Supervised Machine Learning Techniques common algorithms and its application are explained

1. ABSTRACT
In the recent year artificial intelligence used in so many area Machine learning is subfields of
artificial intelligence and supervised machine learning is subfields of machine learning which
is developed now days so many areas. Supervised machine learning a big effect on so many
areas such as medical heath care, legal system, engineering etc.
In this seminar report is the introduction of supervised machine learning, types of supervised
machine learning, and its application areas. Supervised Machine Learning is a subfields of
Machine Learning. Machine learning system are now in routine use in economics, medicine,
engineering and the military, as well as being built into many common home computer software
applications, traditional strategy games like computer chess, 8 puzzle and other video games.
We tried to explain brief ideas of supervised machine learning and its application to various
fields. It cleared the concept of computational and conventional categories. Supervised
Machine learning is used in typical problems such as pattern recognition, natural language
procession and more. This system is working thought the world as an artificial brain,
intelligence involves mechanism, and research has discovered how to make carry out some of
them or not others.it is related to the similar task of using computers to understand human
intelligence. We can learn something about how to make machines solve problems by
observing other people or just by observing our own methods.
So basically we can say supervised machine learning technique is exciting and potentially far
reaching development in computer science is the invention and application of methods of
supervised machine learning. These enable a computer program to automatically analyse a
large body of data and decide what information is most relevant. This crystallized information
can then be used to automatically make predictions or to help people make decisions faster and
more accurately.

2. Table of Contents
Description Page No.
1. Introduction 1
1.1 Overview 1
1.2 Why supervised machine learning technique? 2
1.3 Key terminology of supervised machine learning techniques 2
1.4 List of Common Algorithms 3
1.5 Applications of supervised machine learning algorithms 4
1.6 Supervised machine learning technique Vs Unsupervised machine learning
technique 4
2. Machine learning techniques 5
2.1 Introduction 5
2.2 Definition 5
2.3 Why Machine Learning is Importance 5
2.4 Components are followed by algorithms 6
2.5 Key Terminology of Machine Learning Techniques 6
2.6 History of Machine Learning 7
2.7 Types of Machine Learning Techniques 8
2.8 Domains and Application of Machine Learning 11
2.9 Machine Learning Vs Deep Learning 12
3. Supervised machine learning 13
3.1 Introduction 13
3.2 Definitions 13
3.3 Evolution of Supervised Machine Learning 14
3.4 How it work 14
3.5 General Issues of Supervised Machine Learning Algorithms 16
Kinds of Supervised Machine Learning Techniques 16
Classification Vs Regression 27
Application of Supervised Machine Learning Algorithm/ Technique 27
3.9 Examples of Supervised Machine Learning Techniques 28
Summary 30
References 31

3. 1.1. Overview
Supervised Machine learning techniques is a subfield of Machine learning. The goal of
machine learning generally is to understand the structure of data and fit that data into models
that can be understood and utilized by people.
In supervised learning, the computer is provided with example inputs that are labelled with
their desired outputs. The
accordingly. Supervised learning therefore uses patterns to predict label values on additional
unlabelled data.
Figure1.1 Supervised Machine Learning Technique
A common use case of supervised learning is to use historical data to predict statistically likely
future events. It may use historical stock market information to anticipate upcoming
fluctuations, or be employed to filter out spam emails. In supervised learning, tagged photos of
dogs can be used as input data to classify untagged photos of dogs.
For example, with supervised learning, an algorithm may be fed data with images of sharks
labelled as fish and images of oceans labelled as water. By being trained on this data, the
supervised learning algorithm should be able to later identify unlabelled shark images
as fish and unlabelled ocean images as water.
The process of learning begins with observations or data, such as examples, direct experience,
or instruction, in order to look for patterns in data and make better decisions in the future based

