Charmi Chokshi
April 22, 2023
170

# ML Starter Pack

Presented at GDG Waterloo.

Basics of ML

April 22, 2023

## Transcript

1. ML Starter Pack
A comprehensive guide to Machine Learning
April 22, 2023

2. I'm Charmi!
● ML Grad at Mila and UdeM
● GDE for ML
● International Speaker
● Worked @ AWS, Shipmnts, ISRO
● Tech, Art, Travel

3. Plan of the Day!
● What is Machine Learning?
● Types of ML
● Supervised and Unsupervised techniques
● Classification and Regression
● Basics of Deep Learning
● Basics of NLP, CV, ASR
● The 3D ML Pipeline
● Fun Applications

4. ?
90% have failed to solve this...
=
=
=
33
30
36

5. 99% have failed to solve this...

6. Retrospection time!!
What we just did?
We solved logical math puzzle
in a fraction of time

solved them?

solved them?
We created Rules and Patterns in our brain!!

9. Can machines create
rules on their own?

10. Can machines create
rules on their own?
No, if it’s Classical Programming...

11. Can machines create
rules on their own?
YES, if it’s Machine Learning!!!

12. Classical Programming vs ML
Classical
Programming
Machine
Learning
Rules
Rules
Data
Data

13. Artificial
Intelligence
Deep
Learning
Machine
Learning
Any Technique that enables
computers to mimic human
intelligence & behaviour
A subset of ML exposing
multilayered neural
networks to vast amount of
data
A subset of AI that includes
statistical techniques to
experience

14. Basic Terminologies
● Features
● Labels
● Examples
○ Labelled example
○ Unlabelled example
● Data Split (Train, Valid, Test)
● Models (Train and Test)
○ Classification model
○ Regression model

15. Supervised Learning
● Supervised Learning deals with prediction of values based on given
combinations of data and labels given beforehand
● ML systems learn how to combine input to produce useful
predictions on never-before-seen data
● It is like learning with a teacher

16. Regression and Classification
● A regression model predicts continuous values by fitting a line
○ What is the value of a house in California?
○ What is the probability that a user will click on this ad?
● A classification model predicts discrete values by creating boundaries
○ Is a given email message spam or not spam?
○ Is this an image of a dog, a cat, or a hamster?

17. Iterative Learning

18. Overfitting vs Underfitting
● An overfit model gets a low loss during training but does a poor job
predicting new data
● Overfitting is caused by making a model more complex than necessary
● The fundamental tension of machine learning is between fitting our data
well, but also fitting the data as simply as possible

19. Unsupervised Learning
● It deals with clustering values or forming groups of values
● One aims to infer patterns from the data rather than predicting values
● It is like learning on your own

20. Evaluation Metrics
https://en.wikipedia.org/wiki/Precision_and_recall, https://www.kdnuggets.com/2018/06/right-metric-evaluating-machine-learning-models-2.html

21. Evaluation Metrics
https://en.wikipedia.org/wiki/Precision_and_recall, https://www.kdnuggets.com/2018/06/right-metric-evaluating-machine-learning-models-2.html

22. Deep Learning

23. Neural Network
https://gfycat.com/gifs/search/neural+network

24. When to use or not use DL?
● Deep Learning outperforms other techniques if the data size is large. But
with small data size, traditional Machine Learning algorithms are preferable
● Finding large amount of “Good” data is always a painful task
● Deep Learning techniques need to have high end infrastructure to train in
reasonable time
● When there is lack of domain understanding for feature introspection,
Deep Learning techniques outshines others as you have to worry less about
feature engineering

25. When to use or not use DL?
● Model Training time: a Deep Learning algorithm may take weeks or months
whereas, traditional Machine Learning algorithms take few seconds or hours
● DL never reveals the “how and why” behind the output- it’s a Black Box
● Deep Learning really shines when it comes to complex problems such as
image classification, natural language processing, and speech recognition
● DL excels in tasks where the basic unit (pixel, word) has very little meaning in
itself, but the combination of such units has a useful meaning

26. Natural Language Processing
● Sentiment analysis
● Chatbots
● Machine translation
● Speech recognition
● Text summarization

27. Computer Vision
● Facial recognition
● Object detection
● Medical imaging
● Autonomous vehicles
● Segmentation

28. Speech Understanding
● Voice-controlled personal assistants
● Virtual agents
● Dictation software
● Closed captioning
● Subtitling for movies
● Transcribing

29. The 3D ML Pipeline
Design - Develop - Deploy

30. Data
acquisition
Model
Deployment
Data
Cleaning
Feature
Engineering
Model
Validation
Model
Monitoring
Model
Selection
Model
Testing
Model
Training
Hyper
parameter
tuning

31. Data
acquisition
Model
Deployment
Data
Cleaning
Feature
Engineering
Model
Validation
Model
Monitoring
Model
Selection
Model
Testing
Model
Training
Hyper
parameter
tuning

32. Data
acquisition
Model
Deployment
Data
Cleaning
Feature
Engineering
Model
Validation
Model
Monitoring
Model
Selection
Model
Testing
Model
Training
Hyper
parameter
tuning

33. Data
acquisition
Model
Deployment
Data
Cleaning
Feature
Engineering
Model
Validation
Model
Monitoring
Model
Selection
Model
Testing
Model
Training
Hyper
parameter
tuning

34. Data
acquisition
Model
Deployment
Data
Cleaning
Feature
Engineering
Model
Validation
Model
Monitoring
Model
Selection
Model
Testing
Model
Training
Hyper
parameter
tuning

35. Data
acquisition
Model
Deployment
Data
Cleaning
Feature
Engineering
Model
Validation
Model
Monitoring
Model
Selection
Model
Testing
Model
Training
Hyper
parameter
tuning

36. Data
acquisition
Model
Deployment
Data
Cleaning
Feature
Engineering
Model
Validation
Model
Monitoring
Model
Selection
Model
Testing
Model
Training
Hyper
parameter
tuning

37. Data
acquisition
Model
Deployment
Data
Cleaning
Feature
Engineering
Model
Validation
Model
Monitoring
Model
Selection
Model
Testing
Model
Training
Hyper
parameter
tuning

38. Data
acquisition
Model
Deployment
Data
Cleaning
Feature
Engineering
Model
Validation
Model
Monitoring
Model
Selection
Model
Testing
Model
Training
Hyper
parameter
tuning

39. Data
acquisition
Model
Deployment
Data
Cleaning
Feature
Engineering
Model
Validation
Model
Monitoring
Model
Selection
Model
Testing
Model
Training
Hyper
parameter
tuning

40. Data
acquisition
Model
Deployment
Data
Cleaning
Feature
Engineering
Model
Validation
Model
Monitoring
Model
Selection
Model
Testing
Model
Training
Hyper
parameter
tuning

41. It’s time for exploring
some FUN applications

42. Classification on Custom Data

43. Sketch-RNN
https://magenta.tensorflow.org/assets/sketch_rnn_demo/index.html

44. From where the data came

45. RUNN = 🏃Run + 🤖RNN
http://vibertthio.com/runn/

47. The project Magenta
https://magenta.tensorflow.org/