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1. Artificial
Intelligence and
Machine Learning
PowerPoint
Presentation Slides
1
Your Company Name

2. Table of Content
2
03. Deep Learning
✓ What is Deep Learning?
✓ Deep learning Process
✓ Classification of Neural Networks
✓ Types of Deep Learning Networks
✓ Feed-forward neural networks
✓ Recurrent neural networks (RNNs)
✓ Convolutional neural networks (CNN)
✓ Reinforcement Learning
✓ Examples of deep learning applications
✓ Why is Deep Learning Important?
✓ Limitations of deep learning
02. Machine Learning
✓ What is Machine Learning?
✓ 7 Steps of Machine learning
✓ Machine Learning vs. Traditional Programming
✓ How does machine learning work?
✓ Machine learning Algorithms
✓ Machine learning use cases
✓ How to choose Machine Learning Algorithm
✓ Why to use decision tree algorithm learning
✓ Challenges and Limitations of Machine learning
✓ Application of Machine learning
✓ Why is machine learning important?
04. Difference between AI vs ML vs DL
✓ What is AI?
✓ What is ML?
✓ What is Deep Learning?
✓ Machine Learning Process
✓ Deep Learning Process
✓ Difference between Machine Learning and Deep Learning
✓ Which is better to start AI,ML or Deep learning
01. Introduction
✓ What is AI?
✓ Introduction to AI Levels?
✓ Types of Artificial Intelligence
✓ AI VS machine learning vs deep learning
✓ Where is AI used?
✓ AI use cases
✓ Why is AI booming now?
✓ AI trend in 2020

3. Table of Content
3
Supervised Machine Learning
✓ Types of Machine Learning
✓ What is Supervised Machine Learning?
✓ How Supervised Learning Works
✓ Types of Supervised Machine Learning Algorithms
✓ Supervised vs. Unsupervised Machine learning techniques
✓ Advantages of Supervised Learning
✓ Disadvantages of Supervised Learning
Unsupervised Machine Learning
✓ What is Unsupervised Learning?
✓ How Unsupervised Machine Learning works
✓ Types of Unsupervised Learning
✓ Disadvantages of Unsupervised Learning
Reinforcement Learning
✓ What is reinforcement learning?
✓ How reinforcement learning works
✓ Types of reinforcement learning
✓ Advantage of reinforcement learning
✓ Disadvantage of reinforcement learning
Back Propagation Neural Network in AI
✓ Back Propagation Neural Network in AI
✓ What is Artificial Neural Networks?
✓ What is Backpropagation?
✓ Why We Need Backpropagation?
✓ What is a Feed Forward Network?
✓ Types of Backpropagation Networks
✓ Best practice Backpropagation
Expert System in Artificial Intelligence
✓ What is an Expert System?
✓ Examples of Expert Systems
✓ Characteristic of Expert System
✓ Components of the expert system
✓ Conventional System vs. Expert system
✓ Human expert vs. expert system
✓ Benefits of expert systems
✓ Limitations of the expert system
✓ Applications of expert systems

4. 4
Introduction
✓ What is AI?
✓ Introduction to AI Levels?
✓ Types of Artificial Intelligence
✓ AI VS machine learning vs deep learning
✓ Where is AI used?
✓ AI use cases
✓ Why is AI booming now?
✓ AI trend in 2020
01

5. Artificial Intelligence
Transforming the Nature of Work, Learning, and Learning to Work
5
Artificial intelligence (AI) is a popular branch of computer science that concerns with building “intelligent” smart machines capable
of performing intelligent tasks.
With rapid advancements in deep learning and machine learning, the tech industry is transforming radically.
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Artificial Intelligence
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audience's attention.
Machine Learning
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Deep Learning
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6. Introduction to AI Levels?
6
Types of
Artificial
Intelligence
Artificial Narrow
Intelligence
Artificial Super
Intelligence
Artificial General
Intelligence
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7. Types of Artificial Intelligence
7
Deep Learning
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Machine Learning
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Artificial Intelligence
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8. Artificial Intelligence
8
2016 2017 2018 2019 2020
2020 2019 2018
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35%
2018
2019
2020 65%
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AI
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AI
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Artificial intelligence (AI)
is a popular branch of computer
science that concerns with building
“intelligent” smart machines
capable of performing intelligent
tasks.
With rapid advancements in deep
learning and machine learning, tech
industry is transforming radically.