4. On the examples that we provide. The primary aim is to allow the computers learn
automatically without human intervention or assistance and adjust actions accordingly.
Supervised machine learning algorithms can apply what has been learned in the past to new
data using labelled examples to predict future events. Starting from the analysis of a known
training dataset, the learning algorithm produces an inferred function to make predictions about
the output values. The system is able to provide targets for any new input after sufficient
training. The learning algorithm can also compare its output with the correct, intended output
and find errors in order to modify the model accordingly.
Machine learning is a method of data analysis that automates analytical model building. It is a
branch of artificial intelligence based on the idea that systems can learn from data, identify
patterns and make decisions with minimal human intervention.
1.2. Why supervised machine learning technique?
It was born from pattern recognition and the theory that computers can learn without being
programmed to perform specific tasks researchers interested in artificial intelligence wanted to
see if computers could learn from data. The iterative aspect of machine learning is important
because as models are exposed to new data, they are able to independently adapt. They learn
from previ
but one that has gained fresh momentum.
While many machine learning algorithms have been around for a long time, the ability to
automatically apply complex mathematical calculations to big data over and over, faster
and faster is a recent development.
1.3. Key terminology of supervised machine learning techniques
Data the data is converted into the completely information. Data may be any type of
row facts that is carry from real world. Data likes audios, videos, images, text and csv
etc. these all are the data that is converted into the information.

5. Problem solving tools using supervised machine learning algorithms or supervised
machine learning we can solving may types of problems. Problems like travel and sales
games, tower of Hanoi, 8 puzzle and chees games etc.
Combinations of computer science and engineering and statistics supervised
machine learning is a combinations of computer science and engineering and
mathematics or statistics.
Interprets data and act it
It collects data or produces a data output from the previous experience and examples
Optimize performance criteria using past experience
Supervised machine learning is also known as teaching oriented machine learning.
Supervised machine learning is already known what is output of given input data.
1.4. List of Common Algorithms
Naive Bayes: - It is a classification technique based on with an
assumption of independence between predictors. Bayes theorem provides a way of
calculating posterior probability
Decision Trees: - It is a type of supervised learning algorithm that is mostly used
for classification problems. It works for both categorical and continuous
dependent variables. This is done based on most significant attributes/ independent
variables to make as distinct groups as possible.
Linear Regression: - It is used to estimate real values (cost of houses, number of calls,
total sales etc.) based on continuous variable(s). We establish relationship between
independent and dependent variables by fitting a best line. This best fit line is known
as regression line and represented by a linear equation Y= a *X + b.
Support Vector Machines (SVM): - It is a classification method. In this algorithm,
we plot each data item as a point in n-dimensional space (where n is number of features
you have) with the value of each feature being the value of a particular coordinate.

6. Neural Networks: - Artificial Neural networks (ANN) or neural networks are
computational algorithms. A neural network is an oriented graph. It consists of nodes
which in the biological analogy represent neurons, connected by arcs.
1.5. Applications of supervised machine learning algorithms
Self-driving Google car
Online recommendation offers such as those from Amazon and Netflix
Online Fraud detection
Classifying e-mails as spam
Social Media Services etc.
1.6. Supervised machine learning technique Vs Unsupervised machine
learning technique
Table 1.1 difference between supervised machine learning and unsupervised machine learning techniques.
Based on Supervised machine learning
technique
Unsupervised machine learning
technique
Input Data Algorithms are trained using
labelled
Algorithms are used against data that
has no historical labels
Computational
Complexity
Supervised learning is said to be a
complex method of learning.
Supervised learning affair is the fact
that one has to understand and label
the inputs
Unsupervised method of learning is
less complex. Unsupervised learning,
one is not required to understand and
label the inputs
Accuracy of the
Results
More accurate and reliable less accurate and reliable
Real Time
Learning
supervised method of learning takes
place off-line
Number of
Classes
Number of Classes is Known Number of Classes is not Known

