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AIvsMLvsDLvsDatascience
G.Priyadharshini,(Ph.d)
Researcher in AI & ML
TableofContent
01.Introduction
 WhatisAI?
 IntroductiontoAILevels?
 Typesof ArtificialIntelligence
 AIVSmachine learningvsdeeplearningvsDatascience
 WhereisAIused?
 AIusecases
 WhyisAIboomingnow?
02.MachineLearning
 WhatisMachineLearning?
 7StepsofMachinelearning
 Applicationof Machinelearning
 Machine learningusecases
 What isAI?
 Introduction to AILevels?
 Types of ArtificialIntelligence
 AI VS machine learning vs deeplearning
 Where is AIused?
 AI use cases
 Why is AI booming now?
 AI trend in 2020
01 Introduction
3
• According to the father of Artificial Intelligence, John McCarthy, it is “The
science and engineering of making intelligent machines, especially intelligent
computer programs”.
• Artificial Intelligence is a way of making a computer, a computer-controlled
robot, or a software think intelligently, in the similar manner the intelligent
humans think.
• AI is accomplished by studying how human brain thinks, and how humans
learn, decide, and work while trying to solve a problem, and then using the
outcomes of this study as a basis of developing intelligent software and
systems.
WhatisAI?
Artific ial Intelligenc e
(AI) is a popular branch of computer science that concerns with building
“intelligent” smart machines capable of performing intelligenttasks.
With rapid advancements in deep learning andmachine
learning, the tech industry is transformingradically.
5
MachineLearning
Information
.
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. Itis 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.
6
DeepLearning
Deep Learning isa subfield of machine learning that isconcerned with algorithms inspired by
the brain'sstructure
functions known as artificialneural networks
A computer model can be taught using Deep Learning to run classification actions
using pictures, texts or sounds as input
7
Data Science
• Data science is an interdisciplinary field focused on extracting knowledge from data sets,
which are typically large , and applying the knowledge and actionable insights from data to
solve problems in a wide range of application domains.
• The field encompasses preparing data for analysis, formulating data science problems,
analyzing data, developing data-driven solutions, and presenting findings to inform high-level
decisions in a broad range of application domains.
• As such, it incorporates skills from computer science, statistics, information science,
mathematics, information visualization, data integration, graphic design, complex systems,
communication and business
Examples of ML,DL and NLP in real time
• Take the example of Google Lens. It is an image
recognition app by Google. It allows a smartphone’s
camera to capture images. Using neural network-
based visual analysis, Google Lens throws relevant
information or recommendations related to the
identified object.
• https://www.hdfcbank.com/
• Alexa
• Spam mails
• Amazon ,Nelflix Recommendations
AIVSMachineLearningVSDeepLearningVSDataScience
10
WhereisAIused?
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
11
AIUsecaseinHealthCare
AIandRobotics
Research
Training
Keeping Well
EarlyDetection
Diagnosis
DecisionMaking
Treatment
EndofLife Care
12
AIUseCasesinHumanResource
Employee LifeCycle
Learning
 Curated Training
 Skill Development
Recruiting
 Dynamic Career Sites
 Smart Sourcing
Engagement
 HR Chatbot
 Engagement Surveys
Onboarding
 Automated Messages
 Curated Videos
13
AIChatbotsinHealthcare
Search Engine
Users learn to search for
information
Social Platforms
Like Facebookconnect
users online
Smartphones
Bring the internet online
App Eco-system
Lets users downloadand
use apps easily
MessengerApps
Lets users chat
anywhere, anytime
ArtificialIntelligence
Self Learning machines
becomes smarter themore
they are used
Healthbots
Bring all of the above
together forhealthcare
use cases
14
WhyisAIboomingnow?