9. Machine Learning
9
Machine learning is a type of AI that enables
machines to learn from data and deliver
predictive models.
The machine learning is not dependent on
any explicit programming but the data fed
into it. It is a complicated process.
Based on the data you feed into machine
learning algorithm and the training given to
it, an output is delivered.
A predictive algorithm will create a
predictive model.
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Information
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10. Deep Learning
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&
functions known as artificial
neural networks
A computer model can be taught using
Deep Learning to run classification
actions using pictures, texts or sounds
as input
Deep Learning is a subfield of
machine learning that is concerned
with algorithms inspired by the brain's
structure

11. AI VS Machine Learning VS Deep Learning
11
Artificial Intelligence
✓ Artificial Intelligence originated around
1950s
✓ AI represents simulate intelligence in
machines
✓ AI is a subset of data science
✓ Aim is to build machines which are
capable of thinking like humans
Machine Learning
✓ Machine Learning originated around
1960s
✓ Machine learning is the practice of
getting machines to make decisions
without being programmed
✓ Machine learning is a subset of AI &
Data Science
✓ Aim is to make machines learn through
data so that they can solve problems
Deep Learning
✓ Deep Learning originated around 1970s
✓ Deep Learning is the process of using
artificial neural networks to solve
complex problems
✓ Deep Learning is a subset of Machine
Learning, AI & Data Science
✓ Aim is to build neural networks that
au6tonetically discover patterns for
feature detection
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12. Where is AI used?
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Real-time
Operations Management
Customer Services
Risk Management
& Analytics
Customer Insight
Pricing & Promotion
Predictive Analytics
Customer Experience
Supply Chain
Knowledge Creation
Research & Development
Fraud Detection
Human Resources

13. AI Usecase in HealthCare
13
AI and
Robotics
Training
Research Early Detection
Keeping Well
End of Life Care
Treatment Decision Making
Diagnosis
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14. AI Use Cases in Human Resource
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Recruiting
✓ Dynamic Career Sites
✓ Smart Sourcing
Onboarding
✓ Automated Messages
✓ Curated Videos
Learning
✓ Curated Training
✓ Skill Development
Engagement
✓ HR Chatbot
✓ Engagement Surveys

15. AI in Banking for Fraud Detection
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Cardholder
Profiles
Postings
Payment System
Nonmonetary System
Analyst
Workstation
Payment and
Non-Monetary Transactions
8
Authorization System
Rules Definition
Configuration
Workstation
Case
Management
Database
Case Creation Module
Neural
Network Engine
Scoring Engine
Expert Authorization
Response Module
Expert Rules Base6
Case Creation Rules
Execute
Auth Request1
Expert Rules Execute3
Auth Recommendation4
Auth Request & Score22
Transaction & Score5
Case Information7

16. AI in Supply Chain
16
Structured & Unstructured
Information
Regulatory
Data
B2B Transaction
Data
Inventory
Data
Multimedia
Data
Sensor
Data
Logistics
Data
Trading
Partner
Data
Social
Media
Data
Digital Ecosystem Data Lake
Pervasive
Visibility
Proactive
Replenishment
Predictive
Maintenance
Secure Device
Maintenance
Ecosystem
Integration
Unified
Messaging
Actionable
Insights
IIOT – Securely Provisionally People, System and Things
Secure Access Via Identity Management for Transient Users
Procurement Manufacturing Customers ServiceLogistics

17. Ai Chatbots in Healthcare
17
Search Engine
Users learn to
search for information
Social Platforms
Like Facebook
connect users online
Smartphones
Bring the
internet online
Healthbots
Bring all of the above together
for
healthcare use cases
Artificial Intelligence
Self Learning machines
becomes smarter
the more they are used
Messenger Apps
Lets users chat anywhere,
anytime
App Eco-system
Lets users download and
use apps easily
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18. Why is AI booming now?
18
8,097 7,540 7,336
4,680
4,201
3,714 3,655 3,564 3,169
0
2,000
4,000
6,000
8,000
Static Image Recognition,
Classification and
Tagging
Algorithm Trading
Strategy Performance
Management
Efficient, Scalable
Processing of Patient Data
Predictive Maintenance Object Identification,
detection, classification,
tracking
Text Query of Images Automated Geophysical
Feature Detection
Content Distribution on
Social Media
Object detection &
classification, avoidance,
navigation
22%
24%
27%
36%
37%
51%
60%
60%
66%
71%
0 20 40 60 80 100
Fleet Mobile
Expediting Transactions
Production Floor Systems
Logistics & Supply Chain
Monitoring Through External Devices/Systems
Operational Environment
HR/Workforce Management
Security/Fraud
Customer Relation/Interaction (i.e., chatbots)
External Communication
Global AI Revenue Forecast by 2025, Ranked by Use Case in millions US Dollar
Penetration of Artificial Intelligence Skills, by Country Organizations deploying AI, by Functional Areas
United
States
100% 92%
China
84%
India
54%
Israel
45%
Germany
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19. 10 AI Trend in 2020
19
Robotic Process Automation
AI will make Healthcare more Accurate
Data Modeling will move to the Edge
AI will Come for B2B
Ai-powered Chatbots
AI In Retail
Aerospace and Flight Operations
Controlled by AI
AI Mediated Media and Entertainment
Advanced Cybersecurity
Automated Business Process
AI Trends”
“2020
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20. Machine Learning
02
✓ What is Machine Learning?
✓ 7 Steps of Machine learning
✓ Machine Learning vs. Traditional Programming
✓ How does machine learning work?
✓ Machine learning Algorithms
✓ Machine learning use cases
✓ How to choose Machine Learning Algorithm
✓ Why to use decision tree algorithm learning
✓ Challenges and Limitations of Machine learning
✓ Application of Machine learning
✓ Why is machine learning important?
20