7. MACHINE LEARNING TECHNIQUES
2.1. Introduction
Machine learning is an application of artificial intelligence (AI) that provides systems the
ability to automatically learn and improve from experience without being explicitly
programmed. Machine learning focuses on the development of computer programs that can
access data and use it learn for themselves.
Machine Learning is the science of getting computers to learn and act like humans do, and
improve their learning over time in autonomous fashion, by feeding then data and information
in the form of observations and real-world interactions.
2.2. Definition
A computer program is said learn from experience E. It is passed data with respect to some
class of task T and performance measured P; if its performance on task in T, on task in T as
measured by P improve with experience E.
By Tom Mitchell in 1997
Machine learning is programmed computers to optimize a performance criterion using data or
past experience.
So, learning is about using experience data that is from passed problem, solving data and there
is a task and the task belongs to a class of tasks T and the task are evaluated by performance
measure P, and a machine is said to learn tasks in T if, if performance at task as measured by
P improves with experience E.
2.3. Why Machine Learning is Importance
Some task cannot be defined well, except by example (e.g. recognizing people).
Relationship and correlation can be hidden within large amounts of data. Machine
learning/Data Miming may be able to find these relationships.

8. Human designers often produce machine that do not work as well as desired in the
environments in which they are used.
The amount of knowledge available about certain tasks might be too large for explicit
encoding by humans (e.g. medical diagnostic).
Environments change over time.
New knowledge about task is constantly being discovered by humans. It may be
difficult to continuously re-
2.4. Components are followed by algorithms
2.4.1. Task (T): - Task is the behaviours of the task, behaviours of the task that the learn
program is seeking to improve. For example there are different types of task like
prediction, classification, acting in an environments etc.
2.4.2. Experience/ Data (E): - Experience is also called data. This is what is used for
improving at the task.
2.4.3. Measured (P): - measure of improvement P. For Example you might want to increase
Accuracy in predication or you might want to have new skill to the agent which it did
not earlier process or improve efficiency of problem solving, corresponding to this you
can define the performance measure.
2.5. Key Terminology of Machine Learning Techniques
2.5.1. Labelled data: - Data consisting of a set of training examples, where each example is
a pair consisting of an input and a desired output value (also called the supervisory
signal, labels, etc.)
2.5.2. Classification: - The goal is to predict discrete values, e.g. {1, 0}, {True, False},
{spam, not spam}.
2.5.3. Regression: - The goal is to predict continuous values, e.g. home prices.

9. Experience Data Problem Task
Background Knowledge Accurate performance
Figure 2.1 Block Diagram of Machine learning
It is a box to which we feed the experience or the data and there is a problem or a task, that
requires solution and you can also give background knowledge, which will help the system.
And for this problem or this task the learning program comes up with an answer or a solution
and its corresponding performance can be measured. So, this the schematic diagram of a
machine learning system or a learning system inside there are two components, two main
components, the learner L and the reasoned.
2.6. History of Machine Learning
2.6.1. 1950s
-playing program
2.6.2. 1960s
Pattern Recognition
2.6.3. 1970s
Symbolic concept induction
Expert system and knowledge acquisition bottleneck
Natural language processing (symbolic).
2.6.4. 1980s

10. Advanced decision tree and rule learning
Learning and planning and problem solving
Resurgence of neural network
Valiant PAC learning theory
2.6.5. 1990s
Support vector machine (SVM).
Data mining
Adaptive agents and web application
Text learning
2.6.6. 2009s
Google builds self-driving car
2.6.7. 2014
Human vision surpassed by machine learning system
2.7. Types of Machine Learning Techniques
There are three types of Machine Learning Algorithms.
Figure 2.2. Types of machine learning technique.

11. 2.7.1. Supervised Learning
Supervised learning, in the context of artificial intelligence (AI) and machine learning, is a type
of system in which both input and desired output data are provided. Input and output data are
labelled for classification to provide a learning basis for future data processing.
Supervised learning algorithms try to model relationships and dependencies between the target
prediction output and the input features such that we can predict the output values for new data
based on those relationships which it learned from the previous data sets.
List of common algorithms
Naive Bayes
Decision Trees
Linear Regression
Support Vector Machines (SVM)
Neural Networks
2.7.2. Unsupervised Learning
Unsupervised learning is the training of an artificial intelligence (AI) algorithm using
information that is neither classified nor labelled and allowing the algorithm to act on that
information without guidance.
In unsupervised learning, an AI system may group unsorted information according to
similarities and differences even though there are no categories provided. The family of
machine learning algorithms which are mainly used in pattern detection and descriptive
modelling. There are no output categories or labels here based on which the algorithm can try
to model relationships. These algorithms try to use techniques on the input data to mine for
rules, detect patterns, and summarize and group the data points which help in deriving
meaningful insights and describe the data better to the users.
List of Common Algorithms
k-means clustering

