This Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms. Below topics are covered in this Machine Learning Algorithms Presentation: 1. Real world applications of Machine Learning 2. What is Machine Learning? 3. Processes involved in Machine Learning 4. Type of Machine Learning Algorithms 5. Popular Algorithms with a hands-on demo - Linear regression - Logistic regression - Decision tree and Random forest - N Nearest neighbor 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 - - - - - - -

2. Machine Learning Example
How does
Snapchat
filter work?

3. Machine Learning Example

4. Machine Learning Example
Matrix of numbers

5. Machine Learning Example
Matrix of numbers
Looks for color patterns
on face

6. Machine Learning Example
Matrix of numbers
Looks for color patterns
on face
Points align
themselves and
look for areas of
contrast

7. Machine Learning Example
Matrix of numbers
Looks for color patterns
on face
Points align
themselves and
look for areas of
contrast

8. What’s in it for you?
Types of Machine Learning Algorithms
Real World Applications of Machine Learning
Processes involved in Machine Learning
What is Machine Learning?
Logistic Regression
Linear Regression
Decision Tree and Random forest
K Nearest Neighbors
Popular Algorithms with Hands On Demo

9. Real World Applications of
Machine Learning

10. Real World Applications of Machine Learning
Face Recognition Healthcare Industry Weather Forecasting
Produce a Web Series Prepare a new Drink
Siri and Cortana

11. What is Machine Learning?

12. What is Machine Learning?
Machine Learning is the science of making computers learn and act like humans by feeding
data and information without being explicitly programmed.

13. What is Machine Learning?
Past Data
Machine Learning is the science of making computers learn and act like humans by feeding
data and information without being explicitly programmed.

14. What is Machine Learning?
Past Data
Analyses
Machine Learning is the science of making computers learn and act like humans by feeding
data and information without being explicitly programmed.

15. What is Machine Learning?
Past Data
Analyses
Trains
Machine Learning is the science of making computers learn and act like humans by feeding
data and information without being explicitly programmed.

16. What is Machine Learning?
Past Data
Analyses
Predicts
Trains
Machine Learning is the science of making computers learn and act like humans by feeding
data and information without being explicitly programmed.
Output

17. Processes involved in Machine Learning
Data
Gathering
7Machine Learning
Processes

18. Processes involved in Machine Learning
Data
Gathering
7Machine Learning
Processes
Data
Pre-Processing

19. Processes involved in Machine Learning
Data
Gathering
7Machine Learning
Processes
Data
Pre-Processing
Choose
Model

20. Processes involved in Machine Learning
Data
Gathering
7Machine Learning
Processes
Data
Pre-Processing
Choose
Model
Train Model

21. Processes involved in Machine Learning
Data
Gathering
7Machine Learning
Processes
Data
Pre-Processing
Choose
Model
Train Model
Test Model

22. Processes involved in Machine Learning
Data
Gathering
7Machine Learning
Processes
Data
Pre-Processing
Choose
Model
Train Model
Test Model
Tune Model

23. Processes involved in Machine Learning
Data
Gathering
7Machine Learning
Processes
Data
Pre-Processing
Choose
Model
Train Model
Test Model
Tune Model
Prediction

24. Types of Machine Learning Algorithms
VSupervised
Learning
Un-Supervised
Learning
Reinforcement
Learning
Machine
Learning
Association
• Market Basket Analysis
• Text Mining
• Face Recognition
Classification
• Fraud Detection
• Email Spam Detection
• Image Classification
Regression
• Weather Forecasting
• Risk Assessment
• Score Prediction
Clustering
• Medical Research
• City Planning
• Targeted Marketing
Reinforcement Learning
• Gaming
• Robot Navigation
• Stock Trading
• Assembly Line Processes

25. Popular Algorithms in Machine Learning
Algorithm 1
Linear
Regression

26. Popular Algorithms in Machine Learning
Algorithm 1
Algorithm 2
Linear
Regression
Logistic
Regression

27. Popular Algorithms in Machine Learning
Algorithm 1
Decision
Tree
Algorithm 2
Algorithm 3
Linear
Regression
Logistic
Regression

28. Popular Algorithms in Machine Learning
Algorithm 1
Decision
Tree
Random
Forest
Algorithm 2
Algorithm 3
Algorithm 4
Linear
Regression
Logistic
Regression

