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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

Network:Machine Learning with Python India4 600 Education2 402...

📈 Analytical overview of Telegram channel Machine Learning with Python

Channel Machine Learning with Python (@codeprogrammer) in the English language segment is an active participant. Currently, the community unites 68 536 subscribers, ranking 2 402 in the Education category and 4 600 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 68 536 subscribers.

According to the latest data from 29 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 74 over the last 30 days and by -5 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 3.84%. Within the first 24 hours after publication, content typically collects 1.47% reactions from the total number of subscribers.
Post reach: On average, each post receives 2 629 views. Within the first day, a publication typically gains 1 007 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 9.
Thematic interests: Content is focused on key topics such as insidead, learning, degree, evaluation, algorithm.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Thanks to the high frequency of updates (latest data received on 30 October, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

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68 536

Subscribers

-524 hours

-847 days

+7430 days

2 629

Post views

~ 1 00724 hours

~ 1 52948 hours

3.84%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

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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

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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

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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

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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 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 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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Кринжовые танцы, снег, пародия на Стекловату — полный набор классного новогоднего поздравления от приложения для общения с друзьями 🎄 https://t.me/blinkmap/1110

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https://t.me/InsideAds_bot/open?startapp=r_148350890_utm_source-insideadsInternal-utm_medium-notification-utm_campaign-mailwithdrawnotify-variantA I invite you to InsideAds — a platform to grow and monetize Telegram channels monthly profits: +150$

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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 re

Create a password check function:

def 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"

Check a few examples:

print(check_password_strength("Qwerty123"))
print(check_password_strength("Qw!8zYt@1"))

Output example:

⚠️ Weak password  
✅ Reliable password

🔥 Example of how to check a string for compliance with several conditions using code - and practice with regular expressions. 🚪 @DataScience4

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Repost from Github Top Repositories

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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 https://github.com/rendercv/rendercv 👉 https://t.me/DataScienceN

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🚀 Master Data Science & Programming! Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today! 🔰 Machine Learning with Python Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. https://t.me/CodeProgrammer 🔖 Machine Learning Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications. https://t.me/DataScienceM 🧠 Code With Python This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills. https://t.me/DataScience4 🎯 PyData Careers | Quiz Python Data Science jobs, interview tips, and career insights for aspiring professionals. https://t.me/DataScienceQ 💾 Kaggle Data Hub Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects. https://t.me/datasets1 🧑‍🎓 Udemy Coupons | Courses The first channel in Telegram that offers free Udemy coupons https://t.me/DataScienceC 😀 ML Research Hub Advancing research in Machine Learning – practical insights, tools, and techniques for researchers. https://t.me/DataScienceT 💬 Data Science Chat An active community group for discussing data challenges and networking with peers. https://t.me/DataScience9 🐍 Python Arab| بايثون عربي The largest Arabic-speaking group for Python developers to share knowledge and help. https://t.me/PythonArab 🖊 Data Science Jupyter Notebooks Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post. https://t.me/DataScienceN 📺 Free Online Courses | Videos Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners. https://t.me/DataScienceV 📈 Data Analytics Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making. https://t.me/DataAnalyticsX 🎧 Learn Python Hub Master Python with step-by-step courses – from basics to advanced projects and practical applications. https://t.me/Python53 ⭐️ Research Papers Professional Academic Writing & Simulation Services https://t.me/DataScienceY ━━━━━━━━━━━━━━━━━━ Admin: @HusseinSheikho

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