The document outlines an agenda for a two-day programming and AI event. Day 1 covers introduction to Python programming, essential Python for AI, machine learning programming, and machine learning project deployment. Day 2 covers fullstack web development, machine learning integration, AI integration, and a case study. Each day includes registration periods, topic sessions, and coffee breaks.

1. P RO G R A M M I N G
A I & D E E P L E A R N I N G
F O R S M A R T T E C H N O L O G Y E N T R E P R E N E U R © W I C H I T 2 S

2. A G E N DA
Day 1
08.30 - 09.00 Registration
09.00 - 10.30 Introduction to Programming with Python
10.30 - 10.45 Coffee break
10.45 - 12.00 Essential Python for AI Programming
12.00 - 13.00 Lunch break
13.00 - 14.30 Machine Learning Programming
14.30 - 14.45 Coffee break
14.45 - 16.30 Machine Learning Project Deployment
Day 2
08.30 - 09.00 Registration
09.00 - 10.30 Fullstack Web Development
10.30 - 10.45 Coffee break
10.45 - 12.00 Machine Learning Integration
12.00 - 13.00 Lunch break
13.00 - 14.30 AI Integration
14.30 - 14.45 Coffee break
14.45 - 16.30 Case Study

3. DAY 1
08.30 - 09.00 Registration
09.00 - 10.30 Introduction to Programming with Python
10.30 - 10.45 Coffee break
10.45 - 12.00 Essential Python for AI Programming
12.00 - 13.00 Lunch break
13.00 - 14.30 Machine Learning Programming
14.30 - 14.45 Coffee break
14.45 - 16.30 Machine Learning Project Deployment

4. A I E V E N T S T I M E L I N E
1956
Articial Intelligence
1959
G
eneral Problem
Solver
1966
ELIZA
N
LP
1979
R1/XCO
N
expert system
1997
D
eep
Blue
defeated
G
arry
2011
IBM
W
atson
Jeopardy
2012
D
eep
learning top
Im
ageN
et
2014
G
oogle
AlphaG
o
2018
O
penAI’s G
PT-2
2020
G
PT-3
&
AI as a service

5. I N T R O D U C T I O N
T O
PYTHON
W I C H I T 2 S

6. TA B L E O F C O N T E N T S
COMPUTER AND
PROGRAMS
INSTALLATION BASIC
PROGRAMMING
NUMERIC AND
OPERATIONS
FUNCTION DATA
STRUCTURES

7. TA B L E O F C O N T E N T S
COMPUTER AND
PROGRAMS
INSTALLATION BASIC
PROGRAMMING
NUMERIC AND
OPERATIONS
FUNCTION DATA
STRUCTURES

8. C O M P U T E R
CPU
RAM
Output
Input
HDD

9. C O M P U T E R
CPU
RAM
Output
Input
HDD

10. Program/App/Software
C O M P U T E R
CPU
RAM
Output
Input
HDD

11. M A K I N G S O F T WA R E
Program/App/Software
Programming
Language
IDE
Compiled
C,C++,Go
C#,Java,Swift
Rust,Kotlin
Interpreted
JavaScript,Python
PHP,R,Lisp

12. M A K I N G S O F T WA R E
Program/App/Software
Programming
Language
IDE

13. P Y T H O N
•General OO programming language
•Support many OSes
•Easy to code & Understand
•Multiple packages for many domains

14. W H Y P Y T H O N ?
• Popularity in general software development
• Top 10 Most Popular Programming Languages to Learn in 2020 Northe Eastern University
• 10 Most Popular Programming Languages in October 2020: Learn To Code guru99
• TIOBE Index

15. W H Y P Y T H O N ?
• Popularity in Data Science
• Top programming languages use to compete in kaggle competition kaggle
• Top 10 Data Science Programming Languages for 2020 analyticsinsight
• Top Programming Languages for Data Science in 2020 geeksforgeeks
• Top Data Science Programming Languages jelvix
• Top 7 Best Programming Languages for Data Science techbiason

16. W H Y P Y T H O N ?
• Popularity in IoT
• Top Programming Languages for IoT Projects orangesoft
• 7 Best Languages to Learn IoT Development in 2020 geeksforgeeks
• Top 8 Programming Languages for IoT (with Examples) howtocreateapps
• What programming languages rule the Internet of Things? networkworld
• Best Programming Languages for IoT in 2020 electronicsmedia

