Machine Learning with Python

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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

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Сеть:Machine Learning with Python Индия5 035 Образование2 411...

📈 Аналитический обзор Telegram-канала Machine Learning with Python

Канал Machine Learning with Python (@codeprogrammer) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 67 820 подписчиков, занимая 2 411 место в категории Образование и 5 035 место в регионе Индия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 67 820 подписчиков.

Согласно последним данным от 06 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 55, а за последние 24 часа — -2, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 2.54%. В первые 24 часа после публикации контент обычно набирает 2.53% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 1 720 просмотров. В течение первых суток публикация набирает 1 714 просмотров.
Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 6.
Тематические интересы: Контент сосредоточен на ключевых темах, таких как insidead, learning, degree, evaluation, algorithm.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Благодаря высокой частоте обновлений (последние данные получены 08 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.

67 820

Подписчики

-224 часа

+327 дней

+5530 день

1 720

Просмотры поста

~ 1 71424 часа

~ 2 28448 часов

2.54%

Коэффициент вовлеченности

~ 3

Постов в день

Ads index

beta

Архив постов

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Amazing NumPy Cheat Sheet Snippet with 100 exercises for practicing the concept to get hands on to clear the coding round in the interviews #NumPy #codingexams https://t.me/DataScienceM

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Learn Complete #MatPlotLib with most asked #interview questions 👉 @codeprogrammer

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Have you tried running the code? yes 👍 | No 👎

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Create a Wi-Fi QR code in Python in a couple of seconds

pip install wifi_qrcode_generator

import wifi_qrcode_generator.generator
from PIL import Image

ssid = "CLCoding_WIFI"
password = "supersecret123"
security = "WPA"

from wifi_qrcode_generator.generator import wifi_qrcode
qr = wifi_qrcode(ssid, False, security, password)

qr.make_image().save("wifi_qr.png")
Image.open("wifi_qr.png")

👉 @codeprogrammer

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❔ Interview question What do globals() and locals() do? Answer: Functions globals() and locals() return a dictionary with variables of the current scope. ▶️ globals() — provides access to all global names in the current module (i.e., variables, functions, and classes defined at the top level) ▶️ locals() — returns local variables in the current context: inside a function, class, or block They are useful for dynamic analysis, debugging, metaprogramming, and when working with eval() / exec(). tags: #interview ➡ @codeprogrammer

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Your interaction with us drives us more free information and knowledge

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This GitHub repo is a gold mine for EVERY data scientist! (full of hands-on and interactive tutorials) DS Interactive Python provides several dashboards to interactively learn about statistics, ML models, and other related theoretical concepts! Some key topics you can understand interactively: • PCA • Bagging and boosting • Linear regression and OLS • Bayesian and frequentist statistics • confidence intervals • clustering (kmeans, spectral clustering, etc.) • central limit theorem • neural networks (the backpropagation animation is really good) • and many more. Link: https://github.com/GeostatsGuy/DataScienceInteractivePython https://t.me/CodeProgrammer

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Repost from Python Courses & Resources

Create artificial intelligence using Python In the video the author explains the basics of neural networks and shows how to implement your own simple #AI from scratch in #Python

https://t.me/Python53

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Repost from Machine Learning with Python

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Data Science Formulas Cheat Sheet.pdf1.75 KB

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https://t.me/DataScienceY

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