21. Machine Learning
21This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
Traditional
Programming
Data
(Input)
Program
Output
Data
(Input)
Output
Machine
Learning
Program
Machine Learning is the result of General AI that involves developing machines that can
deliver results better than humans
Input Data:
Feed Learner
Various Data
Output Data:
Present Rules
“Learning”
Machine
Learning System

22. 7 Steps of Machine Learning
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01
02
03
04
05
06
07
Choosing a ModelTraining
Preparing that DataEvaluation
Prediction
Gathering DataHyperparameter Tuning

23. Machine Learning vs. Traditional Programming
23
Prediction
ResultComputer
Data
Handcrafted
ModelLearning
Model
Prediction
Result
New Data
Model
Sample Data
Expected
Result
Computer
Computer
Traditional Modelling
Machine Learning
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24. How does Machine Learning Work?
24
Collect data from hospitals,
health insurance companies,
social service agencies,
police and fire dept.
Collect Data
Identify, the problem to
be solved and create a
clear objective.
Define Objectives
Preparing data is a crucial
step and involves building
workflows to clean, match
and blend the data.
Prepare Data
Depend on the problem to be
solved and the type of data
an appropriate algorithm will
be chosen.
Select Algorithm
Data is fed as input and the
algorithm configured with the
required parameters. A
percent of the data can be
utilized to train the model.
Train Model
Publish the prepared
experiment as a web
service, so applications can
use the model.
Integrate Model
The remaining data is utilized to
test the model, for accuracy.
Depending on the results,
improvements, can be performed
in the “Train model’ and/or “Select
Algorithm” phases, iteratively.
Test Model
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25. Machine Learning Algorithms
25
✓ KNN
✓ Trees
✓ Logistic Regression
✓ Naïve-Bayes
✓ SVM
Reinforcement
Categorical
Machine Learning
✓ Apriori
✓ FP-Growth
Association
Anlaysis
Hidden
Markov Model
✓ SVD
✓ PCA
✓ K-means
Clustering
Unsupervised
✓ Linear
✓ Polynomial
Regression
Decision Tree
Classification
Supervised
Random Forest
Continuous
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26. Machine Learning Use Cases
26
Energy Feedstock
& Utilities
✓ Power Usage Analytics
✓ Seismic Data Processing
✓ Your Text Here
✓ Smart Grid Management
✓ Energy Demand & Supply
Optimization
Financial
Services
✓ Risk Analytics & Regulation
✓ Customer Segmentation
✓ Your Text Here
✓ Credit Worthiness Evaluation
Travel
& Hospitality
✓ Aircraft Scheduling
✓ Dynamic Pricing
✓ Your Text Here
✓ Traffic Patterns &
Congestion Management
Manufacturing
✓ Predictive Maintenance
or Condition Monitoring
✓ Your Text Here
✓ Demand Forecasting
✓ Process Optimization
✓ Telematics
Retail
✓ Predictive Inventory
Planning
✓ Recommendation
Engines
✓ Your Text Here
✓ Customer ROI & Lifetime
Value
Healthcare &
Life Sciences
✓ Alerts & Diagnostics from
Real-time Patient Data
✓ Your Text Here
✓ Predictive Health
Management
✓ Healthcare Provider
Sentiment Analysis
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27. How to Choose Machine Learning Algorithm
27
What do you want to do
with your Data?
Algorithm Cheat Sheet
Additional Requirements Accuracy Linearity Number of
Parameters
Training Time Number of
Features
How to Select Machine Learning Algorithms
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28. Why use Decision Tree Machine Learning Algorithm?
28This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
To Classify
Non-linear Relationship
between Predictors &
Response
Linear Relationship
between Predictors
& Response
Use c4.5
Implementation
Use Standard
Regression Tree
Responsible Variable
has only 2 Categories
Use Standard
Classification here
Use c4.5
Implementation
To Predict
Responsible variable
is Continuous
Decision Trees
Response Variable has
Multiple Categories

