🔖 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

🤖 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.

🚀 711Proxy provides real residential IPs across 200+ countries and regions, with SOCKS5 support and sticky sessions — useful for data collection, testing, and other location-based ML workflows.

🎁 1GB free for testing
New users can use 711TRIAL to get 1GB of residential proxy traffic.
👉 https://www.711proxy.com
After registration, contact 711Proxy support and mention “711TRIAL” to claim the trial.
Available to eligible new users.

Uniface

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

https://github.com/yakhyo/uniface

https://xn--r1a.website/MachineLearning9

Cheat sheet for Scapy: creating and configuring packets, working with IP addresses, sending and receiving packets, sniffing traffic, fuzzing, and viewing packet structure. 📦🔍

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

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If you're just starting to learn machine learning and want to delve deeper into the mathematics required for machine learning and deep learning, I recommend trying this platform. It's something like LeetCode for machine learning.

This is not an advertisement: I personally used it and decided to share it with you.

https://deep-ml.com

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Pandas vs Polars — 14-section course cheatshee

https://xn--r1a.website/MachineLearning9

Directions for the development of hardware for deep learning – a lecture by Bill Dally at Georgia Tech, 2024.

youtu.be/gofI47kfD28

https://xn--r1a.website/MachineLearning9

"Trigonometry" is a free, open-source textbook on trigonometry, with over 1000 pages, covering the subject from basic concepts to advanced topics.

The book covers angles and triangles, trigonometric relationships, the unit circle, sine, cosine, and tangent functions, graphs and their transformations, radians, solving triangles, the sine and cosine theorems, trigonometric identities and equations, inverse trigonometric functions, and formulas for the sum, difference, and double angle.

Later chapters also cover vectors, the dot product, polar coordinates, and complex numbers in polar form.

Each section contains numerous exercises, making the textbook particularly useful for reinforcing theoretical knowledge through practical application as you progress through the material.

https://louis.pressbooks.pub/trigonometry/

"Learning Mathematics: Why Memory, Practice, and Technique are More Important than Talent"

To master mathematics, it is necessary to memorize a large amount of information, rules, methods of simplification, and problem-solving techniques. There is no other way. Theory is useful, but in moderation.

It's like learning a language. If you focus too much on grammar, you will never learn to speak it fluently.

https://algebrica.org/learning-mathematics/

I found a great resource for interactive learning about machine learning and AI – VizLearn.

You can experiment with gradient descent, SVM, PCA, the Bayesian method, BPE tokenization, Q/K/V, KV-cache, quantization, and much more. You can change the input data and see how the calculations themselves change.

It's free and doesn't require registration.

https://vizlearn.in

https://xn--r1a.website/MachineLearning9