This document provides an overview of machine learning concepts including the machine learning workflow, algorithms, and techniques. It discusses the machine learning workflow which includes asking the right question, preparing data, selecting an algorithm, training a model, and testing the model. It also covers machine learning algorithms like SVM and techniques such as feature scaling. The document is intended to introduce readers to artificial intelligence using machine learning.

1. Machine Learning
03
Computers are able to see, hear
and learn. Welcome to the
future.” ~Dave Waters

2. CONTENT OF THIS COURSE
Here’s what you’ll find in this Course :
● Machine Learning Workflow.
● Machine Learning Algorithms
● Machine Learning Techniques.
● Building Real world Projects.

3. Introduction to AI with ML
Machine Learning Workflow

4. Introduction to AI with ML
Machine Learning Workflow
Asking the
Right
Question
Preparing
data
Selecting
the
algorithm
Training the
model
Testing the
model

5. Introduction to AI with ML
Machine Learning Workflow
“I can’t make bricks without clay”.
-Arthur Conan Doyle

6. Introduction to AI with ML
Machine Learning Workflow
“A baby learns to crawl, walk and then run.
We are in the crawling stage when it comes to applying machine learning.”
-Dave Waters

8. Introduction to AI with ML
Machine Learning Workflow

9. Introduction to AI with ML

10. Introduction to AI with ML

11. Introduction to AI with ML
Data Gathering
Might depend on humanwork
• Manual labeling forsupervisedlearning.
• Domain knowledge.Maybe evenexperts.
May come forfree,or “sortof”
• E.g.,Machine Translation.
Themorethebetter:Some algorithmsneed large amounts of data tobe useful (e.g.,
neuralnetworks).
The quantity and quality of data dictatethe modelaccuracy

12. Introduction to AI with ML
Machine Learning Workflow
Asking the
Right
Question
Preparing
data
Selecting
the
algorithm
Training the
model
Testing the
model

13. Introduction to AI with ML

14. Introduction to AI with ML
Data Gathering
"Big Data is not about the Big data" -Gray king, Harvard University
making point that the data is plentiful ,but the real value is in analytics.

15. Introduction to AI with ML

16. Introduction to AI with ML
Data Preprocessing
Is there anything wrong with thedata?
• Missing values
• Outliers
• Bad encoding (fortext)
• Wrongly-labeled examples
• Biased data
• Do I have many more samples of one class than therest?
Need to fix/remove data?

17. Introduction to AI with ML
Data Preprocessing
What is missing data?
'In statistics, missing data, or missing values, occur when no
data value is stored for the variable in an observation. Missing
data are a common occurrence and can have a significant effect
on the conclusions that can be drawn from the data.'

18. Introduction to AI with ML
Data Preprocessing
Is there anything wrong with thedata?
• Missing values
• Outliers
• Bad encoding (fortext)
• Wrongly-labeled examples
• Biased data
• Do I have many more samples of one class than therest?
Need to fix/remove data?

19. Introduction to AI with ML
Machine Learning Workflow
Asking the
Right
Question
Preparing
data
Selecting
the
algorithm
Training the
model
Testing the
model

20. Introduction to AI with ML
Machine Learning Workflow
Testing the
model

21. Introduction to AI with ML
Select the algorithm

22. Introduction to AI with ML
Machine Learning Workflow
Asking the
Right
Question
Preparing
data
Selecting
the
algorithm
Training the
model
Testing the
model

23. Introduction to AI with ML
Train the Algorithm

25. Introduction to AI with ML
Select the algorithm

26. Introduction to AI with ML
Select the algorithm

27. Introduction to AI with ML
Select the algorithm

28. Introduction to AI with ML
Machine Learning Workflow
Asking the
Right
Question
Preparing
data
Selecting
the
algorithm
Training the
model
Testing the
model

29. Introduction to AI with ML
Test Score

30. Introduction to AI with ML
Test Score

31. Introduction to AI with ML
Test Score

32. Introduction to AI with ML
Test Score

33. Introduction to AI with ML
Test Score

34. Introduction to AI with ML
Machine Learning work Flow Guidelines
 Data is never as you need it
 More data is better.
 Expect to go backward

35. Introduction to AI with ML
Test Score

36. Introduction to AI with ML
SVM Algorithm

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SVM Algorithm

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SVM Algorithm
Tuning Parameters
SVM
1. Kernel
2. Regularization
3. Gamma
4. Margin

39. Introduction to AI with ML
Regularization
Also the ‘ C ‘ parameter in Python’s SkLearn Library
Optimises SVM classifier to avoid misclassifying the data.
C → large
C → small
Margin of hyperplane → small
Margin of hyperplane → large
misclassification(possible)
C ---> large , chance of overfit
C ---> small , chance of underfitting

40. Introduction to AI with ML
Overfitting vs Underfitting

41. Introduction to AI with ML
Overfitting vs Underfitting

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Overfitting vs Underfitting

43. Introduction to AI with ML
SVM Algorithm

44. Introduction to AI with ML
Margin
• Margin is the perpendicular distance between the closest data
points and the Hyperplane ( on both sides )
• The best optimised line ( hyperplane ) with maximum margin
is termed as Margin Maximal Hyper plane.
• The closest points where the margin distance is calculated are
considered as the support vectors.

45. Introduction to AI with ML
Gamma
Defines how far influences the calculation of of plausible line of
separation.
Low gamma -----> points far from plausible line are considered for
calculation
High gamma -----> points close to plausible line are considered for
calculation
High Gamma Value Low Gamma Value

46. Introduction to AI with ML
Kernel
Mathematical functions for transforming data
using some linear algebra
Different SVM algorithms use different types of kernel functions
1. Linear kernel
2. Non - linear kernel
3. Radial basis function ( RBF )
4. Sigmoid
5. Polynomial
6. Exponential

47. Introduction to AI with ML
Feature Scaling

48. Introduction to AI with ML
Feature Scaling

49. Introduction to AI with ML
Feature Scaling
 If features do not have the same numerical scale in values, will cause
issues in training a mode.
 If the scale of one independent variable (feature) is greater than another
independent variable, the model will give more importance (skew) to the
independent variable with the larger range.
 Toeliminate this problem, one converts all the independent
variables to use the same scale.
Normalization
Standardization ( -1 to 1 )