29. Challenges and Limitations of Machine learning
29
Advantages
Easily Identifies Trends and Patterns
No Human Intervention needed
Handling multi-dimensional
& multi-variety Data
Continuous Improvement
Wide Applications
Data
Acquisition
Time and
Resources
High error-
Susceptibility
Interpretation
Results
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Disadvantages

30. Application of Machine Learning
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Automatic Language
Translation
Medical Diagnosis
Stock Market Trading
Online Fraud Detection
Virtual Personal Assistant
Email Spam and Malware FilteringSelf Driving Cars
Product Recommendations
Traffic Prediction
Speech Recognition
Image Recognition

31. Why is Machine Learning Important?
31
Phase 1 : Learning
Training Data
Phase 2: Prediction
✓ Precision/recall
✓ Over fitting
✓ Test/cross Validation
data, etc.
Error Analysis
✓ Normalization
✓ Dimension Reduction
✓ Image Processing, etc.
Pre-Processing
✓ Supervised
✓ Unsupervised
✓ Minimization, etc.
Learning
Predicted DataPrediction
New Data
Model
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32. Deep Learning
03
✓ What is Deep Learning?
✓ Deep learning Process
✓ Classification of Neural Networks
✓ Types of Deep Learning Networks
✓ Feed-forward neural networks
✓ Recurrent neural networks (RNNs)
✓ Convolutional neural networks (CNN)
✓ Reinforcement Learning
✓ Examples of deep learning applications
✓ Why is Deep Learning Important?
✓ Limitations of deep learning
32

33. What is Deep Learning?
33
Deep Learning is a subfield of machine learning that is concerned with algorithms inspired by the brain's
structure and functions known as artificial neural networks.
A computer model can be taught using Deep Learning to run classification actions using pictures,
texts or sounds as input.
Car
Not Car
OutputInput Feature Extraction
+ Classification
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What is Deep Learning?

34. Deep Learning Process
34
Understand
the Problem
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Identify
Data
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Select Deep Learning
Algorithms
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Training
the Model
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Test the
Model
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35. Classification of Neural Networks
35
Input Layer Hidden Layer Output Layer
x1
x2
xn
v11
v12
vpn
w11
w22
wmp ym
y2
y1
V1n
1
2
p
w1p
1
2
m
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36. Types of Deep Learning Networks
36
Supervised
Artificial Neural Networks Used for Regression & Classification
Convolutional Neural Networks Used for Computer Vision
Recurrent Neutral Networks Used for Time Series Analysis
Unsupervised
Self-Organizing Maps Used for Feature Detection
Deep Boltzmann Machines Used for Recommendation Systems
AutoEncoders Used for Recommendation Systems
Deep Learning
Models
Supervised
✓ Artificial Neural Networks (ANN)
✓ Convolutional Neural Networks
(CNN)
✓ Recurrent Neural Networks (RNN)
Unsupervised
✓ Self Organizing Maps (SOM)
✓ Boltzmann Machines (BM)
✓ AutoEncoders (AE)
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37. Feed-forward Neural Networks
37
Input Layer Hidden Layer Output Layer
Variable- #1
Variable- #2
Variable- #3
Variable- # 4
Output
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38. Recurrent Neural Networks (RNNs)
38
x1
x2
y
Input Layer
Recurrent Network
Output Layer
Hidden Layers
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39. Convolutional Neural Networks (CNN)
39
Take Car = A1 Truck = B1 VAN = C1 Bicycle = D1 Rest all be Same
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40. Reinforcement Learning
40
State, Reward
Action
Agent Environment
Exploration Policy Neural Networks Filters
Memory Algorithm
Reinforcement Learning
uses rewards and punishment to train
computing models to perform a
sequence of selections. Here
computing faces a game-like scenario
where it employs trial and error to
answer. Based on the action it
performs, computing gets either
rewards or penalties. Its goal is to
maximize the rewards.
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41. Examples of Deep Learning Applications
41
Image
Recognition
Natural Language
Processing
Portfolio Management &
Prediction of Stock Price
Movements
Drug Discovery & Better
Diagnostics of Diseases
in Healthcare
Speech
Recognition
Robots and Self -
Driving Cars
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42. Why is Deep Learning Important?
42This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
Performance
Data
Deep Learning
Other Learning
Algorithms

