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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 68 521 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 68 521 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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68 521

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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Statistics for Data Science Notes.pdf2.06 MB

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🚀 𝐄𝐩𝐨𝐜𝐡 𝐯𝐬 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 – 𝐓𝐡𝐞 𝐌𝐨𝐬𝐭 𝐂𝐨𝐦𝐦𝐨𝐧𝐥𝐲 𝐂𝐨𝐧𝐟𝐮𝐬𝐞𝐝 𝐓𝐞𝐫𝐦𝐬! When training a neural network, two words confuse most beginners: 🔹 𝐄𝐩𝐨𝐜𝐡 𝘈𝘯 𝘦𝘱𝘰𝘤𝘩 𝘮𝘦𝘢𝘯𝘴 𝘵𝘩𝘦 𝘮𝘰𝘥𝘦𝘭 𝘩𝘢𝘴 𝘴𝘦𝘦𝘯 𝘵𝘩𝘦 𝘦𝘯𝘵𝘪𝘳𝘦 𝘥𝘢𝘵𝘢𝘴𝘦𝘵 𝘰𝘯𝘤𝘦. 𝘐𝘧 𝘺𝘰𝘶 𝘵𝘳𝘢𝘪𝘯 𝘧𝘰𝘳 10 𝘦𝘱𝘰𝘤𝘩𝘴, 𝘺𝘰𝘶𝘳 𝘮𝘰𝘥𝘦𝘭 𝘨𝘰𝘦𝘴 𝘵𝘩𝘳𝘰𝘶𝘨𝘩 𝘵𝘩𝘦 𝘸𝘩𝘰𝘭𝘦 𝘥𝘢𝘵𝘢 10 𝘵𝘪𝘮𝘦𝘴. 🔹 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝘈𝘯 𝘪𝘵𝘦𝘳𝘢𝘵𝘪𝘰𝘯 𝘪𝘴 𝘰𝘯𝘦 𝘸𝘦𝘪𝘨𝘩𝘵 𝘶𝘱𝘥𝘢𝘵𝘦, 𝘣𝘢𝘴𝘦𝘥 𝘰𝘯 𝘢 𝘴𝘪𝘯𝘨𝘭𝘦 𝘣𝘢𝘵𝘤𝘩 𝘰𝘧 𝘥𝘢𝘵𝘢. If you have: 10,000 𝘳𝘦𝘤𝘰𝘳𝘥𝘴 𝘉𝘢𝘵𝘤𝘩 𝘴𝘪𝘻𝘦 = 100 👉 𝘛𝘩𝘦𝘯 𝘺𝘰𝘶 𝘨𝘦𝘵 100 𝘪𝘵𝘦𝘳𝘢𝘵𝘪𝘰𝘯𝘴 𝘱𝘦𝘳 𝘦𝘱𝘰𝘤𝘩. ✔️ 𝐒𝐢𝐦𝐩𝐥𝐞 𝐀𝐧𝐚𝐥𝐨𝐠𝐲 𝐓𝐡𝐢𝐧𝐤 𝐨𝐟 𝐠𝐨𝐢𝐧𝐠 𝐭𝐨 𝐭𝐡𝐞 𝐠𝐲𝐦: 𝐄𝐩𝐨𝐜𝐡 = 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐞𝐧𝐭𝐢𝐫𝐞 𝐰𝐨𝐫𝐤𝐨𝐮𝐭 𝐩𝐥𝐚𝐧 𝐨𝐧𝐜𝐞 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 = 𝐝𝐨𝐢𝐧𝐠 𝐨𝐧𝐞 𝐬𝐞𝐭 𝐢𝐧𝐬𝐢𝐝𝐞 𝐭𝐡𝐚𝐭 𝐰𝐨𝐫𝐤𝐨𝐮𝐭 The model becomes stronger with every iteration, and improves overall with more epochs. Read More: https://telegra.ph/Demystifying-Epochs--Iterations-in-Deep-Learning-11-24 👉 @codeprogrammer

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Python For Data Science Cheat Sheet #python #datascience #DataAnalysis https://t.me/CodeProgrammer React ♥️ for more amazing content

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Amazing NumPy Cheat Sheet Snippet with 100 exercises for practicing the concept to get hands on to clear the coding round in the interviews #NumPy #codingexams https://t.me/DataScienceM

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Learn Complete #MatPlotLib with most asked #interview questions 👉 @codeprogrammer

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Have you tried running the code? yes 👍 | No 👎

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Create a Wi-Fi QR code in Python in a couple of seconds

pip install wifi_qrcode_generator

import wifi_qrcode_generator.generator
from PIL import Image

ssid = "CLCoding_WIFI"
password = "supersecret123"
security = "WPA"

from wifi_qrcode_generator.generator import wifi_qrcode
qr = wifi_qrcode(ssid, False, security, password)

qr.make_image().save("wifi_qr.png")
Image.open("wifi_qr.png")

👉 @codeprogrammer

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❔ Interview question What do globals() and locals() do? Answer: Functions globals() and locals() return a dictionary with variables of the current scope. ▶️ globals() — provides access to all global names in the current module (i.e., variables, functions, and classes defined at the top level) ▶️ locals() — returns local variables in the current context: inside a function, class, or block They are useful for dynamic analysis, debugging, metaprogramming, and when working with eval() / exec(). tags: #interview ➡ @codeprogrammer

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Your interaction with us drives us more free information and knowledge

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This GitHub repo is a gold mine for EVERY data scientist! (full of hands-on and interactive tutorials) DS Interactive Python provides several dashboards to interactively learn about statistics, ML models, and other related theoretical concepts! Some key topics you can understand interactively: • PCA • Bagging and boosting • Linear regression and OLS • Bayesian and frequentist statistics • confidence intervals • clustering (kmeans, spectral clustering, etc.) • central limit theorem • neural networks (the backpropagation animation is really good) • and many more. Link: https://github.com/GeostatsGuy/DataScienceInteractivePython https://t.me/CodeProgrammer

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Repost from Python Courses & Resources

Create artificial intelligence using Python In the video the author explains the basics of neural networks and shows how to implement your own simple #AI from scratch in #Python

https://t.me/Python53

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