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:📈 Аналитический обзор Telegram-канала Machine Learning with Python
Канал Machine Learning with Python (@codeprogrammer) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 68 523 подписчиков, занимая 2 402 место в категории Образование и 4 600 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 68 523 подписчиков.
Согласно последним данным от 29 октября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 74, а за последние 24 часа — -5, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 3.84%. В первые 24 часа после публикации контент обычно набирает 1.47% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 2 629 просмотров. В течение первых суток публикация набирает 1 007 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 9.
- 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 30 октября, 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.
68 523
Подписчики
-524 часа
-847 дней
+7430 дней
Архив постов
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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 serverIt 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")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"
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Repost from Learn Python Coding
Checking the reliability of a password with Python!
Sometimes you need to quickly check how secure a password is. Let's look at a simple example using regular expressions - a good opportunity to practice with
re and conditional logic.
Import the module:
import redef check_password_strength(password):
length = len(password) >= 8
upper = re.search(r"[A-Z]", password)
lower = re.search(r"[a-z]", password)
digit = re.search(r"\d", password)
special = re.search(r"[@$!%*?&]", password)
if all([length, upper, lower, digit, special]):
return "✅ Reliable password"
else:
return "⚠️ Weak password"print(check_password_strength("Qwerty123"))
print(check_password_strength("Qw!8zYt@1"))⚠️ Weak password
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Repost from Github Top Repositories
Create a perfect resume without messing with templates.
You write content in YAML and generate a PDF.
The project is called RenderCV, and it's open-source
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Admin: @HusseinSheikho
