SlideShare a Scribd company logo
Machine
Learning
What is the Machine learning?
By Siddharth Adelkar,
People’s Archive of Rural India
Copyright
1. A general idea of what these terms mean: AI, ML, Deep Learning, NLP ?
2. How do they relate to each other?
3. To look deeper into the Machine Learning workflow
4. To install the first basic Python infrastructure for data science
experiments
5. To write our first ML program
Goals of this workship
Agenda
Let’s code!What is AI/ML? Workflow Game Your project
What is machine
learning?
What are the
steps in ML?
Learning on the
Skin MNIST
Dataset
Use ML in your
own project
01
04
Objectives Wall
What are your objectives?
AI Wall
Your understanding of the
buzzwords in AI
03
Project Wall
Do you have an application
in mind?
02
Game wall
Let’s play an ML game
OKR Wall
● What is your Objective
today?
● What are some key results
you expect?
Write on a stickie paste it.
Project Wall
One Artificial Intelligence
project idea that you want to
implement.
AI Wall
Draw a Venn Diagram
● Artificial Intelligence
● Machine Learning
● Natural Language Processing
● Digital Signal Processing
● Image Processing
● Statistics
● Deep Learning
● Neural Networks
● Blockchain
● Deepfakes
What is AI?
Write on a stickie. Paste it.
01
—John McCarthy, coined term ‘AI’[1]
“It is the science and engineering of
making intelligent machines, especially
intelligent computer programs. It is
related to the similar task of using
computers to understand human
intelligence, but AI does not have to
confine itself to methods that are
biologically observable.”
—Merriam Webster Dictionary [2]
“The capability of a
machine to imitate
intelligent human
behavior.”
Some intelligent behaviors?
What does the mind do?
Please write on your AI wall
Intelligent human behaviors?
Learning
Machine Learning
Language
Natural Language Processing
Planning & Decisions
Multi-agents, Robotics,
Operations Research
Recognition
Sensory -- Visual, Audio
Reasoning
Logical Theorist, Symbolic
Reasoning
Thinking?
Turing test
LEARNING
● Write on your wall
● Cases of learning/
Examples of learning
3 specific characterizations of Learning
Unsupervised
Finding some "structure" -
groups, dimensions,
clusters in unlabeled data
Supervised
Learn to predict a
characteristic of a thing,
given its other labels
Reinforcement
Out of scope
Humans can do these easily. Can they? How do humans do it? Let's play.
Classical programming vs Machine Learning
Credit: “Deep Learning with Python”, François Chollet
CREDITS: This presentation template was created
by Slidesgo, including icons by Flaticon, and
infographics & images by Freepik.
Concluding
Session 1
● We conclude that Mind does a large number of things.
● AI constitutes identifying these and then mimicking them
● One of these is learning. But learning itself is very large,
deep and enigmatic.
● ML focuses on specific types of learning
The Machine Learning
Workflow Game
Let’s play!
02
● Your team is given 50 cards. Shuffle
● Roll the dice. This is the number of
classes.
● Group the cards in to those many
categories using your logic.
● Write down your logic on a stickie.
Hide it. Write classes on back of
card.
● This was unsupervised learning. You
were the machine. You did the