8,097 7,540 7,336
4,680
4,201
3,714 3,655 3,564 3,169
2,000
0
4,000
6,000
8,000
Static Image Recognition,
Classification and Tagging
Algorithm Trading
StrategyPerformance
Management
Efficient, Scalable Predictive Maintenance
Processing of Patient Data
Object Identification, Text Query of Images
detection, classification,
tracking
Automated Geophysical Content Distribution on
Feature Detection Social Media
Object detection &
classification,avoidance,
navigation
Global AIRevenue Forecast by 2025,Ranked by UseCase inmillions USDollar
PenetrationofArtificialIntelligenceSkills,by Country Organizationsdeploying AI,by FunctionalAreas
66%
71%
0 20 40 60 80 100
Security/Fraud 60%
HR/Workforce Management 60%
Operational Environment 51%
Monitoring Through External Devices/Systems 37%
Logistics & Supply Chain 36%
Production Floor Systems 27%
Expediting Transactions 24%
Fleet Mobile 22%
Customer Relation/Interaction (i.e., chatbots)
External Communication
100%
United
States
92%
China
84%
India
54%
Israel
45%
Germany
15
 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
 Application of Machine learning
 Why is machine learning important?
02 MachineLearning
20
MachineLearning
Machine Learning is the result of General AI that involves
developing machines that can deliver results better than humans
Traditional Programming
Data
(Input)
Program
Output
Data
(Input)
Output
MachineLearning Program
“Learning”
Machine
Learning System
InputData:
Feed Learner VariousData
Output Data:
Present Rules
21
7StepsofMachineLearning
07
22
Gathering Data
Preparing that Data
Choosing a Model
Training
Evaluation
Hyperparameter Tuning
Prediction
MachineLearningvs.TraditionalProgramming
Machine Learning
TraditionalModelling
Prediction
Result
Learning
Model
Prediction
Result
Computer
New Data
Model
SampleData
Expected
Result
Data
Handcrafted
Model
Computer
Computer
19
HowdoesMachineLearningWork?
Collect Data
Collect data from hospitals,
health insurance companies,
social service agencies, police
and fire dept.
Select Algorithm
Depend on the problem to be
solved and the type of data an
appropriate algorithm will be
chosen.
TestModel
The remaining data is utilized to test the
model, for accuracy. Depending on the
results, improvements, can be performedin
the “Train model’ and/or “Select Algorithm”
phases, iteratively.
Define Objectives
Identify, the problem to
be solved and create a
clear objective.
Prepare Data
Preparing data is a crucial
step and involves building
workflows to clean, match
and blend the data.
TrainModel
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.
Integrate Model
Publish the prepared
experiment as a web
service, so applications
can use the model.
20
MachineLearningAlgorithms
KNN
Trees
Logistic Regression
Naïve-Bayes
SVM
Reinforcement
Categorical
Machine Learning
Apriori
FP-Growth
Association
Anlaysis
Hidden
MarkovModel
SVD
PCA
K-means
Clustering
Unsupervised
Linear
Polynomial
Regression
Decision Tree
Classification
Supervised
Random Forest
Continuous
21
MachineLearningUseCases
Energy
Feedstock &Utilities
Financial
Services
Travel&
Hospitality Manufacturing Retail
Healthcare &
LifeSciences
 Power Usage Analytics
 Seismic Data Processing
 Your Text Here
 Smart Grid Management
 Energy Demand &
SupplyOptimization
 Risk Analytics & Regulation
 Customer Segmentation
 Your Text Here
 Credit Worthiness
Evaluation
 Aircraft Scheduling
 Dynamic Pricing
 Your Text Here
 Traffic Patterns &
Congestion Management
 Predictive Maintenance
or Condition Monitoring
 Your Text Here
 Demand Forecasting
 Process Optimization
 Telematics
 Predictive Inventory
Planning
 Recommendation
Engines
 Your Text Here
 Customer ROI &
Lifetime Value
 Alerts & Diagnostics
from Real-time Patient
Data
 Your Text Here
 Predictive Health
Management
 Healthcare Provider
Sentiment Analysis
22
HowtoChooseMachineLearningAlgorithm
Whatdo you wanttodo
withyourData?
HowtoSelect Machine LearningAlgorithms
Additional
Requirements
Accuracy Linearity Number of
Parameters
TrainingTime Number of
Features
Algorithm Cheat Sheet
23
ApplicationofMachineLearning
Automatic Language
Translation
Medical Diagnosis
Stock MarketTrading
Online FraudDetection
Virtual PersonalAssistant
Email Spam and Malware Filtering
Self Driving Cars
Product Recommendations
Traffic Prediction
Speech Recognition
Image Recognition
24
WhyisMachineLearningImportant?