43. Limitations of Deep Learning
43
Limitations of
Deep Learning
Amount of
Data
Statistical
Reasoning
Interpretability
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44. Difference between
AI vs ML vs DL
04
✓ What is AI?
✓ What is ML?
✓ What is Deep Learning?
✓ Machine Learning Process
✓ Deep Learning Process
✓ Difference between Machine Learning and Deep Learning
✓ Which is better to start AI,ML or Deep learning
44

45. Difference between AI vs ML vs DL
45
Machine
Learning
Ability to learn without
being explicitly
programmed
Deep
Learning
Learning based on
deep neural network
Artificial
Intelligence
Engineering of making
intelligent machines
and programs
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46. What is AI?
46This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
Artificial
Intelligence
Artificial
Intelligence
With rapid advancements in deep
learning and machine learning, the tech
industry is transforming radically.
(AI) is a popular branch of computer
science that concerns with building
“intelligent” smart machines capable of
performing intelligent tasks.

47. What is ML?
47
Learns
Predicts
Improves
Machine
Learning
Ordinary
System
With
AI
Machine Learning
is a type of AI that enables machines to learn from data and deliver predictive models. The machine learning is not
dependent on any explicit programming but the data fed into it. It is a complicated process. Based on the data you
feed into machine learning algorithm and the training given to it, an output is delivered. A predictive algorithm will
create a predictive model.
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Introduction to Machine learning

48. What is Deep Learning?
48
Artificial intelligence (AI) is a popular
branch of computer science that
concerns with building “intelligent”
smart machines capable of
performing intelligent tasks.
With rapid advancements in deep
learning and machine learning, the
tech industry is transforming
radically.
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49. Machine Learning Process
49
Data
Raw &
Training Data
Modelling
Candidate
& Final
Visualisation
Predictions
& Strategy
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Data
Gathering
Data
Cleaning
Selecting
Right Algorithms
Building
Model & Finalising
Data Transformation
into Predictions

50. Deep Learning Process
50
Test the
Model
Understand
the Problem
Identify Data
Select Deep Learning
Algorithm
Training the
Model
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51. Difference between Machine Learning and Deep Learning
51This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
Input Feature
Extraction
Classifica
-tion
Output
Car
Not Car
Input Feature Extraction +
Classification
Output
Car
Not Car

52. Which is better to start AI,ML or DL?
52
Any Technique which enables computers to mimic
human behavior.
Artificial Intelligence
Subset of AI Techniques which use Statistical Methods to
Enable Machines to Improve with Experiences.
Machine Learning
Subset of ML which make the Computation of Multi-layer
Neural Networks Feasible.
Deep Learning
01
02
03
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53. Supervised
Machine Learning
05
✓ Types of Machine Learning
✓ What is Supervised Machine Learning?
✓ How Supervised Learning Works
✓ Types of Supervised Machine Learning Algorithms
✓ Supervised vs. Unsupervised Machine learning techniques
✓ Advantages of Supervised Learning:
✓ Disadvantages of Supervised Learning
53

54. Types of Machine Learning
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Inputs
Training
Inputs
Makes Machine Learn
Explicitly
Data with Clearly defined
Output is given
Direct feedback is given
Predicts outcome/future
Resolves Classification and
Regression Problems
Supervised Learning Unsupervised Learning
Machine Understands the data
(Identifies Patterns/ Structures)
Evaluation is Qualitative or
Indirect
Does not Predict/Find
anything Specific
Reinforcement Learning
An approach to AI
Reward Based Learning
Learning form +ve & +ve
Reinforcement
Machine Learns how to act in
a Certain Environment
To Maximize Rewards
Inputs
Rewards
Outputs Outputs Outputs

55. What is Supervised Machine Learning?
55
Input Raw Data Output
ProcessingAlgorithm
Training
Data set
Desired
Output
Supervisor
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Supervised Learning

56. How Supervised Machine Learning works
56
Classification
Sorting Items into Categories
Regression
Identifying Real Values
(Dollars, Weight, etc.)
Machine
Label
“Group 1”
Step1
Provide the Machine Learning Algorithm
Categorized or “labeled” Input and Output
Data from to Learn
Machine
Group 1
Group 2
Step2
Feed the Machine New, Unlabeled Information to
See if it Tags New Data Appropriately. If not,
Continue Refining the Algorithm
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Types of Problems to which it’s Suited

