Webinar topic: Machine Learning with Python Presenter: Achmad Mardiansyah In this webinar series, We are discussing Machine Learning with Python Please share your feedback or webinar ideas here: http://bit.ly/glcfeedback Check our schedule for future events: https://www.glcnetworks.com/en/schedule/ Follow our social media for updates: Facebook, Instagram, YouTube Channel, and telegram Recording available on Youtube https://youtu.be/wXCM_smb8M8

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Machine learning
with python
(opencv)
GLC webinar, 19 Aug 2021
Achmad Mardiansyah
achmad@glcnetworks.com
GLC Networks, Indonesia
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Agenda
● Introduction
● Review prerequisite knowledge
● Machine learning
● opencv
● Live practice
● Q & A
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introduction
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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
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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
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Past experience
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● 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

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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
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Please introduce yourself
● Your name
● Your company/university?
● Your networking experience?
● Your mikrotik experience?
● Your expectation from this course?
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Prerequisite
● This presentation some prerequisite knowledge
● We assume you already know:
○ How networking works
○ Able to setup a simple network
○ Familiar network devices
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Review prerequisite knowledge
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Basic stuffs
● Variable
● Command (keywords)
● Operation (+, -, *, /, %)
● Array (list, tuple, dictionary)
● Loop
○ For loop
○ While loop
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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

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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
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x = "Python is "
y = "awesome"
z = x + y
print(z)
fruits = ["apple", "banana", "cherry"]
for x in fruits:
print(x)
Source: www.w3schools.com

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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.
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def my_function(fname, lname):
print(fname + " " + lname)
my_function("Emil", "Refsnes")
Source: www.w3schools.com

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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.
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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

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Machine learning
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Machine learning vs data science
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From data to intelligence
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Data analyses level
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The present is result from the past.
AND
The past the the key for the future.

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Machine learning
Purpose:
● Learn from the data with provided methods
● Get insight from the data
Methods:
● Supervised learning
● Unsupervised learning
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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
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Data Mining algoritm

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Open Computer Vision
(OpenCV)
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What is OpenCV
● A library (python, others)
● Capabilities:
○ Image processing
○ Video processing
● Machine learning related
○ Face detection
○ Classification
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Some examples
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LIVE practice
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preparation
● SSH client
● SSH parameters
○ SSH address
○ SSH port
○ SSH username
○ SSH password
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Q & A
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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
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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/
● Recording (youtube): https://www.youtube.com/c/GLCNetworks
● Stay tune with our schedule
● Any questions?
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