12. 2.7.3. Reinforcement Learning
It allows machines and software agents to automatically determine the ideal behaviour within
a specific context, in order to maximize its performance. Simple reward feedback is required
for the agent to learn its behaviour this is known as the reinforcement signal.
Figure 2.3 Reinforcement Learning
List of common algorithms
Q-Learning
Temporal Difference (TD)
Deep Adversarial Networks
2.8. Domains and Application of Machine Learning
2.8.1. Medicine
Diagnose a disease
Input: -
Output: - one of set of possible
Data mine historical medical records to learn which future will respond best to which
treatments.
2.8.2. Computer Vision

13. Say what object appear in an image
Convert hand-
2.8.3. Robot control
Design automats mobile robots that learn to navigate from their own experience
2.8.4. Financial
Predict if a stock will rise or fall in the next few milliseconds
Predict if user will click on ad or not in order to decide which ad to show
2.8.5. Application in Business intelligence
Identifying cross selling promotional opportunities for consumer goods.
Optimize product location at a super market retail outlet.
Identify the price sensitivity of a consumer product and identify the optimum price that
maximize net profit.
2.8.6. Other Application
Fraud detection
Understand consumer sentiment based off of unstructured text data.
Speech recognition
Machine translation
2.9. Machine Learning Vs Deep Learning
Table 2.1 machine learning vs deep learning
MACHINE LEARNING DEEP LEARNING
Focuses on prediction, based on known
properties learning from the training data.
Focuses on the discovery of (previously)
unknown properties on the data.
Performances is usually evaluated with
respect to the ability to reproduce known
knowledge.
The key task is the discovery of previously
unknown knowledge Evaluated with respect
to known knowledge
This is the algorithm part of the data mining
process
Application of algorithms to search for
patterns and relationship that may exist in
large databases.

14. CHAPTER 3
3.1. Introduction
Supervised learning, in the context of artificial intelligence (AI) and machine learning, is a type
of system in which both input and desired output data are provided. Input and output data are
labelled for classification to provide a learning basis for future data processing.
In supervised learning, the system tries to learn from the previous examples that are given.
Supervised machine learning systems provide the learning algorithms with known quantities
to support future judgments.
The Supervised Learning algorithm will learn the relation between training examples and their
associated target variables and apply that learned relationship on the new attribute data to
predict corresponding target attribute.
Supervised machine learning algorithms can apply what has been learned in the past to new
data using labelled examples to predict future events. Starting from the analysis of a known
training dataset, the learning algorithm produces an inferred function to make predictions about
the output values. The system is able to provide targets for any new input after sufficient
training. The learning algorithm can also compare its output with the correct, intended output
and find errors in order to modify the model accordingly.
3.2. Definitions
dataset (called the training dataset) to make predications. The learning dataset includes input
and response values from it the supervised learning algorithm seek so build a model that can
3.3. Evolution of Supervised Machine Learning
Given
A target feature Y

15. A set of training example where the values for the input feature and the target features
are given for each example
A new example, where only the values for the input feature are given
Figure 3.1 Block Diagram of Supervised Machine Learning
Predict the values for the target feature for the new example.
Classification when Y is discrete
Regression when Y is continuous
3.4. How it work
This algorithm consist of a target / outcome variable (or dependent variable) which is to be
predicted from a given set of predictors (independent variables). Using these set of variables,
we generate a function that map inputs to desired outputs. The training process continues until
the model achieves a desired level of accuracy on the training data.