29. Popular Algorithms in Machine Learning
Algorithm 1
Decision
Tree
Random
Forest
K Nearest
Neighbors
Algorithm 2
Algorithm 3
Algorithm 4
Algorithm 5
Linear
Regression
Logistic
Regression

30. Linear Regression

31. History of Linear Regression
Linear Regression: Brief history on how Regression came into picture.
Francis Galton Heights of Father and Son
Studied
Best fit
line
Regression Line
Analyzed
Height Data
Mean Height of all People
Regressed

32. Linear Regression
Linear Regression is a linear modelling approach to find relationship between one or more independent variables (predictors)
denoted as X and dependent variable (target) denoted as Y.
Temperature
Sales
Temperature
Sales
Regression line to predict the sales
Plotting the sales of ice cream based
on temperature
Predict sales of Ice Cream based on temperature:

33. Linear Regression
Finding the best fit line: The best fit line can be found out by minimizing the distance between all the data points and the distance
to the regression line. Ways to minimize this distance are sum of squared errors, sum of absolute errors etc.
Distance between the data points
and the line is maximum
Not the best fit line

34. Finding the best fit line: The best fit line can be found out by minimizing the distance between all the data points and the distance
to the regression line. Ways to minimize this distance are sum of squared errors, sum of absolute errors etc.
Linear Regression
Not the best fit line
Distance between the data points
and the line can still be reduced

35. Linear Regression
Finding the best fit line: The best fit line can be found out by minimizing the distance between all the data points and the distance
to the regression line. Ways to minimize this distance are sum of squared errors, sum of absolute errors etc.
Distance between the data points
and the line is the least
Best fit regression line

36. Implementation of Linear Regression
Linear Regression: Predict Employee Salary based on Years of Experience.
HR team trying to figure out how much
to pay to a new joinee?
Predict Salary
How much do we pay her?
New Joinee
Years of experience
Salary
Employee data of Salary
based on Years of experience

37. Implementation of Linear Regression
1. Load the libraries:
2. Import the Dataset:

38. Implementation of Linear Regression
3. Visualize the data:

39. Implementation of Linear Regression
4. Split the data into training and testing set:
5. Fit Simple Linear Regression to the training dataset:

40. Implementation of Linear Regression
6. Predict the test set results:

41. Implementation of Linear Regression
7. Visualize the train set results:

42. Implementation of Linear Regression
8. Visualize the test set results:

43. Implementation of Linear Regression
9. Calculating the residuals:
Since the RMSE value is too less, the model is a good model

44. Logistic Regression

45. Logistic Regression
Logistic Regression is a Classification algorithm used to predict discrete/ categorical values
Who will default on their credit card payment?

46. Logistic Regression
Logistic Regression is a Classification algorithm used to predict discrete/ categorical values
Who will default on their credit card payment?
Credit card users

47. Logistic Regression
Logistic Regression is a Classification algorithm used to predict discrete/ categorical values
Who will default on their credit card payment?
Credit card users Make credit card
transaction

48. Logistic Regression
Logistic Regression is a Classification algorithm used to predict discrete/ categorical values
Who will default on their credit card payment?
Credit card users
Plot of monthly credit card balance
and annual income
Make credit card
transaction

49. Logistic Regression
Plotting the Logistic Regression Curve: The Logistic Regression curve is known as the Sigmoid curve (S curve)
Predicted Y lies in the range 0 and 1
1
1 + e-zP =
Predicted Y can exceed the range 0 and 1
Y = m1x+c0

50. Logistic Regression
Plotting the Logistic Regression Curve: The Logistic Regression curve is known as the Sigmoid curve (S curve)
Y = m1x+c0
0.5
Threshold
value
0.5
Cutoff point at 0.5, anything below it
results in 0 and above is 1
Red data point will default as it is above
the threshold value of 0.5 and green data
point won’t as it is below the threshold
value

51. Implementation of Logistic Regression
Logistic Regression: Predict if a person will buy an SUV based on their Age and Estimated Salary
Age Estimated
Salary

52. Implementation of Logistic Regression
1. Load the libraries:
2. Import the dataset and extract the independent and dependent variables:

53. Implementation of Logistic Regression
3. Visualize the dataset:

54. Implementation of Logistic Regression
4. Split the dataset into Training and Testing set:
5. Feature Scaling:
6. Fit Logistic Regression to Training dataset:

