This document discusses Adham Nour trying to create a machine learning model at home. It provides an overview of machine learning workflows, including getting data, preparing the data, selecting an algorithm, training the model, and testing the model. It also discusses different machine learning problems like supervised, unsupervised, and reinforcement learning. Key machine learning algorithms are described like support vector machines and how the C parameter impacts regularization. Overall the document serves as an introduction to machine learning concepts for someone trying to build their own model.

1. What if Adham Nour tried to make
a Machine Learning Model, at Home!
By. Mohamed Essam

2. Whoa!
“Computers are able to see, hear and
learn. Welcome to the future.”
—~Dave Waters

3. What Artificial Intelligence is!

4. What Artificial Intelligence is!

5. Different between traditional programming and
Machine Learning Programming
Traditional programming
I know the equation
Example BMI function is
BMI=
𝒘𝒆𝒊𝒈𝒉𝒕
𝒉𝒆𝒊𝒈𝒉𝒕𝟐
Machine Learning Programming
I use Function called (train) to get the equation.
Example if there is the house cost 600$ and it’s a
200m then the equation will be
price= 𝟑𝒙
100 200 300 400
1200
900
600
300
size

6. How To Create a Machine Learning
model
ML Model

7. How To Create a Machine Learning
model
ML Model

9. Machine Learning and
Starbucks coffee Workflow

10. Machine Learning Workflow
Getting the
Data

11. “I can’t make bricks without clay”.
-Arthur Conan Doyle

12. “I can’t make bricks without clay”.
-Arthur Conan Doyle

14. Data Gathering
1
Might depend on humanwork
• Manual labeling for supervisedlearning.
• Domain knowledge. Maybe evenexperts.
May come for free, or “sortof”
• E.g.,Machine Translation.
The more the better:Some algorithms need large amounts of data to be useful
(e.g.,neural networks).
The quantity and quality of data dictate the modelaccuracy

15. Machine Learning Workflow
Getting the
Data
Preparing
data

18. Data Preprocessing
Is there anything wrong withthedata?
• Missing values
• Outliers
• Bad encoding (fortext)
• Wrongly-labeled examples
• Biased data
• Do I have manymore samples of one class
than therest?
Need to fix/removedata?
1

19. Machine Learning Workflow
Getting the
Data
Preparing
data
Selecting
The
Algorithm

22. Machine Learning Workflow
Getting the
Data
Preparing
data
Selecting
The
Algorithm
Training the
model

28. Machine Learning Workflow
Getting the
Data Preparing
data
Selecting
The
Algorithm
Training the
model
Testing the
model

35. Machine Learning work Flow
Guidelines
• Data is never as you need it
• More data is better.
• Expect to go backward
3

36. Machine Learning work Flow
Guidelines
• Data is never as you need it
• More data is better.
• Expect to go backward
3

37. Machine Learning isn’t Magic?
No, more like Adham Nour
Coffee Seeds = Data
Coffee Machines= Algorithms
Adham Nour= You
Cup of Coffee= Model

38. Types of Machine LearningProblems
Supervised
Unsupervised
Reinforcement
Output is a discrete
variable (e.g.,cat/dog)
Classification
Regression
Output is continuous
(e.g.,price, temperature)

39. Types of Machine LearningProblems
Unsupervised
There is no desired output. Learn somethingabout
the data. Latent relationships.
I want to find anomalies in the credit cardusage
patterns of my customers.
3
I have photos and want to put them in 20
groups.
Supervised
Reinforcement

40. Types of Machine LearningProblems
Supervised
Reinforcement
 Environment gives feedback via a positiveor
negative reward signal.
 An agent interacts with an environment and
watches the result of the interaction.
Un Supervised

41. Supervised Learning

42. Supervised Learning

43. Supervised Learning Algorithms
Supervised
• Linear classifier
• Naive Bayes
• Support VectorMachines (SVM(
• Decision Tree
• Random Forests
• k-Nearest Neighbors
• Neural Networks (Deeplearning)
Unsupervised
• PCA
• t-SNE
• k-means
• DBSCAN
Reinforcement
• SARSA–λ
• Q-Learning

44. SVM Algorithm

45. SVM Algorithm 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

46. Any Questions?
Mohamed Essam
!

47. CREDITS: This presentation template was created by Slidesgo,
including icons by Flaticon, and infographics & images by Freepik
THANKS!
Contacts
Mhmd96.essam@gmail.com
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