learning and you clustered the
dataset.
You are machine
● Shuffle cards. Take out 10, 5 of
these are Dev Set. 5 rest are Test
Set.
● Now go to the stage. Face the
audience. Audience is machine.
● Train the machine. You can't talk.
You can only show the two sides of
the card.
● Epoch is when you go through all
cards.
● Too slow? Change batch size.
Audience is Machine
● Training data was not labelled.
● You did not know what you are
interested in.
● The problem at hand was
clustering in meaningful
categories
● Machine discovered features
● Machine created Clusters
● You knew what you were interested in
● You knew the i/p and o/p of Training
Data
● Problem at hand was -- given i/p,
predict o/p
● Machine predicted given past
experience.
Conclusions
Unsupervised Supervised
Why Dev Set? Bias - Variance
Credit: MartinThoma,
Wikimedia Foundation
Model Performance
Quality?
How do you evaluate model quality?
True/False X
Positive/Negative
Class = Yes Class = No
Class = Yes True Positive False Negative
Class = No False Positive True Negative
Predicted labels
True labels
Accuracy
TP+TN/TP+
FP+FN+TN
Simply, correct predictions to total
predictions.
“Wolf! Wolf! Wolf!” Accuracy = 100%
Precision
TP/TP+FP
Make sure that you don’t have too many
false positives!
“He does not have TB.” COSTLY mistake!
Recall
TP/TP+FN
Make sure you are not claiming too
many False Negatives.
F1 Score
2*(Recall * Precision)
/ (Recall + Precision)
When distribution is uneven, a
useful composite metric
Confusion Matrix
Class 1 Class 2 Class 3 Class 4
Class 1
Class 2
Class 3
Class 4
Predicted labels
True labels
Concluding Session 2
● ML Workflow
1. Preparing the dataset- structuring, cleaning
2. Creating the test, dev and train sets
3. Deciding on an architecture
4. Iterating on dataset
5. Output model
6. Test model on dev
7. Happy? No? Go back to step 3
8. Yes? Test on test set. Final quality.
● We learned how to test model quality
Buzzword Alert
Remember the hype cycle. All
great technologies with
potential go through a hype
cycle. Don’t fall prey to over
optimism. Don’t think too
pessimistically either.
4. Anaconda Navigator > Install
Jupyter Notebooks
5. On Kaggle, go to Kernels. Download
the top two kernel codes.
6. Upload and open both on Jupyter
1. Download “SkinMNIST” data
from Kaggle.
2. Anaconda (Python 3.7)
3. conda create -n keras python=3.7
numpy scipy keras matplotlib
pandas seaborn pillow
scikit-learn
03 Let’s hack!
1. Go to
http://skin.test.woza.work/
2. Upload an image of a skin
lesion
3. The model predicts likelihood of
condition
Model, what is it good for?
1. Skin Lesion Analyzer + Tensorflow.js
Web App by Marsh
https://www.kaggle.com/vbookshelf/skin-lesion-analyzer-tensorflow-js-web-app
2. Step wise Approach : CNN Model
(77.0344% Accuracy) by Manu
Siddhartha
https://www.kaggle.com/sid321axn/step-wise-approach-cnn-model-77-0344-accuracy
3. HAM100000 “SKIN MNIST” Data
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T
Thank you kagglers
CREDITS: This presentation template was created
by Slidesgo, including icons by Flaticon, and
infographics & images by Freepik.
Thanks!
Do you have any questions?
siddharth@ruralindiaonline.org
+91 869 287 2055
https://ruralindiaonline.org

