This Logistic Regression Presentation will help you understand how a Logistic Regression algorithm works in Machine Learning. In this tutorial video, you will learn what is Supervised Learning, what is Classification problem and some associated algorithms, what is Logistic Regression, how it works with simple examples, the maths behind Logistic Regression, how it is different from Linear Regression and Logistic Regression applications. At the end, you will also see an interesting demo in Python on how to predict the number present in an image using Logistic Regression. Below topics are covered in this Machine Learning Algorithms Presentation: 1. What is supervised learning? 2. What is classification? what are some of its solutions? 3. What is logistic regression? 4. Comparing linear and logistic regression 5. Logistic regression applications 6. Use case - Predicting the number in an image What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - -

1. Machine
Learning Basics
An Introduction

2. Surviving the Titanic
Suppose, you have to build a model
to predict how many people survived
the Titanic shipwreck

3. Surviving the Titanic
Suppose, you have to build a model
to predict how many people survived
the Titanic shipwreck

4. Surviving the Titanic
• ID
• Survived
• Class
• Name
• Sex
• Age
• Siblings
• Parents/children abroad
• Ticket
• Fare
• Cabin
• Place of Embarkment
Teaching the model with the
passenger dataset
Dropping the non-essential
components of the dataset
Determining the survival of
passengers and evaluating the
model

5. Agenda
What is Supervised Learning?
What is Classification? What are some of its solutions?
What is Logistic Regression?
Comparing Linear and Logistic Regression
Logistic Regression applications
Use Case – Predicting the number in an image

6. What is Supervised Learning?

7. What is Supervised Learning?
That’s an
apple!
apple
Teacher teaches child Child recognizes an apple when she sees it again
A model is able to make predictions based on past data

8. Where does Logistic Regression fit it?
Machine Learning
Supervised Learning Unsupervised Learning
AssociationClusteringClassification Regression
The systems predicts future outcomes based on training from past input

9. Solutions to Classification

10. A few Classification Solutions
A B
We take decisions using a tree structure. Each
branch node represents a choice, and leaf node
represents a decision
Decision Trees

11. A few Classification Solutions
A B
Decision Trees
We take decisions using a tree structure. Each
branch node represents a choice, and leaf node
represents a decision
It helps determine what the given object is, based on
its similarity to the objects it is compared toK=3
K=7
K-Nearest Neighbor

12. It helps determine what the given object is, based on
its similarity to the objects it is compared toK=3
K=7
K-Nearest Neighbor
Decision Trees
A few Classification Solutions
A B
We determine the probability of an event occurring
with the help of a tree structure
A dataset with one or more independent variables is
used to determine binary output of the dependent
variable
Logistic Regression

13. What is Logistic Regression?

14. What is Logistic Regression?
Imagine it’s been a few years since
you serviced your car.
One day you wonder…

15. What is Logistic Regression?
It is a classification algorithm, used to predict binary outcomes for a given set of independent
variables. The dependent variable’s outcome is discrete.
Regression model created based on other
users’ experience
0.60
0.20
0.40
0.80
1.00
1 2 3 4 5 6
Years since service
Probabilityofbreakdown
How long until the
car breaks down?
You provide years since
last service

16. What is Logistic Regression?
Probability>0.50
Value rounded off to
1 : The car will
breakdown
Probability<0.50
Value rounded off to 0:
The car will not
breakdown
Here, the threshold
value 0.50 indicates
that the car is more
likely to breakdown
after 3.5 years of
usage
Model makes predictions
0.60
0.20
0.40
0.80
1.00
1 2 3 4 5 6
Years since last service
Probabilityofbreakdown
0.50
0.29
0.90
Threshold Value

17. What is Linear Regression?

18. Linear Regression
It is a statistical method that helps find the relationship between an independent and dependent variable,
both of which are continuous
He performed
well in the last
quarter. How
much raise
should he get?