57. Types of Supervised Machine Learning Algorithms
57
Classification
✓ Fraud Detection
✓ Email Spam Detection
✓ Diagnostics
✓ Image Classification.
Regression
✓ Risk Assessment
✓ Score Prediction
Supervised
Learning
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58. Supervised vs. Unsupervised Machine Learning Techniques
58
VS
Supervised Learning
✓ Classification
✓ Regression
Input & Output Data
Predictions &
Predictive Models
Unsupervised Learning
✓ Clustering
✓ Association
Input Data
Patterns / Structure
Discovery
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59. It allows you to be very specific about the definition of the labels. In other words, you'll train the
algorithm to differentiate different classes where you'll set a perfect decision boundary.
You are ready to determine the amount of classes you would like to possess.
The input file is extremely documented and is labeled.
The results produced by the supervised method are more accurate and reliable as compared to the
results produced by the unsupervised techniques of machine learning. this is often mainly because
the input file within the supervised algorithm is documented and labeled. this is often a key difference
between supervised and unsupervised learning.
The answers within the analysis and therefore the output of your algorithm are likely to be
known thanks to that each one the classes used are known.
Advantages of Supervised Learning
59
Advantages
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60. Disadvantages of Supervised Learning
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Supervised learning are often a posh method as compared with the unsupervised
method. The key reason is that you simply need to understand alright and label the
inputs in supervised learning.
It doesn’t happen in real time while the unsupervised learning is about the
important time. this is often also a serious difference between supervised and unsupervised
learning. Supervised machine learning uses of-line analysis.
It is needed tons of computation time for training.
If you've got a dynamic big and growing data, you're unsure of the labels to predefine the
principles. this will be a true challenge.

61. Unsupervised
Machine Learning
06
✓ What is Unsupervised Learning?
✓ How Unsupervised Machine Learning works
✓ Types of Unsupervised Learning
✓ Disadvantages of Unsupervised Learning
61

62. What is Unsupervised Learning?
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Input Raw Data OutputAlgorithm
Interpretation Processing
✓ Unknown output
✓ No Training Data Set
Unsupervised Learning

63. How Unsupervised Machine Learning works
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Machine
Step1
Provide the machine learning
algorithm uncategorized, unlabeled
input data to see what patterns it finds
Similar Group 1
Similar Group 2
Machine
Step2
Observe and learn from the patterns the
machine identifies
Types of Problems to Which it’s Suited
Clustering
Identifying similarities in groups
For Example: Are there patterns in the data to
indicate certain patients will respond better to
this treatment than others?
Anomaly Detection
Identifying abnormalities in data
For Example: Is a hacker intruding in
our network?

64. Types of Unsupervised Learning
64
Dimensionality
Reduction
✓ Text Mining
✓ Face Recognition
✓ Big Data Visualization
✓ Image Recognition
Clustering
✓ Biology
✓ City Planning
✓ Targeted Marketing
Unsupervised
Learning
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65. Disadvantages of Unsupervised Learning
65
You cannot get very specific about the definition of the info sorting and therefore the output. This is
often because the info utilized in unsupervised learning is labeled and not known. It's employment of
the machine to label and group the data before determining the hidden patterns.
Less accuracy of the results. This is often also because the input file isn't known and not labeled by
people beforehand , which suggests that the machine will got to do that alone.
The results of the analysis can't be ascertained. there's no prior knowledge within the unsupervised
method of machine learning. Additionally, the numbers of classes also are not known. It results in the
lack to determine the results generated by the analysis.
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66. Reinforcement learning
07
✓ What is Unsupervised Learning?
✓ How Unsupervised Machine Learning works
✓ Types of Unsupervised Learning
✓ Disadvantages of Unsupervised Learning
66

67. What is Reinforcement Learning?
67
Reinforced
Response
Input
Input
Response
It’s a
mango
Feedback
Wrong!
It’s an apple
Learns
Noted
It’s an
Apple
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68. How Reinforcement Learning Works?
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Input Raw Data
Reward
State
Selection of
Algorithm
Best Action
Environment
Agent
Output
Reinforcement Learning

69. Types of Reinforcement Learning
69
Gaming
Finance Sector
Inventory Management
Robot Navigation
Manufacturing
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70. Disadvantage of Reinforcement Learning
70This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
You cannot get very specific about the definition of the info sorting and therefore the output. this is often because the info utilized
in unsupervised learning is labeled and not known. it's employment of the machine to label and group the data before determining
the hidden patterns.
Less accuracy of the results. this is often also because the input file isn't known and not labeled by people beforehand , which
suggests that the machine will got to do that alone.
The results of the analysis can't be ascertained. There's no prior knowledge within the unsupervised method of machine learning.
Additionally, the numbers of classes also are not known. It results in the lack to determine the results generated by the analysis
Reinforcement learning as a framework is wrong in many various ways, but it's precisely this quality that creates it useful.
Too much reinforcement learning can cause an overload of states which may diminish the results.
Reinforcement learning isn't preferable to use for solving simple problems
Reinforcement learning needs tons of knowledge and tons of computation. it's data-hungry. that's why it works rather well in
video games because one can play the sport again and again and again, so getting many data seems feasible.