More Related Content

Similar to Machine Learning Workshop, TSEC 2020

Lessons learned from building practical deep learning systems
Lessons learned from building practical deep learning systemsLessons learned from building practical deep learning systems
Lessons learned from building practical deep learning systems
Xavier Amatriain
 
ML in Android
ML in AndroidML in Android
ML in Android
Jose Antonio Corbacho
 
Tensorflow demo - Teach a computer to add 2 number
Tensorflow demo - Teach a computer to add 2 numberTensorflow demo - Teach a computer to add 2 number
Tensorflow demo - Teach a computer to add 2 number
Willem Hendriks
 
Mastering python lesson1
Mastering python lesson1Mastering python lesson1
Mastering python lesson1
Ruth Marvin
 
Deep learning with tensorflow
Deep learning with tensorflowDeep learning with tensorflow
Deep learning with tensorflow
Charmi Chokshi
 
Meetup 29042015
Meetup 29042015Meetup 29042015
Meetup 29042015
lbishal
 
Deep Learning Class #0 - You Can Do It
Deep Learning Class #0 - You Can Do ItDeep Learning Class #0 - You Can Do It
Deep Learning Class #0 - You Can Do It
Holberton School
 
DL Classe 0 - You can do it
DL Classe 0 - You can do itDL Classe 0 - You can do it
DL Classe 0 - You can do it
Gregory Renard
 
The State of ML for iOS: On the Advent of WWDC 2018 🕯
The State of ML for iOS: On the Advent of WWDC 2018 🕯The State of ML for iOS: On the Advent of WWDC 2018 🕯
The State of ML for iOS: On the Advent of WWDC 2018 🕯
Meghan Kane
 
Automating Tinder w/ Eigenfaces and StanfordNLP
Automating Tinder w/ Eigenfaces and StanfordNLPAutomating Tinder w/ Eigenfaces and StanfordNLP
Automating Tinder w/ Eigenfaces and StanfordNLP
Justin Long
 
tensorflow.pptx
tensorflow.pptxtensorflow.pptx
tensorflow.pptx
JoanJeremiah
 
An Introduction to Machine Learning
An Introduction to Machine LearningAn Introduction to Machine Learning
An Introduction to Machine Learning
Angelo Simone Scotto
 
How to implement artificial intelligence solutions
How to implement artificial intelligence solutionsHow to implement artificial intelligence solutions
How to implement artificial intelligence solutions
Carlos Toxtli
 
Deep learning introduction
Deep learning introductionDeep learning introduction
Deep learning introduction
Adwait Bhave
 
Coding Unplugged_Focus on problem solvin
Coding Unplugged_Focus on problem solvinCoding Unplugged_Focus on problem solvin
Coding Unplugged_Focus on problem solvin
EnkelejdaMica1
 
Assessing computational thinking
Assessing computational thinkingAssessing computational thinking
Assessing computational thinking
Daniel Duckworth
 
Artificial intelligence
Artificial intelligenceArtificial intelligence
Artificial intelligence
Gangeshwar Krishnamurthy
 
Deep Learning, an interactive introduction for NLP-ers
Deep Learning, an interactive introduction for NLP-ersDeep Learning, an interactive introduction for NLP-ers
Deep Learning, an interactive introduction for NLP-ers
Roelof Pieters
 
Testing for the deeplearning folks
Testing for the deeplearning folksTesting for the deeplearning folks
Testing for the deeplearning folks
Vishwas N
 
Business Analyst Technical Interview
Business Analyst Technical InterviewBusiness Analyst Technical Interview
Business Analyst Technical Interview
Neka Allen
 

Similar to Machine Learning Workshop, TSEC 2020 (20)

Lessons learned from building practical deep learning systems
Lessons learned from building practical deep learning systemsLessons learned from building practical deep learning systems
Lessons learned from building practical deep learning systems
 
ML in Android
ML in AndroidML in Android
ML in Android
 
Tensorflow demo - Teach a computer to add 2 number
Tensorflow demo - Teach a computer to add 2 numberTensorflow demo - Teach a computer to add 2 number
Tensorflow demo - Teach a computer to add 2 number
 
Mastering python lesson1
Mastering python lesson1Mastering python lesson1
Mastering python lesson1
 
Deep learning with tensorflow
Deep learning with tensorflowDeep learning with tensorflow
Deep learning with tensorflow
 
Meetup 29042015
Meetup 29042015Meetup 29042015
Meetup 29042015
 
Deep Learning Class #0 - You Can Do It
Deep Learning Class #0 - You Can Do ItDeep Learning Class #0 - You Can Do It
Deep Learning Class #0 - You Can Do It
 
DL Classe 0 - You can do it
DL Classe 0 - You can do itDL Classe 0 - You can do it
DL Classe 0 - You can do it
 
The State of ML for iOS: On the Advent of WWDC 2018 🕯
The State of ML for iOS: On the Advent of WWDC 2018 🕯The State of ML for iOS: On the Advent of WWDC 2018 🕯
The State of ML for iOS: On the Advent of WWDC 2018 🕯
 
