machine learning

1. Machine Learners
INTRODUCING
MACHINE
LEARNING

2. 1
What is
Machine learning?
Machine learning is a
Model Training Process.

3. It’s a data analytics technique that teaches
computers to do what comes naturally to
humans and animals: learn from experience.
Machine Learning Defined 3

4. Do Machines Learn Automatically?

5. Yes! Machines learn by studying
data to detect patterns from its
previous experiences by applying
Machine Learning.
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6. 2
How Machine
Learning Works?

7. Machine Learning Technique
Machine learning works in two
types of techniques:
Supervised learning and
Unsupervised learning.
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8. Machine Learning Technique
MACHINE LEARNING
CLUSTERING
CLASSIFICATION
REGRESSION
UNSUPERVISED
LEARNING
Group and interpret data
based only on input data
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SUPERVISED
LEARNING
Develop predictive model
based on both input and
output data

9. Machine Learning Technique
Clustering is the most common
learning technique. It is used for
exploratory data analysis to find
hidden patterns or groupings in
data.
CLUSTERING
MODEL
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10. Machine Learning Defined
CLUSTERING
EXAMPLE
10Machine Learning Technique

11. Machine Learning Defined
CLUSTERING
EXAMPLE 2
11Machine Learning Technique

12. Machine Learning Technique
Classification techniques predict
discrete responses—for example,
whether an email is genuine or
spam.
REGRESSION
MODEL
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13. Machine Learning Defined
CLASSIFICATION
EXAMPLE
13Machine Learning Technique

14. Machine Learning Technique
Regression techniques predict
continuous responses— for
example, changes in
temperature or fluctuations in
power demand.
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REGRESSION
MODEL

15. 3
When Should
You Use Machine
Learning?

16. When We Should Use Machine Learning
Consider using machine learning when you
have a complex task or problem involving a
large amount of data and lots of variables,
but no existing formula or equation.
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17. When We Should Use Machine Learning
Equations are too
complex—as in face
recognition and speech
recognition.
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18. When We Should Use Machine Learning
The rules of a task are
constantly changing—as
in fraud detection from
transaction records.
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19. When We Should Use Machine Learning
The nature of the data
keeps changing, and the
program needs to adapt
—as in automated trading, energy demand
forecasting, and predicting shopping trends.
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20. 4
Real-World Example
of Machine Learning

21. Machine Learning DefinedA Real life example
Identifying People and Things In Pictures
21
Social media platforms utilize machine learning
to automatically tag people and identify
common objects such as landmarks in uploaded
photos.

22. Machine Learning Defined
Self-
Driving
Car
A Real life example 22
Machine learning can identify the best routes from
point A to B, predict transit conditions and travel
time and predict the best route based on current,
evolving road conditions.
Machine Learning can drive a car without requiring
input from a driver.

23. Thank you 
Machine Learners