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www.glcnetworks.com
Machine learning
with python
GLC Webinar,
18 nov 2021
Achmad Mardiansyah
achmad@glcnetworks.com
GLC Networks, Indonesia
1
Source: python.org
www.glcnetworks.com
Agenda
● Introduction
● Review prerequisite knowledge
● Machine learning
● Python for machine learning
● Live practice
● Q & A
2
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introduction
3
www.glcnetworks.com
What is GLC?
● Garda Lintas Cakrawala (www.glcnetworks.com)
● Based in Bandung, Indonesia
● Areas: Training, IT Consulting
● Certified partner for: Mikrotik, Ubiquity, Linux foundation
● Product: GLC radius manager
● Regular event
4
www.glcnetworks.com
Trainer Introduction
● Name: Achmad Mardiansyah
● Base: bandung, Indonesia
● Linux user since 1999, mikrotik user since 2007, UBNT
2011
● Mikrotik Certified Trainer
(MTCNA/RE/WE/UME/INE/TCE/IPv6)
● Mikrotik/Linux Certified Consultant
● Website contributor: achmadjournal.com, mikrotik.tips,
asysadmin.tips
● More info:
http://au.linkedin.com/in/achmadmardiansyah
5
www.glcnetworks.com
Past experience
6
● 2021 (Congo DRC, Malaysia): network support,
radius/billing integration
● 2020 (Congo DRC, Malaysia): IOT integration,
network automation
● 2019, Congo (DRC): build a wireless ISP from
ground-up
● 2018, Malaysia: network revamp, develop billing
solution and integration, setup dynamic routing
● 2017, Libya (north africa): remote wireless migration
for a new Wireless ISP
● 2016, United Kingdom: workshop for wireless ISP,
migrating a bridged to routed network
www.glcnetworks.com
About GLC webinar?
● First webinar: january 1, 2010 (title:
tahun baru bersama solaris - new year
with solaris OS)
● As a sharing event with various topics:
linux, networking, wireless, database,
programming, etc
● Regular schedule
● Irregular schedule: as needed
● Checking schedule:
http://www.glcnetworks.com/schedule
● You are invited to be a presenter
○ No need to be an expert
○ This is a forum for sharing: knowledge,
experiences, information
7
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Please introduce yourself
● Your name
● Your company/university?
● Your networking experience?
● Your mikrotik experience?
● Your expectation from this course?
8
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Prerequisite
● This presentation some prerequisite knowledge
● We assume you already understand:
○ Python programming
○ Machine learning
9
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Review prerequisite knowledge
10
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Basic stuffs
● Variable
● Command (keywords)
● Operation (+, -, *, /, %)
● Array (list, tuple, dictionary)
● Loop
○ For loop
○ While loop
11
x = "Python is "
y = "awesome"
z = x + y
print(z)
fruits = ["apple", "banana", "cherry"]
for x in fruits:
print(x)
i = 1
while i < 6:
print(i)
i += 1
Source: www.w3schools.com
www.glcnetworks.com
Procedural Python
● Code executed sequentially
● Easy to understand
● Faster to implement
● Natural
● Program lines can be very long
● Need a way to architect to:
○ Manage our code physically
○ Manage our application logic
12
x = "Python is "
y = "awesome"
z = x + y
print(z)
fruits = ["apple", "banana", "cherry"]
for x in fruits:
print(x)
Source: www.w3schools.com
www.glcnetworks.com
Efficient code: using functions
● A function is a block of code which
only runs when it is called.
● You can pass data, known as
parameters, into a function.
● A function can return data as a
result.
13
def my_function(fname, lname):
print(fname + " " + lname)
my_function("Emil", "Refsnes")
Source: www.w3schools.com
www.glcnetworks.com
Efficient code: using external functions (library)
● Consider a module to be the same
as a code library.
● A file containing a set of functions
you want to include in your
application.
14
vim mymodule.py
person1 = {
"name": "John",
"age": 36,
"country": "Norway"
}
vim myapp.py
import mymodule
a = mymodule.person1["age"]
print(a)
Source: www.w3schools.com
www.glcnetworks.com
Machine learning
15
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Machine learning vs data science
16
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From data to intelligence
17
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Data analyses level
18
The present is result from the past.