17. I N T R O D U C T I O N T O P Y T H O N
COMPUTER AND
PROGRAMS
INSTALLATION BASIC
PROGRAMMING
NUMERIC AND
OPERATIONS
FUNCTION DATA
STRUCTURES

18. P O W E R S H E L L

19. I N S TA L L AT I O N
scoop
1
anaconda3
2
Pycharm
3
Set-ExecutionPolicy RemoteSigned –Scope CurrentUser
irm get.scoop.sh | iex

20. I N S TA L L AT I O N
scoop
1
anaconda3
2
Pycharm
3
scoop bucket add extras
scoop install anaconda3

21. I N S TA L L AT I O N
scoop
1
anaconda3
2
Pycharm
3
scoop bucket add versions
scoop install pycharm-rc

25. V I D E O S

26. I N T R O D U C T I O N T O P Y T H O N
COMPUTER AND
PROGRAMS
INSTALLATION BASIC
PROGRAMMING
NUMERIC AND
OPERATIONS
FUNCTION DATA
STRUCTURES

27. A P P L I C AT I O N
P C A P P M O B I L E A P P W E B A P P

28. C O M P O N E N T S
• Names / Identifiers
• Expressions
• Output statements
• Assignment statements
• Simultaneous assignment

29. C O M P O N E N T S
• Names / Identifiers
• Expressions
• Output statements
• Assignment statements
• Simultaneous assignment
x = 20
year = 2023
weight0 = 69.20
weight1 = 71.12
weight2 = 72.34
is_clicked = False
gpa_list = [ 3.44, 3.22, 3.00, 4.00 ]
name = ‘Wichit Sombat’
name_set = { ‘Annie’, ‘Becky’, ‘Cathy’ }

30. C O M P O N E N T S
• Names / Identifiers
• Expressions
• Output statements
• Assignment statements
• Simultaneous assignment
born = 1999
age = 2023 - born
m = max(gpa_list)
is_less = m < 4.0
name = ‘Wichit’ + ‘Sombat’
eq = 9*20 == 3**9

31. C O M P O N E N T S
• Names / Identifiers
• Expressions
• Output statements
• Assignment statements
• Simultaneous assignment
print()
print('Your GPA is ', 3.44)
print('. You are ', 2561 - 2540, ' years old.')
print(4, 5, 6, 7, 8, sep=' ', end='n’)
print(4, 5, 6, 7, 8, sep=' ‘)
print(4, 5, 6, 7, 8, sep=',', end='n’)
print(4, 5, 6, 7, 8, sep=',’)
print(4, 5, 6, 7, 8, sep='', end='n’)
print(4, 5, 6, 7, 8, sep='', end='')

32. C O M P O N E N T S
• Names / Identifiers
• Expressions
• Output statements
• Assignment statements
• Simultaneous assignment
F = 9 * C / 5 + 32
x = 20
y = x ** 9
x = x + 1
x = x**2 - 2*x*y
name = 'Paul Phoenix’
user_name = input()
born = int(input(‘Year '))

33. C O M P O N E N T S
• Names / Identifiers
• Expressions
• Output statements
• Assignment statements
• Simultaneous assignment
x, y, z = 1, 2, 3
a, b, c, d = 2**2, 2**3, 2**4, 2*8 + 2
name, gpa = ‘Paul Phoenix', 3.25
x, y = y, x
x, y, z = eval( input(‘Enter x,y,z: ’) )

34. I N T R O D U C T I O N T O P Y T H O N
COMPUTER AND
PROGRAMS
INSTALLATION BASIC
PROGRAMMING
NUMERIC AND
OPERATIONS
FUNCTION DATA
STRUCTURES

35. N U M E R I C T Y P E S
F = 20
x = 23*300
print( type(F) )
gpa = 3.456
height = 8.0
print( type(height) )
int float

36. O P E R ATO R S
Add, Subtract, Multiply, Divide
+ - * /
Exponential
**
Integer division
//
Modulus
%

37. I N T R O D U C T I O N T O P Y T H O N
COMPUTER AND
PROGRAMS
INSTALLATION BASIC
PROGRAMMING
NUMERIC AND
OPERATIONS
FUNCTION DATA
STRUCTURES