71. Back Propagation
Neural Network in AI
08
✓ Back Propagation Neural Network in AI
✓ What is Artificial Neural Networks?
✓ What is Backpropagation?
✓ Why We Need Backpropagation?
✓ What is a Feed Forward Network?
✓ Types of Backpropagation Networks
✓ Best practice Backpropagation
71

72. Back Propagation Neural Network in AI
72
1 2
i1
i2 h2
w1
b1 b2
net out
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73. What is Artificial Neural Networks?
73
Feed-Forward
Network Output
Input Layer
Network Inputs
Hidden Layer
Back Propagation
Output Layer
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74. What is Backpropagation Neural Networking?
74
x
x
x
w
w
w
w
Difference in
Desired Values
Backprop Output Layer
Input Layer
1
1
Hidden Layer(s)
3
Output Layer
5
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75. Why We Need Backpropagation?
75
Mostprominentadvantagesof
BackpropagationAre: Backpropagation is fast, simple and
straightforward to program.
It has no parameters to tune aside from the
numbers of input.
It is a typical method that generally works well.
It doesn't need any special mention of the features of the
function to be learned.
It is a versatile method because it doesn't require
prior knowledge about the network.

76. What is a Feed Forward Network?
76
Input Layer
Hidden Layer
Output Layer
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77. Types of Backpropagation Networks
77
✓ Static Back-propagation
✓ Recurrent Backpropagation
It is one quite backpropagation network which
produces a mapping of a static input for static
output. it's useful to unravel static classification
issues like optical character recognition.
Static Back-propagation
Recurrent backpropagation is fed forward until a
hard and fast value is achieved. Then, the error is
computed and propagated backward.
Recurrent Backpropagation
The main difference between both of those methods is: that the
mapping is rapid in static back-propagation while it's nonstatic in
recurrent backpropagation
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78. Best Practice Backpropagation
78
A neural network is a group of connected it I/O units where each
connection features a weight related to its computer programs.
Backpropagation is fast, simple and
straightforward to program.
A feedforward neural network is a man-
made neural network.
Backpropagation may be a short form for "backward
propagation of errors." it's a typical method of
coaching artificial neural networks.
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79. Expert System in
Artificial Intelligence
09
✓ What is an Expert System?
✓ Examples of Expert Systems
✓ Characteristic of Expert System
✓ Components of the expert system
✓ Conventional System vs. Expert system
✓ Human expert vs. expert system
✓ Benefits of expert systems
✓ Limitations of the expert system
✓ Applications of expert systems
79

80. Types of Deep Learning Networks
80This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
The Expert System in AI are computer applications. Also,
with the assistance of this development, we will solve complex
problems. it's level of human intelligence and expertise
Knowledge
Base
Inference
Engine
User
Interface
User
(May not be an expert)
Human Expert
Knowledge
Engineer

81. Examples of Expert Systems
81
The Highest Level of Expertise
The expert system offers the very best level of experience. It provides
efficiency, accuracy and imaginative problem-solving.
Right on Time Reaction
An Expert System interacts during a very reasonable period of your time with the
user. the entire time must be but the time taken by an expert to urge the
foremost accurate solution for an equivalent problem
Good Reliability
The expert system must be reliable, and it must not make any an error.
Flexible
It is significant that it remains flexible because it the is possessed by an Expert
system.
Capable of Handling Challenging Decision & Problems
An expert system is capable of handling challenging decision problems and
delivering solutions.
Effective Mechanism
Expert System must have an efficient mechanism to administer the
compilation of the prevailing knowledge in it.
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Expert System
Inference
Engine
Non-expert
User
Knowledge
from an expert
User
Interface
Knowledge
Base
Query
Advice