AND
The past the the key for the future.
www.glcnetworks.com
Machine learning
Purpose:
● Learn from the data with provided methods
● Get insight from the data
Methods:
● Supervised learning
● Unsupervised learning
19
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1. Estimation:
○ Linear Regression, Neural Network, Support Vector Machine,
Deep Learning, etc
2. Prediction/Forecasting:
○ Linear Regression, Neural Network, Support Vector Machine,
Deep Learning, etc
3. Classification:
○ Decision Tree (CART, ID3, C4.5, Credal DT, Credal C4.5,
DynamicCC4.5), Naive Bayes, K-Nearest Neighbor, Linear
Discriminant Analysis, Logistic Regression, etc
4. Clustering:
○ K-Means, K-Medoids, Self-Organizing Map (SOM), Fuzzy
C-Means, etc
5. Association:
○ FP-Growth, A Priori, Coefficient of Correlation, Chi Square, etc
20
Data Mining algoritm
www.glcnetworks.com
21
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LIVE practice
22
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preparation
● SSH client
● SSH parameters
○ SSH address
○ SSH port
○ SSH username
○ SSH password
23
www.glcnetworks.com
Q & A
24
www.glcnetworks.com
Interested? Just come to our training...
● Topics are arranged in systematic and logical way
● You will learn from experienced teacher
● Not only learn the materials, but also sharing experiences, best-practices, and
networking
25
www.glcnetworks.com
End of slides
● Thank you for your attention
● Please submit your feedback: http://bit.ly/glcfeedback
● Find our further event on our website : https://www.glcnetworks.com/en/
● Like our facebook page: https://www.facebook.com/glcnetworks
● Slide: https://www.slideshare.net/glcnetworks/
● Discord (bahasa indonesia): (https://discord.gg/6MZ3KUHHBX)
● Recording (youtube): https://www.youtube.com/c/GLCNetworks
● Stay tune with our schedule
● Any questions?
26

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Machine Learning with Python

  • 1. www.glcnetworks.com Machine learning with python GLC Webinar, 18 nov 2021 Achmad Mardiansyah achmad@glcnetworks.com GLC Networks, Indonesia 1 Source: python.org
  • 2. www.glcnetworks.com Agenda ● Introduction ● Review prerequisite knowledge ● Machine learning ● Python for machine learning ● Live practice ● Q & A 2
  • 4. www.glcnetworks.com What is GLC? ● Garda Lintas Cakrawala (www.glcnetworks.com) ● Based in Bandung, Indonesia ● Areas: Training, IT Consulting ● Certified partner for: Mikrotik, Ubiquity, Linux foundation ● Product: GLC radius manager ● Regular event 4
  • 5. www.glcnetworks.com Trainer Introduction ● Name: Achmad Mardiansyah ● Base: bandung, Indonesia ● Linux user since 1999, mikrotik user since 2007, UBNT 2011 ● Mikrotik Certified Trainer (MTCNA/RE/WE/UME/INE/TCE/IPv6) ● Mikrotik/Linux Certified Consultant ● Website contributor: achmadjournal.com, mikrotik.tips, asysadmin.tips ● More info: http://au.linkedin.com/in/achmadmardiansyah 5
  • 6. www.glcnetworks.com Past experience 6 ● 2021 (Congo DRC, Malaysia): network support, radius/billing integration ● 2020 (Congo DRC, Malaysia): IOT integration, network automation ● 2019, Congo (DRC): build a wireless ISP from ground-up ● 2018, Malaysia: network revamp, develop billing solution and integration, setup dynamic routing ● 2017, Libya (north africa): remote wireless migration for a new Wireless ISP ● 2016, United Kingdom: workshop for wireless ISP, migrating a bridged to routed network
  • 7. www.glcnetworks.com About GLC webinar? ● First webinar: january 1, 2010 (title: tahun baru bersama solaris - new year with solaris OS) ● As a sharing event with various topics: linux, networking, wireless, database, programming, etc ● Regular schedule ● Irregular schedule: as needed ● Checking schedule: http://www.glcnetworks.com/schedule ● You are invited to be a presenter ○ No need to be an expert ○ This is a forum for sharing: knowledge, experiences, information 7