16. Following steps are follow by supervised machine learning
Step 1
The first step is collecting the dataset.
Step 2
If a requisite expert is available, could suggest which field (attributes, features) are the most
informative.
Step 3
If the not, then the simple methods -
available in the hope that the right (informative, relevant) feature can be isolated.
Figure 3.2. Data flows in supervised machine learning
Testing
Training

17. Figure 3.3. Workflow of supervised machine learning
3.5. General Issues of Supervised Machine Learning Algorithms
Inductive machine learning is the process of learning a set of rules from a set of rules from
instances(example in a training set), or more generally speaking, creating a classifier that can
be used to generalize from new instances.
What can be wrong with data? There is a hierarchy of problems that are often encountered in
data preparation and pre-processing:
Impossible or unlikely values have been inputted.
No values have been inputted (missing values).
Irrelevant input feature are present in the data at hand.
Impossible values (noise) should be checked for by the data handling software, ideally at the
point of input so that they can be re-entered. If correct values cannot be entered, the problem
is converted into missing values category, by simply removing the data. Incomplete data is an
unavoidable problem in dealing with most world data sources.
3.6. Kinds of Supervised Machine Learning Techniques
Supervised learning problems can be further divided into two parts, namely classification, and
regression.

18. Supervised Machine Learning
Algorithms
Classification Supervised
Machine Learning Algoriths
Regression Supervised Mahine
Learning Algorithms

20. Classification
supervised
machine learning
Logistic
Regression
Support vector
machine
Naïve Bayes
classifier
Decision tree

21. first plot these two variables in two dimensional space where each point has two co-ordinates
(these co-ordinates are known as Support Vectors)

22. Figure 3.8 support vector machine
Now, we find some line that splits the data between the two differently classified groups of
data. This will be the line such that the distances from the closest in each of two groups will be
farthest away.
In the example shown above, the line which splits the data into two differently classified groups
is the black line, the two closet point are the farthest apart from line. This line our classifier.
Naïve Bayes classifier
It is a classification technique based on
between predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a
particular feature in a class is unrelated to the presence of any other feature.

23. Naïve Bayesian model is easy to build and particularly useful for very large data sets. Along
with simplicity, Naïve Bayes is known to outperform even highly sophisticated classification
methods.
Bayes theorems provides a way of calculating posterior probability P (c|x) from P(c), P(x) and
P (x|c). Look at the equation below:
Here
P (c|x) is the posterior probability of class (target) given predictor (attribute).
P (c) is the prior probability of class.
P (x|c) is the likelihood which is the probability of predictor given class.
P (x) is the prior probability of predictor.
Naïve Bayes uses a similar method to predict the probability of different class based on various
attributes. This algorithms is mostly used in the text classification and with problems having
multiple classes.
In the image above, you can see that population is classified into four different groups based
heterogeneous groups, it uses various technique.

24. 3.6.2. Regression Supervised Machine Learning Technique
Regression Algorithms are used to calculate numeric values. A regression problem is when the
Relationship between variables.
Example: - price of a used car
x: car attributes
y: price
y=g(x, )
g () model, parameters
Figure 3.9 Regression supervised machine learning algorithms
Types of Regression Supervised Machine Learning Technique
Dependents Regression

25. Independents Regression
For Example
In this equation
Y Dependent Variable
a Slope
X Independent variable
b Intercept
What will be temperature be tomorrows?
How much discount can you give on a particular items?
Regression supervised
machine learning
alogrithms
Linear resgression
algorithms
Neural network

26. These coefficients a and b are derived on minimizing the sum of squared difference of distance
between data point and regression line.
Look at the below example. Here we have identified the best fit line having linear equation
y=0.2811x+13.9.
Figure3.11 graph of linear regression
Linear Regression is of mainly two types: Simple Linear Regression and Multiple Linear
Regression. Simple Linear Regression is characterized by one independent variable. And,
Multiple Linear Regression (as the name suggests) is characterized by multiple (more than 1)
independent variable. While finding best fit line, you can fit a polynomial or curvilinear
regression. And these are also known as polynomial or curvilinear regression.
An Artificial Neuron Network (ANN)
An artificial neuron network (ANN) is a computational model based on the structure and
functions of biological neural networks. Information that flows through the network affects the
structure of the ANN because a neural network changes - or learns, in a sense - based on that
input and output.
ANNs are considered nonlinear statistical data modelling tools where the complex relationships
between inputs and outputs are modelled or patterns are found. ANN is also known as a neural
network.