55. Implementation of Logistic Regression
7. Predicting the Test set results:

56. Implementation of Logistic Regression
8. Visualize the Train set results:

57. Implementation of Logistic Regression
9. Visualize the Test set results:

58. Implementation of Logistic Regression
11. Evaluating the model:
Understanding the Confusion Matrix:
N = 134 Predicted: No Predicted: Yes
Actual: No TN=79 FP=6
Actual: Yes FN=11 TP=38
Accuracy:
(TN+TP)/N
(79+38)/134 = 0.87
Misclassification Rate:
(FP+FN)/N
(6+11)/134 = 0.13

59. Decision Tree and
Random Forest

60. Decision Tree
Decision Tree is a tree where each node represents a feature (attribute), each link (branch) represents a decision and each leaf
represents an outcome (categorical or continuous value).
Should the person accept a new job offer?
Yes
Salary >
$60,000
Root Node
Accept a job offer?
Reject
Offer
No
Leaf node
Accept
offer
Yes
Reject
Offer
No
Reject
Offer
YesNo
Performance
Incentives
Decision
node
Commute > 1
hour

61. Implementation of Decision Tree
Decision Tree and Random Forest: Does Kyphosis exist after a surgery?
Bunch of kids Kyphosis surgery Predict kyphosis
present or absent?

62. Implementation of Decision Tree
Classification Tree for Kyphosis:
Start>=8.5
Absent 64/17
Present
8/11
No
Start>=14.5
Absent 56/6
Yes
Absent
12/0
Yes
Absent
29/0
Yes
Absent
12/2
Yes
Age<55
Absent 27/6
No
Age>=111
Absent 15/6
No
Present
3/4
No

63. Implementation of Decision Tree
1. Load the libraries:
2. Import the dataset and extract the independent and dependent variables:

64. Implementation of Decision Tree
3. Visualize the data:

65. Implementation of Decision Tree
4. Split the data into Train and Test set:
5. Train a Decision Tree:

66. Implementation of Decision Tree
6. Predict the Model:

67. Implementation of Decision Tree
N = 25 Predicted: No Predicted: Yes
Actual: No TN=16 FP=4
Actual: Yes FN=2 TP=3
7. Evaluate the Model:
Understanding the confusion matrix: Accuracy:
(TN+TP)/N
(16+3)/25 = 0.76
Misclassification Rate:
(FP+FN)/N
(4+2)/25 = 0.24

68. Improving performance using Random Forest
Compare the model using Random Forest Tree:
Accuracy:
(TN+TP)/N
(18+2)/25 = 0.80
Misclassification Rate:
(FP+FN)/N
(2+3)/25 = 0.20

69. Algorithm 5
K Nearest Neighbors

70. K Nearest Neighbors (KNN)
K Nearest Neighbors: KNN is a Classification algorithm generally used to predict categorical values.
Height(ft)
Weight (lbs)
Class Dog
1.5
3.5
2.0
3.0
2.5
1.0
0.5
0 65 75 9585 105
Class Cat
Weight (lbs)
Class Dog
1.5
3.5
2.0
3.0
2.5
1.0
0.5
0 65 75 9585 105
Height(ft)
New data
point
Class Cat
To find if a new data point is a or a ?

71. Choosing a K will define what class a new data point is assigned to:
K Nearest Neighbors
Height(ft)
Class Dog
Weight (lbs)
0.5
2.5
2.0
1.5
3.0
1.0
3.5
10565 75 85 95
Class Cat
K=3
If K=3, the new data point
belongs to class Cat
K=7
If K=7, the new data point
belongs to class Dog

72. Implementation of KNN
Predict if a person will buy an SUV based on Age and Estimated Salary
Age Estimated
Salary

73. Implementation of KNN
1. Load the Libraries:
2. Load the Dataset:

74. Implementation of KNN
3. Visualize the dataset:

75. Implementation of KNN
4. Split the data into Train and Test set:
5. Feature Scaling:

76. Implementation of KNN
6. Fit KNN to Train set:
7. Predict the Test set results:

77. Implementation of KNN
8. Visualize the Train set results:

78. Implementation of KNN
9. Visualize the Test set results:

79. Implementation of KNN
10. Evaluate the model by creating a confusion matrix:
Understanding the confusion matrix:
N = 100 Predicted: No Predicted: Yes
Actual: No TN=64 FP=4
Actual: Yes FN=3 TP=29
Accuracy:
(TN+TP)/N
(64+29)/100 = 0.93
Misclassification Rate:
(FP+FN)/N
(4+3)/100 = 0.07

80. Summary