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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](https://image.slidesharecdn.com/2021-11-18machinelearningwithpython-211119112406/85/Machine-Learning-with-Python-11-320.jpg)
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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
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](https://image.slidesharecdn.com/2021-11-18machinelearningwithpython-211119112406/85/Machine-Learning-with-Python-12-320.jpg)
![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](https://image.slidesharecdn.com/2021-11-18machinelearningwithpython-211119112406/85/Machine-Learning-with-Python-14-320.jpg)
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Machine Learning with Python
This document contains the slides for a webinar on machine learning with Python presented by Achmad Mardiansyah from GLC Networks, Indonesia. The webinar agenda includes an introduction to machine learning and Python, a review of prerequisite Python knowledge, a discussion of machine learning concepts, and a live coding practice session. The slides provide background on the presenter and GLC Networks, an overview of machine learning methods, and instructions for participating in the live coding demo.
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Machine Learning with Python
- 1. www.glcnetworks.com Machine learning with python GLCWebinar, 18 nov 2021 Achmad Mardiansyah achmad@glcnetworks.com GLC Networks, Indonesia 1 Source: python.org
- 2. www.glcnetworks.com Agenda ● Introduction ● Reviewprerequisite knowledge ● Machine learning ● Python for machine learning ● Live practice ● Q & A 2
- 3.
- 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 presentationsome prerequisite knowledge ● We assume you already understand: ○ Python programming ○ Machine learning 9
- 10.
- 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 ● Codeexecuted 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: usingfunctions ● 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: usingexternal 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
- 15.
- 16. www.glcnetworks.com Machine learning vsdata science 16
- 17. www.glcnetworks.com From data tointelligence 17
- 18. www.glcnetworks.com Data analyses level 18 Thepresent is result from the past. AND The past the the key for the future.
- 19. www.glcnetworks.com Machine learning Purpose: ● Learnfrom the data with provided methods ● Get insight from the data Methods: ● Supervised learning ● Unsupervised learning 19
- 20. www.glcnetworks.com 1. Estimation: ○ LinearRegression, 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
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- 23. www.glcnetworks.com preparation ● SSH client ●SSH parameters ○ SSH address ○ SSH port ○ SSH username ○ SSH password 23
- 24.
- 25. www.glcnetworks.com Interested? Just cometo 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