentiality.
As the human population
rises, the need for technology also arises. Automatic processes are introduced
to tackle complex problems and find accurate solutions. Work done by machines
is called an automatic process. Is it possible that machines do their jobs
similar to humans? Yes, it is possible with the concept called MachineLearning.
What is the need
for Machine Learning?
Every suitable solution is found when
the Necessity arises. Let’s have a look at the need for machine learning:
1. To reduce the minor errors done in the manual
process.
2. The utilization of a large amount of time is
reduced compared to the manual process.
3. It takes the human generation to the next
level of technology
4. It is mandatory in the Medical and Research
fields for its fast and accurate performance.
5. Machine Learning boosts people to find more
technological innovations and gives them a feeling that the world is developing
to its next level.
A brief discussion about Machine Learning
The process
of making machines, to learn new methods by the data input by the user. The
methods of making machines learn are called Artificial Intelligence. It allows
the user to input certain algorithms, and the system reads and analyzes the
algorithms and takes decisions based on the history. It will repeat the process
and learns the technology from humans. The decisions decided by the machines by
analyzing their past behavior are called Artificial Intelligence.
Programming Language involved in Machine
Learning
The input data is based on the user's facility.
Depending upon their requirements and applications, the input is decided.
The input is nothing but the set of
instructions or algorithms which is called Programming Language
The familiar programming language which
is used in trending Machine Learning Technology are as follows,
o
Python-which is a
trending language for machine language
o
C++
o
JavaScript
o
Java
o
Julia and Lisp
Types of Machine Learning
Supervised
Machine Learning
It
is subdivided into two types
1.
Classification
Machine Learning
2.
Regression
Machine Learning
Classification
Making the
machines learn the labeled set of data as input and predict the output based on
the input. It takes the decision based upon the input data. There always exists
a relationship between input and output data.
Regression
The
regression supervised learning algorithm is used to predict future values.
Based on the given input data, it predicts the output data. The input data
contains independent and dependent values, which contain labels and data
points. The types of regression are as follows:
1. Linear Regression
2. Logistic Regression
3. Polynomial Regression
4. Stepwise Regression
Linear Regression
The linear regression is based upon constant slope based upon ‘X’ variable ‘Y’ output.
Mainly it is used in Marketing, Share market industries and so on.
Logistic Regression
It
denotes the relationship between the independent and dependent variables. Logistic Regression
determines the output using predictive analysis of input data.
Polynomial Regression
It is used
for various research and experimental purposes. The calculations are performed
using the independent X variable and dependent Y variable to get the desired ‘N’th
polynomial as a result.
Stepwise Regression
In
stepwise regression, the process is carried out by step-by-step process by
adding or subtracting necessary variables to get the predictive output. Below
given chart is an example of stepwise regression.
Unsupervised Machine Learning
As the title indicates, unsupervised learning is the opposite of supervised learning. The input data contains a variety of unlabeled models, and the machine has to classify the data set based on its artificial intelligence.
It is subdivided into two types,
1. Clustering
2. Association
Based on the types mentioned above, it is subdivided into
various types of familiar algorithms based upon the user's facility.
Reinforcement Machine Learning
The reinforcement method completely involves artificial Intelligence learning. It involves two main concepts, such as environment and agent. It's a trial and error method, where the agent has to work based upon the environment's reaction. It's every process affects the environment with positive and negative feedback.
Reinforcement is subdivided into four types
a)
Positive
Reinforcement
b)
Negative
Reinforcement
c)
Extinction
Reinforcement
d) Punishment Reinforcement
Positive
Reinforcement
As
reinforcement is a trial and error method, if the outcome is positive, it has a
higher frequency based on the environment and agent. The possibility of finding
a better solution is higher.
Negative
Reinforcement
If
the action between agent and environment is not satisfied, that process is
stopped or eliminated to reduce the lower frequency.
Extinction
Reinforcement
It
is the process of stopping the actions of negative impacts between the agent
and the environment. This avoids the action of performing that process again
and again.
Punishment
Reinforcement
It
involves removing or encouraging bad and good behavior. Positive punishment
decreases the negative things while negative punishment increases the bad
actions. The punishment takes place between the environment and agent on the
basis of rewards.
Conclusion
From the above article, we have discussed the working process of machine learning. Apart from that, various programming algorithms are used by programmers for developing various applications using machine language.
In the future, the
upcoming technologies will take machine learning to the next level. We people
are now trying to make machines to learn technology equal to human beings,
tackle various complex problems and reduce the people’s burden.
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