Machine Learning
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Real Machine Learning β€” simple, practical, and built on experience.
Learn step by step with clear explanations and working code.

Admin: @HusseinSheikho || @Hussein_Sheikho
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πŸ”– Free book on graph theory

"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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πŸ”– 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
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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 – 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/
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πŸ€” Mathos AI β€” a neural network for solving and learning mathematics!

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.

πŸ“Œ Here's the link: mathos.ai

https://t.me/MachineLearning9
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πŸ€– 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:
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.

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Available to eligible new users.
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Machine Learning pinned Β«πŸ€– 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…»
OpenAI researcher Alice Liu went through 57 interviews before being hired, and then openly shared her entire preparation and job search journey.

If you're preparing for Research Scientist or MTS positions, this is one of the most comprehensive resources available.

You can use her notes directly, or simply use the list of topics to study them yourself. This kind of information is rarely published.

Notes on LLMs:
https://alisawuffles.notion.site/alisa-s-book-of-llms

Mathematics:
https://alisawuffles.notion.site/math-notes

Analysis of the job search and interview process:
https://alisawuffles.github.io/blog/job-search/

https://t.me/MachineLearning9 πŸ«€
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Uniface

Automate face detection, recognition, and analysis of key facial landmarks with the Uniface Python library.

https://github.com/yakhyo/uniface

https://t.me/MachineLearning9
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Cheat sheet for Scapy: creating and configuring packets, working with IP addresses, sending and receiving packets, sniffing traffic, fuzzing, and viewing packet structure. πŸ“¦πŸ”

#Scapy #Networking #Python #CyberSecurity #PacketCrafting #Hacking

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