27. Artificial Neural networks (ANN) or neural networks are computational algorithms. A neural
network is an oriented graph. It consists of nodes which in the biological analogy represent
neurons, connected by arcs.
3.7. Classification Vs Regression
Table 3.2 classification Vs regression
Classification Regression
Classification is the task of predicting a
discrete class label.
Regression is the task of predicting a
continuous quantity.
A problem with two classes is often called a
two-class or binary classification problem.
A problem with multiple input variables is
often called a multivariate regression
problem.
A problem with more than two classes is
often called a multi-class classification
problem.
A regression problem where input variables
are ordered by time is called a time series
forecasting problem.
3.8. Application of Supervised Machine Learning Algorithm/Technique
Bioinformatics
Bioinformatics is an interdisciplinary field that develops methods and software tools for
understanding biological data. As an interdisciplinary field of science, bioinformatics
combines biology, computer science, mathematics and statistics to analyse and interpret
biological data. Bioinformatics has been used for in analyses of biological queries using
mathematical and statistical techniques.
An Artificial Neuron Network (ANN)
An artificial neuron network (ANN) is a computational model based on the structure and
functions of biological neural networks. Information that flows through the network affects the
structure of the ANN because a neural network changes - or learns, in a sense - based on that
input and output.
ANNs are considered nonlinear statistical data modelling tools where the complex
relationships between inputs and outputs are modelled or patterns are found. ANN is also
known as a neural network.

28. Learning Automaton
A learning automaton is one type of machine learning algorithm studied since 1970s. Learning
automata select their current action based on past experiences from the environment. It will fall
into the range of reinforcement learning.
The actions are chosen according to a specific probability distribution which is updated based
on the environment response the automaton obtains by performing a particular action.
Case-Based Reasoning
Case-based reasoning (CBR), broadly construed, is the process of solving new problems based
on the solutions of similar past problems.
It has been argued that case-based reasoning is not only a powerful method for computer
reasoning, but also a pervasive behaviour in everyday human problem solving; or, more
radically, that all reasoning is based on past cases personally experienced.
3.9. Examples of Supervised Machine Learning Techniques
Classifying e-mails as spam
There are a number of spam filtering approaches that email clients use. To ascertain that these
spam filters are continuously updated, they are powered by machine learning. When rule-based
spam filtering is done, it fails to track the latest tricks adopted by spammers. Multi-Layer
Perceptron, C 4.5 Decision Tree Induction are some of the spam filtering techniques that are
powered by ML.
Figure3.12 Classifying e-mails as spam

29. Online Fraud Detection
Machine learning is proving its potential to make cyberspace a secure place and tracking
monetary frauds online is one of its examples.
Social Media Services
From personalizing your news feed to better ads targeting, social media platforms are utilizing
machine learning for their own and user benefits. Here are a few examples that you must be
noticing, using, and loving in your social media accounts, without realizing that these
wonderful features are nothing but the applications of ML.

30. SUMMARY
These days, supervised machine learning techniques are being widely used to solve real-world
problems by storing, manipulating, extracting and retrieving data from large sources.
Supervised machine learning techniques have been widely adopted however these techniques
prove to be very expensive when the systems are implemented over wide range of data. This is
due to the fact that significant amount of effort and cost is involved because of obtaining large
labelled data sets. Thus active learning provides a way to reduce the labelling costs by labelling
only the most useful.
Supervised machine learning approaches applied in systematic reviews of complex research
fields such as quality improvement may assist in the title and abstract inclusion screening
process. Machine learning approaches are of particular interest considering steadily increasing
search outputs and accessibility of the existing evidence is a particular challenge of the research
field quality improvement. Increased reviewer agreement appeared to be associated with
improved predictive performance.

31. REFERENCES
Overview and definition of machine learning techniques and supervised machine
learning techniques available at
1. https://www.expertsystem.com/machine-learning-definition/
2. https://www.youtube.com/watch?v=BRMS3T11Cdw&list=PLYihddLF-
CgYuWNL55Wg8ALkm6u8U7gps
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Common machine learning algorithms available at: -
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Application of machine learning available at: -
7. https://medium.com/app-affairs/9-applications-of-machine-learning-from-day-to-
day-life-112a47a429d0