The document discusses artificial intelligence, machine learning, and deep learning. It defines each term and explains their relationships. Artificial intelligence is defined as human intelligence exhibited by machines. Machine learning is an approach to achieve artificial intelligence using algorithms that train on data to make predictions. Deep learning is a technique for implementing machine learning based on feature learning from data representations. The document also briefly discusses data science and its intersection with artificial intelligence.

1. Presented by
MUHAMMAD AMMAR

2.  Intuition of AI vs ML vs DL
 Artificial Intelligence
 Machine Learning
 Deep Learning
 Data Science

3. Artificial
Intelligence
Machine
learning
Deep
learning

4.  (AI) is a subfield of computer science .
 AI term was coined by JOHN McCARTHY in year
1956s
 Artificial Intelligence ,term was meant to describe
the goal that machine will be able to have humans
like intelligence in futher.

5. 1. Applied AI (weak AI )
2. Generalized AI (Strong AI )
1)Applied AI (weak AI) perform some specific tasks
2)Generalized AI (Strong AI) acts like human

6.  Machine learning is a subset or AI
 Machine learning is a set of algorithms that train on
a data set to make predictions or take actions in
order to optimize some system.

7.  Deep learning is a next evolution of machine
learning .
 The deep lraening was introduced to the machine
learning community in 1986.
 Deep learning is a subset of machine learning
where learning method is based on data
representation on feature learning.

8.  Artificial Intelligence: Human intelligence exhibited
by machines.
 Machine learning: An apporch to achieve Artificial
Intelligence.
 Deep Learning: A technique for implementing
machine learning.

9.  Data science has an intersection with artificial
intelligence but is not a subset of artificial
intelligence.
 Data science is the art and science of drawing
actionable insights from the data.
 Some Applictions: Retail,Bank,E-Commerce
,Healthcare and telecom etc

10. Data
collection
Data
preprocessing
Data
Analyzing
Data
visualization
Model
Building
Deployment

11. Importing
data
Data
clean
Model
building
Improve
Eficiency
Test
model
Train
model
Prediction

12. THE END