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Machine Learning with Python

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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πŸ“ˆ 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 67 804 subscribers, ranking 2 419 in the Education category and 4 901 in the India region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 67 804 subscribers.

According to the latest data from 23 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 107 over the last 30 days and by 23 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 4.73%. Within the first 24 hours after publication, content typically collects 1.30% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 3 210 views. Within the first day, a publication typically gains 884 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 4.
  • Thematic interests: Content is focused on key topics such as insidead, learning, degree, evaluation, algorithm.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œLearn 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 24 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.

67 804
Subscribers
+2324 hours
-367 days
+10730 days
Posts Archive
Guide to scalable system design and interviews πŸš€πŸ“Š https://github.com/karanpratapsingh/system-design #SystemDesign #Scalabil
Guide to scalable system design and interviews πŸš€πŸ“Š https://github.com/karanpratapsingh/system-design #SystemDesign #Scalability #TechInterviews #SoftwareEngineering #DevCommunity #Coding ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A πŸš€ Level up your AI & Data Science skills with HelloEncyclo β€” a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more. βœ… 13 courses live + 40+ coming soon 🎯 One access, lifetime updates πŸ”‘ Use code: PRESALE-BOOK-WAVE-2GFG πŸ‘‰ https://helloencyclo.com/?ref=HUSSEINSHEIKHO

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My favorite way to work with multiple filters in pandas.Series β€” not a chain of .loc, but a single mask. 🐼 The chain looks neat, but breaks on real data and easily gives unexpected results:
s = pd.Series([10, 15, 20, 25, 30])
s.loc[s > 20].loc[s % 2 == 1]
The problem is that the second .loc again looks at the original s, not the already filtered result. The logic gets messy. 🀯 It's more reliable to gather everything into one expression:
s = pd.Series([10, 15, 20, 25, 30])

mask = (s > 20) & (s % 2 == 1)
result = s.loc[mask]
One mask, one point of truth. βœ… It's easier to debug. Fewer surprises when the code grows. πŸš€ #Pandas #Python #DataScience #CodingTips #DataEngineering #Debugging ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A πŸš€ Level up your AI & Data Science skills with HelloEncyclo β€” a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more. βœ… 13 courses live + 40+ coming soon 🎯 One access, lifetime updates πŸ”‘ Use code: PRESALE-BOOK-WAVE-2GFG πŸ‘‰ https://helloencyclo.com/?ref=HUSSEINSHEIKHO

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Machine Learning & Python Data Science for Business and AI Learn Python Programming, Data Analysis, and Machine Learning Tech
Machine Learning & Python Data Science for Business and AI Learn Python Programming, Data Analysis, and Machine Learning Techniques to Solve Real World Business Challenges with AI… 🏷 Category: development 🌍 Language: English (US) πŸ‘₯ Students: 9,960 students ⭐️ Rating: 4.2/5.0 (55 reviews) πŸƒβ€β™‚οΈ Enrollments Left: N/A ⏳ Expires In: 0D:3H:3M πŸ’° Price: $9.59 ⟹ FREE πŸ†” Coupon: MT260622G1 ⚠️ Watch 2 short ads to unlock your free access. πŸ’Ž By: https://t.me/Udemy26 #Programming #Coding #Development #Tech #Python #DataScience

Learn AI for free directly from top companies. πŸš€ 1 - Anthropic: anthropic.skilljar.com 2 - Google: grow.google/ai 3 - Meta: ai.meta.com/resources/ 4 - NVIDIA: developer.nvidia.com/cuda 5 - Microsoft: learn.microsoft.com/en-us/training/ 6 - OpenAI: academy.openai.com 7 - IBM: skillsbuild.org 8 - AWS: skillbuilder.aws 9 - DeepLearning.AI: deeplearning.ai 10 - Hugging Face: huggingface.co/learn πŸ’¬ Comment "Learning" if you find this helpful. πŸ”„ Repost so others can take help. πŸ”– Must bookmark for future reference. #AI #MachineLearning #Tech #FreeLearning #DataScience #AIForAll

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