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Machine Learning with Python

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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📈 Аналитический обзор Telegram-канала Machine Learning with Python

Канал Machine Learning with Python (@codeprogrammer) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 67 812 подписчиков, занимая 2 404 место в категории Образование и 5 049 место в регионе Индия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 67 812 подписчиков.

Согласно последним данным от 05 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 77, а за последние 24 часа — 9, при этом общий охват остаётся высоким.

  • Статус верификации: Не верифицирован
  • Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 2.60%. В первые 24 часа после публикации контент обычно набирает 2.50% реакций от общего числа подписчиков.
  • Охват публикаций: В среднем каждый пост получает 1 767 просмотров. В течение первых суток публикация набирает 1 695 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 07 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.

67 812
Подписчики
+924 часа
+587 дней
+7730 день
Архив постов
nature papers: 1400$ Q1 and  Q2 papers    900$ Q3 and Q4 papers   500$ Doctoral thesis (complete)    700$ M.S thesis         300$ paper simulation   200$ Contact me https://t.me/m/-nTmpj5vYzNk

200$ to 20k$ SOL Challenge! As promised, i will do another challenge for those who missed the previous one! Last one we compl
200$ to 20k$ SOL Challenge! As promised, i will do another challenge for those who missed the previous one! Last one we completed in 6 days, let’s do this one even quicker! Join my free group Before closing 👇 https://t.me/+DAKLP7eUy9Y3ZjY0 #ad InsideAds

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This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visua
This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0https://t.me/Codeprogrammer

Learn how LLMs work in less than 10 minutes And honestly? This is probably the best visualization of #LLMs ever made. https://t.me/Python53

“I almost lost my main accounts to bans—until I found this.” Managing dozens of profiles seemed impossible. Then I tried GoLo
“I almost lost my main accounts to bans—until I found this.” Managing dozens of profiles seemed impossible. Then I tried GoLogin: safe, fast, and insanely reliable. Thousands already trust it to run ads without fear. Ready to change how you manage accounts? Try it here before your competitors do. #ad InsideAds

Awesome open-source project to learn more about Generative Adversarial Networks. We found this interactive website that shows
Awesome open-source project to learn more about Generative Adversarial Networks. We found this interactive website that shows you visually how #GANs work. GAN Lab Website: https://lnkd.in/eYV8QvrJ https://t.me/CodeProgrammer 🩷

For those interested in joining our Premium channel, which contains over 5,000 books and 200 courses in machine learning, programming languages, data analysis, LLM, and other related fields with daily updates> We offer a 15-minute free trial to explore the channel's content. Afterward, you can subscribe to the channel for $50 – a permanent subscription. Contact me: t.me/HusseinSheikho

All assignments for the #Stanford The Modern Software Developer course are now available online. This is the first full-fledg
All assignments for the #Stanford The Modern Software Developer course are now available online. This is the first full-fledged university course that covers how code-generative #LLMs are changing every stage of the development lifecycle. The assignments are designed to take you from a beginner to a confident expert in using AI to boost productivity in development. Enjoy your studies! ✌️ (https://github.com/mihail911/modern-software-dev-assignments

I never believed a simple bot could change my workflow — until I met @MegaRenamerV2Bot. It renames thousands of MEGA files in
I never believed a simple bot could change my workflow — until I met @MegaRenamerV2Bot. It renames thousands of MEGA files in seconds, saving me hours every day. No more manual hassle, just pure efficiency. Curious how it works? Discover the secret here 👉 https://t.me Join RSC Bots and step up your file game! #ad InsideAds

How to test code without a real database During unit testing, connecting to a real DB is unnecessary: • tests run slowly • be
How to test code without a real database
During unit testing, connecting to a real DB is unnecessary: • tests run slowly • become unstable • require a working server
It is much better to mock the call to pandas.read_sql and return dummy data Example function:
def query_user_data(user_id):
    query = f"SELECT id, name FROM users WHERE id = {user_id}"
    return pd.read_sql(query, "postgresql://localhost/mydb")
Test with mock:
from unittest.mock import patch
import pandas as pd

@patch("pandas.read_sql")
def test_database_query_mocked(mock_read_sql):
    mock_read_sql.return_value = pd.DataFrame(
        {"id": [123], "name": ["Alice"]}
    )

    result = query_user_data(user_id=123)
    assert result["name"].iloc[0] == "Alice"
This way you test only the business logic — quickly, reliably, and without unnecessary dependencies https://t.me/CodeProgrammer

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200$ to 20k$ SOL Challenge! As promised, i will do another challenge for those who missed the previous one! Last one we compl
200$ to 20k$ SOL Challenge! As promised, i will do another challenge for those who missed the previous one! Last one we completed in 6 days, let’s do this one even quicker! Join my free group Before closing 👇 https://t.me/+DAKLP7eUy9Y3ZjY0 #ad InsideAds

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