19. Linear Regression
41 2 3 5
5
10
15
20
25
Employee rating
Salaryhike
Collection of ratings and corresponding
hikes Linear Regression is performed on data
The management provides the
corresponding salary hike
Employee rating

20. Linear and Logistic Regression
Here’s the graph of how linear
regression would be, for a given
scenario
x
y

21. Linear and Logistic Regression
What if you wanted to know whether the
employee would get a promotion or not
based on their rating
41 2 3 5
0 =No
1=Yes
Employee rating
Probabilityofgettinga
promotion

22. Linear and Logistic Regression
This graph would not be able to
make such a prediction. So we clip
the line at 0 and 1.
41 2 3 5
Employee rating
0 =No
1=Yes
Probabilityofgettinga
promotion

23. Linear and Logistic Regression
So, how did this… …become this?
41 2 3 5
Employee rating
41 2 3 5
Employee rating
0 =No
1=Yes
Probabilityofgettinga
promotion
0 =No
1=Yes
Probabilityofgettinga
promotion

24. The Math behind Logistic Regression
To understand Logistic Regression, let’s talk
about the odds of success
Odds (𝜃) =
Probability of an
event happening
Probability of an
event not
happening
or 𝜃 =
𝑝
1 − 𝑝
The values of odds range from 0 to ∞
The values of probability change from 0 to 1

25. The Math behind Logistic Regression
Type equation here.
Take the equation of the straight line
𝛽0
x
y
Here, 𝛽0 is the y-intercept
𝛽1 is the slope of the line
x is the value of the x co-ordinate
y is the value of the prediction
The equation would be: 𝑦 = 𝛽0 + 𝛽1 𝑥

26. The Math behind Logistic Regression
Type equation here.
Now, we predict the odds of success
e𝑙𝑛
𝑝 𝑥
1 − 𝑝 𝑥
= e 𝛽0+𝛽1 𝑥
log
𝑝 𝑥
1−𝑃 𝑥
= 𝛽0 + 𝛽1 𝑥
Exponentiating both sides:
𝑝 𝑥
1 − 𝑝 𝑥
= e 𝛽0+𝛽1 𝑥
Let Y = e 𝛽0+𝛽1 𝑥
Then
𝑝 𝑥
1−𝑝 𝑥
= Y
𝑝 𝑥 = 𝑌 1 − 𝑝 𝑥
𝑝 𝑥 = 𝑌 − 𝑌 𝑝 𝑥
𝑝 𝑥 + 𝑌 𝑝 𝑥 = 𝑌
𝑝 𝑥 1 + 𝑌 = 𝑌
𝑝 𝑥 =
𝑌
1 + 𝑌
𝑝 𝑥 =
e 𝛽0+𝛽1 𝑥
1 + e 𝛽0+𝛽1 𝑥
The equation of a sigmoid function:
𝑝 𝑥 =
e 𝛽0+𝛽1 𝑥
1 + e 𝛽0+𝛽1 𝑥
𝑝 𝑥 =
1
1 + e−(𝛽0+𝛽1 𝑥)

27. The Math behind Logistic Regression
Type equation here.
Now, we predict the odds of success
e𝑙𝑛
𝑝 𝑥
1 − 𝑝 𝑥
= e 𝛽0+𝛽1 𝑥
log
𝑝 𝑥
1−𝑃 𝑥
= 𝛽0 + 𝛽1 𝑥
Exponentiating both sides:
𝑝 𝑥
1 − 𝑝 𝑥
= e 𝛽0+𝛽1 𝑥
Let Y = e 𝛽0+𝛽1 𝑥
Then
𝑝 𝑥
1−𝑝 𝑥
= Y
𝑝 𝑥 = 𝑌 1 − 𝑝 𝑥
𝑝 𝑥 = 𝑌 − 𝑌 𝑝 𝑥
𝑝 𝑥 + 𝑌 𝑝 𝑥 = 𝑌
𝑝 𝑥 1 + 𝑌 = 𝑌
𝑝 𝑥 =
𝑌
1 + 𝑌
𝑝 𝑥 =
e 𝛽0+𝛽1 𝑥
1 + e 𝛽0+𝛽1 𝑥
The equation of a sigmoid function:
𝑝 𝑥 =
e 𝛽0+𝛽1 𝑥
1 + e 𝛽0+𝛽1 𝑥
𝑝 𝑥 =
1
1 + e−(𝛽0+𝛽1 𝑥)
41 2 3 5
0
0.25
0.50
0.75
1
Employee rating
Probabilityofgettinga
promotion
A sigmoid curve is obtained!

