This seminar on Supervised Machine Learning Techniques brief overview about its

1. SUPERVISED MACHINE
LEARNING TECHNIQUES
TARA RAM
MCA 6th SEM

2. 1.
Introduction
Terminology, type of supervised learning, Common Algorithms
& it’s applications
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3. Instructions
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▸ Supervised machine learning is subfields
machine learning.
▸ Supervised learning uses patterns to predict
label values on additional unlabeled data.
▸ Supervised learning is to use historical data
to predict statistically likely future events.

4. Cont..
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▸ 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.
▸ In supervised learning, the system tries to
learn from the previous examples and
labeled data that are given.

5. Learning Process
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Training Data
Machine learning
Algorithm
ClassifierNew Data Prediction

6. Supervised Vs Unsupervised Machine Learning Technique
Based On SML UML
Input Data
Trained using
labelled data
Trained using
unlabelled data
Accuracy of the
Result
More accurate and
reliable
Less acurate and
reliable
Number of classes Known Unknown
Real Time Learning
Learning tasks
place off-line
Learning takes
place in real time.
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7. Terminology
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▸ Data
▸ Problem solving tools
▸ Combinations of computer science and
engineering and statistics
▸ Optimize performance criteria using past
experience

8. Types of Supervised Machine
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Supervised Machine Learning
Algorithms
Classification Supervised Machine
Learning Algorithms
Regression Supervised Mahine
Learning Algorithms

9. Classification Supervised Machine Learning Algorithms
▸ Classification algorithms
are used to classify a
records.
▸ A classification problem
is when the output
variable is a category or a
group.
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10. Regression Supervised Machine Learning Technique
▸ Regression Algorithms are used to calculate
numeric values.
▸ A regression problem is when the output
variable is a real value, such as “Rupees” or
“height.”
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11. Comman Algorithms
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▸ Linear Regression
▸ Decision Trees
▸ Logistic Regression
▸ Naïve Bayes classifier

12. Linear Regression
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▸ It is used to estimate real values (cost of houses,
number of calls, total sales etc.) based on
continuous variable(s).
▸ Establish relationship between independent
and dependent variables by fitting a best
line.
▸ This best fit line is known as regression
line.

13. Cont..
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▸ A Standard and simple mathematical
techniques for predicting numeric
outcomes.
▸ Oldest and most widely predictive model

14. Decision Trees
▸ It is mostly used for classification problems.
▸ Decision trees classify instances or examples
by starting at the root of the tree and moving
through it until a leaf node
▸ Decision tree is a classifier in the form of a
tree structure.
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15. Decision Tree
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Age
Young Middle Old
Has_Job Own_House Credit_Rating
True false True False Fair Good
Yes No Yes No No Yes
Excellence
Yes

16. Logistic Regression
▸ It is used to estimate discrete values (Binary values
like 0/1, yes/no, true/false) based on given set of
independent variable(s).
▸ It predicts the probability of occurrence of an event
by fitting data to a logit function.
▸ Its output values lies between 0 and 1 (as
expected).
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17. Naïve Bayes classifier
▸ Naïve Bayes Classifier technique based
on Bayes theorem.
▸ Naïve Bayesian model is easy to build.
▸ Naïve Bayes Classifier is used in large
data set.
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18. Applications
▸ Speech Recognition
▸ Effective Web Search
▸ Machine Translation(Languages
translation)
▸ Email Spam Filtering
▸ Fraud Detection
▸ Medical Diagnosis
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19. Cont..
▸ Stock Market Analysis
▸ Structural Health Monitoring
▸ Image Search (Similarity)
▸ Recommendation System
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20. 20
THANKS!
Tara Ram
MCA 6th SEM