Phase1:Learning
Phase2:Prediction
TrainingData
Pre-Processing
Normalization
DimensionReduction
Image Processing,etc.
Learning
Supervised
Unsupervised
Minimization, etc.
PredictedData
Prediction
Model
NewData
ErrorAnalysis
Precision/recall
Over fitting
Test/crossValidation
data, etc
25
 What isDeep Learning?
 Deep learningProcess
 Types of Deep Learning Networks
 Examples of deep learning applications
 Why isDeep LearningImportant?
03 DeepLearning
32
WhatisDeep Learning?
Deep Learning is a subfield of machine learning that is concerned with algorithms inspired by the brain's structure and
functions known as artificial neuralnetworks.
A computer model can be taught using Deep Learning to run classification actions using
pictures, texts or sounds as input.
WhatisDeep Learning?
FeatureExtraction +
Classification
Input
Car
NotCar
Output
27
DeepLearningProcess
Understand
theProblem
.
Identify
Data
Select Deep Learning
Algorithms
.
Training
theModel
Testthe
Model
28
ExamplesofDeep LearningApplications
Image Recognition Portfolio Management &
Prediction ofStock Price
Movements
Speech
Recognition
Natural LanguageProcessing
Drug Discovery & Better Diagnostics of
Diseases inHealthcare Robots and Self-Driving
Cars
29
DifferencebetweenMachine Learningand Deep Learning
Input Feature
Extraction
Classification Output
Car
Not Car
Input Feature Extraction +
Classification
Output
Car
Not Car
30
Step by step procedure to learn data science
Data
science
Programming
language
Machine
learning
IDE
Web scraping
Math
Data
visualization
Data Analysis
Python,R,Java
Classification,Regression,Reinforc
ement, DL , Dimesionality
Reduction,Clustering
Pycharm,Jupyter,
Spyder
Beautiful
soup,Scrapy,URLLib
Statistics,Linear
Algebra, Differential
Calculus
Tableau,Power BI ,
MatPlotLib.GGplot,
Seaborn
Feature engineering
,Data wrangling,
EDA
WhichisbettertostartAI,MLor DL?
Artificial Intelligence
Deep Learning
Machine Learning
Artificial Intelligence
Any Technique which enables computers to mimic human
behavior.
Deep Learning
Subset of ML which make the Computation of Multi-layer Neural
Networks Feasible.
Machine Learning
Subset ofAI Techniques which use Statistical Methods to Enable
Machines to Improve with Experiences.
32
What is NLP
• NLP stands for Natural Language
Processing, which is a part of Computer
Science, Human language, and Artificial
Intelligence. It is the technology that is
used by machines to understand, analyse,
manipulate, and interpret human's
languages. It helps developers to organize
knowledge for performing tasks such as
translation, automatic summarization,
Named Entity Recognition (NER), speech
recognition, relationship extraction, and
topic segmentation.
Applications of NLP
Chatbot
• Implementing the Chatbot
is one of the important
applications of NLP. It is
used by many companies
to provide the customer's
chat services.
Machine Translation
• Machine translation is
used to translate text or
speech from one natural
language to another
natural language.
Question Answering
• Question Answering
focuses on building
systems that automatically
answer the questions
asked by humans in a
natural language.

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demo AI ML.pptx

  • 2. TableofContent 01.Introduction  WhatisAI?  IntroductiontoAILevels?  Typesof ArtificialIntelligence  AIVSmachine learningvsdeeplearningvsDatascience  WhereisAIused?  AIusecases  WhyisAIboomingnow? 02.MachineLearning  WhatisMachineLearning?  7StepsofMachinelearning  Applicationof Machinelearning  Machine learningusecases
  • 3.  What isAI?  Introduction to AILevels?  Types of ArtificialIntelligence  AI VS machine learning vs deeplearning  Where is AIused?  AI use cases  Why is AI booming now?  AI trend in 2020 01 Introduction 3
  • 4. • According to the father of Artificial Intelligence, John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs”. • Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think. • AI is accomplished by studying how human brain thinks, and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems.