28. Comparing Linear and Logistic
Regression

29. How is Linear and Logistic Regression different?
Linear Regression Logistic Regression
• Used to solve Regression
Problems

30. How is Linear and Logistic Regression different?
Linear Regression Logistic Regression
• Used to solve Classification
Problems
• Used to solve Regression
Problems

31. How is Linear and Logistic Regression different?
Linear Regression Logistic Regression
• Used to solve Classification
Problems
• Used to solve Regression
Problems
• The response variables are
continuous in nature

32. How is Linear and Logistic Regression different?
Linear Regression Logistic Regression
• Used to solve Classification
Problems
• The response variable is
categorical in nature
• Used to solve Regression
Problems
• The response variables are
continuous in nature

33. How is Linear and Logistic Regression different?
Linear Regression Logistic Regression
• Used to solve Classification
Problems
• The response variable is
categorical in nature
• Used to solve Regression
Problems
• The response variables are
continuous in nature
• It helps estimate the dependent
variable when there is a change
in the independent variable.

34. How is Linear and Logistic Regression different?
Linear Regression Logistic Regression
• Used to solve Classification
Problems
• The response variable is
categorical in nature
• It helps calculate the possibility
of a particular event taking
place.
• Used to solve Regression
Problems
• The response variables are
continuous in nature
• It helps estimate the dependent
variable when there is a change
in the independent variable.

35. How is Linear and Logistic Regression different?
Linear Regression Logistic Regression
• Used to solve Classification
Problems
• The response variable is
categorical in nature
• It helps calculate the possibility
of a particular event taking
place.
• Used to solve Regression
Problems
• The response variables are
continuous in nature
• It helps estimate the dependent
variable when there is a change
in the independent variable.
• Is a straight line.

36. How is Linear and Logistic Regression different?
Linear Regression Logistic Regression
• Used to solve Classification
Problems
• The response variable is
categorical in nature
• It helps calculate the possibility
of a particular event taking
place.
• An S-curve. (S = Sigmoid)
• Used to solve Regression
Problems
• The response variables are
continuous in nature
• It helps estimate the dependent
variable when there is a change
in the independent variable.
• Is a straight line.

37. Logistic Regression Applications
Weather Prediction
Helps determine the kind of weather that
can be expected

38. Identifies the different components
that are present in the image, and
helps categorize them
Logistic Regression Applications
Humans Animals Vehicles
Image Categorization

39. Determines the possibility of patient
survival, taking age, ISS and RTS into
consideration
Logistic Regression Applications
Healthcare (TRISS)
Patient survival %
Revised
Trauma Score
Injury Severity
Score
Age

40. Use Case – Predicting numbers in
images

41. Use Case – Predicting numbers
Can you guess
what number I am? Are you a 3? 4?
I don’t know!
8x8 image

42. Use Case – Predicting numbers
Dividing the data set
Training
set
Test set
The model being trained
Model identifies number in
images
Test set applied
A number 4
A number 1

43. Use Case – Implementation
Importing libraries and their associated methods
Determining the total number of images and labels

44. Use Case – Implementation
Displaying some of the images and labels

45. Use Case – Implementation
Dividing dataset into Training and Test set

46. Use Case – Implementation
Import the Logistic Regression model
Making an instance of the model and training it
Predicting the output of the first element of the test set
Predicting the output of the first 10 elements of the test set

47. Use Case – Implementation
Predicting for the entire dataset
Determining the accuracy of the model
Representing the confusion matrix in a heat map

48. Use Case – Implementation
Accurately predicting the image to
contain a zero
Inaccurately predicting the image to
contain a seven

49. Use Case – Implementation
Presenting predictions and actual output

50. Use Case – Predicting numbers
Dividing the data set
Training
set
Test
set
The model being trained Model identifies number in
images
Test set applied
A number 4
A number 2

51. Here’s What You’ve Learnt so Far

52. So what’s
your next step?