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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Network:📈 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 68 103 subscribers, ranking 2 374 in the Education category and 4 765 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 68 103 subscribers.
According to the latest data from 28 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 75 over the last 30 days and by -18 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 4.69%. Within the first 24 hours after publication, content typically collects 1.70% reactions from the total number of subscribers.
- Post reach: On average, each post receives 3 194 views. Within the first day, a publication typically gains 1 155 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 29 August, 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.
68 103
Subscribers
-1824 hours
-907 days
+7530 days
Posts Archive
200$ to 20k$ SOL Challenge!
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👉 @codeprogrammer
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𝗧𝗵𝗼𝘀𝗲 𝘄𝗵𝗼 𝘄𝗮𝗻𝘁 𝗿𝗲𝗳𝗲𝗿𝗿𝗮𝗹𝘀 𝗮𝗻𝗱 𝗝𝗼𝗯𝘀 𝗮𝗻𝗱 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽𝘀 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀 𝗳𝗿𝗼𝗺 𝗧𝗼𝗽 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗕𝗮𝘀𝗲𝗱, 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗕𝗮𝘀𝗲𝗱 𝗮𝗻𝗱 𝗦𝘁𝗮𝗿𝘁 𝘂𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗹𝗶𝗸𝗲 𝗔𝗺𝗮𝘇𝗼𝗻, 𝗚𝗼𝗼𝗴𝗹𝗲, 𝗔𝗽𝗽𝗹𝗲, 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁, 𝗜𝗕𝗠, 𝗧𝗖𝗦, 𝗖𝗼𝗴𝗻𝗶𝘇𝗮𝗻𝘁, 𝗪𝗶𝗽𝗿𝗼, 𝗖𝗧𝗦, 𝗚𝗼𝗹𝗱𝗺𝗮𝗻 𝗦𝗮𝗰𝗵𝘀, 𝗢𝗹𝗮, 𝗨𝗯𝗲𝗿, 𝗭𝗼𝗺𝗮𝘁𝗼, 𝗦𝘄𝗶𝗴𝗴𝘆, 𝘂𝗽𝗚𝗿𝗮𝗱, 𝗖𝘂𝗿𝗲 𝗙𝗶𝘁, 𝗛𝗮𝗰𝗸𝗲𝗿𝗿𝗮𝗻𝗸, 𝗚𝗲𝗲𝗸𝘀𝗳𝗼𝗿𝗴𝗲𝗲𝗸𝘀 𝗮𝗻𝗱 𝗺𝗮𝗻𝘆 𝗺𝗼𝗿𝗲, 𝗰𝗮𝗻 𝗷𝗼𝗶𝗻 𝘁𝗵𝗲𝘀𝗲 𝗯𝗲𝗹𝗼𝘄 𝗰𝗵𝗮𝗻𝗻𝗲𝗹𝘀
👇👇
https://t.me/jobsandinternshipsupdates
💛 Top 10 Best Websites to Learn Machine Learning ⭐️
by [@codeprogrammer]
---
🧠 Google’s ML Course
🔗 https://developers.google.com/machine-learning/crash-course
📈 Kaggle Courses
🔗 https://kaggle.com/learn
🧑🎓 Coursera – Andrew Ng’s ML Course
🔗 https://coursera.org/learn/machine-learning
⚡️ Fast.ai
🔗 https://fast.ai
🔧 Scikit-Learn Documentation
🔗 https://scikit-learn.org
📹 TensorFlow Tutorials
🔗 https://tensorflow.org/tutorials
🔥 PyTorch Tutorials
🔗 https://docs.pytorch.org/tutorials/
🏛️ MIT OpenCourseWare – Machine Learning
🔗 https://ocw.mit.edu/courses/6-867-machine-learning-fall-2006/
✍️ Towards Data Science (Blog)
🔗 https://towardsdatascience.com
---
💡 Which one are you starting with? Drop a comment below! 👇
#MachineLearning #LearnML #DataScience #AI #TechWithRam
Do you see yourself as a programmer, researcher, or engineer?
Repost from Machine Learning with Python
+1
These Google Colab-notebooks help to implement all machine learning algorithms from scratch 🤯
Repo: https://udlbook.github.io/udlbook/
👉 @codeprogrammer
Build your own AI agent from scratch for free in 5 minutes
In this article, I will show you how to build your first AI agent from scratch using Google’s ADK (Agent Development Kit). This is an open-source framework that makes it easier to create agents, test them, add tools, and even build multi-agent systems.
Read: https://habr.com/en/articles/974212/
Repost from Data Analytics
This repository collects everything you need to use AI and LLM in your projects.
120+ libraries, organized by development stages:
→ Model training, fine-tuning, and evaluation
→ Deploying applications with LLM and RAG
→ Fast and scalable model launch
→ Data extraction, crawlers, and scrapers
→ Creating autonomous LLM agents
→ Prompt optimization and security
Repo: https://github.com/KalyanKS-NLP/llm-engineer-toolkit
🥺 https://t.me/DataAnalyticsX
😎 Machine Learning Cheatsheet — a structured ML guide!
There are no courses here, no unnecessary theory or long lectures, but there are clear formulas, algorithms, the logic of ML pipelines, and a neatly structured knowledge base. It's perfect for quickly refreshing your understanding of algorithms or having it handy as an ML cheat sheet during work.
📌 Here's the link: ml-cheatsheet.readthedocs.io
🚪 @codeprogrammer | #resource
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Repost from ADMINOTEKA
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