industrial training on machine learning

1. Intelligence
Machine Learning
Deep Learning PPT
PowerPoint Presentation Slide Templates
Your Company Name
INDUSTRIAL
TRAINING ON
MACHINE
LEARNING TO
DEEP LEARNING.

2. Table of Contents
Introduction
o What is AI?
o Introduction to AI Levels?
o Types of Artificial Intelligence
o AI VS machine learning vs deep learning
o Where is AI used?
o AI use cases
o Why is AI boomingnow?
o AI trend in 2020
Machine Learning
o What is Machine Learning?
o 7 Steps of Machine learning
o Machine Learning vs. Traditional Programming
o How does machine learning work?
o Machine learning Algorithms
o Machine learning use cases
o How to choose Machine Learning Algorithm
o Why to use decision tree algorithmlearning
o Challenges and Limitations of Machine learning
o Application of Machine learning
o Why is machine learning important?
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Deep Learning
o What is Deep Learning?
o Deep learning Process
o Classification of Neural Networks
o Types of Deep Learning Networks
o Feed-forward neural networks
o Recurrent neural networks (RNNs)
o Convolutional neural networks (CNN)
o Reinforcement Learning
o Examples of deep learning applications
o Why is Deep Learning Important?
o Limitations of deep learning
03
Difference between AI vs ML vs DL
o What is AI?
o What is ML?
o What is Deep Learning?
o Machine Learning Process
o Deep Learning Process
o Difference between Machine Learning and Deep
Learning
o Which is better to start AI,ML or Deep learning
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3. Table of Contents
Unsupervised Machine Learning
o What is Unsupervised Learning?
o How Unsupervised Machine Learning works
o Types of Unsupervised Learning
o Disadvantages of Unsupervised Learning
06
Reinforcement Learning
o What is reinforcementlearning?
o How reinforcement learning works
o Types of reinforcement learning
o Advantage of reinforcement learning
o Disadvantage of reinforcement learning
07
Back Propagation Neural Network in AI
o Back Propagation Neural Network in AI
o What is Artificial Neural Networks?
o What is Backpropagation?
o Why We NeedBackpropagation?
o What is a Feed Forward Network?
o Types of BackpropagationNetworks
o Best practice Backpropagation
08
Supervised Machine Learning
o Types of Machine Learning
o What is Supervised Machine Learning?
o How Supervised Learning Works
o Types of Supervised Machine Learning Algorithms
o Supervised vs. Unsupervised Machine learning
techniques
o Advantages of Supervised Learning
o Disadvantages of Supervised Learning
05
Expert System in Artificial Intelligence
o What is an Expert System?
o Examples of Expert Systems
o Characteristic of Expert System
o Components of the expert system
o Conventional System vs. Expert system
o Human expert vs. expert system
o Benefits of expert systems
o Limitations of the expert system
o Applications of expert systems
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4. Artificial intelligence (AI) is a popular branch of computer science that concerns withbuilding
“intelligent” smart machines capable of performing intelligenttasks.
With rapid advancements in deep learning and machine learning, the tech industryis transforming
radically.
Artificial Intelligence
Transforming the Nature of Work, Learning, and Learning to Work
Artificial Intelligence
Machine Learning
Deep Learning
4
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5. Types of Artificial Intelligence
Deep Learning Machine Learning Artificial Intelligence
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6. Where is AI
used?
Customer Experience
Supply Chain
Human Resources
Knowledge Creation
Research & Development
Fraud Detection
Real-time Operations Management
Customer Services
Risk Management &Analytics
Customer Insight
Pricing & Promotion
PredictiveAnalytics
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7. AI Usecase in HealthCare
Research
Training Keeping Well
Early Detection
Diagnosis
Decision Making
Treatment
End of Life Care
AI and
Robotic
s
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8. Machine Learning
Machine Learning is the result of General AI that involves developingmachines
that can deliver results better than humans
Traditional
Programming
Data
(Input)
Program
Output
Data
(Input)
Output
Machine
Learning
Program
“Learning”
Machine
Learning System
Input Data:
Feed Learner
Various Data
Output Data:
Present Rules
8
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9. Machine Learning
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 acomplicated
process.
Based on the data you feed into machinelearning
algorithm and the training given to it, an output is
delivered.
