In this slideshow we will effortlessly learn about supervised and unsupervised machine learning with examples.

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Supervised
and
Unsupervised
learning
Supervised
and
Unsupervised
learning
Supervised
and
Unsupervised
learning

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What is Supervised learning?
You have separate baskets for yellow
banana, golden pineapple, black grapes
and so on. Now if I give you a golden
pineapple you know exactly what it is and
in which basket you need to keep it.
The labeled fruits help you train your
brain about their respective correct
baskets.
Supervised learning is a learning in which
we teach or train the machine using data
which are properly or rather correctly
labeled.

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The supervised learning process
Let’s say, you want to predict for a group of people their chances of becoming
Coronavirus infected.
You will need training dataset from previous cases.
You will discover new direct relationships via a feedback loop.
Your model will evolve and become more and more accurate.
Training data
Machine learning
algorithm
Predictive model
Model
evaluation
Feedback loop
Labeled

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I give you 100 items that are not
labeled. And then I give you a new item,
another puppy, and ask you what it is.
Will you be able to tell me?
I have not labeled the items for you to
understand. You have learnt about
them on your own.
Unsupervised learning is the learning
of machine using information that is
neither classified nor labeled and
allowing the algorithm to act on that
information without guidance.
What is Unsupervised learning?

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The unsupervised learning process
There are many animals, snakes, birds and insects that you have never ever seen in
your life.
But when you see a new bird, that no one has labeled as bird for you to understand,
you can still make out that it is a bird because it has feather, it has beak, it can fly etc.
This is unsupervised learning. Computationally complex and less accurate.
Input data
Machine learning
algorithm
Outputl
Unlabeled

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Supervised learning algorithms
When the output variable is categorical
or discrete, such as good or bad, yes or
no, prone to disease or not prone to
disease, the problem that we need to
solve is a classification problem.
When the output variable is a real value
that is continuous, such as age, weight
or price, the problem that we need to
solve is a regression problem.
Please note, logistic regression in not a
regression algorithm.
Machine Learning
Supervised
Learning
Unsupervised
Learning
RegressionClassification
Decision Tree
Discriminant
Analysis
Naïve Bayes
Logistic Regression
Support Vector
Machine
Linear Regression
SVR
Regression Tree
Ensemble Methods
GLM

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Unsupervised learning algorithms
We use clustering model when we want
to discover the inherent groupings in
the data.
We use association rule when we want
to discover rules that describe large
portions of our data.
Using association rules we discover
relationships between variables in large
dataset.
Machine Learning
Supervised
Learning
Unsupervised
Learning
AssociationClustering
Hierarchical
K-means
Hidden Markov
Model
Gaussian Mixture
Fuzzy C-means

8. Why do we need supervised learning
❖ Supervised learning allows us to collect data or produce a data output from the
previous experience.
❖ It helps us optimize performance criteria using experience.
❖ Supervised machine learning helps us solve various types of real-world
computation problems.

9. Why do we need unsupervised learning
❖ Unsupervised machine learning finds all kinds of unknown patterns in data.
❖ Unsupervised methods help us find features which can be useful for
categorization.
❖ It is takes place in real time, so all the input data to be analyzed and labeled in
the presence of the learners.
❖ It is easier to get unlabeled data from a computer than labeled data. Because
labeling of data needs manual intervention.

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Supervised vs Unsupervised learning
Supervised Learning
Process: input output
variables
Input data: Labeled
data
Computational
Complexity: Simple
Use of Data: Learns
from training data
Accuracy: Highly
accurate
Learning: Offline
Unsupervised Learning
Number of classes:
Known
Drawback: Classifying
big data is a challenge
Process: Only input
data
Input data: Unlabeled
data
Computational
Complexity: Complex
Use of Data: Does not
learn from output data
Accuracy: Less accurate
Learning: Realtime
Number of classes:
Unknown
Drawback: No precise
data sorting formation

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That’s all for today.