  • 5. WhatisAI? Artific ial Intelligenc e (AI) is a popular branch of computer science that concerns with building “intelligent” smart machines capable of performing intelligenttasks. With rapid advancements in deep learning andmachine learning, the tech industry is transformingradically. 5
  • 6. MachineLearning Information . 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. Itis 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. 6
  • 7. DeepLearning Deep Learning isa subfield of machine learning that isconcerned with algorithms inspired by the brain'sstructure functions known as artificialneural networks A computer model can be taught using Deep Learning to run classification actions using pictures, texts or sounds as input 7
  • 8. Data Science • Data science is an interdisciplinary field focused on extracting knowledge from data sets, which are typically large , and applying the knowledge and actionable insights from data to solve problems in a wide range of application domains. • The field encompasses preparing data for analysis, formulating data science problems, analyzing data, developing data-driven solutions, and presenting findings to inform high-level decisions in a broad range of application domains. • As such, it incorporates skills from computer science, statistics, information science, mathematics, information visualization, data integration, graphic design, complex systems, communication and business
  • 9. Examples of ML,DL and NLP in real time • Take the example of Google Lens. It is an image recognition app by Google. It allows a smartphone’s camera to capture images. Using neural network- based visual analysis, Google Lens throws relevant information or recommendations related to the identified object. • https://www.hdfcbank.com/ • Alexa • Spam mails • Amazon ,Nelflix Recommendations
  • 11. WhereisAIused? 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 11
  • 13. AIUseCasesinHumanResource Employee LifeCycle Learning  Curated Training  Skill Development Recruiting  Dynamic Career Sites  Smart Sourcing Engagement  HR Chatbot  Engagement Surveys Onboarding  Automated Messages  Curated Videos 13
  • 14. AIChatbotsinHealthcare Search Engine Users learn to search for information Social Platforms Like Facebookconnect users online Smartphones Bring the internet online App Eco-system Lets users downloadand use apps easily MessengerApps Lets users chat anywhere, anytime ArtificialIntelligence Self Learning machines becomes smarter themore they are used Healthbots Bring all of the above together forhealthcare use cases 14
  • 15. WhyisAIboomingnow? 8,097 7,540 7,336 4,680 4,201 3,714 3,655 3,564 3,169 2,000 0 4,000 6,000 8,000 Static Image Recognition, Classification and Tagging Algorithm Trading StrategyPerformance Management Efficient, Scalable Predictive Maintenance Processing of Patient Data Object Identification, Text Query of Images detection, classification, tracking Automated Geophysical Content Distribution on Feature Detection Social Media Object detection & classification,avoidance, navigation Global AIRevenue Forecast by 2025,Ranked by UseCase inmillions USDollar PenetrationofArtificialIntelligenceSkills,by Country Organizationsdeploying AI,by FunctionalAreas 66% 71% 0 20 40 60 80 100 Security/Fraud 60% HR/Workforce Management 60% Operational Environment 51% Monitoring Through External Devices/Systems 37% Logistics & Supply Chain 36% Production Floor Systems 27% Expediting Transactions 24% Fleet Mobile 22% Customer Relation/Interaction (i.e., chatbots) External Communication 100% United States 92% China 84% India 54% Israel 45% Germany 15
  • 16.  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  Application of Machine learning  Why is machine learning important? 02 MachineLearning 20
  • 17. MachineLearning Machine Learning is the result of General AI that involves developing machines that can deliver results better than humans Traditional Programming Data (Input) Program Output Data (Input) Output MachineLearning Program “Learning” Machine Learning System InputData: Feed Learner VariousData Output Data: Present Rules 21
  • 18. 7StepsofMachineLearning 07 22 Gathering Data Preparing that Data Choosing a Model Training Evaluation Hyperparameter Tuning Prediction