Automating Tinder w/ Eigenfaces and StanfordNLP
Automating Tinder w/ Eigenfaces and StanfordNLPAutomating Tinder w/ Eigenfaces and StanfordNLP
Automating Tinder w/ Eigenfaces and StanfordNLP
 
tensorflow.pptx
tensorflow.pptxtensorflow.pptx
tensorflow.pptx
 
An Introduction to Machine Learning
An Introduction to Machine LearningAn Introduction to Machine Learning
An Introduction to Machine Learning
 
How to implement artificial intelligence solutions
How to implement artificial intelligence solutionsHow to implement artificial intelligence solutions
How to implement artificial intelligence solutions
 
Deep learning introduction
Deep learning introductionDeep learning introduction
Deep learning introduction
 
Coding Unplugged_Focus on problem solvin
Coding Unplugged_Focus on problem solvinCoding Unplugged_Focus on problem solvin
Coding Unplugged_Focus on problem solvin
 
Assessing computational thinking
Assessing computational thinkingAssessing computational thinking
Assessing computational thinking
 
Artificial intelligence
Artificial intelligenceArtificial intelligence
Artificial intelligence
 
Deep Learning, an interactive introduction for NLP-ers
Deep Learning, an interactive introduction for NLP-ersDeep Learning, an interactive introduction for NLP-ers
Deep Learning, an interactive introduction for NLP-ers
 
Testing for the deeplearning folks
Testing for the deeplearning folksTesting for the deeplearning folks
Testing for the deeplearning folks
 
Business Analyst Technical Interview
Business Analyst Technical InterviewBusiness Analyst Technical Interview
Business Analyst Technical Interview
 

Recently uploaded

JavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green MasterplanJavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green Masterplan
Miro Wengner
 
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
Edge AI and Vision Alliance
 
Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)
Jakub Marek
 
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup SlidesProgramming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
Zilliz
 
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing InstancesEnergy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Alpen-Adria-Universität
 
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsConnector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
DianaGray10
 
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframeDigital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Precisely
 
FREE A4 Cyber Security Awareness Posters-Social Engineering part 3
FREE A4 Cyber Security Awareness  Posters-Social Engineering part 3FREE A4 Cyber Security Awareness  Posters-Social Engineering part 3
FREE A4 Cyber Security Awareness Posters-Social Engineering part 3
Data Hops
 
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptxChoosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
Brandon Minnick, MBA
 
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdfMonitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Tosin Akinosho
 
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfHow to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
Chart Kalyan
 
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERPBest 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
Pixlogix Infotech
 
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptxNordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptx
MichaelKnudsen27
 
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
saastr
 
What is an RPA CoE? Session 1 – CoE Vision
What is an RPA CoE?  Session 1 – CoE VisionWhat is an RPA CoE?  Session 1 – CoE Vision
What is an RPA CoE? Session 1 – CoE Vision
DianaGray10
 
Astute Business Solutions | Oracle Cloud Partner |
Astute Business Solutions | Oracle Cloud Partner |Astute Business Solutions | Oracle Cloud Partner |
Astute Business Solutions | Oracle Cloud Partner |
AstuteBusiness
 
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAUHCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
panagenda
 
Public CyberSecurity Awareness Presentation 2024.pptx
Public CyberSecurity Awareness Presentation 2024.pptxPublic CyberSecurity Awareness Presentation 2024.pptx
Public CyberSecurity Awareness Presentation 2024.pptx
marufrahmanstratejm
 
June Patch Tuesday
June Patch TuesdayJune Patch Tuesday
June Patch Tuesday
Ivanti
 
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
Jason Yip
 

Recently uploaded (20)

JavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green MasterplanJavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green Masterplan
 
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
 
Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)
 
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup SlidesProgramming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
 
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing InstancesEnergy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
 
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsConnector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
 
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframeDigital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
 
