Machine Learning with Python

Open in Telegram

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 India5 049 Education2 404...

📈 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 67 812 subscribers, ranking 2 404 in the Education category and 5 049 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 67 812 subscribers.

According to the latest data from 05 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 77 over the last 30 days and by 9 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 2.60%. Within the first 24 hours after publication, content typically collects 2.50% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 767 views. Within the first day, a publication typically gains 1 695 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 6.
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 07 June, 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.

67 812

Subscribers

+924 hours

+587 days

+7730 days

1 767

Post views

~ 1 69524 hours

~ 2 25748 hours

2.60%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

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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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Repost from Python Courses & Resources

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Repost from ADMINOTEKA

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Repost from Learn Python Coding

A cheat sheet about functions and techniques in Python: shows useful built-in functions, working with iterators, strings, and collections, as well as popular tricks with unpacking, zip, enumerate, map, filter, and dictionaries @DataScience4

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Repost from Learn Python Coding

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𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐪𝐮𝐢𝐜𝐤 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐭𝐨𝐩 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 🔥👇⁣⁣ ⁣⁣ ✅ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘢 𝘛𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮𝘦𝘳 𝘢𝘯𝘥 𝘸𝘩𝘺 𝘸𝘢𝘴 𝘪𝘵 𝘪𝘯𝘵𝘳𝘰𝘥𝘶𝘤𝘦𝘥?⁣⁣ 𝘐𝘵 𝘴𝘰𝘭𝘷𝘦𝘥 𝘵𝘩𝘦 𝘭𝘪𝘮𝘪𝘵𝘢𝘵𝘪𝘰𝘯𝘴 𝘰𝘧 𝘙𝘕𝘕𝘴 & 𝘓𝘚𝘛𝘔𝘴 𝘣𝘺 𝘶𝘴𝘪𝘯𝘨 𝘴𝘦𝘭𝘧-𝘢𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯, 𝘦𝘯𝘢𝘣𝘭𝘪𝘯𝘨 𝘱𝘢𝘳𝘢𝘭𝘭𝘦𝘭 𝘱𝘳𝘰𝘤𝘦𝘴𝘴𝘪𝘯𝘨 𝘢𝘯𝘥 𝘤𝘢𝘱𝘵𝘶𝘳𝘪𝘯𝘨 𝘭𝘰𝘯𝘨-𝘳𝘢𝘯𝘨𝘦 𝘥𝘦𝘱𝘦𝘯𝘥𝘦𝘯𝘤𝘪𝘦𝘴 𝘭𝘪𝘬𝘦 𝘯𝘦𝘷𝘦𝘳 𝘣𝘦𝘧𝘰𝘳𝘦!⁣⁣ ⁣⁣ ✅ 𝘚𝘦𝘭𝘧-𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 – 𝘛𝘩𝘦 𝘮𝘢𝘨𝘪𝘤 𝘣𝘦𝘩𝘪𝘯𝘥 𝘪𝘵⁣⁣ 𝘌𝘷𝘦𝘳𝘺 𝘸𝘰𝘳𝘥 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥𝘴 𝘪𝘵𝘴 𝘤𝘰𝘯𝘵𝘦𝘹𝘵 𝘪𝘯 𝘳𝘦𝘭𝘢𝘵𝘪𝘰𝘯 𝘵𝘰 𝘰𝘵𝘩𝘦𝘳𝘴—𝘮𝘢𝘬𝘪𝘯𝘨 𝘦𝘮𝘣𝘦𝘥𝘥𝘪𝘯𝘨𝘴 𝘴𝘮𝘢𝘳𝘵𝘦𝘳 𝘢𝘯𝘥 𝘮𝘰𝘥𝘦𝘭𝘴 𝘮𝘰𝘳𝘦 𝘤𝘰𝘯𝘵𝘦𝘹𝘵-𝘢𝘸𝘢𝘳𝘦.⁣⁣ ⁣⁣ ✅ 𝘔𝘶𝘭𝘵𝘪-𝘏𝘦𝘢𝘥 𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 – 𝘚𝘦𝘦𝘪𝘯𝘨 𝘧𝘳𝘰𝘮 𝘮𝘶𝘭𝘵𝘪𝘱𝘭𝘦 𝘢𝘯𝘨𝘭𝘦𝘴⁣⁣ 𝘋𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘢𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 𝘩𝘦𝘢𝘥𝘴 𝘧𝘰𝘤𝘶𝘴 𝘰𝘯 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘳𝘦𝘭𝘢𝘵𝘪𝘰𝘯𝘴𝘩𝘪𝘱𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘥𝘢𝘵𝘢. 𝘐𝘵’𝘴 𝘭𝘪𝘬𝘦 𝘩𝘢𝘷𝘪𝘯𝘨 𝘮𝘶𝘭𝘵𝘪𝘱𝘭𝘦 𝘦𝘹𝘱𝘦𝘳𝘵𝘴 𝘢𝘯𝘢𝘭𝘺𝘻𝘦 𝘵𝘩𝘦 𝘴𝘢𝘮𝘦 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯!⁣⁣ ⁣⁣ ✅ 𝘗𝘰𝘴𝘪𝘵𝘪𝘰𝘯𝘢𝘭 𝘌𝘯𝘤𝘰𝘥𝘪𝘯𝘨 – 𝘛𝘦𝘢𝘤𝘩𝘪𝘯𝘨 𝘵𝘩𝘦 𝘮𝘰𝘥𝘦𝘭 𝘰𝘳𝘥𝘦𝘳 𝘮𝘢𝘵𝘵𝘦𝘳𝘴⁣⁣ 𝘚𝘪𝘯𝘤𝘦 𝘛𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮𝘦𝘳𝘴 𝘥𝘰𝘯’𝘵 𝘱𝘳𝘰𝘤𝘦𝘴𝘴 𝘥𝘢𝘵𝘢 𝘴𝘦𝘲𝘶𝘦𝘯𝘵𝘪𝘢𝘭𝘭𝘺, 𝘵𝘩𝘪𝘴 𝘵𝘳𝘪𝘤𝘬 𝘦𝘯𝘴𝘶𝘳𝘦𝘴 𝘵𝘩𝘦𝘺 “𝘬𝘯𝘰𝘸” 𝘵𝘩𝘦 𝘱𝘰𝘴𝘪𝘵𝘪𝘰𝘯 𝘰𝘧 𝘦𝘢𝘤𝘩 𝘵𝘰𝘬𝘦𝘯.⁣⁣ ⁣⁣ ✅ 𝘓𝘢𝘺𝘦𝘳 𝘕𝘰𝘳𝘮𝘢𝘭𝘪𝘻𝘢𝘵𝘪𝘰𝘯 – 𝘚𝘵𝘢𝘣𝘪𝘭𝘪𝘻𝘪𝘯𝘨 𝘵𝘩𝘦 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘱𝘳𝘰𝘤𝘦𝘴𝘴⁣⁣ 𝘐𝘵 𝘴𝘱𝘦𝘦𝘥𝘴 𝘶𝘱 𝘵𝘳𝘢𝘪𝘯𝘪𝘯𝘨 𝘢𝘯𝘥 𝘢𝘷𝘰𝘪𝘥𝘴 𝘷𝘢𝘯𝘪𝘴𝘩𝘪𝘯𝘨 𝘨𝘳𝘢𝘥𝘪𝘦𝘯𝘵𝘴, 𝘭𝘦𝘵𝘵𝘪𝘯𝘨 𝘮𝘰𝘥𝘦𝘭𝘴 𝘨𝘰 𝘥𝘦𝘦𝘱𝘦𝘳 𝘢𝘯𝘥 𝘭𝘦𝘢𝘳𝘯 𝘣𝘦𝘵𝘵𝘦𝘳.⁣⁣