82. Characteristic of Expert System
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The system must be capable of responding at A level of
competency adequate to or better than an expert
system within the field. the standard of the
recommendation given by the system should be during
a high level integrity and that the performance ratio
should be also very high
High level Performance
Expert systems are typically very domain specific. For
ex., a diagnostic expert system for troubleshooting
computers must actually perform all the required data
manipulation as a person's expert would. The
developer of such a system must limit his or her scope
of the system to only what's needed to unravel the
target problem. Special tools or programming
languages are often needed to accomplish the
precise objectives of the system
Domain Specificity
The expert system must be as reliable as a
person's expert
Good Reliability
The system should be designed in
such how that it's ready to perform
within alittle amount of your time , like or better than
the time taken by a person's expert to succeed in at a
choice point. An expert system that takes a year to
succeed in a choice compared to a person's expert’s
time of 1 hour wouldn't be useful
Adequate Response Time
The system should be understandable i.e. be ready
to explain the steps of reasoning while executing. The
expert system should have an
evidence capability almost like the reasoning ability of
human experts
Understandable
Expert systems use symbolic representations
for knowledge (rules, networks or frames) and
perform their inference through symbolic
computations that closely resemble
manipulations of tongue
Use Symbolic
Representations

83. Components of the Expert System
83
Explanation
Inference Engine
Knowledge
Base
Acquisition Facility
User
Interface
Experts and
Knowledge
Engineers
Users
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84. Conventional System vs. Expert System
84
vs
Knowledge domain break away the
mechanism processing
01
The program could have
made an error
02
Not necessarily need all
the input/data
03
Changes within the rule are
often made with ease
04
The system can work only with
the rule as a tittle
05
Information and processing
combined during a sequential file
01
The program isn't wrong 02
Need all the input file 03
Changes to the program are
inconvenient
04
The system works if it's complete 05
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85. Human Expert vs. Expert System
85
Human Experts (Artificial ) Expert Systems
PermanentPerishable
Easy to TransferDifficult to Transfer
Easy to DocumentDifficult to Document
Affordable, costly to develop, but cheap
to operate
Expensive, especially top notch
Add Your Text HereAdd Your Text Here

86. Benefits of Expert Systems
86
01
Easy to Develop and
Modify the System
02
Fast
Response
03
Low
Accessibility Cost
04
Error Rate are Very
Low
05
Humans Emotions are not
Affected
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87. Limitations of the Expert System
87
Its Developed for a Specific Domain
It cant Deal with the Mixed Knowledge
There are Chances of Errors
Don’t Have Decision Making Power Like Humans
Expert System is not Widely used or Tested
Its Difficult to Maintain
Development Cost is High
Not Able to Explain the Logic Behind the Decision
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88. Applications of Expert Systems
88
Medical domain
(Diagnosis system,
medical operations
Process
Control System
Finance/Commerce (Stock
market trading, airline
scheduling cargo
scheduling)
Warehousing
Optimization
Knowledge domain
(Finding out the faults
in vehicles, computer)
RepairingMonitoring
system
Design domain (Camera
lens design,automobile
design)
Shipping

89. Artificial Intelligence and Machine Learning PowerPoint Presentation Slides Icons Slide
89

90. 90
Good ideas start with
brainstorming Great ideas start
with coffee.
Coffee Break

91. Additional Slides
91
Bar Chart01
Stacked Line With Markers02
Welcome to Our Agenda03
Timeline04
Circular Diagram05
Venn06
Thank You07

92. Bar Chart
92
This graph/chart is linked to excel, and changes
automatically based on data. Just left click on it and
select “Edit Data”.
Product02
This graph/chart is linked to excel, and changes
automatically based on data. Just left click on it and
select “Edit Data”.
Product01
0
10
20
30
40
50
60
70
80
90
100
Jan Feb Mar Apr May Jun
Sales(inUSDmillions)
Year 2020
100%
Product01
Product02

93. Stacked Line With Markers
93
This graph/chart is linked to excel, and changes
automatically based on data. Just left click on it and
select “Edit Data”.
Product02
This graph/chart is linked to excel, and changes
automatically based on data. Just left click on it and
select “Edit Data”.
Product01
3628.4
3573.9
3484.0
3532.1
3740.3
3881.7
3528.4
3873.9
3584.0
3732.1
3640.3
3981.7
3400
3500
3600
3700
3800
3900
4000
4100
2015 2016 2017 2018 2019 2020
InMillions
YEARS

94. This slide is 100% editable. Adapt it to your needs and capture your
audience's attention.
Agenda 01
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Agenda 02
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Agenda 04
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Agenda 05
Welcome to Our Agenda
94

95. Timeline
95
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Four
2020
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2019
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2018
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2017

96. Circular Diagram
96
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04
05
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97. Venn
97
01 02 03
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98. Thank You
98
# street number, city, state
Address
0123456789
Contact Number
emailaddress123@gmail.com
Email Address