FREE A4 Cyber Security Awareness Posters-Social Engineering part 3
FREE A4 Cyber Security Awareness  Posters-Social Engineering part 3FREE A4 Cyber Security Awareness  Posters-Social Engineering part 3
FREE A4 Cyber Security Awareness Posters-Social Engineering part 3
 
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptxChoosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
 
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdfMonitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
 
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfHow to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
 
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERPBest 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
 
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptxNordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptx
 
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
 
What is an RPA CoE? Session 1 – CoE Vision
What is an RPA CoE?  Session 1 – CoE VisionWhat is an RPA CoE?  Session 1 – CoE Vision
What is an RPA CoE? Session 1 – CoE Vision
 
Astute Business Solutions | Oracle Cloud Partner |
Astute Business Solutions | Oracle Cloud Partner |Astute Business Solutions | Oracle Cloud Partner |
Astute Business Solutions | Oracle Cloud Partner |
 
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAUHCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
 
Public CyberSecurity Awareness Presentation 2024.pptx
Public CyberSecurity Awareness Presentation 2024.pptxPublic CyberSecurity Awareness Presentation 2024.pptx
Public CyberSecurity Awareness Presentation 2024.pptx
 
June Patch Tuesday
June Patch TuesdayJune Patch Tuesday
June Patch Tuesday
 
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
 

Machine Learning Workshop, TSEC 2020

  • 1. Machine Learning What is the Machine learning? By Siddharth Adelkar, People’s Archive of Rural India Copyright
  • 2. 1. A general idea of what these terms mean: AI, ML, Deep Learning, NLP ? 2. How do they relate to each other? 3. To look deeper into the Machine Learning workflow 4. To install the first basic Python infrastructure for data science experiments 5. To write our first ML program Goals of this workship
  • 3. Agenda Let’s code!What is AI/ML? Workflow Game Your project What is machine learning? What are the steps in ML? Learning on the Skin MNIST Dataset Use ML in your own project
  • 4. 01 04 Objectives Wall What are your objectives? AI Wall Your understanding of the buzzwords in AI 03 Project Wall Do you have an application in mind? 02 Game wall Let’s play an ML game
  • 5. OKR Wall ● What is your Objective today? ● What are some key results you expect? Write on a stickie paste it.
  • 6. Project Wall One Artificial Intelligence project idea that you want to implement.
  • 7. AI Wall Draw a Venn Diagram ● Artificial Intelligence ● Machine Learning ● Natural Language Processing ● Digital Signal Processing ● Image Processing ● Statistics ● Deep Learning ● Neural Networks ● Blockchain ● Deepfakes
  • 8. What is AI? Write on a stickie. Paste it. 01
  • 9. —John McCarthy, coined term ‘AI’[1] “It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”
  • 10. —Merriam Webster Dictionary [2] “The capability of a machine to imitate intelligent human behavior.”
  • 11. Some intelligent behaviors? What does the mind do? Please write on your AI wall
  • 12. Intelligent human behaviors? Learning Machine Learning Language Natural Language Processing Planning & Decisions Multi-agents, Robotics, Operations Research Recognition Sensory -- Visual, Audio Reasoning Logical Theorist, Symbolic Reasoning Thinking? Turing test
  • 13. LEARNING ● Write on your wall ● Cases of learning/ Examples of learning
  • 14. 3 specific characterizations of Learning Unsupervised Finding some "structure" - groups, dimensions, clusters in unlabeled data Supervised Learn to predict a characteristic of a thing, given its other labels Reinforcement Out of scope Humans can do these easily. Can they? How do humans do it? Let's play.
  • 15. Classical programming vs Machine Learning Credit: “Deep Learning with Python”, François Chollet