  • 20. HowdoesMachineLearningWork? Collect Data Collect data from hospitals, health insurance companies, social service agencies, police and fire dept. Select Algorithm Depend on the problem to be solved and the type of data an appropriate algorithm will be chosen. TestModel The remaining data is utilized to test the model, for accuracy. Depending on the results, improvements, can be performedin the “Train model’ and/or “Select Algorithm” phases, iteratively. Define Objectives Identify, the problem to be solved and create a clear objective. Prepare Data Preparing data is a crucial step and involves building workflows to clean, match and blend the data. TrainModel 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. Integrate Model Publish the prepared experiment as a web service, so applications can use the model. 20
  • 22. MachineLearningUseCases Energy Feedstock &Utilities Financial Services Travel& Hospitality Manufacturing Retail Healthcare & LifeSciences  Power Usage Analytics  Seismic Data Processing  Your Text Here  Smart Grid Management  Energy Demand & SupplyOptimization  Risk Analytics & Regulation  Customer Segmentation  Your Text Here  Credit Worthiness Evaluation  Aircraft Scheduling  Dynamic Pricing  Your Text Here  Traffic Patterns & Congestion Management  Predictive Maintenance or Condition Monitoring  Your Text Here  Demand Forecasting  Process Optimization  Telematics  Predictive Inventory Planning  Recommendation Engines  Your Text Here  Customer ROI & Lifetime Value  Alerts & Diagnostics from Real-time Patient Data  Your Text Here  Predictive Health Management  Healthcare Provider Sentiment Analysis 22
  • 23. HowtoChooseMachineLearningAlgorithm Whatdo you wanttodo withyourData? HowtoSelect Machine LearningAlgorithms Additional Requirements Accuracy Linearity Number of Parameters TrainingTime Number of Features Algorithm Cheat Sheet 23
  • 24. ApplicationofMachineLearning Automatic Language Translation Medical Diagnosis Stock MarketTrading Online FraudDetection Virtual PersonalAssistant Email Spam and Malware Filtering Self Driving Cars Product Recommendations Traffic Prediction Speech Recognition Image Recognition 24
  • 26.  What isDeep Learning?  Deep learningProcess  Types of Deep Learning Networks  Examples of deep learning applications  Why isDeep LearningImportant? 03 DeepLearning 32
  • 27. WhatisDeep Learning? Deep Learning is a subfield of machine learning that is concerned with algorithms inspired by the brain's structure and functions known as artificial neuralnetworks. A computer model can be taught using Deep Learning to run classification actions using pictures, texts or sounds as input. WhatisDeep Learning? FeatureExtraction + Classification Input Car NotCar Output 27
  • 29. ExamplesofDeep LearningApplications Image Recognition Portfolio Management & Prediction ofStock Price Movements Speech Recognition Natural LanguageProcessing Drug Discovery & Better Diagnostics of Diseases inHealthcare Robots and Self-Driving Cars 29
  • 30. DifferencebetweenMachine Learningand Deep Learning Input Feature Extraction Classification Output Car Not Car Input Feature Extraction + Classification Output Car Not Car 30
  • 31. Step by step procedure to learn data science Data science Programming language Machine learning IDE Web scraping Math Data visualization Data Analysis Python,R,Java Classification,Regression,Reinforc ement, DL , Dimesionality Reduction,Clustering Pycharm,Jupyter, Spyder Beautiful soup,Scrapy,URLLib Statistics,Linear Algebra, Differential Calculus Tableau,Power BI , MatPlotLib.GGplot, Seaborn Feature engineering ,Data wrangling, EDA
  • 32. WhichisbettertostartAI,MLor DL? Artificial Intelligence Deep Learning Machine Learning Artificial Intelligence Any Technique which enables computers to mimic human behavior. Deep Learning Subset of ML which make the Computation of Multi-layer Neural Networks Feasible. Machine Learning Subset ofAI Techniques which use Statistical Methods to Enable Machines to Improve with Experiences. 32
  • 33. What is NLP • NLP stands for Natural Language Processing, which is a part of Computer Science, Human language, and Artificial Intelligence. It is the technology that is used by machines to understand, analyse, manipulate, and interpret human's languages. It helps developers to organize knowledge for performing tasks such as translation, automatic summarization, Named Entity Recognition (NER), speech recognition, relationship extraction, and topic segmentation.
  • 34. Applications of NLP Chatbot • Implementing the Chatbot is one of the important applications of NLP. It is used by many companies to provide the customer's chat services.
  • 35. Machine Translation • Machine translation is used to translate text or speech from one natural language to another natural language.
  • 36. Question Answering • Question Answering focuses on building systems that automatically answer the questions asked by humans in a natural language.