A predictive algorithm will create a predictivemodel.
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Information
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10. 7 Steps of Machine Learning
Gathering Data
Preparing that Data Choosing a Model
Training Evaluation
Hyperparameter Tuning Prediction
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11. What is Supervised Machine Learning?
Input Raw Data
Processi
ng
Output
Algorith
m
Training
Data set
Desired
Output
Supervised
Learning
Supervisor
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12. Types of Supervised Machine Learning
Algorithms
Classification
o Fraud Detection
o Email Spam Detection
o Diagnostics
o Image Classification
Regressio
n
o RiskAssessment
o Score Prediction
Supervise
d
Learning
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13. What is Unsupervised
Learning?
Unsupervised
Learning
Input Raw Data Output
Algorithm
Interpretation Processin
g
o Unknown output
o No Training Data Set
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14. Machine Learning vs. Traditional Programming
Traditional Modelling
Prediction
Result
Machine Learning
Learning
Model
Prediction
Result
Computer
New Data
Model
Sample Data
Expected
Result
Data
Handcrafted
Model
Computer
Computer
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15. Application of Machine Learning
Automatic Language
Translation
Medical Diagnosis
Stock Market Trading
Online Fraud Detection
Virtual PersonalAssistant
Email Spam and Malware Filtering
Self Driving Cars
Product Recommendations
Traffic Prediction
Speech Recognition
Image Recognition
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16. Deep Learning
Deep Learning is a subfield of machine learning that is
concerned with algorithms inspired by the brain's structure
&
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
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17. What is Deep 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 neural networks.
A computer model can be taught using Deep Learning to run
classification actions using pictures, texts or sounds as input.
Feature Extraction + Classification
Input Output
Car
Not Car
What is Deep Learning?
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18. Types of Deep Learning Networks
Supervised
Artificial Neural Networks
Convolutional Neural Networks
Recurrent Neutral Networks
Used for Regression & Classification
Used for Computer Vision
Used for Time Series Analysis
Unsupervised
Self-Organizing Maps
Deep Boltzmann Machines
AutoEncoders
Used for Feature Detection
Used for Recommendation Systems
Used for Recommendation Systems
o Artificial Neural Networks (ANN)
o Convolutional Neural Networks (CNN)
o Recurrent Neural Networks (RNN)
o Self Organizing Maps (SOM)
o Boltzmann Machines (BM)
o AutoEncoders (AE)
Supervise
d
Unsupervise
d
Deep Learning
Models
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19. Examples of Deep Learning Applications
Image
Recognition
Portfolio
Management &
Prediction of Stock
Price Movements
Speech
Recognition
Natural Language
Processing
Drug Discovery &
Better Diagnostics of
Diseases in Healthcare
Robots and Self
- Driving Cars
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20. Limitations of Deep
Learning
Limitations of
Deep Learning
Interpretability
Statistical Reasoning
Amount of Data
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21. Reinforcement Learning
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.
Exploration Policy Neural Networks
Filters Memory
Algorithm
Agent Environment
Action
State, Reward
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22. What is Reinforcement
Learning?
Input Response Feedback Learns
It’s a
mango
Wrong!
It’s an
apple
Noted
It’s an
Apple
Reinforced
Response
Input
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23. Types of Reinforcement
Learning
Gaming
Finance Sector
Inventory Management
Manufacturing
Robot Navigation
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24. How Reinforcement Learning
Works?
Reinforcement Learning
Input Raw Data Output
Reward
State
Selection of
Algorithm
BestAction
Environmen
t
Agent
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25. AI VS Machine Learning VS Deep Learning
o Artificial Intelligence originated around
1950s
o AI represents simulate intelligence in
machines
o AI is a subset of data science
o Aim is to build machines which are
capable of thinking like humans
o Machine Learning originated around
1960s
o Machine learning is the practice of
getting machines to makedecisions
without being programmed
o Machine learning is a subset of AI &
Data Science
o Aim is to make machines learn through
data so that they can solveproblems
Artificial Intelligence Machine Learning Deep Learning
o Deep Learning originated around 1970s
o Deep Learning is the process of using
artificial neural networks to solve complex
problems
o Deep Learning is a subset of Machine
Learning, AI & DataScience
o Aim is to build neural networks that
au6tonetically discover patterns for
feature detection
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