  • 16. CREDITS: This presentation template was created by Slidesgo, including icons by Flaticon, and infographics & images by Freepik. Concluding Session 1 ● We conclude that Mind does a large number of things. ● AI constitutes identifying these and then mimicking them ● One of these is learning. But learning itself is very large, deep and enigmatic. ● ML focuses on specific types of learning
  • 17. The Machine Learning Workflow Game Let’s play! 02
  • 18. ● Your team is given 50 cards. Shuffle ● Roll the dice. This is the number of classes. ● Group the cards in to those many categories using your logic. ● Write down your logic on a stickie. Hide it. Write classes on back of card. ● This was unsupervised learning. You were the machine. You did the learning and you clustered the dataset. You are machine
  • 19. ● Shuffle cards. Take out 10, 5 of these are Dev Set. 5 rest are Test Set. ● Now go to the stage. Face the audience. Audience is machine. ● Train the machine. You can't talk. You can only show the two sides of the card. ● Epoch is when you go through all cards. ● Too slow? Change batch size. Audience is Machine
  • 20. ● Training data was not labelled. ● You did not know what you are interested in. ● The problem at hand was clustering in meaningful categories ● Machine discovered features ● Machine created Clusters ● You knew what you were interested in ● You knew the i/p and o/p of Training Data ● Problem at hand was -- given i/p, predict o/p ● Machine predicted given past experience. Conclusions Unsupervised Supervised
  • 21. Why Dev Set? Bias - Variance Credit: MartinThoma, Wikimedia Foundation
  • 22. Model Performance Quality? How do you evaluate model quality?
  • 23. True/False X Positive/Negative Class = Yes Class = No Class = Yes True Positive False Negative Class = No False Positive True Negative Predicted labels True labels
  • 24. Accuracy TP+TN/TP+ FP+FN+TN Simply, correct predictions to total predictions. “Wolf! Wolf! Wolf!” Accuracy = 100%
  • 25. Precision TP/TP+FP Make sure that you don’t have too many false positives! “He does not have TB.” COSTLY mistake!
  • 26. Recall TP/TP+FN Make sure you are not claiming too many False Negatives.
  • 27. F1 Score 2*(Recall * Precision) / (Recall + Precision) When distribution is uneven, a useful composite metric
  • 28. Confusion Matrix Class 1 Class 2 Class 3 Class 4 Class 1 Class 2 Class 3 Class 4 Predicted labels True labels
  • 29. Concluding Session 2 ● ML Workflow 1. Preparing the dataset- structuring, cleaning 2. Creating the test, dev and train sets 3. Deciding on an architecture 4. Iterating on dataset 5. Output model 6. Test model on dev 7. Happy? No? Go back to step 3 8. Yes? Test on test set. Final quality. ● We learned how to test model quality
  • 30. Buzzword Alert Remember the hype cycle. All great technologies with potential go through a hype cycle. Don’t fall prey to over optimism. Don’t think too pessimistically either.
  • 31. 4. Anaconda Navigator > Install Jupyter Notebooks 5. On Kaggle, go to Kernels. Download the top two kernel codes. 6. Upload and open both on Jupyter 1. Download “SkinMNIST” data from Kaggle. 2. Anaconda (Python 3.7) 3. conda create -n keras python=3.7 numpy scipy keras matplotlib pandas seaborn pillow scikit-learn 03 Let’s hack!
  • 32. 1. Go to http://skin.test.woza.work/ 2. Upload an image of a skin lesion 3. The model predicts likelihood of condition Model, what is it good for?
  • 33. 1. Skin Lesion Analyzer + Tensorflow.js Web App by Marsh https://www.kaggle.com/vbookshelf/skin-lesion-analyzer-tensorflow-js-web-app 2. Step wise Approach : CNN Model (77.0344% Accuracy) by Manu Siddhartha https://www.kaggle.com/sid321axn/step-wise-approach-cnn-model-77-0344-accuracy 3. HAM100000 “SKIN MNIST” Data https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T Thank you kagglers
  • 34. CREDITS: This presentation template was created by Slidesgo, including icons by Flaticon, and infographics & images by Freepik. Thanks! Do you have any questions? siddharth@ruralindiaonline.org +91 869 287 2055 https://ruralindiaonline.org