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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Сітка:📈 Аналітичний огляд Telegram-каналу Machine Learning with Python
Канал Machine Learning with Python (@codeprogrammer) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 68 536 підписників, посідаючи 2 402 місце в категорії Освіта та 4 600 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 68 536 підписників.
За останніми даними від 29 жовтня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 74, а за останні 24 години на -5, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 3.84%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.47% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 2 629 переглядів. Протягом першої доби публікація в середньому набирає 1 007 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 9.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як insidead, learning, degree, evaluation, algorithm.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Завдяки високій частоті оновлень (останні дані отримано 30 жовтня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Освіта.
68 536
Підписники
-524 години
-847 днів
+7430 днів
Архів дописів
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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
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Admin: @HusseinSheikho
