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.
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
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
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
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