  • 8. www.glcnetworks.com Please introduce yourself ● Your name ● Your company/university? ● Your networking experience? ● Your mikrotik experience? ● Your expectation from this course? 8
  • 9. www.glcnetworks.com Prerequisite ● This presentation some prerequisite knowledge ● We assume you already understand: ○ Python programming ○ Machine learning 9
  • 11. www.glcnetworks.com Basic stuffs ● Variable ● Command (keywords) ● Operation (+, -, *, /, %) ● Array (list, tuple, dictionary) ● Loop ○ For loop ○ While loop 11 x = "Python is " y = "awesome" z = x + y print(z) fruits = ["apple", "banana", "cherry"] for x in fruits: print(x) i = 1 while i < 6: print(i) i += 1 Source: www.w3schools.com
  • 12. www.glcnetworks.com Procedural Python ● Code executed sequentially ● Easy to understand ● Faster to implement ● Natural ● Program lines can be very long ● Need a way to architect to: ○ Manage our code physically ○ Manage our application logic 12 x = "Python is " y = "awesome" z = x + y print(z) fruits = ["apple", "banana", "cherry"] for x in fruits: print(x) Source: www.w3schools.com
  • 13. www.glcnetworks.com Efficient code: using functions ● A function is a block of code which only runs when it is called. ● You can pass data, known as parameters, into a function. ● A function can return data as a result. 13 def my_function(fname, lname): print(fname + " " + lname) my_function("Emil", "Refsnes") Source: www.w3schools.com
  • 14. www.glcnetworks.com Efficient code: using external functions (library) ● Consider a module to be the same as a code library. ● A file containing a set of functions you want to include in your application. 14 vim mymodule.py person1 = { "name": "John", "age": 36, "country": "Norway" } vim myapp.py import mymodule a = mymodule.person1["age"] print(a) Source: www.w3schools.com
  • 18. www.glcnetworks.com Data analyses level 18 The present is result from the past. AND The past the the key for the future.
  • 19. www.glcnetworks.com Machine learning Purpose: ● Learn from the data with provided methods ● Get insight from the data Methods: ● Supervised learning ● Unsupervised learning 19
  • 20. www.glcnetworks.com 1. Estimation: ○ Linear Regression, Neural Network, Support Vector Machine, Deep Learning, etc 2. Prediction/Forecasting: ○ Linear Regression, Neural Network, Support Vector Machine, Deep Learning, etc 3. Classification: ○ Decision Tree (CART, ID3, C4.5, Credal DT, Credal C4.5, DynamicCC4.5), Naive Bayes, K-Nearest Neighbor, Linear Discriminant Analysis, Logistic Regression, etc 4. Clustering: ○ K-Means, K-Medoids, Self-Organizing Map (SOM), Fuzzy C-Means, etc 5. Association: ○ FP-Growth, A Priori, Coefficient of Correlation, Chi Square, etc 20 Data Mining algoritm
  • 23. www.glcnetworks.com preparation ● SSH client ● SSH parameters ○ SSH address ○ SSH port ○ SSH username ○ SSH password 23
  • 25. www.glcnetworks.com Interested? Just come to our training... ● Topics are arranged in systematic and logical way ● You will learn from experienced teacher ● Not only learn the materials, but also sharing experiences, best-practices, and networking 25
  • 26. www.glcnetworks.com End of slides ● Thank you for your attention ● Please submit your feedback: http://bit.ly/glcfeedback ● Find our further event on our website : https://www.glcnetworks.com/en/ ● Like our facebook page: https://www.facebook.com/glcnetworks ● Slide: https://www.slideshare.net/glcnetworks/ ● Discord (bahasa indonesia): (https://discord.gg/6MZ3KUHHBX) ● Recording (youtube): https://www.youtube.com/c/GLCNetworks ● Stay tune with our schedule ● Any questions? 26