πRound 2 β 14-Day CCNA & CCNP Study Sprint!
Our first 21-Day Sprint was a huge success β we saw amazing check-ins, great discussions, and a community that truly learned together. π
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π Sprint Period: Sep 14 β Sep 27 (UTC+8)
π How It Works:
β DM admin: "I'M IN + cert name" (e.g., I'M IN CCNA 200-301)
β‘ Check in 12 out of 14 days β win prizes π
π Prizes (First come, first served!):
1οΈβ£ $10 SPOTO Universal Coupon Γ2
2οΈβ£ Cisco SD-Access Training ($150 value) Γ1
3οΈβ£ Cisco SD-WAN Training ($150 value) Γ1
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5οΈβ£ Free Cisco Learning Pack β unlimited for all finishers π
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JOIN 14 Days Study Sprint: https://chat.whatsapp.com/KZrAj2HZ3Y5K9UhhNhrApf
Our first 21-Day Sprint was a huge success β we saw amazing check-ins, great discussions, and a community that truly learned together. π
Now we're back with a faster, tighter 14-Day Sprint β same energy, same prizes, easier to finish! πͺ
π Sprint Period: Sep 14 β Sep 27 (UTC+8)
π How It Works:
β DM admin: "I'M IN + cert name" (e.g., I'M IN CCNA 200-301)
β‘ Check in 12 out of 14 days β win prizes π
π Prizes (First come, first served!):
1οΈβ£ $10 SPOTO Universal Coupon Γ2
2οΈβ£ Cisco SD-Access Training ($150 value) Γ1
3οΈβ£ Cisco SD-WAN Training ($150 value) Γ1
4οΈβ£ CCNA Training Pro Package ($59.99 value) Γ10
5οΈβ£ Free Cisco Learning Pack β unlimited for all finishers π
β Referral Bonus: Invite a friend β Get FREE EXAM DEMO
π» DM admin to register: https://wa.me/8619559123054
JOIN 14 Days Study Sprint: https://chat.whatsapp.com/KZrAj2HZ3Y5K9UhhNhrApf
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"Introduction to Graph Theory" by Darij Grinberg is an advanced resource with theorems, an algebraic approach, and plenty of exercises.
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β Link to the book
https://chapterpal.com/ebook/473a1c1b-c584-4430-b986-cd9c57085e67
"Introduction to Graph Theory" by Darij Grinberg is an advanced resource with theorems, an algebraic approach, and plenty of exercises.
You can read it for free with an AI tutor.
β Link to the book
https://chapterpal.com/ebook/473a1c1b-c584-4430-b986-cd9c57085e67
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Forwarded from Machine Learning with Python
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π Python Reference for Data Science and Machine Learning
PY-DS-ML provides practical resources on 30 popular Python libraries for data analysis and machine learning.
You can quickly find the commands, syntax, and examples you need without having to search through extensive documentation.
It includes a search function, organization by difficulty level, cheat sheets, and checklists.
Link: https://py-ds-ml.ru/
#russian #ML
PY-DS-ML provides practical resources on 30 popular Python libraries for data analysis and machine learning.
You can quickly find the commands, syntax, and examples you need without having to search through extensive documentation.
It includes a search function, organization by difficulty level, cheat sheets, and checklists.
Link: https://py-ds-ml.ru/
#russian #ML
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The correct way to learn is like this:
You simply need to solve problems and work on projects.
This approach was used in one of the best books on the fundamentals of statistics β and, incidentally, one of the few that I actually read.
It's very simple:
You read a chapter.
You solve all the problems related to that topic.
https://www.statlearning.com/
You simply need to solve problems and work on projects.
This approach was used in one of the best books on the fundamentals of statistics β and, incidentally, one of the few that I actually read.
It's very simple:
You read a chapter.
You solve all the problems related to that topic.
https://www.statlearning.com/
β€7
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This AI service helps you break down mathematical problems step-by-step, with explanations for each action. You can enter the problem as text, take a photo, or upload a PDF β Mathos will recognize the problem, suggest a solution, and, if necessary, create a graph. You can request not a ready-made answer, but only a hint, to continue solving the problem yourself.
https://t.me/MachineLearning9
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β€5π1
π€ A Practical Tip for ML Data Collection
When building a machine learning project, getting enough useful data is often just as important as the model itself.
If you're collecting public web data for a dataset, you may need to access the same source from different locations or test how location affects the data returned.
A residential proxy can help with this by routing your requests through IPs from different regions.
For example, with Python:
Replace USER, PASSWORD, HOST, and PORT with your proxy credentials.
π 711Proxy provides real residential IPs across 200+ countries and regions, with SOCKS5 support and sticky sessions β useful for data collection, testing, and other location-based ML workflows.
π 1GB free for testing
New users can use 711TRIAL to get 1GB of residential proxy traffic.
π https://www.711proxy.com
After registration, contact 711Proxy support and mention β711TRIALβ to claim the trial.
Available to eligible new users.
When building a machine learning project, getting enough useful data is often just as important as the model itself.
If you're collecting public web data for a dataset, you may need to access the same source from different locations or test how location affects the data returned.
A residential proxy can help with this by routing your requests through IPs from different regions.
For example, with Python:
import requests
proxies = {
"http": "http://USER:PASSWORD@HOST:PORT",
"https": "http://USER:PASSWORD@HOST:PORT"
}
response = requests.get(
"https://example.com",
proxies=proxies
)
print(response.status_code)
Replace USER, PASSWORD, HOST, and PORT with your proxy credentials.
π 711Proxy provides real residential IPs across 200+ countries and regions, with SOCKS5 support and sticky sessions β useful for data collection, testing, and other location-based ML workflows.
π 1GB free for testing
New users can use 711TRIAL to get 1GB of residential proxy traffic.
π https://www.711proxy.com
After registration, contact 711Proxy support and mention β711TRIALβ to claim the trial.
Available to eligible new users.
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Machine Learning pinned Β«π€ A Practical Tip for ML Data Collection When building a machine learning project, getting enough useful data is often just as important as the model itself. If you're collecting public web data for a dataset, you may need to access the same source from differentβ¦Β»