38. M AT H
Numeric ceil(x), fabs(x), factorial(n), floor(x),
fmod(x, y), gcd(a, b), remainder(x, y)
Validation isClose(x, y, rel_tol=1e-09, abs_tol=0.0),
isfinite(x), isnan(x)
Exponential exp(x), log(x, base), log2(x), log10(x),
pow(x, y), sqrt(x)

39. M AT H
Trigonometric acos(x), asin(x), atan(x), atan2(y, x),
cos(x), hypot(x, y), sin(x), tan(x)
Angular degrees(x), radians(x)
Hyperbola acosh(x), asinh(x), atanh(x), cosh(x),
sinh(x), tanh(x)
Constants pi, e, tau, inf, nan

40. I N T R O D U C T I O N T O P Y T H O N
COMPUTER AND
PROGRAMS
INSTALLATION BASIC
PROGRAMMING
NUMERIC AND
OPERATIONS
FUNCTION DATA
STRUCTURES

41. DATA S T R U C T U R E S
Categories Types Example
sequence str ‘Paul Phoenix’
list [3.99, 3.22, 3.33, 3.44]
tuple (1,2,9,7)
Non-sequence set {7, 9, 8, 6}
dict { ‘Annie’: 3.99, ‘Becky’: 3.22 }

42. E S S E N T I A L P Y T H O N
F O R
A I P R O G R A M M I N G
W I C H I T 2 S

43. TA B L E O F C O N T E N T S
DOMAINS DATA
COLLECTION
DATA
MANIPULATION
MACHINE
LEARNING
DEPLOYMENT

44. TA B L E O F C O N T E N T S
DOMAINS DATA
COLLECTION
DATA
MANIPULATION
MACHINE
LEARNING
DEPLOYMENT

45. P R O J E C T

46. P R O J E C T

47. TA B L E O F C O N T E N T S
DOMAINS DATA
COLLECTION
DATA
MANIPULATION
MACHINE
LEARNING
DEPLOYMENT

48. D A T A
C O L L E C T I O N
Databases
Data Lake
Data Warehousing
Web Scraping

49. D A T A
C O L L E C T I O N
Databases
Data Lake
Data Warehousing
Web Scraping

50. D A T A
C O L L E C T I O N
Databases
Data Lake
Data Warehousing
Web Scraping

51. W E B S C R A P I N G
• https://scrapy.org/
• https://wichit2s.gitlab.io/datascience/0401WebScraping/webscraping.html

52. TA B L E O F C O N T E N T S
DOMAINS SCRAPING DATA
MANIPULATION
MACHINE
LEARNING
DEPLOYMENT

53. DATA M A N I P U L AT I O N
• https://pandas.pydata.org
• https://wichit2s.gitlab.io/datascience/04_05_Pandas/index.html

54. TA B L E O F C O N T E N T S
DOMAINS SCRAPING DATA
MANIPULATION
MACHINE
LEARNING
DEPLOYMENT

55. M A C H I N E L E A R N I N G
• https://scikit-learn.org
• https://scikit-learn.org/stable/_static/ml_map.png

56. M A C H I N E L E A R N I N G
• https://wichit2s.gitlab.io/datascience/11_basic_ml/index.html
• https://wichit2s.gitlab.io/datascience/12_regression/index.html
• https://wichit2s.gitlab.io/datascience/13_pca/index.html
• https://wichit2s.gitlab.io/datascience/14_classification/index.html

57. TA B L E O F C O N T E N T S
DOMAINS DATA
COLLECTION
DATA
MANIPULATION
MACHINE
LEARNING
DEPLOYMENT

58. D E P LOY M E N T
• Web-based Cloud Hosting
• AI-as-a-Service Platform
• On-premise Hosting
• Streamlit https://streamlit.io
• Dash https://dash.plotly.com
• Microservices
• Fastapi https://fastapi.tiangolo.com/
• Flask https://flask.palletsprojects.com/
• Full-stack web application
• https://www.djangoproject.com/
• Data warehousing à Datalake opensource solutions